PrimeKG Enrichment and Embedding¶

In this tutorial, we will explain how to perform multimodal enrichment and embedding of PrimeKG nodes.

We will consider the following node types

1. Drugs (PubChem/DrugBank/CTD) - TEXT and SMILES
2. Proteins (NCBI/Gene) - TEXT and amino-acid sequence
3. Pathways (Reactome) - TEXT
4. Phenotypes (HPO) - TEXT
5. Protein function (GO) - TEXT
6. Disease (MONDO) - TEXT
7. Anatomy (UBERON) - TEXT

Prior information about the PrimeKG can be found in the following repositories:

• https://github.com/mims-harvard/PrimeKG
• https://github.com/mims-harvard/TDC/

Note that we are leveraging the PrimeKG provided in Harvard Dataverse, which is publicly available in the following link:

https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IXA7BM

By the time we are writing this tutorial, the latest version of PrimeKG (kg.csv) is 2.1.

First of all, we need to import necessary libraries as follows:

In [17]:

# Import necessary libraries
import sys
import torch
import networkx as nx
from tqdm import tqdm
sys.path.append('../../..')
from aiagents4pharma.talk2knowledgegraphs.datasets.primekg import PrimeKG
from aiagents4pharma.talk2knowledgegraphs.utils.enrichments.uniprot_proteins import EnrichmentWithUniProt
from aiagents4pharma.talk2knowledgegraphs.utils.enrichments.ols_terms import EnrichmentWithOLS
from aiagents4pharma.talk2knowledgegraphs.utils.enrichments.reactome_pathways import EnrichmentWithReactome
from aiagents4pharma.talk2knowledgegraphs.utils.enrichments.pubchem_strings import EnrichmentWithPubChem
from aiagents4pharma.talk2knowledgegraphs.utils.embeddings.huggingface import EmbeddingWithHuggingFace
from aiagents4pharma.talk2knowledgegraphs.utils.pubchem_utils import external_id2pubchem_cid

# Import necessary libraries import sys import torch import networkx as nx from tqdm import tqdm sys.path.append('../../..') from aiagents4pharma.talk2knowledgegraphs.datasets.primekg import PrimeKG from aiagents4pharma.talk2knowledgegraphs.utils.enrichments.uniprot_proteins import EnrichmentWithUniProt from aiagents4pharma.talk2knowledgegraphs.utils.enrichments.ols_terms import EnrichmentWithOLS from aiagents4pharma.talk2knowledgegraphs.utils.enrichments.reactome_pathways import EnrichmentWithReactome from aiagents4pharma.talk2knowledgegraphs.utils.enrichments.pubchem_strings import EnrichmentWithPubChem from aiagents4pharma.talk2knowledgegraphs.utils.embeddings.huggingface import EmbeddingWithHuggingFace from aiagents4pharma.talk2knowledgegraphs.utils.pubchem_utils import external_id2pubchem_cid

Check device availability¶

In [2]:

device = "cuda:0" if torch.cuda.is_available() else "cpu"
device

device = "cuda:0" if torch.cuda.is_available() else "cpu" device

Out[2]:

'cpu'

Load the BioBERT model¶

In [3]:

# Using MSFT's BioBERT
biobert_model = EmbeddingWithHuggingFace(model_name='microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract',
                                     model_cache_dir="../../../../data/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract/",
                                     truncation=False,
                                     device=device)

# Using MSFT's BioBERT biobert_model = EmbeddingWithHuggingFace(model_name='microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract', model_cache_dir="../../../../data/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract/", truncation=False, device=device)

Load PrimeKG¶

The PrimeKG dataset allows to load the data from the Harvard Dataverse server if the data is not available locally.

Otherwise, the data is loaded from the local directory as defined in the local_dir.

In [4]:

# Define primekg data by providing a local directory where the data is stored
primekg_data = PrimeKG(local_dir="../../../../data/primekg/")

# Define primekg data by providing a local directory where the data is stored primekg_data = PrimeKG(local_dir="../../../../data/primekg/")

To load the dataframes of nodes and edges from PrimeKG, we just need to invoke a method as follows.

In [5]:

# Invoke a method to load the data
primekg_data.load_data()

# Get primekg_nodes and primekg_edges
primekg_nodes = primekg_data.get_nodes()
primekg_edges = primekg_data.get_edges()

# Invoke a method to load the data primekg_data.load_data() # Get primekg_nodes and primekg_edges primekg_nodes = primekg_data.get_nodes() primekg_edges = primekg_data.get_edges()

Loading nodes of PrimeKG dataset ...
../../../../data/primekg/primekg_nodes.tsv.gz already exists. Loading the data from the local directory.
Loading edges of PrimeKG dataset ...
../../../../data/primekg/primekg_edges.tsv.gz already exists. Loading the data from the local directory.

Check PrimeKG Dataframes¶

As mentioned before, the primekg_nodes and primekg_edges are the dataframes of nodes and edges, respectively.

We can further analyze the dataframes to extract the information we need.

For instance, we can construct a graph from the nodes and edges dataframes using the networkx library.

PrimeKG Nodes¶

primekg_nodes is a dataframe of nodes, which has the following columns:

• node_index: the index of the node
• node: the node name
• node_id: the id of the node (currently set as node name itself, for visualization purposes)
• node_uid: the unique identifier of the node (source name + unique id)
• node_type: the type of the node

We can check a sample of the primekg nodes to see the list of nodes in the PrimeKG dataset as follows.

In [6]:

# Check a sample of the primekg nodes
primekg_nodes.head()

# Check a sample of the primekg nodes primekg_nodes.head()

Out[6]:

	node_index	node_name	node_source	node_id	node_type
0	0	PHYHIP	NCBI	9796	gene/protein
1	1	GPANK1	NCBI	7918	gene/protein
2	2	ZRSR2	NCBI	8233	gene/protein
3	3	NRF1	NCBI	4899	gene/protein
4	4	PI4KA	NCBI	5297	gene/protein

The current version of PrimeKG has about 130K of nodes in total as we can observe in the following cell.

In [7]:

# Check dimensions of the primekg nodes
primekg_nodes.shape

# Check dimensions of the primekg nodes primekg_nodes.shape

Out[7]:

(129375, 5)

We can breakdown the statistics of the primekg nodes by their types as follows.

In [8]:

# Show node types and their counts
primekg_nodes['node_type'].value_counts()

# Show node types and their counts primekg_nodes['node_type'].value_counts()

Out[8]:

node_type
biological_process    28642
gene/protein          27671
disease               17080
effect/phenotype      15311
anatomy               14035
molecular_function    11169
drug                   7957
cellular_component     4176
pathway                2516
exposure                818
Name: count, dtype: int64

PrimeKG was built using various sources, as we can observe from their unique node sources as follows.

In [9]:

# Show source of the primekg nodes
primekg_nodes['node_source'].value_counts()

# Show source of the primekg nodes primekg_nodes['node_source'].value_counts()

Out[9]:

node_source
GO               43987
NCBI             27671
MONDO            15813
HPO              15311
UBERON           14035
DrugBank          7957
REACTOME          2516
MONDO_grouped     1267
CTD                818
Name: count, dtype: int64

In [10]:

primekg_nodes[primekg_nodes['node_source'] == 'CTD']
# primekg_edges.head()

primekg_nodes[primekg_nodes['node_source'] == 'CTD'] # primekg_edges.head()

Out[10]:

	node_index	node_name	node_source	node_id	node_type
61677	61677	1-hydroxyphenanthrene	CTD	C092102	exposure
61678	61678	1-hydroxypyrene	CTD	C033146	exposure
61679	61679	1-naphthol	CTD	C029350	exposure
61680	61680	2,2',3',4,4',5-hexachlorobiphenyl	CTD	C029790	exposure
61681	61681	2,2',3,5,5',6-hexachlorobiphenyl	CTD	C066675	exposure
...	...	...	...	...	...
127593	127593	Heptanes	CTD	D006536	exposure
127594	127594	octane	CTD	C026728	exposure
127595	127595	pseudocumene	CTD	C010313	exposure
127596	127596	pentane	CTD	C033353	exposure
127597	127597	Butylated Hydroxyanisole	CTD	D002083	exposure

818 rows × 5 columns

In [11]:

test = EnrichmentWithPubChem()

test = EnrichmentWithPubChem()

In [12]:

test.enrich_documents(['24667'])

test.enrich_documents(['24667'])

INFO:aiagents4pharma.talk2knowledgegraphs.utils.pubchem_utils:Load Hydra configuration for PubChem CID description.

Out[12]:

(["Butylated Hydroxyanisole can cause cancer according to The World Health Organization's International Agency for Research on Cancer (IARC)."],
 ['CC(C)(C)C1=C(C=CC(=C1)O)OC.CC(C)(C)C1=C(C=CC(=C1)OC)O'])

Create a directed graph using the egdes¶

In [13]:

kg = nx.DiGraph()

## Make a KG using the edgelist
G = nx.from_pandas_edgelist(
    primekg_edges,
    source="head_name",
    target="tail_name",
    edge_key="relation",
    # edge_attr=["edge_id", "edge_type", "feature_value", "feature_id"],
    create_using=nx.DiGraph(),
)
kg = nx.compose(G, kg)

kg = nx.DiGraph() ## Make a KG using the edgelist G = nx.from_pandas_edgelist( primekg_edges, source="head_name", target="tail_name", edge_key="relation", # edge_attr=["edge_id", "edge_type", "feature_value", "feature_id"], create_using=nx.DiGraph(), ) kg = nx.compose(G, kg)

Add additional node attributes (e.g. source, id and type)¶

In [14]:

# Start by extracting slicing the df to include only thge head nodes
df_head_nodes = primekg_edges[['head_name', 'head_source', 'head_id', 'head_type']]
# Rename the columns
df_head_nodes = df_head_nodes.rename(columns={
    'head_name': 'node_name',
    'head_source': 'node_source',
    'head_id': 'node_id',
    'head_type': 'node_type'
})
# Set the node_name as index
df_head_nodes = df_head_nodes.set_index('node_name')
# Add the additional attributes to graph
G.add_nodes_from((n, dict(d)) for n, d in df_head_nodes.iterrows())
# Recompose the graph
kg = nx.compose(G, kg)

# Start by extracting slicing the df to include only thge head nodes df_head_nodes = primekg_edges[['head_name', 'head_source', 'head_id', 'head_type']] # Rename the columns df_head_nodes = df_head_nodes.rename(columns={ 'head_name': 'node_name', 'head_source': 'node_source', 'head_id': 'node_id', 'head_type': 'node_type' }) # Set the node_name as index df_head_nodes = df_head_nodes.set_index('node_name') # Add the additional attributes to graph G.add_nodes_from((n, dict(d)) for n, d in df_head_nodes.iterrows()) # Recompose the graph kg = nx.compose(G, kg)

CTD enrichment¶

We will map CTD IDs to their corresponding PubChem IDs, and extract their descriptions and SMILES representation using EnrichmentWithPubChem.

In [15]:

from dataclasses import dataclass
# Create a dataclass to hold the node attributes
@dataclass
class PubChemAttr:
    """Dataclass to hold the attributes of a node."""
    pubchem_cid: str
    name: str
    # Make description optional
    # If not provided, it will be set to None
    description: str = None
    smiles: str = None

from dataclasses import dataclass # Create a dataclass to hold the node attributes @dataclass class PubChemAttr: """Dataclass to hold the attributes of a node.""" pubchem_cid: str name: str # Make description optional # If not provided, it will be set to None description: str = None smiles: str = None

Go iteratively over every CTD ID and fetch its description and SMILES rep¶

In [23]:

list_pubchem_attrs = []
# For the sake of space and time, we will enrich only the first 5 nodes of each DB
# Extract all gene IDs from the graph
pubchem_obj = EnrichmentWithPubChem()
pubchem_cids = []
count = 0
for node in tqdm(kg.nodes):
    if kg.nodes[node].get('node_source') != 'CTD':
        continue
    count += 1
    # Get the node attributes
    node_attr = kg.nodes[node]
    # Cnvert CTD ID into PubChem ID
    pubchem_cid = external_id2pubchem_cid('Comparative Toxicogenomics Database', node_attr.get('node_id'))
    # Save all PubChem CIDs
    pubchem_cids.append(pubchem_cid)
    # Create a ReactomeAttr object
    pubchem_attr = PubChemAttr(
        pubchem_cid=pubchem_cid,
        name=node
    )
    list_pubchem_attrs.append(pubchem_attr)
    if count == 2:
        break

# Enrich PubChem attr
for pubchem_attr in list_pubchem_attrs:
    # Fetch descriptions and SMILES representation
    description, smiles = pubchem_obj.enrich_documents([pubchem_attr.pubchem_cid])
    # Add descriptions to the corresponding Reactome attributes
    pubchem_attr.description = description[0]
    pubchem_attr.smiles = smiles[0]

list_pubchem_attrs = [] # For the sake of space and time, we will enrich only the first 5 nodes of each DB # Extract all gene IDs from the graph pubchem_obj = EnrichmentWithPubChem() pubchem_cids = [] count = 0 for node in tqdm(kg.nodes): if kg.nodes[node].get('node_source') != 'CTD': continue count += 1 # Get the node attributes node_attr = kg.nodes[node] # Cnvert CTD ID into PubChem ID pubchem_cid = external_id2pubchem_cid('Comparative Toxicogenomics Database', node_attr.get('node_id')) # Save all PubChem CIDs pubchem_cids.append(pubchem_cid) # Create a ReactomeAttr object pubchem_attr = PubChemAttr( pubchem_cid=pubchem_cid, name=node ) list_pubchem_attrs.append(pubchem_attr) if count == 2: break # Enrich PubChem attr for pubchem_attr in list_pubchem_attrs: # Fetch descriptions and SMILES representation description, smiles = pubchem_obj.enrich_documents([pubchem_attr.pubchem_cid]) # Add descriptions to the corresponding Reactome attributes pubchem_attr.description = description[0] pubchem_attr.smiles = smiles[0]

  0%|          | 0/129262 [00:00<?, ?it/s]INFO:aiagents4pharma.talk2knowledgegraphs.utils.pubchem_utils:Load Hydra configuration for PubChem ID conversion.
  9%|▊         | 11282/129262 [00:00<00:04, 24243.44it/s]INFO:aiagents4pharma.talk2knowledgegraphs.utils.pubchem_utils:Load Hydra configuration for PubChem ID conversion.
 14%|█▍        | 18354/129262 [00:01<00:06, 18164.34it/s]
INFO:aiagents4pharma.talk2knowledgegraphs.utils.pubchem_utils:Load Hydra configuration for PubChem CID description.
INFO:aiagents4pharma.talk2knowledgegraphs.utils.pubchem_utils:Load Hydra configuration for PubChem CID description.

Add descrioptions to the CTD nodes and recompose the graph¶

In [24]:

for pubchem_attr in list_pubchem_attrs:
    node = pubchem_attr.name
    description = pubchem_attr.description
    # print (f"node: {node}, description: {description}")
    G.add_nodes_from([(node, {'description': description})])

# Recompose the graph
kg = nx.compose(G, kg)

for pubchem_attr in list_pubchem_attrs: node = pubchem_attr.name description = pubchem_attr.description # print (f"node: {node}, description: {description}") G.add_nodes_from([(node, {'description': description})]) # Recompose the graph kg = nx.compose(G, kg)

Please follow the notebook link below to know how to generate embeddings of SMILES representation¶

https://virtualpatientengine.github.io/AIAgents4Pharma/notebooks/talk2knowledgegraphs/tutorial_primekg_smiles_enrich_embed/

Generate embedding of CTD descriptions¶

In [25]:

for i, node in tqdm(enumerate(kg.nodes)):
    node_id = kg.nodes[node].get('node_id')    
    if kg.nodes[node].get('description') is None:
        continue
    print (node)
    desc = kg.nodes[node].get('description')
    # print (desc)
    outputs = biobert_model.embed_documents([desc])
    # print (outputs)
    G.add_nodes_from([(node, {'description_embedding': outputs})])
    # torch.cuda.synchronize()
    # torch.cuda.empty_cache()

# Recompose the graph
kg = nx.compose(G, kg)

for i, node in tqdm(enumerate(kg.nodes)): node_id = kg.nodes[node].get('node_id') if kg.nodes[node].get('description') is None: continue print (node) desc = kg.nodes[node].get('description') # print (desc) outputs = biobert_model.embed_documents([desc]) # print (outputs) G.add_nodes_from([(node, {'description_embedding': outputs})]) # torch.cuda.synchronize() # torch.cuda.empty_cache() # Recompose the graph kg = nx.compose(G, kg)

11282it [00:00, 93365.28it/s]

DDT
Copper

129262it [00:00, 429209.15it/s]

Display a DF with results¶

In [26]:

import pandas as pd
dic = {'node':[],
       'node_source':[],
       'node_id':[],
       'description':[],
       'description_embedding':[]}
for node in tqdm(kg.nodes):
    node_id = kg.nodes[node].get('node_id')
    if kg.nodes[node].get('description') is None:
        continue
    dic['node'].append(node)
    dic['node_source'].append(kg.nodes[node].get('node_source'))
    dic['node_id'].append(kg.nodes[node].get('node_id'))
    dic['description'].append(kg.nodes[node].get('description'))
    dic['description_embedding'].append(kg.nodes[node].get('description_embedding'))
    # print (node, kg.nodes[node].get('description'), kg.nodes[node].get('sequence'), kg.nodes[node].get('description_embedding'))

df = pd.DataFrame(dic)
df

import pandas as pd dic = {'node':[], 'node_source':[], 'node_id':[], 'description':[], 'description_embedding':[]} for node in tqdm(kg.nodes): node_id = kg.nodes[node].get('node_id') if kg.nodes[node].get('description') is None: continue dic['node'].append(node) dic['node_source'].append(kg.nodes[node].get('node_source')) dic['node_id'].append(kg.nodes[node].get('node_id')) dic['description'].append(kg.nodes[node].get('description')) dic['description_embedding'].append(kg.nodes[node].get('description_embedding')) # print (node, kg.nodes[node].get('description'), kg.nodes[node].get('sequence'), kg.nodes[node].get('description_embedding')) df = pd.DataFrame(dic) df

100%|██████████| 129262/129262 [00:00<00:00, 859282.23it/s]

Out[26]:

	node	node_source	node_id	description	description_embedding
0	DDT	CTD	D003634	DDT (Dichlorodiphenyltrichloroethane) can caus...	[[tensor(-0.1798), tensor(0.1512), tensor(0.26...
1	Copper	CTD	D003300	Copper atom is a copper group element atom and...	[[tensor(-0.2902), tensor(0.0179), tensor(0.33...

Reactome pathway enrichment¶

We will use Reactome API services to extract textual descriptions of pathways using the EnrichmentWithReactome class.

