Human interactome¶

This Jupyter notebook provides an example of using the Python package gravis. The .ipynb file can be found here.

It visualizes protein-protein interactions (PPi) taken from the Human Reference Interactome (HuRI) and HuRI combined with other systematic screening efforts at CCSB (HI-union).

References¶

Center for Cancer Systems Biology (CCSB)
- The Human Reference Protein Interactome Mapping Project
  - Download
    - HuRI.tsv with 52569 interactions (Ensembl gene IDs)
    - HI-union.tsv with 64006 interactions
    - Preprint paper
      - “The dataset, versioned HI-III-19 (Human Interactome obtained from screening Space III, published in 2019), contains 52,569 verified PPIs involving 8,275 proteins (Supplementary Table 6). Given its systematic nature, completeness and scale, we consider HI-III-19 to be the first draft of the Human Reference Interactome (HuRI).”
      - “Combining HuRI with all previously published systematic screening efforts at CCSB yields 64,006 binary PPIs involving 9,094 proteins (HI-union)”
EMBL-EBI
- Ensembl: Ensembl is a genome browser for vertebrate genomes
  - About: In order to improve consistency between the data provided by different genome browsers, Ensembl has entered into an agreement with UCSC and NCBI with regard to sequence identifiers
UniProt
- About: The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data.

[1]:

import csv
import os

import gravis as gv
import networkx as nx

Load protein-protein interaction (PPi) data¶

The data