In [15]:

from dataclasses import dataclass
# Create a dataclass to hold the node attributes
@dataclass
class ReactomeAttr:
    """Dataclass to hold the attributes of a node."""
    pathway_id: str
    name: str
    # Make description optional
    # If not provided, it will be set to None
    description: str = None

from dataclasses import dataclass # Create a dataclass to hold the node attributes @dataclass class ReactomeAttr: """Dataclass to hold the attributes of a node.""" pathway_id: str name: str # Make description optional # If not provided, it will be set to None description: str = None

Go iteratively over every pathway and fetch its description¶

In [20]:

list_reactome_attrs = []
# For the sake of space and time, we will enrich only the first 5 nodes of each DB
# Extract all gene IDs from the graph
reactome_obj = EnrichmentWithReactome()
count = 0
for node in tqdm(kg.nodes):
    if kg.nodes[node].get('node_source') != 'REACTOME':
        continue
    count += 1
    # Get the node attributes
    node_attr = kg.nodes[node]
    # Create a ReactomeAttr object
    reactome_attr = ReactomeAttr(
        pathway_id=node_attr.get('node_id'),
        name=node,
        description=node_attr.get('description')
    )
    list_reactome_attrs.append(reactome_attr)
    if count == 2:
        break
for reactome_attr in list_reactome_attrs:
    # Fetch descriptions
    description = reactome_obj.enrich_documents([reactome_attr.pathway_id])
    # Add descriptions to the corresponding Reactome attributes
    reactome_attr.description = description[0]
print (list_reactome_attrs)

list_reactome_attrs = [] # For the sake of space and time, we will enrich only the first 5 nodes of each DB # Extract all gene IDs from the graph reactome_obj = EnrichmentWithReactome() count = 0 for node in tqdm(kg.nodes): if kg.nodes[node].get('node_source') != 'REACTOME': continue count += 1 # Get the node attributes node_attr = kg.nodes[node] # Create a ReactomeAttr object reactome_attr = ReactomeAttr( pathway_id=node_attr.get('node_id'), name=node, description=node_attr.get('description') ) list_reactome_attrs.append(reactome_attr) if count == 2: break for reactome_attr in list_reactome_attrs: # Fetch descriptions description = reactome_obj.enrich_documents([reactome_attr.pathway_id]) # Add descriptions to the corresponding Reactome attributes reactome_attr.description = description[0] print (list_reactome_attrs)

 47%|████▋     | 60717/129262 [00:00<00:00, 1510221.05it/s]
INFO:aiagents4pharma.talk2knowledgegraphs.utils.enrichments.reactome_pathways:Load Hydra configuration for reactome enrichment
INFO:aiagents4pharma.talk2knowledgegraphs.utils.enrichments.reactome_pathways:Load Hydra configuration for reactome enrichment

[ReactomeAttr(pathway_id='R-HSA-8877627', name='Vitamin E', description='Vitamins A, D, E and K are lipophilic compounds, the so-called fat-soluble vitamins. Because of their lipophilicity, fat-soluble vitamins are solubilised and transported by intracellular carrier proteins to exert their actions. Alpha-tocopherol, the main form of vitamin E found in the body, is transported by alpha-tocopherol transfer protein (TTPA) in hepatic cells (Kono & Arai 2015, Schmolz et al. 2016).'), ReactomeAttr(pathway_id='R-HSA-5334118', name='DNA methylation', description='Methylation of cytosine is catalyzed by a family of DNA methyltransferases (DNMTs): DNMT1, DNMT3A, and DNMT3B transfer methyl groups from S-adenosylmethionine to cytosine, producing 5-methylcytosine and homocysteine (reviewed in Klose and Bird 2006, Ooi et al. 2009, Jurkowska et al. 2011, Moore et al. 2013). (DNMT2 appears to methylate RNA rather than DNA.) DNMT1, the first enzyme discovered, preferentially methylates hemimethylated CG motifs that are produced by replication (template strand methylated, synthesized strand unmethylated). Thus it maintains existing methylation through cell division. DNMT3A and DNMT3B catalyze de novo methylation at unmethylated sites that include both CG dinucleotides and non-CG motifs. DNA from adult humans contains about 0.76 to 1.00 mole percent 5-methylcytosine (Ehrlich et al. 1982, reviewed in Klose and Bird 2006, Ooi et al. 2009, Moore et al. 2013). Methylation of DNA occurs at cytosines that are mainly located in CG dinucleotides. CG dinucleotides are unevenly distributed in the genome. Promoter regions tend to have a high CG-content, forming so-called CG-islands (CGIs), while the CG-content in the remaining part of the genome is much lower. CGIs tend to be unmethylated, while the majority of CGs outside CGIs are methylated. Methylation in promoters and first exons tends to repress transcription while methylation in gene bodies (regions of genes downstream of the promoter and first exon) correlates with transcription (reviewed in Ehrlich and Lacey 2013, Kulis et al. 2013). Proteins such as MeCP2 and MBDs specifically bind 5-methylcytosine and may recruit other factors. Mammalian development has two major episodes of genome-wide demethylation and remethylation (reviewed in Zhou 2012, Guibert and Weber 2013, Hackett and Surani 2013, Dean 2014). In mice about 1 day after fertilization the paternal genome is actively demethylated by TET proteins together with thymine DNA glycosylase and the maternal genome is demethylated by passive dilution during replication, however methylation at imprinted sites is maintained. The genome has its lowest methylation level about 3.5 days post-fertilization. Remethylation occurs by 6.5 days post-fertilization. The second demethylation-remethylation event occurs in primordial germ cells of the developing embryo about 12.5 days post-fertilization. DNMT3A and DNMT3B, together with the non-catalytic DNMT3L, play major roles in the remethylation events (reviewed in Chen and Chan 2014). How the methyltransferases are directed to particular regions of the genome remains an area of active research. The mechanisms at each locus may differ in detail but a connection between histone modifications and DNA methylation has been observed (reviewed in Rose and Klose 2014).')]

Add descriptions to the Reactome nodes and recompose the graph¶

In [21]:

for reactome_attr in list_reactome_attrs:
    node = reactome_attr.name
    description = reactome_attr.description
    # print (f"node: {node}, description: {description}")
    G.add_nodes_from([(node, {'description': description})])

# Recompose the graph
kg = nx.compose(G, kg)

for reactome_attr in list_reactome_attrs: node = reactome_attr.name description = reactome_attr.description # print (f"node: {node}, description: {description}") G.add_nodes_from([(node, {'description': description})]) # Recompose the graph kg = nx.compose(G, kg)

Generate embeddings of descriptions of reactome pathways¶

In [31]:

for i, node in tqdm(enumerate(kg.nodes)):
    node_id = kg.nodes[node].get('node_id')    
    if kg.nodes[node].get('description') is None:
        continue
    print (node)
    desc = kg.nodes[node].get('description')
    # print (desc)
    outputs = biobert_model.embed_documents([desc])
    # print (outputs)
    G.add_nodes_from([(node, {'description_embedding': outputs})])
    # torch.cuda.synchronize()
    # torch.cuda.empty_cache()

# Recompose the graph
kg = nx.compose(G, kg)

for i, node in tqdm(enumerate(kg.nodes)): node_id = kg.nodes[node].get('node_id') if kg.nodes[node].get('description') is None: continue print (node) desc = kg.nodes[node].get('description') # print (desc) outputs = biobert_model.embed_documents([desc]) # print (outputs) G.add_nodes_from([(node, {'description_embedding': outputs})]) # torch.cuda.synchronize() # torch.cuda.empty_cache() # Recompose the graph kg = nx.compose(G, kg)

93148it [00:00, 456878.51it/s]

Vitamin E
DNA methylation

129262it [00:00, 531214.21it/s]

Display the results in a DF¶

In [32]:

import pandas as pd
dic = {'node':[],
       'node_source':[],
       'node_id':[],
       'description':[],
       'description_embedding':[]}
for node in tqdm(kg.nodes):
    node_id = kg.nodes[node].get('node_id')
    if kg.nodes[node].get('description') is None:
        continue
    dic['node'].append(node)
    dic['node_source'].append(kg.nodes[node].get('node_source'))
    dic['node_id'].append(kg.nodes[node].get('node_id'))
    dic['description'].append(kg.nodes[node].get('description'))
    dic['description_embedding'].append(kg.nodes[node].get('description_embedding'))
    # print (node, kg.nodes[node].get('description'), kg.nodes[node].get('sequence'), kg.nodes[node].get('description_embedding'))

df = pd.DataFrame(dic)
df

import pandas as pd dic = {'node':[], 'node_source':[], 'node_id':[], 'description':[], 'description_embedding':[]} for node in tqdm(kg.nodes): node_id = kg.nodes[node].get('node_id') if kg.nodes[node].get('description') is None: continue dic['node'].append(node) dic['node_source'].append(kg.nodes[node].get('node_source')) dic['node_id'].append(kg.nodes[node].get('node_id')) dic['description'].append(kg.nodes[node].get('description')) dic['description_embedding'].append(kg.nodes[node].get('description_embedding')) # print (node, kg.nodes[node].get('description'), kg.nodes[node].get('sequence'), kg.nodes[node].get('description_embedding')) df = pd.DataFrame(dic) df

100%|██████████| 129262/129262 [00:00<00:00, 995538.19it/s]

Out[32]:

	node	node_source	node_id	description	description_embedding
0	Vitamin E	REACTOME	R-HSA-8877627	Vitamins A, D, E and K are lipophilic compound...	[[tensor(-0.4649), tensor(0.2769), tensor(0.73...
1	DNA methylation	REACTOME	R-HSA-5334118	Methylation of cytosine is catalyzed by a fami...	[[tensor(-0.5609), tensor(0.4334), tensor(0.49...

OLS terms enrichments¶

OLS is the Ontology Lookup Service by EMBL/EBI. We will use their API services to extract textual descriptions of the following terms using the EnrichmentWithOLS class.

1. GO
2. HPO
3. UBERON
4. MONDO
5. MONDO_grouped

In [14]:

from dataclasses import dataclass
# Create a dataclass to hold the node attributes
@dataclass
class OLSAttr:
    """Dataclass to hold the attributes of a node."""
    term_id: str
    name: str
    label: str = None
    # Make description optional
    # If not provided, it will be set to None
    description: str = None

from dataclasses import dataclass # Create a dataclass to hold the node attributes @dataclass class OLSAttr: """Dataclass to hold the attributes of a node.""" term_id: str name: str label: str = None # Make description optional # If not provided, it will be set to None description: str = None

In [53]:

# Define a dictionary to store DB name and its OLS code
dic_ols = {
    'GO': 'GO',
    'HPO': 'HP',
    'UBERON': 'UBERON',
    'MONDO': 'MONDO',
}

# Define a dictionary to store DB name and its OLS code dic_ols = { 'GO': 'GO', 'HPO': 'HP', 'UBERON': 'UBERON', 'MONDO': 'MONDO', }

Go iteratively over every DB in OLS and store results in a dic¶

In [54]:

list_ols_attrs = []
term_ids = []
# For the sake of space and time, we will enrich only the first 5 nodes of each DB
# Extract all gene IDs from the graph
ols_obj = EnrichmentWithOLS()
for source in ['GO', 'MONDO', 'HPO', 'UBERON']:
    count = 0
    for node in tqdm(kg.nodes):
        if kg.nodes[node].get('node_source') != source:
            continue
        count += 1
        # Get the node attributes
        node_attr = kg.nodes[node]
        # Term ID
        # OLS term must contain 7-digit integer code
        # Hence, prefix with 0s such that total number
        # of characters is 7
        term_id = dic_ols[source] + '_' + str("{:07}".format(int(node_attr.get('node_id'))))
        term_ids.append(term_id)
        # Create a OLSAttr object
        ols_attr = OLSAttr(
            term_id=term_id,
            name=node,
            label=node,
            description=node_attr.get('description')
        )
        list_ols_attrs.append(ols_attr)
        if count == 2:
            break
# Fetch descriptions
descriptions = ols_obj.enrich_documents(term_ids)
# Add descriptions to the corresponding OLS attributes
for ols_attr, description in zip(list_ols_attrs, descriptions):
    ols_attr.description = description

list_ols_attrs = [] term_ids = [] # For the sake of space and time, we will enrich only the first 5 nodes of each DB # Extract all gene IDs from the graph ols_obj = EnrichmentWithOLS() for source in ['GO', 'MONDO', 'HPO', 'UBERON']: count = 0 for node in tqdm(kg.nodes): if kg.nodes[node].get('node_source') != source: continue count += 1 # Get the node attributes node_attr = kg.nodes[node] # Term ID # OLS term must contain 7-digit integer code # Hence, prefix with 0s such that total number # of characters is 7 term_id = dic_ols[source] + '_' + str("{:07}".format(int(node_attr.get('node_id')))) term_ids.append(term_id) # Create a OLSAttr object ols_attr = OLSAttr( term_id=term_id, name=node, label=node, description=node_attr.get('description') ) list_ols_attrs.append(ols_attr) if count == 2: break # Fetch descriptions descriptions = ols_obj.enrich_documents(term_ids) # Add descriptions to the corresponding OLS attributes for ols_attr, description in zip(list_ols_attrs, descriptions): ols_attr.description = description

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INFO:aiagents4pharma.talk2knowledgegraphs.utils.enrichments.ols_terms:Load Hydra configuration for OLS enrichments.

True
['Any process that stops, prevents, or reduces the frequency, rate or extent of the directed movement of a neurotransmitter into a neuron or glial cell.']
negative regulation of neurotransmitter uptake
True
['Any process that stops, prevents, or reduces the frequency, rate or extent of the directed movement of serotonin into a cell.']
negative regulation of serotonin uptake
True
['Persistently high systemic arterial blood pressure. Based on multiple readings (blood pressure determination), hypertension is currently defined as when systolic pressure is consistently greater than 140 mm Hg or when diastolic pressure is consistently 90 mm Hg or more.']
hypertensive disorder
True
['A condition that occurs while resting or lying in bed; it is characterized by an irresistible urgency to move the legs to obtain relief from a strange and uncomfortable sensation in the legs.']
restless legs syndrome
True
[]
Graves disease
True
[]
Horner syndrome
True
['Nonsynovial joint in which the articulating bones are connected by bone. Examples: epiphyseal junction, manubriosternal synostosis.']
synostosis
True
['A cover or envelope partly or wholly surrounding a structure. Examples: egg shell, articular capsules, renal capsules[WP].', "see also: protective structure surrounding some species of bacteria and fungi. Note that FMA has classes such as Articular capsule that are not subtypes of Capsule (which is undefined in FMA). GO has 'capsule' which is a fungal structure, and 'external encapsulating structure' which is restricted to cells"]
capsule

Repeat the same for MONDO_grouped expect concatenate descriptions of all IDs in a group¶

In [55]:

# For the sake of space and time, we will enrich only the first 5 nodes of each DB
# Extract all gene IDs from the graph
ols_obj = EnrichmentWithOLS()
count = 0
for node in tqdm(kg.nodes):
    if kg.nodes[node].get('node_source') != 'MONDO_grouped':
        continue
    count += 1
    # Get the node attributes
    node_attr = kg.nodes[node]
    # MONDO_grouped contains multiple codes
    # separated by a '_'
    # OLS term must contain 7-digit integer code
    # Hence, prefix with 0s such that total number
    # of characters is 7
    codes = node_attr.get('node_id')
    codes = codes.split('_')
    # print (codes)
    term_ids = []
    for code in codes:
        term_id = 'MONDO_' + str("{:07}".format(int(code)))
        term_ids.append(term_id)
    # Create a OLSAttr object
    ols_attr = OLSAttr(
        term_id=node_attr.get('node_id'),
        name=node,
        label=node,
        description=node_attr.get('description')
    )
    # Fetch descriptions
    descriptions = ols_obj.enrich_documents(term_ids)
    # Add descriptions to the corresponding OLS attributes
    ols_attr.description = '\n'.join(descriptions)
    list_ols_attrs.append(ols_attr)
    if count == 2:
        break
print (list_ols_attrs)

# For the sake of space and time, we will enrich only the first 5 nodes of each DB # Extract all gene IDs from the graph ols_obj = EnrichmentWithOLS() count = 0 for node in tqdm(kg.nodes): if kg.nodes[node].get('node_source') != 'MONDO_grouped': continue count += 1 # Get the node attributes node_attr = kg.nodes[node] # MONDO_grouped contains multiple codes # separated by a '_' # OLS term must contain 7-digit integer code # Hence, prefix with 0s such that total number # of characters is 7 codes = node_attr.get('node_id') codes = codes.split('_') # print (codes) term_ids = [] for code in codes: term_id = 'MONDO_' + str("{:07}".format(int(code))) term_ids.append(term_id) # Create a OLSAttr object ols_attr = OLSAttr( term_id=node_attr.get('node_id'), name=node, label=node, description=node_attr.get('description') ) # Fetch descriptions descriptions = ols_obj.enrich_documents(term_ids) # Add descriptions to the corresponding OLS attributes ols_attr.description = '\n'.join(descriptions) list_ols_attrs.append(ols_attr) if count == 2: break print (list_ols_attrs)

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True
['High blood pressure caused by an underlying medical condition.']
secondary hypertension
True
['Hypertension that presents without an identifiable cause.']
essential hypertension
True
["OBSOLETE. An instance of hypertension that is caused by a modification of the individual's genome."]
obsolete genetic hypertension

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True
['Increased blood pressure in the portal venous system. It is most commonly caused by cirrhosis. Other causes include portal vein thrombosis, Budd-Chiari syndrome, and right heart failure. Complications include ascites, esophageal varices, encephalopathy, and splenomegaly.']
portal hypertension
True
['A severe medical condition which is estimated to appear in 9-18% of hypertensive patients, in which treatment with 3 or more antihypertensive drugs including diuretics are ineffective.']
resistant hypertension
True
['Any Parkinson disease in which the cause of the disease is a mutation in the LRRK2 gene.']
autosomal dominant Parkinson disease 8
True
['Any Parkinson disease in which the cause of the disease is a mutation in the PARK7 gene.']
autosomal recessive early-onset Parkinson disease 7
True
['Any Parkinson disease in which the cause of the disease is a mutation in the VPS35 gene.']
Parkinson disease 17
True
['A Parkinson disease that begins after around the age of 50.']
late-onset Parkinson disease
True
['Any hereditary late onset Parkinson disease in which the cause of the disease is a mutation in the DNAJC13 gene.']
Parkinson disease 21
True
['Any Parkinson disease in which the cause of the disease is a mutation in the PINK1 gene.']
autosomal recessive early-onset Parkinson disease 6
True
['Any Parkinson disease in which the cause of the disease is a mutation in the SYNJ1 gene.']
early-onset Parkinson disease 20
True
['A late onset Parkinson disease that has material basis in heterozygous triplication of the alpha-synuclein gene (SNCA) on chromosome 4q22.']
autosomal dominant Parkinson disease 4
True
['Editor note: DO def states any mutation in SNCA, but this would include PARK4; def needs to state that this is het mutation']
autosomal dominant Parkinson disease 1
True
['Any young-onset Parkinson disease in which the cause of the disease is a mutation in the VPS13C gene.']
autosomal recessive early-onset Parkinson disease 23
True
['A form of Parkinson disease (PD) characterized by an age of onset between 21-45 years, rigidity, painful cramps followed by tremor, bradykinesia, dystonia, gait complaints and falls, and other non-motor symptoms. A slow disease progression and a more pronounced response to dopaminergic therapy are also observed in most YOPD forms.']
young-onset Parkinson disease
True
['A progressive degenerative disorder of the central nervous system characterized by loss of dopamine producing neurons in the substantia nigra and the presence of Lewy bodies in the substantia nigra and locus coeruleus. Signs and symptoms include tremor which is most pronounced during rest, muscle rigidity, slowing of the voluntary movements, a tendency to fall back, and a mask-like facial expression.']
Parkinson disease
True
['A condition with a clinical picture similar to that of Parkinson disease, but which is caused by external factors, including medication.']
secondary Parkinson disease
True
['Editor notes: check onset axioms']
juvenile-onset Parkinson disease
True
[]
Parkinson disease, mitochondrial

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True
[]
parkinson disease 12
True
[]
parkinson disease 10
True
[]
parkinson disease 16
[OLSAttr(term_id='GO_0051581', name='negative regulation of neurotransmitter uptake', label='negative regulation of neurotransmitter uptake', description='Any process that stops, prevents, or reduces the frequency, rate or extent of the directed movement of a neurotransmitter into a neuron or glial cell.'), OLSAttr(term_id='GO_0051612', name='negative regulation of serotonin uptake', label='negative regulation of serotonin uptake', description='Any process that stops, prevents, or reduces the frequency, rate or extent of the directed movement of serotonin into a cell.'), OLSAttr(term_id='MONDO_0005044', name='hypertensive disorder', label='hypertensive disorder', description='Persistently high systemic arterial blood pressure. Based on multiple readings (blood pressure determination), hypertension is currently defined as when systolic pressure is consistently greater than 140 mm Hg or when diastolic pressure is consistently 90 mm Hg or more.'), OLSAttr(term_id='MONDO_0005391', name='restless legs syndrome', label='restless legs syndrome', description='A condition that occurs while resting or lying in bed; it is characterized by an irresistible urgency to move the legs to obtain relief from a strange and uncomfortable sensation in the legs.'), OLSAttr(term_id='HP_0100647', name='Graves disease', label='Graves disease', description='Graves disease'), OLSAttr(term_id='HP_0002277', name='Horner syndrome', label='Horner syndrome', description='Horner syndrome'), OLSAttr(term_id='UBERON_0010361', name='synostosis', label='synostosis', description='Nonsynovial joint in which the articulating bones are connected by bone. Examples: epiphyseal junction, manubriosternal synostosis.'), OLSAttr(term_id='UBERON_0003893', name='capsule', label='capsule', description="A cover or envelope partly or wholly surrounding a structure. Examples: egg shell, articular capsules, renal capsules[WP].\nsee also: protective structure surrounding some species of bacteria and fungi. Note that FMA has classes such as Articular capsule that are not subtypes of Capsule (which is undefined in FMA). GO has 'capsule' which is a fungal structure, and 'external encapsulating structure' which is restricted to cells"), OLSAttr(term_id='1200_1134_15512_5080_100078', name='hypertension', label='hypertension', description="High blood pressure caused by an underlying medical condition.\nHypertension that presents without an identifiable cause.\nOBSOLETE. An instance of hypertension that is caused by a modification of the individual's genome.\nIncreased blood pressure in the portal venous system. It is most commonly caused by cirrhosis. Other causes include portal vein thrombosis, Budd-Chiari syndrome, and right heart failure. Complications include ascites, esophageal varices, encephalopathy, and splenomegaly.\nA severe medical condition which is estimated to appear in 9-18% of hypertensive patients, in which treatment with 3 or more antihypertensive drugs including diuretics are ineffective."), OLSAttr(term_id='11764_11658_13625_8199_14604_11613_14233_11562_8200_14796_17279_5180_6966_828_10796_10360_11737_13167', name='Parkinson disease', label='Parkinson disease', description='Any Parkinson disease in which the cause of the disease is a mutation in the LRRK2 gene.\nAny Parkinson disease in which the cause of the disease is a mutation in the PARK7 gene.\nAny Parkinson disease in which the cause of the disease is a mutation in the VPS35 gene.\nA Parkinson disease that begins after around the age of 50.\nAny hereditary late onset Parkinson disease in which the cause of the disease is a mutation in the DNAJC13 gene.\nAny Parkinson disease in which the cause of the disease is a mutation in the PINK1 gene.\nAny Parkinson disease in which the cause of the disease is a mutation in the SYNJ1 gene.\nA late onset Parkinson disease that has material basis in heterozygous triplication of the alpha-synuclein gene (SNCA) on chromosome 4q22.\nEditor note: DO def states any mutation in SNCA, but this would include PARK4; def needs to state that this is het mutation\nAny young-onset Parkinson disease in which the cause of the disease is a mutation in the VPS13C gene.\nA form of Parkinson disease (PD) characterized by an age of onset between 21-45 years, rigidity, painful cramps followed by tremor, bradykinesia, dystonia, gait complaints and falls, and other non-motor symptoms. A slow disease progression and a more pronounced response to dopaminergic therapy are also observed in most YOPD forms.\nA progressive degenerative disorder of the central nervous system characterized by loss of dopamine producing neurons in the substantia nigra and the presence of Lewy bodies in the substantia nigra and locus coeruleus. Signs and symptoms include tremor which is most pronounced during rest, muscle rigidity, slowing of the voluntary movements, a tendency to fall back, and a mask-like facial expression.\nA condition with a clinical picture similar to that of Parkinson disease, but which is caused by external factors, including medication.\nEditor notes: check onset axioms\nParkinson disease, mitochondrial\nparkinson disease 12\nparkinson disease 10\nparkinson disease 16')]

Add descrioptions to the OLS nodes and recompose the graph¶

In [56]:

for ols_attr in list_ols_attrs:
    node = ols_attr.name
    description = ols_attr.description
    # print (f"node: {node}, description: {description}")
    G.add_nodes_from([(node, {'description': description})])

# Recompose the graph
kg = nx.compose(G, kg)

for ols_attr in list_ols_attrs: node = ols_attr.name description = ols_attr.description # print (f"node: {node}, description: {description}") G.add_nodes_from([(node, {'description': description})]) # Recompose the graph kg = nx.compose(G, kg)

Generate embedding for all the nodes with textual descriptions¶

In [57]:

for i, node in tqdm(enumerate(kg.nodes)):
    node_id = kg.nodes[node].get('node_id')    
    if kg.nodes[node].get('description') is None:
        continue
    print (node)
    desc = kg.nodes[node].get('description')
    outputs = biobert_model.embed_documents([desc])
    G.add_nodes_from([(node, {'description_embedding': outputs})])
    # torch.cuda.synchronize()
    # torch.cuda.empty_cache()

# Recompose the graph
kg = nx.compose(G, kg)

for i, node in tqdm(enumerate(kg.nodes)): node_id = kg.nodes[node].get('node_id') if kg.nodes[node].get('description') is None: continue print (node) desc = kg.nodes[node].get('description') outputs = biobert_model.embed_documents([desc]) G.add_nodes_from([(node, {'description_embedding': outputs})]) # torch.cuda.synchronize() # torch.cuda.empty_cache() # Recompose the graph kg = nx.compose(G, kg)

24716it [00:00, 202653.96it/s]

hypertensive disorder
hypertension
restless legs syndrome
Parkinson disease

59436it [00:00, 125677.84it/s]

Graves disease
Horner syndrome
synostosis
negative regulation of neurotransmitter uptake
negative regulation of serotonin uptake

129262it [00:00, 218477.31it/s]

capsule

Display the results in a DF¶

In [58]:

import pandas as pd
dic = {'node':[],
       'node_source':[],
       'node_id':[],
       'description':[],
       'description_embedding':[]}
for node in tqdm(kg.nodes):
    node_id = kg.nodes[node].get('node_id')
    if kg.nodes[node].get('description') is None:
        continue
    dic['node'].append(node)
    dic['node_source'].append(kg.nodes[node].get('node_source'))
    dic['node_id'].append(kg.nodes[node].get('node_id'))
    dic['description'].append(kg.nodes[node].get('description'))
    dic['description_embedding'].append(kg.nodes[node].get('description_embedding'))
    # print (node, kg.nodes[node].get('description'), kg.nodes[node].get('sequence'), kg.nodes[node].get('description_embedding'))

df = pd.DataFrame(dic)
df

import pandas as pd dic = {'node':[], 'node_source':[], 'node_id':[], 'description':[], 'description_embedding':[]} for node in tqdm(kg.nodes): node_id = kg.nodes[node].get('node_id') if kg.nodes[node].get('description') is None: continue dic['node'].append(node) dic['node_source'].append(kg.nodes[node].get('node_source')) dic['node_id'].append(kg.nodes[node].get('node_id')) dic['description'].append(kg.nodes[node].get('description')) dic['description_embedding'].append(kg.nodes[node].get('description_embedding')) # print (node, kg.nodes[node].get('description'), kg.nodes[node].get('sequence'), kg.nodes[node].get('description_embedding')) df = pd.DataFrame(dic) df

100%|██████████| 129262/129262 [00:00<00:00, 953488.63it/s]

Out[58]:

	node	node_source	node_id	description	description_embedding
0	hypertensive disorder	MONDO	5044	Persistently high systemic arterial blood pres...	[[tensor(-0.3774), tensor(0.0026), tensor(0.33...
1	hypertension	MONDO_grouped	1200_1134_15512_5080_100078	High blood pressure caused by an underlying me...	[[tensor(-0.2972), tensor(0.1923), tensor(0.38...
2	restless legs syndrome	MONDO	5391	A condition that occurs while resting or lying...	[[tensor(-0.0092), tensor(0.1624), tensor(0.47...
3	Parkinson disease	MONDO_grouped	11764_11658_13625_8199_14604_11613_14233_11562...	Any Parkinson disease in which the cause of th...	[[tensor(-0.0157), tensor(0.0820), tensor(0.17...
4	Graves disease	HPO	100647	Graves disease	[[tensor(-0.6299), tensor(0.1351), tensor(0.77...
5	Horner syndrome	HPO	2277	Horner syndrome	[[tensor(-0.5213), tensor(-0.1533), tensor(0.7...
6	synostosis	UBERON	10361	Nonsynovial joint in which the articulating bo...	[[tensor(0.0200), tensor(-0.2079), tensor(0.38...
7	negative regulation of neurotransmitter uptake	GO	51581	Any process that stops, prevents, or reduces t...	[[tensor(0.0589), tensor(0.3241), tensor(0.057...
8	negative regulation of serotonin uptake	GO	51612	Any process that stops, prevents, or reduces t...	[[tensor(-0.0227), tensor(0.3225), tensor(-0.0...
9	capsule	UBERON	3893	A cover or envelope partly or wholly surroundi...	[[tensor(-0.0116), tensor(0.2860), tensor(0.49...

Protein enrichments¶

Now, we will encrich the protein nodes with their description and sequence. We will query the UniProt via API to get the descp and sequence. For this, we will first need to get all the node IDs.

In [11]:

from dataclasses import dataclass
# Create a dataclass to hold the node attributes
@dataclass
class GeneAttr:
    """Dataclass to hold the attributes of a gene node."""
    id: str
    name: str
    # Make description optional
    # If not provided, it will be set to None
    description: str = None
    sequence: str = None

from dataclasses import dataclass # Create a dataclass to hold the node attributes @dataclass class GeneAttr: """Dataclass to hold the attributes of a gene node.""" id: str name: str # Make description optional # If not provided, it will be set to None description: str = None sequence: str = None

Get node IDs¶

In [37]:

# Extract all gene IDs from the graph
dic_gene_ids = {}
for n in tqdm(kg.nodes):
    if kg.nodes[n].get('node_type') != 'gene/protein' and kg.nodes[n].get('node_source') != 'NCBI':
        continue
    # Get the node attributes
    node_attr = kg.nodes[n]
    # Create a GeneAttr object
    gene_attr = GeneAttr(
        id=node_attr.get('node_id'),
        name=n,
        description=node_attr.get('description'),
        sequence=node_attr.get('sequence')
    )
    # Add the gene_attr object to the dictionary
    dic_gene_ids[node_attr.get('node_id')] = gene_attr
# Check the number of gene IDs
len(dic_gene_ids)

# Extract all gene IDs from the graph dic_gene_ids = {} for n in tqdm(kg.nodes): if kg.nodes[n].get('node_type') != 'gene/protein' and kg.nodes[n].get('node_source') != 'NCBI': continue # Get the node attributes node_attr = kg.nodes[n] # Create a GeneAttr object gene_attr = GeneAttr( id=node_attr.get('node_id'), name=n, description=node_attr.get('description'), sequence=node_attr.get('sequence') ) # Add the gene_attr object to the dictionary dic_gene_ids[node_attr.get('node_id')] = gene_attr # Check the number of gene IDs len(dic_gene_ids)

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Out[37]:

27609

Submit a job to UniProt to map the Gene ID to its description and sequence¶

Here we show 2 ways to get description and sequence of a gene:

1. Most of the biomedical graphs offer gene names, which can be used to extract sequence and description using the EnrichmentWithUniProt class in the utils of T2KG
2. Some graphs, like PrimeKG, also offer gene IDs, which can also be used to extract sequence and descriotion using the snippet defined (borrowed from UniProt)

In [15]:

import time
import requests
from requests.adapters import HTTPAdapter, Retry
from urllib.parse import urlparse, parse_qs, urlencode

# Define variables to perform UniProt ID mapping
# Adopted from https://www.uniprot.org/help/id_mapping
API_URL = "https://rest.uniprot.org"
POLLING_INTERVAL = 5
retries = Retry(total=5, backoff_factor=0.25, status_forcelist=[500, 502, 503, 504])
session = requests.Session()
session.mount("https://", HTTPAdapter(max_retries=retries))

def submit_id_mapping(from_db, to_db, ids) -> str:
    """
    Function to submit a job to perform ID mapping.

    Args:
        from_db (str): The source database.
        to_db (str): The target database.
        ids (list): The list of IDs to map.

    Returns:
        str: The job ID.
    """
    request = requests.post(f"{API_URL}/idmapping/run",
                            data={"from": from_db,
                                  "to": to_db,
                                  "ids": ",".join(ids)},)
    try:
        request.raise_for_status()
    except requests.HTTPError:
        print(request.json())
        raise

    return request.json()["jobId"]

def check_id_mapping_results_ready(job_id):
    """
    Function to check if the ID mapping results are ready.

    Args:
        job_id (str): The job ID.

    Returns:
        bool: True if the results are ready, False otherwise.
    """
    while True:
        request = session.get(f"{API_URL}/idmapping/status/{job_id}")

        try:
            request.raise_for_status()
        except requests.HTTPError:
            print(request.json())
            raise

        j = request.json()
        if "jobStatus" in j:
            if j["jobStatus"] in ("NEW", "RUNNING"):
                print(f"Retrying in {POLLING_INTERVAL}s")
                time.sleep(POLLING_INTERVAL)
            else:
                raise Exception(j["jobStatus"])
        else:
            return bool(j["results"] or j["failedIds"])

def get_id_mapping_results_link(job_id):
    """
    Function to get the link to the ID mapping results.

    Args:
        job_id (str): The job ID.

    Returns:
        str: The link to the ID mapping results.
    """
    url = f"{API_URL}/idmapping/details/{job_id}"
    request = requests.Session().get(url)

    try:
        request.raise_for_status()
    except requests.HTTPError:
        print(request.json())
        raise

    return request.json()["redirectURL"]

def decode_results(response, file_format, compressed):
    """
    Function to decode the ID mapping results.

    Args:
        response (requests.Response): The response object.
        file_format (str): The file format of the results.
        compressed (bool): Whether the results are compressed.

    Returns:
        str: The ID mapping results
    """

    if compressed:
        decompressed = zlib.decompress(response.content, 16 + zlib.MAX_WBITS)
        if file_format == "json":
            j = json.loads(decompressed.decode("utf-8"))
            return j
        elif file_format == "tsv":
            return [line for line in decompressed.decode("utf-8").split("\n") if line]
        elif file_format == "xlsx":
            return [decompressed]
        elif file_format == "xml":
            return [decompressed.decode("utf-8")]
        else:
            return decompressed.decode("utf-8")
    elif file_format == "json":
        return response.json()
    elif file_format == "tsv":
        return [line for line in response.text.split("\n") if line]
    elif file_format == "xlsx":
        return [response.content]
    elif file_format == "xml":
        return [response.text]
    return response.text

def get_id_mapping_results_stream(url):
    """
    Function to get the ID mapping results from a stream.

    Args:
        url (str): The URL to the ID mapping results.

    Returns:
        str: The ID mapping results.
    """
    if "/stream/" not in url:
        url = url.replace("/results/", "/results/stream/")

    request = session.get(url)

    try:
        request.raise_for_status()
    except requests.HTTPError:
        print(request.json())
        raise

    parsed = urlparse(url)
    query = parse_qs(parsed.query)
    file_format = query["format"][0] if "format" in query else "json"
    compressed = (
        query["compressed"][0].lower() == "true" if "compressed" in query else False
    )
    return decode_results(request, file_format, compressed)

import time import requests from requests.adapters import HTTPAdapter, Retry from urllib.parse import urlparse, parse_qs, urlencode # Define variables to perform UniProt ID mapping # Adopted from https://www.uniprot.org/help/id_mapping API_URL = "https://rest.uniprot.org" POLLING_INTERVAL = 5 retries = Retry(total=5, backoff_factor=0.25, status_forcelist=[500, 502, 503, 504]) session = requests.Session() session.mount("https://", HTTPAdapter(max_retries=retries)) def submit_id_mapping(from_db, to_db, ids) -> str: """ Function to submit a job to perform ID mapping. Args: from_db (str): The source database. to_db (str): The target database. ids (list): The list of IDs to map. Returns: str: The job ID. """ request = requests.post(f"{API_URL}/idmapping/run", data={"from": from_db, "to": to_db, "ids": ",".join(ids)},) try: request.raise_for_status() except requests.HTTPError: print(request.json()) raise return request.json()["jobId"] def check_id_mapping_results_ready(job_id): """ Function to check if the ID mapping results are ready. Args: job_id (str): The job ID. Returns: bool: True if the results are ready, False otherwise. """ while True: request = session.get(f"{API_URL}/idmapping/status/{job_id}") try: request.raise_for_status() except requests.HTTPError: print(request.json()) raise j = request.json() if "jobStatus" in j: if j["jobStatus"] in ("NEW", "RUNNING"): print(f"Retrying in {POLLING_INTERVAL}s") time.sleep(POLLING_INTERVAL) else: raise Exception(j["jobStatus"]) else: return bool(j["results"] or j["failedIds"]) def get_id_mapping_results_link(job_id): """ Function to get the link to the ID mapping results. Args: job_id (str): The job ID. Returns: str: The link to the ID mapping results. """ url = f"{API_URL}/idmapping/details/{job_id}" request = requests.Session().get(url) try: request.raise_for_status() except requests.HTTPError: print(request.json()) raise return request.json()["redirectURL"] def decode_results(response, file_format, compressed): """ Function to decode the ID mapping results. Args: response (requests.Response): The response object. file_format (str): The file format of the results. compressed (bool): Whether the results are compressed. Returns: str: The ID mapping results """ if compressed: decompressed = zlib.decompress(response.content, 16 + zlib.MAX_WBITS) if file_format == "json": j = json.loads(decompressed.decode("utf-8")) return j elif file_format == "tsv": return [line for line in decompressed.decode("utf-8").split("\n") if line] elif file_format == "xlsx": return [decompressed] elif file_format == "xml": return [decompressed.decode("utf-8")] else: return decompressed.decode("utf-8") elif file_format == "json": return response.json() elif file_format == "tsv": return [line for line in response.text.split("\n") if line] elif file_format == "xlsx": return [response.content] elif file_format == "xml": return [response.text] return response.text def get_id_mapping_results_stream(url): """ Function to get the ID mapping results from a stream. Args: url (str): The URL to the ID mapping results. Returns: str: The ID mapping results. """ if "/stream/" not in url: url = url.replace("/results/", "/results/stream/") request = session.get(url) try: request.raise_for_status() except requests.HTTPError: print(request.json()) raise parsed = urlparse(url) query = parse_qs(parsed.query) file_format = query["format"][0] if "format" in query else "json" compressed = ( query["compressed"][0].lower() == "true" if "compressed" in query else False ) return decode_results(request, file_format, compressed)

For the sake of time, we will use only the first 5 nodes

In [40]:

# Add the top 5 gene IDs to a list
inputs = list(dic_gene_ids.keys())[:5]
# Submit the job to perform ID mapping
job_id = submit_id_mapping(
    from_db="GeneID", to_db="UniProtKB", ids=inputs
)
# Print the job ID
print (f"Job ID: {job_id}")
# Check the status of the job
status = check_id_mapping_results_ready(job_id)
# Print the status of the job
print (f"Job status: {status}")

# Add the top 5 gene IDs to a list inputs = list(dic_gene_ids.keys())[:5] # Submit the job to perform ID mapping job_id = submit_id_mapping( from_db="GeneID", to_db="UniProtKB", ids=inputs ) # Print the job ID print (f"Job ID: {job_id}") # Check the status of the job status = check_id_mapping_results_ready(job_id) # Print the status of the job print (f"Job status: {status}")

Job ID: 29s22juOq2
Job status: True

Check and get the ID mapping results

In [41]:

if check_id_mapping_results_ready(job_id):
    link = get_id_mapping_results_link(job_id)
    mapping_results = get_id_mapping_results_stream(link)
    print(mapping_results)

if check_id_mapping_results_ready(job_id): link = get_id_mapping_results_link(job_id) mapping_results = get_id_mapping_results_stream(link) print(mapping_results)

{'results': [{'from': '9796', 'to': {'entryType': 'UniProtKB reviewed (Swiss-Prot)', 'primaryAccession': 'Q92561', 'secondaryAccessions': ['D3DSR1', 'Q8N4I9'], 'uniProtkbId': 'PHYIP_HUMAN', 'entryAudit': {'firstPublicDate': '1997-11-01', 'lastAnnotationUpdateDate': '2025-04-09', 'lastSequenceUpdateDate': '1997-02-01', 'entryVersion': 176, 'sequenceVersion': 1}, 'annotationScore': 4.0, 'organism': {'scientificName': 'Homo sapiens', 'commonName': 'Human', 'taxonId': 9606, 'lineage': ['Eukaryota', 'Metazoa', 'Chordata', 'Craniata', 'Vertebrata', 'Euteleostomi', 'Mammalia', 'Eutheria', 'Euarchontoglires', 'Primates', 'Haplorrhini', 'Catarrhini', 'Hominidae', 'Homo']}, 'proteinExistence': '1: Evidence at protein level', 'proteinDescription': {'recommendedName': {'fullName': {'value': 'Phytanoyl-CoA hydroxylase-interacting protein'}}, 'alternativeNames': [{'fullName': {'value': 'Phytanoyl-CoA hydroxylase-associated protein 1'}, 'shortNames': [{'value': 'PAHX-AP1'}, {'value': 'PAHXAP1'}]}]}, 'genes': [{'geneName': {'value': 'PHYHIP'}, 'synonyms': [{'value': 'DYRK1AP3'}, {'value': 'KIAA0273'}]}], 'comments': [{'texts': [{'value': 'Its interaction with PHYH suggests a role in the development of the central system'}], 'commentType': 'FUNCTION'}, {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '10686344'}], 'value': 'Interacts with PHYH and ADGRB1'}], 'commentType': 'SUBUNIT'}, {'commentType': 'INTERACTION', 'interactions': [{'interactantOne': {'uniProtKBAccession': 'Q92561', 'intActId': 'EBI-716478'}, 'interactantTwo': {'uniProtKBAccession': 'Q9NXK6', 'geneName': 'PAQR5', 'intActId': 'EBI-10316423'}, 'numberOfExperiments': 2, 'organismDiffer': False}]}, {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '10686344'}], 'value': 'Highly expressed in the brain'}], 'commentType': 'TISSUE SPECIFICITY'}, {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000305'}], 'value': 'Belongs to the PHYHIP family'}], 'commentType': 'SIMILARITY'}, {'commentType': 'SEQUENCE CAUTION', 'sequenceCautionType': 'Erroneous initiation', 'sequence': 'BAA13402.2', 'evidences': [{'evidenceCode': 'ECO:0000305'}]}], 'features': [{'type': 'Chain', 'location': {'start': {'value': 1, 'modifier': 'EXACT'}, 'end': {'value': 330, 'modifier': 'EXACT'}}, 'description': 'Phytanoyl-CoA hydroxylase-interacting protein', 'featureId': 'PRO_0000058416'}, {'type': 'Domain', 'location': {'start': {'value': 6, 'modifier': 'EXACT'}, 'end': {'value': 115, 'modifier': 'EXACT'}}, 'description': 'Fibronectin type-III', 'evidences': [{'evidenceCode': 'ECO:0000255', 'source': 'PROSITE-ProRule', 'id': 'PRU00316'}]}, {'type': 'Glycosylation', 'location': {'start': {'value': 14, 'modifier': 'EXACT'}, 'end': {'value': 14, 'modifier': 'EXACT'}}, 'description': 'N-linked (GlcNAc...) asparagine', 'evidences': [{'evidenceCode': 'ECO:0000255'}], 'featureId': ''}, {'type': 'Glycosylation', 'location': {'start': {'value': 325, 'modifier': 'EXACT'}, 'end': {'value': 325, 'modifier': 'EXACT'}}, 'description': 'N-linked (GlcNAc...) asparagine', 'evidences': [{'evidenceCode': 'ECO:0000255'}], 'featureId': ''}, {'type': 'Natural variant', 'location': {'start': {'value': 21, 'modifier': 'EXACT'}, 'end': {'value': 21, 'modifier': 'EXACT'}}, 'description': 'in dbSNP:rs11547660', 'featureCrossReferences': [{'database': 'dbSNP', 'id': 'rs11547660'}], 'featureId': 'VAR_018475', 'alternativeSequence': {'originalSequence': 'R', 'alternativeSequences': ['S']}}, {'type': 'Sequence conflict', 'location': {'start': {'value': 150, 'modifier': 'EXACT'}, 'end': {'value': 150, 'modifier': 'EXACT'}}, 'description': 'in Ref. 3; AAH34034/AAH35940', 'evidences': [{'evidenceCode': 'ECO:0000305'}], 'alternativeSequence': {'originalSequence': 'Q', 'alternativeSequences': ['E']}}, {'type': 'Sequence conflict', 'location': {'start': {'value': 245, 'modifier': 'EXACT'}, 'end': {'value': 245, 'modifier': 'EXACT'}}, 'description': 'in Ref. 3; AAH34034/AAH35940', 'evidences': [{'evidenceCode': 'ECO:0000305'}], 'alternativeSequence': {'originalSequence': 'L', 'alternativeSequences': ['M']}}, {'type': 'Sequence conflict', 'location': {'start': {'value': 254, 'modifier': 'EXACT'}, 'end': {'value': 254, 'modifier': 'EXACT'}}, 'description': 'in Ref. 3; AAH34034/AAH35940', 'evidences': [{'evidenceCode': 'ECO:0000305'}], 'alternativeSequence': {'originalSequence': 'L', 'alternativeSequences': ['R']}}], 'keywords': [{'id': 'KW-0325', 'category': 'PTM', 'name': 'Glycoprotein'}, {'id': 'KW-1267', 'category': 'Technical term', 'name': 'Proteomics identification'}, {'id': 'KW-1185', 'category': 'Technical term', 'name': 'Reference proteome'}], 'references': [{'referenceNumber': 1, 'citation': {'id': '9039502', 'citationType': 'journal article', 'authors': ['Nagase T.', 'Seki N.', 'Ishikawa K.', 'Ohira M.', 'Kawarabayasi Y.', 'Ohara O.', 'Tanaka A.', 'Kotani H.', 'Miyajima N.', 'Nomura N.'], 'citationCrossReferences': [{'database': 'PubMed', 'id': '9039502'}, {'database': 'DOI', 'id': '10.1093/dnares/3.5.321'}], 'title': 'Prediction of the coding sequences of unidentified human genes. VI. The coding sequences of 80 new genes (KIAA0201-KIAA0280) deduced by analysis of cDNA clones from cell line KG-1 and brain.', 'publicationDate': '1996', 'journal': 'DNA Res.', 'firstPage': '321', 'lastPage': '329', 'volume': '3'}, 'referencePositions': ['NUCLEOTIDE SEQUENCE [LARGE SCALE MRNA]'], 'referenceComments': [{'value': 'Bone marrow', 'type': 'TISSUE'}]}, {'referenceNumber': 2, 'citation': {'id': 'CI-5GBD0VIIJ7C63', 'citationType': 'submission', 'authors': ['Mural R.J.', 'Istrail S.', 'Sutton G.G.', 'Florea L.', 'Halpern A.L.', 'Mobarry C.M.', 'Lippert R.', 'Walenz B.', 'Shatkay H.', 'Dew I.', 'Miller J.R.', 'Flanigan M.J.', 'Edwards N.J.', 'Bolanos R.', 'Fasulo D.', 'Halldorsson B.V.', 'Hannenhalli S.', 'Turner R.', 'Yooseph S.', 'Lu F.', 'Nusskern D.R.', 'Shue B.C.', 'Zheng X.H.', 'Zhong F.', 'Delcher A.L.', 'Huson D.H.', 'Kravitz S.A.', 'Mouchard L.', 'Reinert K.', 'Remington K.A.', 'Clark A.G.', 'Waterman M.S.', 'Eichler E.E.', 'Adams M.D.', 'Hunkapiller M.W.', 'Myers E.W.', 'Venter J.C.'], 'publicationDate': 'SEP-2005', 'submissionDatabase': 'EMBL/GenBank/DDBJ databases'}, 'referencePositions': ['NUCLEOTIDE SEQUENCE [LARGE SCALE GENOMIC DNA]']}, {'referenceNumber': 3, 'citation': {'id': '15489334', 'citationType': 'journal article', 'authoringGroup': ['The MGC Project Team'], 'citationCrossReferences': [{'database': 'PubMed', 'id': '15489334'}, {'database': 'DOI', 'id': '10.1101/gr.2596504'}], 'title': 'The status, quality, and expansion of the NIH full-length cDNA project: the Mammalian Gene Collection (MGC).', 'publicationDate': '2004', 'journal': 'Genome Res.', 'firstPage': '2121', 'lastPage': '2127', 'volume': '14'}, 'referencePositions': ['NUCLEOTIDE SEQUENCE [LARGE SCALE MRNA]'], 'referenceComments': [{'value': 'Brain', 'type': 'TISSUE'}]}, {'referenceNumber': 4, 'citation': {'id': '10686344', 'citationType': 'journal article', 'authors': ['Lee Z.H.', 'Kim H.-H.', 'Ahn K.Y.', 'Seo K.H.', 'Kim J.K.', 'Bae C.S.', 'Kim K.K.'], 'citationCrossReferences': [{'database': 'PubMed', 'id': '10686344'}, {'database': 'DOI', 'id': '10.1016/s0169-328x(99)00304-6'}], 'title': 'Identification of a brain specific protein that associates with a Refsum disease gene product, phytanoyl-CoA alpha-hydroxylase.', 'publicationDate': '2000', 'journal': 'Brain Res. Mol. Brain Res.', 'firstPage': '237', 'lastPage': '247', 'volume': '75'}, 'referencePositions': ['TISSUE SPECIFICITY', 'INTERACTION WITH PHYH']}], 'uniProtKBCrossReferences': [{'database': 'EMBL', 'id': 'D87463', 'properties': [{'key': 'ProteinId', 'value': 'BAA13402.2'}, {'key': 'Status', 'value': 'ALT_INIT'}, {'key': 'MoleculeType', 'value': 'mRNA'}]}, {'database': 'EMBL', 'id': 'CH471080', 'properties': [{'key': 'ProteinId', 'value': 'EAW63693.1'}, {'key': 'Status', 'value': '-'}, {'key': 'MoleculeType', 'value': 'Genomic_DNA'}]}, {'database': 'EMBL', 'id': 'CH471080', 'properties': [{'key': 'ProteinId', 'value': 'EAW63694.1'}, {'key': 'Status', 'value': '-'}, {'key': 'MoleculeType', 'value': 'Genomic_DNA'}]}, {'database': 'EMBL', 'id': 'BC034034', 'properties': [{'key': 'ProteinId', 'value': 'AAH34034.1'}, {'key': 'Status', 'value': '-'}, {'key': 'MoleculeType', 'value': 'mRNA'}]}, {'database': 'EMBL', 'id': 'BC035940', 'properties': [{'key': 'ProteinId', 'value': 'AAH35940.1'}, {'key': 'Status', 'value': '-'}, {'key': 'MoleculeType', 'value': 'mRNA'}]}, {'database': 'CCDS', 'id': 'CCDS43723.1', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'RefSeq', 'id': 'NP_001092805.1', 'properties': [{'key': 'NucleotideSequenceId', 'value': 'NM_001099335.2'}]}, {'database': 'RefSeq', 'id': 'NP_001350240.1', 'properties': [{'key': 'NucleotideSequenceId', 'value': 'NM_001363311.2'}]}, {'database': 'RefSeq', 'id': 'NP_001350241.1', 'properties': [{'key': 'NucleotideSequenceId', 'value': 'NM_001363312.2'}]}, {'database': 'RefSeq', 'id': 'NP_055574.3', 'properties': [{'key': 'NucleotideSequenceId', 'value': 'NM_014759.3'}]}, {'database': 'RefSeq', 'id': 'XP_006716479.1', 'properties': [{'key': 'NucleotideSequenceId', 'value': 'XM_006716416.1'}]}, {'database': 'AlphaFoldDB', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'SMR', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'BioGRID', 'id': '115139', 'properties': [{'key': 'Interactions', 'value': '43'}]}, {'database': 'IntAct', 'id': 'Q92561', 'properties': [{'key': 'Interactions', 'value': '39'}]}, {'database': 'STRING', 'id': '9606.ENSP00000320017', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'GlyCosmos', 'id': 'Q92561', 'properties': [{'key': 'glycosylation', 'value': '2 sites, No reported glycans'}]}, {'database': 'GlyGen', 'id': 'Q92561', 'properties': [{'key': 'glycosylation', 'value': '3 sites, 1 O-linked glycan (1 site)'}]}, {'database': 'iPTMnet', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'PhosphoSitePlus', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'BioMuta', 'id': 'PHYHIP', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'DMDM', 'id': '2495731', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'jPOST', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'MassIVE', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'PaxDb', 'id': '9606-ENSP00000415491', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'PeptideAtlas', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'ProteomicsDB', 'id': '75317', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'Antibodypedia', 'id': '5280', 'properties': [{'key': 'antibodies', 'value': '142 antibodies from 23 providers'}]}, {'database': 'DNASU', 'id': '9796', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'Ensembl', 'id': 'ENST00000321613.7', 'properties': [{'key': 'ProteinId', 'value': 'ENSP00000320017.3'}, {'key': 'GeneId', 'value': 'ENSG00000168490.14'}]}, {'database': 'Ensembl', 'id': 'ENST00000454243.7', 'properties': [{'key': 'ProteinId', 'value': 'ENSP00000415491.2'}, {'key': 'GeneId', 'value': 'ENSG00000168490.14'}]}, {'database': 'GeneID', 'id': '9796', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'KEGG', 'id': 'hsa:9796', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'MANE-Select', 'id': 'ENST00000454243.7', 'properties': [{'key': 'ProteinId', 'value': 'ENSP00000415491.2'}, {'key': 'RefSeqNucleotideId', 'value': 'NM_014759.5'}, {'key': 'RefSeqProteinId', 'value': 'NP_055574.3'}]}, {'database': 'UCSC', 'id': 'uc003xbj.5', 'properties': [{'key': 'OrganismName', 'value': 'human'}]}, {'database': 'AGR', 'id': 'HGNC:16865', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'CTD', 'id': '9796', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'DisGeNET', 'id': '9796', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'GeneCards', 'id': 'PHYHIP', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'HGNC', 'id': 'HGNC:16865', 'properties': [{'key': 'GeneName', 'value': 'PHYHIP'}]}, {'database': 'HPA', 'id': 'ENSG00000168490', 'properties': [{'key': 'ExpressionPatterns', 'value': 'Tissue enriched (brain)'}]}, {'database': 'MIM', 'id': '608511', 'properties': [{'key': 'Type', 'value': 'gene'}]}, {'database': 'neXtProt', 'id': 'NX_Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'OpenTargets', 'id': 'ENSG00000168490', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'PharmGKB', 'id': 'PA33281', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'VEuPathDB', 'id': 'HostDB:ENSG00000168490', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'eggNOG', 'id': 'ENOG502QQIT', 'properties': [{'key': 'ToxonomicScope', 'value': 'Eukaryota'}]}, {'database': 'GeneTree', 'id': 'ENSGT00390000014563', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'HOGENOM', 'id': 'CLU_054218_1_0_1', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'InParanoid', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'OMA', 'id': 'SPGDHFC', 'properties': [{'key': 'Fingerprint', 'value': '-'}]}, {'database': 'OrthoDB', 'id': '6101761at2759', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'PhylomeDB', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'TreeFam', 'id': 'TF314485', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'PathwayCommons', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'SignaLink', 'id': 'Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'BioGRID-ORCS', 'id': '9796', 'properties': [{'key': 'hits', 'value': '15 hits in 1155 CRISPR screens'}]}, {'database': 'CD-CODE', 'id': 'FB4E32DD', 'properties': [{'key': 'EntryName', 'value': 'Presynaptic clusters and postsynaptic densities'}]}, {'database': 'ChiTaRS', 'id': 'PHYHIP', 'properties': [{'key': 'OrganismName', 'value': 'human'}]}, {'database': 'GenomeRNAi', 'id': '9796', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'Pharos', 'id': 'Q92561', 'properties': [{'key': 'DevelopmentLevel', 'value': 'Tdark'}]}, {'database': 'PRO', 'id': 'PR:Q92561', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'Proteomes', 'id': 'UP000005640', 'properties': [{'key': 'Component', 'value': 'Chromosome 8'}]}, {'database': 'RNAct', 'id': 'Q92561', 'properties': [{'key': 'moleculeType', 'value': 'protein'}]}, {'database': 'Bgee', 'id': 'ENSG00000168490', 'properties': [{'key': 'ExpressionPatterns', 'value': 'Expressed in right hemisphere of cerebellum and 132 other cell types or tissues'}]}, {'database': 'ExpressionAtlas', 'id': 'Q92561', 'properties': [{'key': 'ExpressionPatterns', 'value': 'baseline and differential'}]}, {'database': 'GO', 'id': 'GO:0005737', 'properties': [{'key': 'GoTerm', 'value': 'C:cytoplasm'}, {'key': 'GoEvidenceType', 'value': 'IDA:UniProtKB'}], 'evidences': [{'evidenceCode': 'ECO:0000314', 'source': 'PubMed', 'id': '15694837'}]}, {'database': 'GO', 'id': 'GO:1990782', 'properties': [{'key': 'GoTerm', 'value': 'F:protein tyrosine kinase binding'}, {'key': 'GoEvidenceType', 'value': 'IPI:UniProtKB'}], 'evidences': [{'evidenceCode': 'ECO:0000353', 'source': 'PubMed', 'id': '15694837'}]}, {'database': 'GO', 'id': 'GO:0008104', 'properties': [{'key': 'GoTerm', 'value': 'P:protein localization'}, {'key': 'GoEvidenceType', 'value': 'IDA:UniProtKB'}], 'evidences': [{'evidenceCode': 'ECO:0000314', 'source': 'PubMed', 'id': '15694837'}]}, {'database': 'CDD', 'id': 'cd00063', 'properties': [{'key': 'EntryName', 'value': 'FN3'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'FunFam', 'id': '2.60.40.10:FF:000277', 'properties': [{'key': 'EntryName', 'value': 'Phytanoyl-CoA hydroxylase-interacting protein-like protein'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'Gene3D', 'id': '2.60.40.10', 'properties': [{'key': 'EntryName', 'value': 'Immunoglobulins'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'InterPro', 'id': 'IPR003961', 'properties': [{'key': 'EntryName', 'value': 'FN3_dom'}]}, {'database': 'InterPro', 'id': 'IPR036116', 'properties': [{'key': 'EntryName', 'value': 'FN3_sf'}]}, {'database': 'InterPro', 'id': 'IPR013783', 'properties': [{'key': 'EntryName', 'value': 'Ig-like_fold'}]}, {'database': 'InterPro', 'id': 'IPR042868', 'properties': [{'key': 'EntryName', 'value': 'PHYHIP/PHYHIPL'}]}, {'database': 'InterPro', 'id': 'IPR045545', 'properties': [{'key': 'EntryName', 'value': 'PHYIP/PHIPL_C'}]}, {'database': 'PANTHER', 'id': 'PTHR15698:SF9', 'properties': [{'key': 'EntryName', 'value': 'PHYTANOYL-COA HYDROXYLASE-INTERACTING PROTEIN'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'PANTHER', 'id': 'PTHR15698', 'properties': [{'key': 'EntryName', 'value': 'PROTEIN CBG15099'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'Pfam', 'id': 'PF19281', 'properties': [{'key': 'EntryName', 'value': 'PHYHIP_C'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'SUPFAM', 'id': 'SSF49265', 'properties': [{'key': 'EntryName', 'value': 'Fibronectin type III'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'PROSITE', 'id': 'PS50853', 'properties': [{'key': 'EntryName', 'value': 'FN3'}, {'key': 'MatchStatus', 'value': '1'}]}], 'sequence': {'value': 'MELLSTPHSIEINNITCDSFRISWAMEDSDLERVTHYFIDLNKKENKNSNKFKHRDVPTKLVAKAVPLPMTVRGHWFLSPRTEYSVAVQTAVKQSDGEYLVSGWSETVEFCTGDYAKEHLAQLQEKAEQIAGRMLRFSVFYRNHHKEYFQHARTHCGNMLQPYLKDNSGSHGSPTSGMLHGVFFSCNTEFNTGQPPQDSPYGRWRFQIPAQRLFNPSTNLYFADFYCMYTAYHYAILVLAPKGSLGDRFCRDRLPLLDIACNKFLTCSVEDGELVFRHAQDLILEIIYTEPVDLSLGTLGEISGHQLMSLSTADAKKDPSCKTCNISVGR', 'length': 330, 'molWeight': 37573, 'crc64': '777199E6BB071D1C', 'md5': 'DE19CBC5CB9CD947C17A4920C019DFC8'}, 'extraAttributes': {'countByCommentType': {'FUNCTION': 1, 'SUBUNIT': 1, 'INTERACTION': 1, 'TISSUE SPECIFICITY': 1, 'SIMILARITY': 1, 'SEQUENCE CAUTION': 1}, 'countByFeatureType': {'Chain': 1, 'Domain': 1, 'Glycosylation': 2, 'Natural variant': 1, 'Sequence conflict': 3}, 'uniParcId': 'UPI0000139557'}}}, {'from': '56992', 'to': {'entryType': 'UniProtKB reviewed (Swiss-Prot)', 'primaryAccession': 'Q9NS87', 'secondaryAccessions': ['Q17RV9', 'Q69YL6', 'Q96JX7', 'Q9H280'], 'uniProtkbId': 'KIF15_HUMAN', 'entryAudit': {'firstPublicDate': '2008-04-08', 'lastAnnotationUpdateDate': '2025-04-09', 'lastSequenceUpdateDate': '2000-10-01', 'entryVersion': 177, 'sequenceVersion': 1}, 'annotationScore': 5.0, 'organism': {'scientificName': 'Homo sapiens', 'commonName': 'Human', 'taxonId': 9606, 'lineage': ['Eukaryota', 'Metazoa', 'Chordata', 'Craniata', 'Vertebrata', 'Euteleostomi', 'Mammalia', 'Eutheria', 'Euarchontoglires', 'Primates', 'Haplorrhini', 'Catarrhini', 'Hominidae', 'Homo']}, 'proteinExistence': '1: Evidence at protein level', 'proteinDescription': {'recommendedName': {'fullName': {'value': 'Kinesin-like protein KIF15'}}, 'alternativeNames': [{'fullName': {'value': 'Kinesin-like protein 2'}, 'shortNames': [{'value': 'hKLP2'}]}, {'fullName': {'value': 'Kinesin-like protein 7'}}, {'fullName': {'value': 'Serologically defined breast cancer antigen NY-BR-62'}}]}, 'genes': [{'geneName': {'value': 'KIF15'}, 'synonyms': [{'value': 'KLP2'}, {'value': 'KNSL7'}]}], 'comments': [{'texts': [{'evidences': [{'evidenceCode': 'ECO:0000250'}], 'value': 'Plus-end directed kinesin-like motor enzyme involved in mitotic spindle assembly'}], 'commentType': 'FUNCTION'}, {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '10878014'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '12612055'}], 'value': 'Interacts with MKI67 and TPX2'}], 'commentType': 'SUBUNIT'}, {'commentType': 'INTERACTION', 'interactions': [{'interactantOne': {'uniProtKBAccession': 'Q9NS87', 'intActId': 'EBI-712159'}, 'interactantTwo': {'uniProtKBAccession': 'P46013', 'geneName': 'MKI67', 'intActId': 'EBI-876367'}, 'numberOfExperiments': 3, 'organismDiffer': False}]}, {'commentType': 'SUBCELLULAR LOCATION', 'note': {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000250'}], 'value': 'Detected during the interphase in the cytoplasm as finely punctuate pattern and irregularly shaped dots. Detected during mitosis on the mitotic spindle. Colocalizes with TPX2 in mitosis. Localizes at the central spindle at anaphase (By similarity). Localizes at the sites of invaginating cell membranes, a position that corresponds to the location of the contractile actomyosin ring of dividing cells (By similarity). Colocalizes with actin in interphase (By similarity). Colocalizes in dendrites and in growth cone of axons with microtubules (By similarity)'}]}, 'subcellularLocations': [{'location': {'value': 'Cytoplasm', 'id': 'SL-0086'}}, {'location': {'value': 'Cytoplasm, cytoskeleton, spindle', 'id': 'SL-0251'}}]}, {'commentType': 'ALTERNATIVE PRODUCTS', 'events': ['Alternative splicing'], 'isoforms': [{'name': {'value': '1'}, 'isoformIds': ['Q9NS87-1'], 'isoformSequenceStatus': 'Displayed'}, {'name': {'value': '2'}, 'isoformIds': ['Q9NS87-2'], 'sequenceIds': ['VSP_032753'], 'isoformSequenceStatus': 'Described'}, {'name': {'value': '3'}, 'isoformIds': ['Q9NS87-3'], 'sequenceIds': ['VSP_032752'], 'isoformSequenceStatus': 'Described'}, {'name': {'value': '4'}, 'isoformIds': ['Q9NS87-4'], 'sequenceIds': ['VSP_032754', 'VSP_032755'], 'isoformSequenceStatus': 'Described'}]}, {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '12747765'}], 'value': 'Expressed in testis, colon, thymus and in breast cancer'}], 'commentType': 'TISSUE SPECIFICITY'}, {'commentType': 'DISEASE', 'disease': {'diseaseId': 'Braddock-Carey syndrome 2', 'diseaseAccession': 'DI-06453', 'acronym': 'BRDCS2', 'description': 'An autosomal recessive disease characterized by microcephaly, congenital thrombocytopenia, and facial dysmorphisms including Pierre-Robin sequence.', 'diseaseCrossReference': {'database': 'MIM', 'id': '619981'}, 'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '28150392'}]}, 'note': {'texts': [{'value': 'The disease may be caused by variants affecting the gene represented in this entry'}]}}, {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000255', 'source': 'PROSITE-ProRule', 'id': 'PRU00283'}], 'value': 'Belongs to the TRAFAC class myosin-kinesin ATPase superfamily. Kinesin family. KLP2 subfamily'}], 'commentType': 'SIMILARITY'}, {'commentType': 'SEQUENCE CAUTION', 'sequenceCautionType': 'Frameshift', 'sequence': 'AAG48261.1', 'evidences': [{'evidenceCode': 'ECO:0000305'}]}], 'features': [{'type': 'Chain', 'location': {'start': {'value': 1, 'modifier': 'EXACT'}, 'end': {'value': 1388, 'modifier': 'EXACT'}}, 'description': 'Kinesin-like protein KIF15', 'featureId': 'PRO_0000328684'}, {'type': 'Domain', 'location': {'start': {'value': 26, 'modifier': 'EXACT'}, 'end': {'value': 363, 'modifier': 'EXACT'}}, 'description': 'Kinesin motor', 'evidences': [{'evidenceCode': 'ECO:0000255', 'source': 'PROSITE-ProRule', 'id': 'PRU00283'}]}, {'type': 'Region', 'location': {'start': {'value': 1, 'modifier': 'EXACT'}, 'end': {'value': 25, 'modifier': 'EXACT'}}, 'description': 'Disordered', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Region', 'location': {'start': {'value': 1228, 'modifier': 'EXACT'}, 'end': {'value': 1250, 'modifier': 'EXACT'}}, 'description': 'Disordered', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Coiled coil', 'location': {'start': {'value': 368, 'modifier': 'EXACT'}, 'end': {'value': 1388, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0000255'}]}, {'type': 'Compositional bias', 'location': {'start': {'value': 10, 'modifier': 'EXACT'}, 'end': {'value': 22, 'modifier': 'EXACT'}}, 'description': 'Polar residues', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Binding site', 'location': {'start': {'value': 109, 'modifier': 'EXACT'}, 'end': {'value': 116, 'modifier': 'EXACT'}}, 'description': '', 'featureCrossReferences': [{'database': 'ChEBI', 'id': 'CHEBI:30616'}], 'evidences': [{'evidenceCode': 'ECO:0000255', 'source': 'PROSITE-ProRule', 'id': 'PRU00283'}], 'ligand': {'name': 'ATP', 'id': 'ChEBI:CHEBI:30616'}}, {'type': 'Modified residue', 'location': {'start': {'value': 399, 'modifier': 'EXACT'}, 'end': {'value': 399, 'modifier': 'EXACT'}}, 'description': 'Phosphothreonine', 'evidences': [{'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '23186163'}]}, {'type': 'Modified residue', 'location': {'start': {'value': 568, 'modifier': 'EXACT'}, 'end': {'value': 568, 'modifier': 'EXACT'}}, 'description': 'Phosphoserine', 'evidences': [{'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '18669648'}]}, {'type': 'Modified residue', 'location': {'start': {'value': 1009, 'modifier': 'EXACT'}, 'end': {'value': 1009, 'modifier': 'EXACT'}}, 'description': 'N6-acetyllysine', 'evidences': [{'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '19608861'}]}, {'type': 'Modified residue', 'location': {'start': {'value': 1141, 'modifier': 'EXACT'}, 'end': {'value': 1141, 'modifier': 'EXACT'}}, 'description': 'Phosphoserine', 'evidences': [{'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '23186163'}]}, {'type': 'Modified residue', 'location': {'start': {'value': 1169, 'modifier': 'EXACT'}, 'end': {'value': 1169, 'modifier': 'EXACT'}}, 'description': 'Phosphoserine', 'evidences': [{'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '20068231'}]}, {'type': 'Alternative sequence', 'location': {'start': {'value': 1, 'modifier': 'EXACT'}, 'end': {'value': 952, 'modifier': 'EXACT'}}, 'description': 'in isoform 3', 'evidences': [{'evidenceCode': 'ECO:0000303', 'source': 'PubMed', 'id': '14702039'}], 'featureId': 'VSP_032752', 'alternativeSequence': {}}, {'type': 'Alternative sequence', 'location': {'start': {'value': 1, 'modifier': 'EXACT'}, 'end': {'value': 97, 'modifier': 'EXACT'}}, 'description': 'in isoform 2', 'evidences': [{'evidenceCode': 'ECO:0000303', 'source': 'PubMed', 'id': '15489334'}], 'featureId': 'VSP_032753', 'alternativeSequence': {}}, {'type': 'Alternative sequence', 'location': {'start': {'value': 1092, 'modifier': 'EXACT'}, 'end': {'value': 1119, 'modifier': 'EXACT'}}, 'description': 'in isoform 4', 'evidences': [{'evidenceCode': 'ECO:0000303', 'source': 'PubMed', 'id': '12747765'}], 'featureId': 'VSP_032754', 'alternativeSequence': {'originalSequence': 'ELTKKEALIQELQHKLNQKKEEVEQKKN', 'alternativeSequences': ['RTDQERSPDSGTSAQAKPKERGSRTEEE']}}, {'type': 'Alternative sequence', 'location': {'start': {'value': 1200, 'modifier': 'EXACT'}, 'end': {'value': 1388, 'modifier': 'EXACT'}}, 'description': 'in isoform 4', 'evidences': [{'evidenceCode': 'ECO:0000303', 'source': 'PubMed', 'id': '12747765'}], 'featureId': 'VSP_032755', 'alternativeSequence': {}}, {'type': 'Natural variant', 'location': {'start': {'value': 211, 'modifier': 'EXACT'}, 'end': {'value': 211, 'modifier': 'EXACT'}}, 'description': 'in dbSNP:rs34862960', 'featureCrossReferences': [{'database': 'dbSNP', 'id': 'rs34862960'}], 'featureId': 'VAR_042464', 'alternativeSequence': {'originalSequence': 'A', 'alternativeSequences': ['V']}}, {'type': 'Natural variant', 'location': {'start': {'value': 501, 'modifier': 'EXACT'}, 'end': {'value': 1388, 'modifier': 'EXACT'}}, 'description': 'in BRDCS2; uncertain significance; dbSNP:rs1002572191', 'featureCrossReferences': [{'database': 'dbSNP', 'id': 'rs1002572191'}], 'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '28150392'}], 'featureId': 'VAR_087453', 'alternativeSequence': {}}, {'type': 'Natural variant', 'location': {'start': {'value': 996, 'modifier': 'EXACT'}, 'end': {'value': 996, 'modifier': 'EXACT'}}, 'description': 'in dbSNP:rs11710339', 'featureCrossReferences': [{'database': 'dbSNP', 'id': 'rs11710339'}], 'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '12747765'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '17974005'}], 'featureId': 'VAR_042465', 'alternativeSequence': {'originalSequence': 'T', 'alternativeSequences': ['S']}}, {'type': 'Natural variant', 'location': {'start': {'value': 1206, 'modifier': 'EXACT'}, 'end': {'value': 1206, 'modifier': 'EXACT'}}, 'description': 'in dbSNP:rs3804583', 'featureCrossReferences': [{'database': 'dbSNP', 'id': 'rs3804583'}], 'featureId': 'VAR_042466', 'alternativeSequence': {'originalSequence': 'L', 'alternativeSequences': ['M']}}, {'type': 'Natural variant', 'location': {'start': {'value': 1272, 'modifier': 'EXACT'}, 'end': {'value': 1272, 'modifier': 'EXACT'}}, 'description': 'in dbSNP:rs17076986', 'featureCrossReferences': [{'database': 'dbSNP', 'id': 'rs17076986'}], 'featureId': 'VAR_042467', 'alternativeSequence': {'originalSequence': 'E', 'alternativeSequences': ['D']}}, {'type': 'Sequence conflict', 'location': {'start': {'value': 1057, 'modifier': 'EXACT'}, 'end': {'value': 1057, 'modifier': 'EXACT'}}, 'description': 'in Ref. 5; AAG48261', 'evidences': [{'evidenceCode': 'ECO:0000305'}], 'alternativeSequence': {'originalSequence': 'E', 'alternativeSequences': ['K']}}, {'type': 'Beta strand', 'location': {'start': {'value': 28, 'modifier': 'EXACT'}, 'end': {'value': 33, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 50, 'modifier': 'EXACT'}, 'end': {'value': 55, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 58, 'modifier': 'EXACT'}, 'end': {'value': 61, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 64, 'modifier': 'EXACT'}, 'end': {'value': 66, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 68, 'modifier': 'EXACT'}, 'end': {'value': 71, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 73, 'modifier': 'EXACT'}, 'end': {'value': 76, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Helix', 'location': {'start': {'value': 82, 'modifier': 'EXACT'}, 'end': {'value': 98, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 103, 'modifier': 'EXACT'}, 'end': {'value': 108, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Helix', 'location': {'start': {'value': 115, 'modifier': 'EXACT'}, 'end': {'value': 119, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Helix', 'location': {'start': {'value': 135, 'modifier': 'EXACT'}, 'end': {'value': 150, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 158, 'modifier': 'EXACT'}, 'end': {'value': 172, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 174, 'modifier': 'EXACT'}, 'end': {'value': 176, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 186, 'modifier': 'EXACT'}, 'end': {'value': 189, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 195, 'modifier': 'EXACT'}, 'end': {'value': 198, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Helix', 'location': {'start': {'value': 208, 'modifier': 'EXACT'}, 'end': {'value': 226, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 228, 'modifier': 'EXACT'}, 'end': {'value': 231, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 236, 'modifier': 'EXACT'}, 'end': {'value': 250, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Beta strand', 'location': {'start': {'value': 253, 'modifier': 'EXACT'}, 'end': {'value': 265, 'modifier': 'EXACT'}}, 'description': '', 'evidences': [{'evidenceCode': 'ECO:0007829', 'source': 'PDB', 'id': '4BN2'}]}, {'type': 'Helix', 'location': {'start': {'value': 290, 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'Naylor J.', 'Raymond C.', 'Rosetti M.', 'Santos R.', 'Sheridan A.', 'Sougnez C.', 'Stange-Thomann N.', 'Stojanovic N.', 'Subramanian A.', 'Wyman D.', 'Rogers J.', 'Sulston J.', 'Ainscough R.', 'Beck S.', 'Bentley D.', 'Burton J.', 'Clee C.', 'Carter N.', 'Coulson A.', 'Deadman R.', 'Deloukas P.', 'Dunham A.', 'Dunham I.', 'Durbin R.', 'French L.', 'Grafham D.', 'Gregory S.', 'Hubbard T.', 'Humphray S.', 'Hunt A.', 'Jones M.', 'Lloyd C.', 'McMurray A.', 'Matthews L.', 'Mercer S.', 'Milne S.', 'Mullikin J.C.', 'Mungall A.', 'Plumb R.', 'Ross M.', 'Shownkeen R.', 'Sims S.', 'Waterston R.H.', 'Wilson R.K.', 'Hillier L.W.', 'McPherson J.D.', 'Marra M.A.', 'Mardis E.R.', 'Fulton L.A.', 'Chinwalla A.T.', 'Pepin K.H.', 'Gish W.R.', 'Chissoe S.L.', 'Wendl M.C.', 'Delehaunty K.D.', 'Miner T.L.', 'Delehaunty A.', 'Kramer J.B.', 'Cook L.L.', 'Fulton R.S.', 'Johnson D.L.', 'Minx P.J.', 'Clifton S.W.', 'Hawkins T.', 'Branscomb E.', 'Predki P.', 'Richardson P.', 'Wenning S.', 'Slezak T.', 'Doggett 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'MAMTLLEDWCRGMDVNSQRALLVWGIPVNCDEAEIEETLQAAMPQVSYRMLGRMFWREENAKAALLELTGAVDYAAIPREMPGKGGVWKVLFKPPTSDAEFLERLHLFLAREGWTVQDVARVLGFQNPTPTPGPEMPAEMLNYILDNVIQPLVESIWYKRLTLFSGRDIPGPGEETFDPWLEHTNEVLEEWQVSDVEKRRRLMESLRGPAADVIRILKSNNPAITTAECLKALEQVFGSVESSRDAQIKFLNTYQNPGEKLSAYVIRLEPLLQKVVEKGAIDKDNVNQARLEQVIAGANHSGAIRRQLWLTGAGEGPAPNLFQLLVQIREEEAKEEEEEAEATLLQLGLEGHF', 'length': 353, 'molWeight': 39761, 'crc64': 'EB7F5B6AEDA25961', 'md5': 'F38EF6628256BF10939F84F4973927D7'}, 'extraAttributes': {'countByCommentType': {'INTERACTION': 154, 'SUBCELLULAR LOCATION': 1, 'TISSUE SPECIFICITY': 1, 'MISCELLANEOUS': 1, 'SIMILARITY': 1}, 'countByFeatureType': {'Chain': 1, 'Natural variant': 2}, 'uniParcId': 'UPI000003779C'}}}, {'from': '8233', 'to': {'entryType': 'UniProtKB reviewed (Swiss-Prot)', 'primaryAccession': 'Q15696', 'secondaryAccessions': ['Q14D69'], 'uniProtkbId': 'U2AFM_HUMAN', 'entryAudit': {'firstPublicDate': '1997-11-01', 'lastAnnotationUpdateDate': '2025-04-09', 'lastSequenceUpdateDate': '1997-05-01', 'entryVersion': 202, 'sequenceVersion': 2}, 'annotationScore': 5.0, 'organism': {'scientificName': 'Homo sapiens', 'commonName': 'Human', 'taxonId': 9606, 'lineage': ['Eukaryota', 'Metazoa', 'Chordata', 'Craniata', 'Vertebrata', 'Euteleostomi', 'Mammalia', 'Eutheria', 'Euarchontoglires', 'Primates', 'Haplorrhini', 'Catarrhini', 'Hominidae', 'Homo']}, 'proteinExistence': '1: Evidence at protein level', 'proteinDescription': {'recommendedName': {'fullName': {'value': 'U2 small nuclear ribonucleoprotein auxiliary factor 35 kDa subunit-related protein 2'}}, 'alternativeNames': [{'fullName': {'value': 'CCCH type zinc finger, RNA-binding motif and serine/arginine rich protein 2'}}, {'fullName': {'value': 'Renal carcinoma antigen NY-REN-20'}}, {'fullName': {'value': 'U2(RNU2) small nuclear RNA auxiliary factor 1-like 2'}}, {'fullName': {'value': 'U2AF35-related protein'}, 'shortNames': [{'value': 'URP'}]}]}, 'genes': [{'geneName': {'value': 'ZRSR2'}, 'synonyms': [{'value': 'U2AF1-RS2'}, {'value': 'U2AF1L2'}, {'value': 'U2AF1RS2'}, {'value': 'URP'}]}], 'comments': [{'texts': [{'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '21041408'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '9237760'}], 'value': "Pre-mRNA-binding protein required for splicing of both U2- and U12-type introns. Selectively interacts with the 3'-splice site of U2- and U12-type pre-mRNAs and promotes different steps in U2 and U12 intron splicing. Recruited to U12 pre-mRNAs in an ATP-dependent manner and is required for assembly of the pre-spliceosome, a precursor to other spliceosomal complexes. For U2-type introns, it is selectively and specifically required for the second step of splicing"}], 'commentType': 'FUNCTION'}, {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '15146077'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '21041408'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '9237760'}], 'value': 'Component of the U11/U12 snRNPs that are part of the U12-type spliceosome. Interacts (via RS domain) with SRSF1 and SRSF2. Interacts with U2AF2/U2AF65'}], 'commentType': 'SUBUNIT'}, {'commentType': 'INTERACTION', 'interactions': [{'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q9UK58', 'geneName': 'CCNL1', 'intActId': 'EBI-2836773'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'P49760', 'geneName': 'CLK2', 'intActId': 'EBI-750020'}, 'numberOfExperiments': 8, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q92997', 'geneName': 'DVL3', 'intActId': 'EBI-739789'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q7Z7F0-4', 'geneName': 'KHDC4', 'intActId': 'EBI-9089060'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q9Y383', 'geneName': 'LUC7L2', 'intActId': 'EBI-352851'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q6BDI9', 'geneName': 'REP15', 'intActId': 'EBI-12048237'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q9H190', 'geneName': 'SDCBP2', 'intActId': 'EBI-742426'}, 'numberOfExperiments': 7, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q96SB4', 'geneName': 'SRPK1', 'intActId': 'EBI-539478'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'P78362', 'geneName': 'SRPK2', 'intActId': 'EBI-593303'}, 'numberOfExperiments': 8, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'A7MD48', 'geneName': 'SRRM4', 'intActId': 'EBI-3867173'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q13243', 'geneName': 'SRSF5', 'intActId': 'EBI-720503'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q9Y2D8', 'geneName': 'SSX2IP', 'intActId': 'EBI-2212028'}, 'numberOfExperiments': 4, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q96N21', 'geneName': 'TEPSIN', 'intActId': 'EBI-11139477'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q8WVP5', 'geneName': 'TNFAIP8L1', 'intActId': 'EBI-752102'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q15696', 'geneName': 'ZRSR2', 'intActId': 'EBI-6657923'}, 'numberOfExperiments': 3, 'organismDiffer': False}, {'interactantOne': {'uniProtKBAccession': 'Q15696', 'intActId': 'EBI-6657923'}, 'interactantTwo': {'uniProtKBAccession': 'Q15695', 'geneName': 'ZRSR2P1', 'intActId': 'EBI-12270264'}, 'numberOfExperiments': 6, 'organismDiffer': False}]}, {'commentType': 'SUBCELLULAR LOCATION', 'subcellularLocations': [{'location': {'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '15146077'}], 'value': 'Nucleus', 'id': 'SL-0191'}}]}, {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '9237760'}], 'value': 'Widely expressed'}], 'commentType': 'TISSUE SPECIFICITY'}, {'texts': [{'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '9237760'}], 'value': 'Phosphorylated in the RS domain by SRPK1'}], 'commentType': 'PTM'}, {'commentType': 'DISEASE', 'disease': {'diseaseId': 'Orofaciodigital syndrome 21', 'diseaseAccession': 'DI-06953', 'acronym': 'OFD21', 'description': 'A form of orofaciodigital syndrome, a group of heterogeneous disorders characterized by malformations of the oral cavity, face and digits, and associated phenotypic abnormalities that lead to the delineation of various subtypes. OFD21 is an X-linked recessive form characterized by postaxial polydactyly of the hands, hallux duplication, palatal defects, fused incisors, accessory oral frenula and tongue nodules, in association with brain anomalies that range from pituitary anomalies to alobar holoprosencephaly.', 'diseaseCrossReference': {'database': 'MIM', 'id': '301132'}, 'evidences': [{'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '31680349'}, {'evidenceCode': 'ECO:0000269', 'source': 'PubMed', 'id': '38158857'}]}, 'note': {'texts': [{'value': 'The disease is caused by variants affecting the gene represented in this entry'}]}}], 'features': [{'type': 'Chain', 'location': {'start': {'value': 1, 'modifier': 'EXACT'}, 'end': {'value': 482, 'modifier': 'EXACT'}}, 'description': 'U2 small nuclear ribonucleoprotein auxiliary factor 35 kDa subunit-related protein 2', 'featureId': 'PRO_0000082001'}, {'type': 'Domain', 'location': {'start': {'value': 198, 'modifier': 'EXACT'}, 'end': {'value': 304, 'modifier': 'EXACT'}}, 'description': 'RRM', 'evidences': [{'evidenceCode': 'ECO:0000255', 'source': 'PROSITE-ProRule', 'id': 'PRU00176'}]}, {'type': 'Zinc finger', 'location': {'start': {'value': 166, 'modifier': 'EXACT'}, 'end': {'value': 194, 'modifier': 'EXACT'}}, 'description': 'C3H1-type 1', 'evidences': [{'evidenceCode': 'ECO:0000255', 'source': 'PROSITE-ProRule', 'id': 'PRU00723'}]}, {'type': 'Zinc finger', 'location': {'start': {'value': 306, 'modifier': 'EXACT'}, 'end': {'value': 333, 'modifier': 'EXACT'}}, 'description': 'C3H1-type 2', 'evidences': [{'evidenceCode': 'ECO:0000255', 'source': 'PROSITE-ProRule', 'id': 'PRU00723'}]}, {'type': 'Region', 'location': {'start': {'value': 1, 'modifier': 'EXACT'}, 'end': {'value': 59, 'modifier': 'EXACT'}}, 'description': 'Disordered', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Region', 'location': {'start': {'value': 115, 'modifier': 'EXACT'}, 'end': {'value': 135, 'modifier': 'EXACT'}}, 'description': 'Disordered', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Region', 'location': {'start': {'value': 351, 'modifier': 'EXACT'}, 'end': {'value': 482, 'modifier': 'EXACT'}}, 'description': 'Disordered', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Compositional bias', 'location': {'start': {'value': 14, 'modifier': 'EXACT'}, 'end': {'value': 31, 'modifier': 'EXACT'}}, 'description': 'Basic residues', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Compositional bias', 'location': {'start': {'value': 46, 'modifier': 'EXACT'}, 'end': {'value': 58, 'modifier': 'EXACT'}}, 'description': 'Acidic residues', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Compositional bias', 'location': {'start': {'value': 360, 'modifier': 'EXACT'}, 'end': {'value': 375, 'modifier': 'EXACT'}}, 'description': 'Basic and acidic residues', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Compositional bias', 'location': {'start': {'value': 383, 'modifier': 'EXACT'}, 'end': {'value': 398, 'modifier': 'EXACT'}}, 'description': 'Basic and acidic residues', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Compositional bias', 'location': {'start': {'value': 399, 'modifier': 'EXACT'}, 'end': {'value': 412, 'modifier': 'EXACT'}}, 'description': 'Basic residues', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Compositional bias', 'location': {'start': {'value': 413, 'modifier': 'EXACT'}, 'end': {'value': 435, 'modifier': 'EXACT'}}, 'description': 'Basic and acidic residues', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Compositional bias', 'location': {'start': {'value': 436, 'modifier': 'EXACT'}, 'end': {'value': 454, 'modifier': 'EXACT'}}, 'description': 'Basic residues', 'evidences': [{'evidenceCode': 'ECO:0000256', 'source': 'SAM', 'id': 'MobiDB-lite'}]}, {'type': 'Modified residue', 'location': {'start': {'value': 349, 'modifier': 'EXACT'}, 'end': {'value': 349, 'modifier': 'EXACT'}}, 'description': 'Phosphoserine', 'evidences': [{'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '18669648'}, {'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '20068231'}, {'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '23186163'}]}, {'type': 'Modified residue', 'location': {'start': {'value': 384, 'modifier': 'EXACT'}, 'end': {'value': 384, 'modifier': 'EXACT'}}, 'description': 'Phosphoserine', 'evidences': [{'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '17081983'}, {'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '21406692'}]}, {'type': 'Cross-link', 'location': {'start': {'value': 45, 'modifier': 'EXACT'}, 'end': {'value': 45, 'modifier': 'EXACT'}}, 'description': 'Glycyl lysine isopeptide (Lys-Gly) (interchain with G-Cter in SUMO2)', 'evidences': [{'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '28112733'}]}, {'type': 'Cross-link', 'location': {'start': {'value': 62, 'modifier': 'EXACT'}, 'end': {'value': 62, 'modifier': 'EXACT'}}, 'description': 'Glycyl lysine isopeptide (Lys-Gly) (interchain with G-Cter in SUMO2)', 'evidences': [{'evidenceCode': 'ECO:0007744', 'source': 'PubMed', 'id': '28112733'}]}], 'keywords': [{'id': 'KW-1186', 'category': 'Disease', 'name': 'Ciliopathy'}, {'id': 'KW-1017', 'category': 'PTM', 'name': 'Isopeptide bond'}, {'id': 'KW-0479', 'category': 'Ligand', 'name': 'Metal-binding'}, {'id': 'KW-0507', 'category': 'Biological process', 'name': 'mRNA processing'}, {'id': 'KW-0508', 'category': 'Biological process', 'name': 'mRNA splicing'}, {'id': 'KW-0539', 'category': 'Cellular component', 'name': 'Nucleus'}, {'id': 'KW-0597', 'category': 'PTM', 'name': 'Phosphoprotein'}, {'id': 'KW-1267', 'category': 'Technical term', 'name': 'Proteomics identification'}, {'id': 'KW-1185', 'category': 'Technical term', 'name': 'Reference proteome'}, {'id': 'KW-0677', 'category': 'Domain', 'name': 'Repeat'}, {'id': 'KW-0687', 'category': 'Molecular function', 'name': 'Ribonucleoprotein'}, {'id': 'KW-0694', 'category': 'Molecular function', 'name': 'RNA-binding'}, {'id': 'KW-0747', 'category': 'Cellular component', 'name': 'Spliceosome'}, {'id': 'KW-0832', 'category': 'PTM', 'name': 'Ubl conjugation'}, {'id': 'KW-0862', 'category': 'Ligand', 'name': 'Zinc'}, {'id': 'KW-0863', 'category': 'Domain', 'name': 'Zinc-finger'}], 'references': [{'referenceNumber': 1, 'citation': {'id': '8586425', 'citationType': 'journal article', 'authors': ['Kitagawa K.', 'Wang X.', 'Hatada I.', 'Yamaoka T.', 'Nojima H.', 'Inazawa J.', 'Abe T.', 'Mitsuya K.', 'Oshimura M.', 'Murata A.', 'Monden M.', 'Mukai T.'], 'citationCrossReferences': [{'database': 'PubMed', 'id': '8586425'}, {'database': 'DOI', 'id': '10.1006/geno.1995.9879'}], 'title': 'Isolation and mapping of human homologues of an imprinted mouse gene U2af1-rs1.', 'publicationDate': '1995', 'journal': 'Genomics', 'firstPage': '257', 'lastPage': '263', 'volume': '30'}, 'referencePositions': ['NUCLEOTIDE SEQUENCE [MRNA]'], 'referenceComments': [{'value': 'Brain', 'type': 'TISSUE'}]}, {'referenceNumber': 2, 'citation': {'id': '15772651', 'citationType': 'journal article', 'authors': ['Ross M.T.', 'Grafham D.V.', 'Coffey A.J.', 'Scherer S.', 'McLay K.', 'Muzny D.', 'Platzer M.', 'Howell G.R.', 'Burrows C.', 'Bird C.P.', 'Frankish A.', 'Lovell F.L.', 'Howe K.L.', 'Ashurst J.L.', 'Fulton R.S.', 'Sudbrak R.', 'Wen G.', 'Jones M.C.', 'Hurles M.E.', 'Andrews T.D.', 'Scott C.E.', 'Searle S.', 'Ramser J.', 'Whittaker A.', 'Deadman R.', 'Carter N.P.', 'Hunt S.E.', 'Chen R.', 'Cree A.', 'Gunaratne P.', 'Havlak P.', 'Hodgson A.', 'Metzker M.L.', 'Richards S.', 'Scott G.', 'Steffen D.', 'Sodergren E.', 'Wheeler D.A.', 'Worley K.C.', 'Ainscough R.', 'Ambrose K.D.', 'Ansari-Lari M.A.', 'Aradhya S.', 'Ashwell R.I.', 'Babbage A.K.', 'Bagguley C.L.', 'Ballabio A.', 'Banerjee R.', 'Barker G.E.', 'Barlow K.F.', 'Barrett I.P.', 'Bates K.N.', 'Beare D.M.', 'Beasley H.', 'Beasley O.', 'Beck A.', 'Bethel G.', 'Blechschmidt K.', 'Brady N.', 'Bray-Allen S.', 'Bridgeman A.M.', 'Brown A.J.', 'Brown M.J.', 'Bonnin D.', 'Bruford E.A.', 'Buhay C.', 'Burch P.', 'Burford D.', 'Burgess J.', 'Burrill W.', 'Burton J.', 'Bye J.M.', 'Carder C.', 'Carrel L.', 'Chako J.', 'Chapman J.C.', 'Chavez D.', 'Chen E.', 'Chen G.', 'Chen Y.', 'Chen Z.', 'Chinault C.', 'Ciccodicola A.', 'Clark S.Y.', 'Clarke G.', 'Clee C.M.', 'Clegg S.', 'Clerc-Blankenburg K.', 'Clifford K.', 'Cobley V.', 'Cole C.G.', 'Conquer J.S.', 'Corby N.', 'Connor R.E.', 'David R.', 'Davies J.', 'Davis C.', 'Davis J.', 'Delgado O.', 'Deshazo D.', 'Dhami P.', 'Ding Y.', 'Dinh H.', 'Dodsworth S.', 'Draper H.', 'Dugan-Rocha S.', 'Dunham A.', 'Dunn 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'McLaren S.', 'McMurray A.', 'Meidl P.', 'Meitinger T.', 'Milne S.', 'Miner G.', 'Mistry S.L.', 'Morgan M.', 'Morris S.', 'Muller I.', 'Mullikin J.C.', 'Nguyen N.', 'Nordsiek G.', 'Nyakatura G.', "O'Dell C.N.", 'Okwuonu G.', 'Palmer S.', 'Pandian R.', 'Parker D.', 'Parrish J.', 'Pasternak S.', 'Patel D.', 'Pearce A.V.', 'Pearson D.M.', 'Pelan S.E.', 'Perez L.', 'Porter K.M.', 'Ramsey Y.', 'Reichwald K.', 'Rhodes S.', 'Ridler K.A.', 'Schlessinger D.', 'Schueler M.G.', 'Sehra H.K.', 'Shaw-Smith C.', 'Shen H.', 'Sheridan E.M.', 'Shownkeen R.', 'Skuce C.D.', 'Smith M.L.', 'Sotheran E.C.', 'Steingruber H.E.', 'Steward C.A.', 'Storey R.', 'Swann R.M.', 'Swarbreck D.', 'Tabor P.E.', 'Taudien S.', 'Taylor T.', 'Teague B.', 'Thomas K.', 'Thorpe A.', 'Timms K.', 'Tracey A.', 'Trevanion S.', 'Tromans A.C.', "d'Urso M.", 'Verduzco D.', 'Villasana D.', 'Waldron L.', 'Wall M.', 'Wang Q.', 'Warren J.', 'Warry G.L.', 'Wei X.', 'West A.', 'Whitehead S.L.', 'Whiteley M.N.', 'Wilkinson J.E.', 'Willey D.L.', 'Williams G.', 'Williams L.', 'Williamson A.', 'Williamson H.', 'Wilming L.', 'Woodmansey R.L.', 'Wray P.W.', 'Yen J.', 'Zhang J.', 'Zhou J.', 'Zoghbi H.', 'Zorilla S.', 'Buck D.', 'Reinhardt R.', 'Poustka A.', 'Rosenthal A.', 'Lehrach H.', 'Meindl A.', 'Minx P.J.', 'Hillier L.W.', 'Willard H.F.', 'Wilson R.K.', 'Waterston R.H.', 'Rice C.M.', 'Vaudin M.', 'Coulson A.', 'Nelson D.L.', 'Weinstock G.', 'Sulston J.E.', 'Durbin R.', 'Hubbard T.', 'Gibbs R.A.', 'Beck S.', 'Rogers J.', 'Bentley D.R.'], 'citationCrossReferences': [{'database': 'PubMed', 'id': '15772651'}, {'database': 'DOI', 'id': '10.1038/nature03440'}], 'title': 'The DNA sequence of the human X chromosome.', 'publicationDate': '2005', 'journal': 'Nature', 'firstPage': '325', 'lastPage': '337', 'volume': '434'}, 'referencePositions': ['NUCLEOTIDE SEQUENCE [LARGE SCALE GENOMIC DNA]'], 'evidences': [{'evidenceCode': 'ECO:0000313', 'source': 'Ensembl', 'id': 'ENSP00000510773.1'}, {'evidenceCode': 'ECO:0000313', 'source': 'Proteomes', 'id': 'UP000005640'}]}, {'referenceNumber': 4, 'citation': {'id': 'CI-20IMBF1U3E5V5', 'citationType': 'submission', 'authoringGroup': ['Ensembl'], 'publicationDate': 'DEC-2024', 'submissionDatabase': 'UniProtKB'}, 'referencePositions': ['IDENTIFICATION'], 'evidences': [{'evidenceCode': 'ECO:0000313', 'source': 'Ensembl', 'id': 'ENSP00000510773.1'}]}], 'uniProtKBCrossReferences': [{'database': 'EMBL', 'id': 'AC004106', 'properties': [{'key': 'ProteinId', 'value': '-'}, {'key': 'Status', 'value': 'NOT_ANNOTATED_CDS'}, {'key': 'MoleculeType', 'value': 'Genomic_DNA'}]}, {'database': 'EMBL', 'id': 'AC096510', 'properties': [{'key': 'ProteinId', 'value': '-'}, {'key': 'Status', 'value': 'NOT_ANNOTATED_CDS'}, {'key': 'MoleculeType', 'value': 'Genomic_DNA'}]}, {'database': 'EMBL', 'id': 'KF458988', 'properties': [{'key': 'ProteinId', 'value': '-'}, {'key': 'Status', 'value': 'NOT_ANNOTATED_CDS'}, {'key': 'MoleculeType', 'value': 'Genomic_DNA'}]}, {'database': 'RefSeq', 'id': 'XP_011543891.2', 'properties': [{'key': 'NucleotideSequenceId', 'value': 'XM_011545589.2'}]}, {'database': 'SMR', 'id': 'A0A8I5KSD0', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'PeptideAtlas', 'id': 'A0A8I5KSD0', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'Ensembl', 'id': 'ENST00000684799.1', 'properties': [{'key': 'ProteinId', 'value': 'ENSP00000510773.1'}, {'key': 'GeneId', 'value': 'ENSG00000169249.14'}]}, {'database': 'GeneID', 'id': '8233', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'CTD', 'id': '8233', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'DisGeNET', 'id': '8233', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'HGNC', 'id': 'HGNC:23019', 'properties': [{'key': 'GeneName', 'value': 'ZRSR2'}]}, {'database': 'GeneTree', 'id': 'ENSGT00950000183152', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'OrthoDB', 'id': '75923at2759', 'properties': [{'key': 'Description', 'value': '-'}]}, {'database': 'Proteomes', 'id': 'UP000005640', 'properties': [{'key': 'Component', 'value': 'Chromosome X'}]}, {'database': 'GO', 'id': 'GO:0089701', 'properties': [{'key': 'GoTerm', 'value': 'C:U2AF complex'}, {'key': 'GoEvidenceType', 'value': 'IEA:InterPro'}]}, {'database': 'GO', 'id': 'GO:0003723', 'properties': [{'key': 'GoTerm', 'value': 'F:RNA binding'}, {'key': 'GoEvidenceType', 'value': 'IEA:UniProtKB-UniRule'}]}, {'database': 'GO', 'id': 'GO:0008270', 'properties': [{'key': 'GoTerm', 'value': 'F:zinc ion binding'}, {'key': 'GoEvidenceType', 'value': 'IEA:UniProtKB-KW'}]}, {'database': 'GO', 'id': 'GO:0000398', 'properties': [{'key': 'GoTerm', 'value': 'P:mRNA splicing, via spliceosome'}, {'key': 'GoEvidenceType', 'value': 'IEA:InterPro'}]}, {'database': 'CDD', 'id': 'cd12540', 'properties': [{'key': 'EntryName', 'value': 'RRM_U2AFBPL'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'FunFam', 'id': '3.30.70.330:FF:000209', 'properties': [{'key': 'EntryName', 'value': 'U2 small nuclear ribonucleoprotein auxiliary factor 35 kDa subunit-related protein 2'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'Gene3D', 'id': '3.30.70.330', 'properties': [{'key': 'EntryName', 'value': '-'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'InterPro', 'id': 'IPR012677', 'properties': [{'key': 'EntryName', 'value': 'Nucleotide-bd_a/b_plait_sf'}]}, {'database': 'InterPro', 'id': 'IPR035979', 'properties': [{'key': 'EntryName', 'value': 'RBD_domain_sf'}]}, {'database': 'InterPro', 'id': 'IPR000504', 'properties': [{'key': 'EntryName', 'value': 'RRM_dom'}]}, {'database': 'InterPro', 'id': 'IPR003954', 'properties': [{'key': 'EntryName', 'value': 'RRM_dom_euk'}]}, {'database': 'InterPro', 'id': 'IPR009145', 'properties': [{'key': 'EntryName', 'value': 'U2AF_small'}]}, {'database': 'InterPro', 'id': 'IPR000571', 'properties': [{'key': 'EntryName', 'value': 'Znf_CCCH'}]}, {'database': 'PANTHER', 'id': 'PTHR12620', 'properties': [{'key': 'EntryName', 'value': 'U2 SNRNP AUXILIARY FACTOR, SMALL SUBUNIT'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'Pfam', 'id': 'PF00076', 'properties': [{'key': 'EntryName', 'value': 'RRM_1'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'Pfam', 'id': 'PF00642', 'properties': [{'key': 'EntryName', 'value': 'zf-CCCH'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'PRINTS', 'id': 'PR01848', 'properties': [{'key': 'EntryName', 'value': 'U2AUXFACTOR'}]}, {'database': 'SMART', 'id': 'SM00361', 'properties': [{'key': 'EntryName', 'value': 'RRM_1'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'SMART', 'id': 'SM00356', 'properties': [{'key': 'EntryName', 'value': 'ZnF_C3H1'}, {'key': 'MatchStatus', 'value': '2'}]}, {'database': 'SUPFAM', 'id': 'SSF54928', 'properties': [{'key': 'EntryName', 'value': 'RNA-binding domain, RBD'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'PROSITE', 'id': 'PS50102', 'properties': [{'key': 'EntryName', 'value': 'RRM'}, {'key': 'MatchStatus', 'value': '1'}]}, {'database': 'PROSITE', 'id': 'PS50103', 'properties': [{'key': 'EntryName', 'value': 'ZF_C3H1'}, {'key': 'MatchStatus', 'value': '2'}]}], 'sequence': {'value': 'MTFLCFLRALPASLVALCKEEEEDTFIEEQQLEEEKLLERERQRLHEEWLLREQKAQEEFRIKKEKEEAAKKRQEEQERKLKEQWEEQQRKEREEEEQKRQEKKEKEEALQKMLDQAENELENGTTWQNPEPPVDFRVMEKDRANCPFYSKTGACRFGDRCSRKHNFPTSSPTLLIKSMFTTFGMEQCRRDDYDPDASLEYSEEETYQQFLDFYEDVLPEFKNVGKVIQFKVSCNLEPHLRGNVYVQYQSEEECQAALSLFNGRWYAGRQLQCEFCPVTRWKMAICGLFEIQQCPRGKHCNFLHVFRNPNNEFWEANRDIYLSPDRTGSSFGKNSERRERMGHHDDYYSRLRGRRNPSPDHSYKRNGESERKSSRHRGKKSHKRTSKSRERHNSRSRGRNRDRSRDRSRGRGSRSRSRSRSRRSRRSRSQSSSRSRSRGRRRSETGSCYVAQTGGQWLFT', 'length': 460, 'molWeight': 55164, 'crc64': '767508C34EFA4466', 'md5': '778986CF61A1A953A5B1D7F6E9248A50'}, 'extraAttributes': {'countByFeatureType': {'Signal': 1, 'Chain': 1, 'Domain': 3, 'Zinc finger': 2, 'Region': 2, 'Compositional bias': 5}, 'uniParcId': 'UPI0007DC7599'}}}]}

Store the mapping results in a dictionary¶

Key is the gene ID and value is a nested dictionary with keys "description" and "sequence"

In [43]:

dic_gene_id_to_descp_seq = {}
for result in mapping_results['results']:
    # print(result['to'])
    if result['to']['entryType'] == 'UniProtKB reviewed (Swiss-Prot)':
        # print (result['from'], result['to'])
        dic_gene_id_to_descp_seq[result['from']] = {}
        for comment in result['to']['comments']:
            if comment['commentType'] == 'FUNCTION':
                for text in comment['texts']:
                    # print (text['value'])
                    description = text['value']
        dic_gene_id_to_descp_seq[result['from']]['description'] = description
        dic_gene_id_to_descp_seq[result['from']]['sequence'] = result['to']['sequence']['value']

# Display the contents of the dictionary
for gene_id, descp_seq in dic_gene_id_to_descp_seq.items():
    print(f"Gene ID: {gene_id}")
    print(f"Description: {descp_seq['description']}")
    print(f"Sequence: {descp_seq['sequence']}")
    print()
        

dic_gene_id_to_descp_seq = {} for result in mapping_results['results']: # print(result['to']) if result['to']['entryType'] == 'UniProtKB reviewed (Swiss-Prot)': # print (result['from'], result['to']) dic_gene_id_to_descp_seq[result['from']] = {} for comment in result['to']['comments']: if comment['commentType'] == 'FUNCTION': for text in comment['texts']: # print (text['value']) description = text['value'] dic_gene_id_to_descp_seq[result['from']]['description'] = description dic_gene_id_to_descp_seq[result['from']]['sequence'] = result['to']['sequence']['value'] # Display the contents of the dictionary for gene_id, descp_seq in dic_gene_id_to_descp_seq.items(): print(f"Gene ID: {gene_id}") print(f"Description: {descp_seq['description']}") print(f"Sequence: {descp_seq['sequence']}") print()

Gene ID: 9796
Description: Its interaction with PHYH suggests a role in the development of the central system
Sequence: MELLSTPHSIEINNITCDSFRISWAMEDSDLERVTHYFIDLNKKENKNSNKFKHRDVPTKLVAKAVPLPMTVRGHWFLSPRTEYSVAVQTAVKQSDGEYLVSGWSETVEFCTGDYAKEHLAQLQEKAEQIAGRMLRFSVFYRNHHKEYFQHARTHCGNMLQPYLKDNSGSHGSPTSGMLHGVFFSCNTEFNTGQPPQDSPYGRWRFQIPAQRLFNPSTNLYFADFYCMYTAYHYAILVLAPKGSLGDRFCRDRLPLLDIACNKFLTCSVEDGELVFRHAQDLILEIIYTEPVDLSLGTLGEISGHQLMSLSTADAKKDPSCKTCNISVGR

Gene ID: 56992
Description: Plus-end directed kinesin-like motor enzyme involved in mitotic spindle assembly
Sequence: MAPGCKTELRSVTNGQSNQPSNEGDAIKVFVRIRPPAERSGSADGEQNLCLSVLSSTSLRLHSNPEPKTFTFDHVADVDTTQESVFATVAKSIVESCMSGYNGTIFAYGQTGSGKTFTMMGPSESDNFSHNLRGVIPRSFEYLFSLIDREKEKAGAGKSFLCKCSFIEIYNEQIYDLLDSASAGLYLREHIKKGVFVVGAVEQVVTSAAEAYQVLSGGWRNRRVASTSMNRESSRSHAVFTITIESMEKSNEIVNIRTSLLNLVDLAGSERQKDTHAEGMRLKEAGNINRSLSCLGQVITALVDVGNGKQRHVCYRDSKLTFLLRDSLGGNAKTAIIANVHPGSRCFGETLSTLNFAQRAKLIKNKAVVNEDTQGNVSQLQAEVKRLKEQLAELASGQTPPESFLTRDKKKTNYMEYFQEAMLFFKKSEQEKKSLIEKVTQLEDLTLKKEKFIQSNKMIVKFREDQIIRLEKLHKESRGGFLPEEQDRLLSELRNEIQTLREQIEHHPRVAKYAMENHSLREENRRLRLLEPVKRAQEMDAQTIAKLEKAFSEISGMEKSDKNQQGFSPKAQKEPCLFANTEKLKAQLLQIQTELNNSKQEYEEFKELTRKRQLELESELQSLQKANLNLENLLEATKACKRQEVSQLNKIHAETLKIITTPTKAYQLHSRPVPKLSPEMGSFGSLYTQNSSILDNDILNEPVPPEMNEQAFEAISEELRTVQEQMSALQAKLDEEEHKNLKLQQHVDKLEHHSTQMQELFSSERIDWTKQQEELLSQLNVLEKQLQETQTKNDFLKSEVHDLRVVLHSADKELSSVKLEYSSFKTNQEKEFNKLSERHMHVQLQLDNLRLENEKLLESKACLQDSYDNLQEIMKFEIDQLSRNLQNFKKENETLKSDLNNLMELLEAEKERNNKLSLQFEEDKENSSKEILKVLEAVRQEKQKETAKCEQQMAKVQKLEESLLATEKVISSLEKSRDSDKKVVADLMNQIQELRTSVCEKTETIDTLKQELKDINCKYNSALVDREESRVLIKKQEVDILDLKETLRLRILSEDIERDMLCEDLAHATEQLNMLTEASKKHSGLLQSAQEELTKKEALIQELQHKLNQKKEEVEQKKNEYNFKMRQLEHVMDSAAEDPQSPKTPPHFQTHLAKLLETQEQEIEDGRASKTSLEHLVTKLNEDREVKNAEILRMKEQLREMENLRLESQQLIEKNWLLQGQLDDIKRQKENSDQNHPDNQQLKNEQEESIKERLAKSKIVEEMLKMKADLEEVQSALYNKEMECLRMTDEVERTQTLESKAFQEKEQLRSKLEEMYEERERTSQEMEMLRKQVECLAEENGKLVGHQNLHQKIQYVVRLKKENVRLAEETEKLRAENVFLKEKKRSES

Gene ID: 7918
Description: Plus-end directed kinesin-like motor enzyme involved in mitotic spindle assembly
Sequence: MSRPLLITFTPATDPSDLWKDGQQQPQPEKPESTLDGAAARAFYEALIGDESSAPDSQRSQTEPARERKRKKRRIMKAPAAEAVAEGASGRHGQGRSLEAEDKMTHRILRAAQEGDLPELRRLLEPHEAGGAGGNINARDAFWWTPLMCAARAGQGAAVSYLLGRGAAWVGVCELSGRDAAQLAEEAGFPEVARMVRESHGETRSPENRSPTPSLQYCENCDTHFQDSNHRTSTAHLLSLSQGPQPPNLPLGVPISSPGFKLLLRGGWEPGMGLGPRGEGRANPIPTVLKRDQEGLGYRSAPQPRVTHFPAWDTRAVAGRERPPRVATLSWREERRREEKDRAWERDLRTYMNLEF

Gene ID: 9240
Description: Plus-end directed kinesin-like motor enzyme involved in mitotic spindle assembly
Sequence: MAMTLLEDWCRGMDVNSQRALLVWGIPVNCDEAEIEETLQAAMPQVSYRMLGRMFWREENAKAALLELTGAVDYAAIPREMPGKGGVWKVLFKPPTSDAEFLERLHLFLAREGWTVQDVARVLGFQNPTPTPGPEMPAEMLNYILDNVIQPLVESIWYKRLTLFSGRDIPGPGEETFDPWLEHTNEVLEEWQVSDVEKRRRLMESLRGPAADVIRILKSNNPAITTAECLKALEQVFGSVESSRDAQIKFLNTYQNPGEKLSAYVIRLEPLLQKVVEKGAIDKDNVNQARLEQVIAGANHSGAIRRQLWLTGAGEGPAPNLFQLLVQIREEEAKEEEEEAEATLLQLGLEGHF

Gene ID: 8233
Description: Pre-mRNA-binding protein required for splicing of both U2- and U12-type introns. Selectively interacts with the 3'-splice site of U2- and U12-type pre-mRNAs and promotes different steps in U2 and U12 intron splicing. Recruited to U12 pre-mRNAs in an ATP-dependent manner and is required for assembly of the pre-spliceosome, a precursor to other spliceosomal complexes. For U2-type introns, it is selectively and specifically required for the second step of splicing
Sequence: MAAPEKMTFPEKPSHKKYRAALKKEKRKKRRQELARLRDSGLSQKEEEEDTFIEEQQLEEEKLLERERQRLHEEWLLREQKAQEEFRIKKEKEEAAKKRQEEQERKLKEQWEEQQRKEREEEEQKRQEKKEKEEALQKMLDQAENELENGTTWQNPEPPVDFRVMEKDRANCPFYSKTGACRFGDRCSRKHNFPTSSPTLLIKSMFTTFGMEQCRRDDYDPDASLEYSEEETYQQFLDFYEDVLPEFKNVGKVIQFKVSCNLEPHLRGNVYVQYQSEEECQAALSLFNGRWYAGRQLQCEFCPVTRWKMAICGLFEIQQCPRGKHCNFLHVFRNPNNEFWEANRDIYLSPDRTGSSFGKNSERRERMGHHDDYYSRLRGRRNPSPDHSYKRNGESERKSSRHRGKKSHKRTSKSRERHNSRSRGRNRDRSRDRSRGRGSRSRSRSRSRRSRRSRSQSSSRSRSRGRRRSGNRDRTVQSPKSK

Most of the biomedical graphs offer gene names, hence you can choose to also query seqeuences and descriptions via the Gene names using the EnrichmentWithUniprot class¶

In [47]:

for gene_id in inputs:
    # Get the gene name of the gene ID
    gene_name = dic_gene_ids[gene_id].name
    print (f"Gene name: {gene_name}")
    # Create an instance of the EnrichmentWithUniProt class
    enrich_uniprot = EnrichmentWithUniProt()
    # Get the sequence and description for the gene name
    description, sequence = enrich_uniprot.enrich_documents([gene_name])
    dic_gene_id_to_descp_seq[gene_id]['description'] = description
    dic_gene_id_to_descp_seq[gene_id]['sequence'] = sequence
    print (f"Gene name: {gene_name}\nDescription: {description}\nSequence: {sequence}")

for gene_id in inputs: # Get the gene name of the gene ID gene_name = dic_gene_ids[gene_id].name print (f"Gene name: {gene_name}") # Create an instance of the EnrichmentWithUniProt class enrich_uniprot = EnrichmentWithUniProt() # Get the sequence and description for the gene name description, sequence = enrich_uniprot.enrich_documents([gene_name]) dic_gene_id_to_descp_seq[gene_id]['description'] = description dic_gene_id_to_descp_seq[gene_id]['sequence'] = sequence print (f"Gene name: {gene_name}\nDescription: {description}\nSequence: {sequence}")

Gene name: PHYHIP
Gene name: PHYHIP
Description: ['Its interaction with PHYH suggests a role in the development of the central system']
Sequence: ['MELLSTPHSIEINNITCDSFRISWAMEDSDLERVTHYFIDLNKKENKNSNKFKHRDVPTKLVAKAVPLPMTVRGHWFLSPRTEYSVAVQTAVKQSDGEYLVSGWSETVEFCTGDYAKEHLAQLQEKAEQIAGRMLRFSVFYRNHHKEYFQHARTHCGNMLQPYLKDNSGSHGSPTSGMLHGVFFSCNTEFNTGQPPQDSPYGRWRFQIPAQRLFNPSTNLYFADFYCMYTAYHYAILVLAPKGSLGDRFCRDRLPLLDIACNKFLTCSVEDGELVFRHAQDLILEIIYTEPVDLSLGTLGEISGHQLMSLSTADAKKDPSCKTCNISVGR']
Gene name: KIF15
Gene name: KIF15
Description: ['Plus-end directed kinesin-like motor enzyme involved in mitotic spindle assembly']
Sequence: ['MAPGCKTELRSVTNGQSNQPSNEGDAIKVFVRIRPPAERSGSADGEQNLCLSVLSSTSLRLHSNPEPKTFTFDHVADVDTTQESVFATVAKSIVESCMSGYNGTIFAYGQTGSGKTFTMMGPSESDNFSHNLRGVIPRSFEYLFSLIDREKEKAGAGKSFLCKCSFIEIYNEQIYDLLDSASAGLYLREHIKKGVFVVGAVEQVVTSAAEAYQVLSGGWRNRRVASTSMNRESSRSHAVFTITIESMEKSNEIVNIRTSLLNLVDLAGSERQKDTHAEGMRLKEAGNINRSLSCLGQVITALVDVGNGKQRHVCYRDSKLTFLLRDSLGGNAKTAIIANVHPGSRCFGETLSTLNFAQRAKLIKNKAVVNEDTQGNVSQLQAEVKRLKEQLAELASGQTPPESFLTRDKKKTNYMEYFQEAMLFFKKSEQEKKSLIEKVTQLEDLTLKKEKFIQSNKMIVKFREDQIIRLEKLHKESRGGFLPEEQDRLLSELRNEIQTLREQIEHHPRVAKYAMENHSLREENRRLRLLEPVKRAQEMDAQTIAKLEKAFSEISGMEKSDKNQQGFSPKAQKEPCLFANTEKLKAQLLQIQTELNNSKQEYEEFKELTRKRQLELESELQSLQKANLNLENLLEATKACKRQEVSQLNKIHAETLKIITTPTKAYQLHSRPVPKLSPEMGSFGSLYTQNSSILDNDILNEPVPPEMNEQAFEAISEELRTVQEQMSALQAKLDEEEHKNLKLQQHVDKLEHHSTQMQELFSSERIDWTKQQEELLSQLNVLEKQLQETQTKNDFLKSEVHDLRVVLHSADKELSSVKLEYSSFKTNQEKEFNKLSERHMHVQLQLDNLRLENEKLLESKACLQDSYDNLQEIMKFEIDQLSRNLQNFKKENETLKSDLNNLMELLEAEKERNNKLSLQFEEDKENSSKEILKVLEAVRQEKQKETAKCEQQMAKVQKLEESLLATEKVISSLEKSRDSDKKVVADLMNQIQELRTSVCEKTETIDTLKQELKDINCKYNSALVDREESRVLIKKQEVDILDLKETLRLRILSEDIERDMLCEDLAHATEQLNMLTEASKKHSGLLQSAQEELTKKEALIQELQHKLNQKKEEVEQKKNEYNFKMRQLEHVMDSAAEDPQSPKTPPHFQTHLAKLLETQEQEIEDGRASKTSLEHLVTKLNEDREVKNAEILRMKEQLREMENLRLESQQLIEKNWLLQGQLDDIKRQKENSDQNHPDNQQLKNEQEESIKERLAKSKIVEEMLKMKADLEEVQSALYNKEMECLRMTDEVERTQTLESKAFQEKEQLRSKLEEMYEERERTSQEMEMLRKQVECLAEENGKLVGHQNLHQKIQYVVRLKKENVRLAEETEKLRAENVFLKEKKRSES']
Gene name: GPANK1
Gene name: GPANK1
Description: ['']
Sequence: ['MSRPLLITFTPATDPSDLWKDGQQQPQPEKPESTLDGAAARAFYEALIGDESSAPDSQRSQTEPARERKRKKRRIMKAPAAEAVAEGASGRHGQGRSLEAEDKMTHRILRAAQEGDLPELRRLLEPHEAGGAGGNINARDAFWWTPLMCAARAGQGAAVSYLLGRGAAWVGVCELSGRDAAQLAEEAGFPEVARMVRESHGETRSPENRSPTPSLQYCENCDTHFQDSNHRTSTAHLLSLSQGPQPPNLPLGVPISSPGFKLLLRGGWEPGMGLGPRGEGRANPIPTVLKRDQEGLGYRSAPQPRVTHFPAWDTRAVAGRERPPRVATLSWREERRREEKDRAWERDLRTYMNLEF']
Gene name: PNMA1
Gene name: PNMA1
Description: ['']
Sequence: ['MAMTLLEDWCRGMDVNSQRALLVWGIPVNCDEAEIEETLQAAMPQVSYRMLGRMFWREENAKAALLELTGAVDYAAIPREMPGKGGVWKVLFKPPTSDAEFLERLHLFLAREGWTVQDVARVLGFQNPTPTPGPEMPAEMLNYILDNVIQPLVESIWYKRLTLFSGRDIPGPGEETFDPWLEHTNEVLEEWQVSDVEKRRRLMESLRGPAADVIRILKSNNPAITTAECLKALEQVFGSVESSRDAQIKFLNTYQNPGEKLSAYVIRLEPLLQKVVEKGAIDKDNVNQARLEQVIAGANHSGAIRRQLWLTGAGEGPAPNLFQLLVQIREEEAKEEEEEAEATLLQLGLEGHF']
Gene name: ZRSR2
Gene name: ZRSR2
Description: ["Pre-mRNA-binding protein required for splicing of both U2- and U12-type introns. Selectively interacts with the 3'-splice site of U2- and U12-type pre-mRNAs and promotes different steps in U2 and U12 intron splicing. Recruited to U12 pre-mRNAs in an ATP-dependent manner and is required for assembly of the pre-spliceosome, a precursor to other spliceosomal complexes. For U2-type introns, it is selectively and specifically required for the second step of splicing"]
Sequence: ['MAAPEKMTFPEKPSHKKYRAALKKEKRKKRRQELARLRDSGLSQKEEEEDTFIEEQQLEEEKLLERERQRLHEEWLLREQKAQEEFRIKKEKEEAAKKRQEEQERKLKEQWEEQQRKEREEEEQKRQEKKEKEEALQKMLDQAENELENGTTWQNPEPPVDFRVMEKDRANCPFYSKTGACRFGDRCSRKHNFPTSSPTLLIKSMFTTFGMEQCRRDDYDPDASLEYSEEETYQQFLDFYEDVLPEFKNVGKVIQFKVSCNLEPHLRGNVYVQYQSEEECQAALSLFNGRWYAGRQLQCEFCPVTRWKMAICGLFEIQQCPRGKHCNFLHVFRNPNNEFWEANRDIYLSPDRTGSSFGKNSERRERMGHHDDYYSRLRGRRNPSPDHSYKRNGESERKSSRHRGKKSHKRTSKSRERHNSRSRGRNRDRSRDRSRGRGSRSRSRSRSRRSRRSRSQSSSRSRSRGRRRSGNRDRTVQSPKSK']

Map the description and sequence from the dictionary to their corresponding nodes in the graph¶

In [48]:

from tqdm import tqdm
for node in tqdm(kg.nodes):
    if kg.nodes[node].get('node_type') != 'gene/protein':
        continue
    gene_id = kg.nodes[node].get('node_id')
    # Ignore the genes/proteins without description
    if gene_id not in dic_gene_id_to_descp_seq:
        continue
    description = dic_gene_id_to_descp_seq[gene_id]['description']
    sequence = dic_gene_id_to_descp_seq[gene_id]['sequence']
    print (f"node: {node}, gene ID: {gene_id}, description: {description}, sequence: {sequence}")
    G.add_nodes_from([(node, {'description': description, 'sequence': sequence})])

# Recompose the graph
kg = nx.compose(G, kg)

from tqdm import tqdm for node in tqdm(kg.nodes): if kg.nodes[node].get('node_type') != 'gene/protein': continue gene_id = kg.nodes[node].get('node_id') # Ignore the genes/proteins without description if gene_id not in dic_gene_id_to_descp_seq: continue description = dic_gene_id_to_descp_seq[gene_id]['description'] sequence = dic_gene_id_to_descp_seq[gene_id]['sequence'] print (f"node: {node}, gene ID: {gene_id}, description: {description}, sequence: {sequence}") G.add_nodes_from([(node, {'description': description, 'sequence': sequence})]) # Recompose the graph kg = nx.compose(G, kg)

100%|██████████| 129262/129262 [00:00<00:00, 1531104.56it/s]

node: PHYHIP, gene ID: 9796, description: ['Its interaction with PHYH suggests a role in the development of the central system'], sequence: ['MELLSTPHSIEINNITCDSFRISWAMEDSDLERVTHYFIDLNKKENKNSNKFKHRDVPTKLVAKAVPLPMTVRGHWFLSPRTEYSVAVQTAVKQSDGEYLVSGWSETVEFCTGDYAKEHLAQLQEKAEQIAGRMLRFSVFYRNHHKEYFQHARTHCGNMLQPYLKDNSGSHGSPTSGMLHGVFFSCNTEFNTGQPPQDSPYGRWRFQIPAQRLFNPSTNLYFADFYCMYTAYHYAILVLAPKGSLGDRFCRDRLPLLDIACNKFLTCSVEDGELVFRHAQDLILEIIYTEPVDLSLGTLGEISGHQLMSLSTADAKKDPSCKTCNISVGR']
node: KIF15, gene ID: 56992, description: ['Plus-end directed kinesin-like motor enzyme involved in mitotic spindle assembly'], sequence: ['MAPGCKTELRSVTNGQSNQPSNEGDAIKVFVRIRPPAERSGSADGEQNLCLSVLSSTSLRLHSNPEPKTFTFDHVADVDTTQESVFATVAKSIVESCMSGYNGTIFAYGQTGSGKTFTMMGPSESDNFSHNLRGVIPRSFEYLFSLIDREKEKAGAGKSFLCKCSFIEIYNEQIYDLLDSASAGLYLREHIKKGVFVVGAVEQVVTSAAEAYQVLSGGWRNRRVASTSMNRESSRSHAVFTITIESMEKSNEIVNIRTSLLNLVDLAGSERQKDTHAEGMRLKEAGNINRSLSCLGQVITALVDVGNGKQRHVCYRDSKLTFLLRDSLGGNAKTAIIANVHPGSRCFGETLSTLNFAQRAKLIKNKAVVNEDTQGNVSQLQAEVKRLKEQLAELASGQTPPESFLTRDKKKTNYMEYFQEAMLFFKKSEQEKKSLIEKVTQLEDLTLKKEKFIQSNKMIVKFREDQIIRLEKLHKESRGGFLPEEQDRLLSELRNEIQTLREQIEHHPRVAKYAMENHSLREENRRLRLLEPVKRAQEMDAQTIAKLEKAFSEISGMEKSDKNQQGFSPKAQKEPCLFANTEKLKAQLLQIQTELNNSKQEYEEFKELTRKRQLELESELQSLQKANLNLENLLEATKACKRQEVSQLNKIHAETLKIITTPTKAYQLHSRPVPKLSPEMGSFGSLYTQNSSILDNDILNEPVPPEMNEQAFEAISEELRTVQEQMSALQAKLDEEEHKNLKLQQHVDKLEHHSTQMQELFSSERIDWTKQQEELLSQLNVLEKQLQETQTKNDFLKSEVHDLRVVLHSADKELSSVKLEYSSFKTNQEKEFNKLSERHMHVQLQLDNLRLENEKLLESKACLQDSYDNLQEIMKFEIDQLSRNLQNFKKENETLKSDLNNLMELLEAEKERNNKLSLQFEEDKENSSKEILKVLEAVRQEKQKETAKCEQQMAKVQKLEESLLATEKVISSLEKSRDSDKKVVADLMNQIQELRTSVCEKTETIDTLKQELKDINCKYNSALVDREESRVLIKKQEVDILDLKETLRLRILSEDIERDMLCEDLAHATEQLNMLTEASKKHSGLLQSAQEELTKKEALIQELQHKLNQKKEEVEQKKNEYNFKMRQLEHVMDSAAEDPQSPKTPPHFQTHLAKLLETQEQEIEDGRASKTSLEHLVTKLNEDREVKNAEILRMKEQLREMENLRLESQQLIEKNWLLQGQLDDIKRQKENSDQNHPDNQQLKNEQEESIKERLAKSKIVEEMLKMKADLEEVQSALYNKEMECLRMTDEVERTQTLESKAFQEKEQLRSKLEEMYEERERTSQEMEMLRKQVECLAEENGKLVGHQNLHQKIQYVVRLKKENVRLAEETEKLRAENVFLKEKKRSES']
node: GPANK1, gene ID: 7918, description: [''], sequence: ['MSRPLLITFTPATDPSDLWKDGQQQPQPEKPESTLDGAAARAFYEALIGDESSAPDSQRSQTEPARERKRKKRRIMKAPAAEAVAEGASGRHGQGRSLEAEDKMTHRILRAAQEGDLPELRRLLEPHEAGGAGGNINARDAFWWTPLMCAARAGQGAAVSYLLGRGAAWVGVCELSGRDAAQLAEEAGFPEVARMVRESHGETRSPENRSPTPSLQYCENCDTHFQDSNHRTSTAHLLSLSQGPQPPNLPLGVPISSPGFKLLLRGGWEPGMGLGPRGEGRANPIPTVLKRDQEGLGYRSAPQPRVTHFPAWDTRAVAGRERPPRVATLSWREERRREEKDRAWERDLRTYMNLEF']
node: PNMA1, gene ID: 9240, description: [''], sequence: ['MAMTLLEDWCRGMDVNSQRALLVWGIPVNCDEAEIEETLQAAMPQVSYRMLGRMFWREENAKAALLELTGAVDYAAIPREMPGKGGVWKVLFKPPTSDAEFLERLHLFLAREGWTVQDVARVLGFQNPTPTPGPEMPAEMLNYILDNVIQPLVESIWYKRLTLFSGRDIPGPGEETFDPWLEHTNEVLEEWQVSDVEKRRRLMESLRGPAADVIRILKSNNPAITTAECLKALEQVFGSVESSRDAQIKFLNTYQNPGEKLSAYVIRLEPLLQKVVEKGAIDKDNVNQARLEQVIAGANHSGAIRRQLWLTGAGEGPAPNLFQLLVQIREEEAKEEEEEAEATLLQLGLEGHF']
node: ZRSR2, gene ID: 8233, description: ["Pre-mRNA-binding protein required for splicing of both U2- and U12-type introns. Selectively interacts with the 3'-splice site of U2- and U12-type pre-mRNAs and promotes different steps in U2 and U12 intron splicing. Recruited to U12 pre-mRNAs in an ATP-dependent manner and is required for assembly of the pre-spliceosome, a precursor to other spliceosomal complexes. For U2-type introns, it is selectively and specifically required for the second step of splicing"], sequence: ['MAAPEKMTFPEKPSHKKYRAALKKEKRKKRRQELARLRDSGLSQKEEEEDTFIEEQQLEEEKLLERERQRLHEEWLLREQKAQEEFRIKKEKEEAAKKRQEEQERKLKEQWEEQQRKEREEEEQKRQEKKEKEEALQKMLDQAENELENGTTWQNPEPPVDFRVMEKDRANCPFYSKTGACRFGDRCSRKHNFPTSSPTLLIKSMFTTFGMEQCRRDDYDPDASLEYSEEETYQQFLDFYEDVLPEFKNVGKVIQFKVSCNLEPHLRGNVYVQYQSEEECQAALSLFNGRWYAGRQLQCEFCPVTRWKMAICGLFEIQQCPRGKHCNFLHVFRNPNNEFWEANRDIYLSPDRTGSSFGKNSERRERMGHHDDYYSRLRGRRNPSPDHSYKRNGESERKSSRHRGKKSHKRTSKSRERHNSRSRGRNRDRSRDRSRGRGSRSRSRSRSRRSRRSRSQSSSRSRSRGRRRSGNRDRTVQSPKSK']

Check device availability¶

In [ ]:

device = "cuda:0" if torch.cuda.is_available() else "cpu"
device

device = "cuda:0" if torch.cuda.is_available() else "cpu" device

Out[ ]:

'cuda:0'

Load the ESM2 model¶

In [ ]:

emb_model = EmbeddingWithHuggingFace(model_name='facebook/esm2_t6_8M_UR50D',
                                     model_cache_dir="../../../../data/facebook/esm2_t6_8M_UR50D/",
                                     truncation=False,
                                     device=device)

emb_model = EmbeddingWithHuggingFace(model_name='facebook/esm2_t6_8M_UR50D', model_cache_dir="../../../../data/facebook/esm2_t6_8M_UR50D/", truncation=False, device=device)

Some weights of EsmModel were not initialized from the model checkpoint at facebook/esm2_t6_8M_UR50D and are newly initialized: ['esm.pooler.dense.bias', 'esm.pooler.dense.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

Generate sequence embedding and add it to the graph as new attribute "sequence_embedding"¶

In [ ]:

# Embeddings using 1 sample at a time
for node in tqdm(kg.nodes):
    if kg.nodes[node].get('node_type') != 'gene/protein':
        continue
    gene_id = kg.nodes[node].get('node_id')
    if kg.nodes[node].get('sequence') is None:
        continue
    seq = kg.nodes[node].get('sequence')
    # print (node, seq)
    outputs = emb_model.embed_documents([seq])
    G.add_nodes_from([(node, {'sequence_embedding': outputs[0]})])
    torch.cuda.synchronize()
    torch.cuda.empty_cache()

# Recompose the graph
kg = nx.compose(G, kg)

# Embeddings using 1 sample at a time for node in tqdm(kg.nodes): if kg.nodes[node].get('node_type') != 'gene/protein': continue gene_id = kg.nodes[node].get('node_id') if kg.nodes[node].get('sequence') is None: continue seq = kg.nodes[node].get('sequence') # print (node, seq) outputs = emb_model.embed_documents([seq]) G.add_nodes_from([(node, {'sequence_embedding': outputs[0]})]) torch.cuda.synchronize() torch.cuda.empty_cache() # Recompose the graph kg = nx.compose(G, kg)

100%|██████████| 129262/129262 [00:05<00:00, 21864.09it/s]

Protein embedding¶

Load the BioBERT model

In [20]:

# Using MSFT's BioBERT
emb_model = EmbeddingWithHuggingFace(model_name='microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract',
                                     model_cache_dir="../../../../data/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract/",
                                     truncation=False,
                                     device=device)

# Using MSFT's BioBERT emb_model = EmbeddingWithHuggingFace(model_name='microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract', model_cache_dir="../../../../data/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract/", truncation=False, device=device)

Generate description embedding and add it to the graph as new attribute "description_embedding"¶

In [21]:

for i, node in tqdm(enumerate(kg.nodes)):
    if kg.nodes[node].get('node_type') != 'gene/protein':
        continue
    gene_id = kg.nodes[node].get('node_id')
    if kg.nodes[node].get('description') is None:
        continue
    desc = kg.nodes[node].get('description')
    outputs = emb_model.embed_documents([desc])
    G.add_nodes_from([(node, {'description_embedding': outputs})])
    torch.cuda.synchronize()
    torch.cuda.empty_cache()

# Recompose the graph
kg = nx.compose(G, kg)

for i, node in tqdm(enumerate(kg.nodes)): if kg.nodes[node].get('node_type') != 'gene/protein': continue gene_id = kg.nodes[node].get('node_id') if kg.nodes[node].get('description') is None: continue desc = kg.nodes[node].get('description') outputs = emb_model.embed_documents([desc]) G.add_nodes_from([(node, {'description_embedding': outputs})]) torch.cuda.synchronize() torch.cuda.empty_cache() # Recompose the graph kg = nx.compose(G, kg)

129262it [00:00, 372240.20it/s]

Put together all the results so far in a df¶

In [22]:

import pandas as pd
dic = {'gene':[],
       'description':[],
       'sequence':[],
       'description_embedding':[],
       'sequence_embedding':[]}
for node in tqdm(kg.nodes):
    if kg.nodes[node].get('node_type') != 'gene/protein':
        continue
    gene_id = kg.nodes[node].get('node_id')
    if kg.nodes[node].get('description') is None:
        continue
    dic['gene'].append(node)
    dic['description'].append(kg.nodes[node].get('description'))
    dic['sequence'].append(kg.nodes[node].get('sequence'))
    dic['description_embedding'].append(kg.nodes[node].get('description_embedding'))
    dic['sequence_embedding'].append(kg.nodes[node].get('sequence_embedding'))
    # print (node, kg.nodes[node].get('description'), kg.nodes[node].get('sequence'), kg.nodes[node].get('description_embedding'))

df = pd.DataFrame(dic)
df

import pandas as pd dic = {'gene':[], 'description':[], 'sequence':[], 'description_embedding':[], 'sequence_embedding':[]} for node in tqdm(kg.nodes): if kg.nodes[node].get('node_type') != 'gene/protein': continue gene_id = kg.nodes[node].get('node_id') if kg.nodes[node].get('description') is None: continue dic['gene'].append(node) dic['description'].append(kg.nodes[node].get('description')) dic['sequence'].append(kg.nodes[node].get('sequence')) dic['description_embedding'].append(kg.nodes[node].get('description_embedding')) dic['sequence_embedding'].append(kg.nodes[node].get('sequence_embedding')) # print (node, kg.nodes[node].get('description'), kg.nodes[node].get('sequence'), kg.nodes[node].get('description_embedding')) df = pd.DataFrame(dic) df

100%|██████████| 129262/129262 [00:00<00:00, 1170116.51it/s]

Out[22]:

	gene	description	sequence	description_embedding	sequence_embedding
0	PABPC1	(Microbial infection) Positively regulates the...	MNPSAPSYPMASLYVGDLHPDVTEAMLYEKFSPAGPILSIRVCRDM...	[[tensor(-0.1369), tensor(-0.0554), tensor(0.0...	[tensor(0.0133), tensor(-0.0413), tensor(0.192...
1	GTPBP3	GTPase component of the GTPBP3-MTO1 complex th...	MWRGLWTLAAQAARGPRRLCTRRSSGAPAPGSGATIFALSSGQGRC...	[[tensor(-0.0017), tensor(0.2555), tensor(0.45...	[tensor(-0.1717), tensor(-0.2150), tensor(-0.0...
2	SHOX2	May be a growth regulator and have a role in s...	MEELTAFVSKSFDQKVKEKKEAITYREVLESGPLRGAKEPTGCTEA...	[[tensor(0.0310), tensor(0.3633), tensor(0.744...	[tensor(-0.0969), tensor(-0.2075), tensor(0.01...
3	ALDH16A1	May be a growth regulator and have a role in s...	MAATRAGPRAREIFTSLEYGPVPESHACALAWLDTQDRCLGHYVNG...	[[tensor(0.0310), tensor(0.3633), tensor(0.744...	[tensor(-0.2420), tensor(-0.0727), tensor(0.10...
4	GMPR	Catalyzes the irreversible NADPH-dependent dea...	MPRIDADLKLDFKDVLLRPKRSSLKSRAEVDLERTFTFRNSKQTYS...	[[tensor(0.1503), tensor(-0.1371), tensor(0.33...	[tensor(0.0235), tensor(0.0571), tensor(0.0156...
5	INPP4B	Catalyzes the hydrolysis of the 4-position pho...	MEIKEEGASEEGQHFLPTAQANDPGDCQFTSIQKTPNEPQLEFILA...	[[tensor(0.1104), tensor(0.2020), tensor(0.234...	[tensor(-0.0336), tensor(-0.1216), tensor(0.02...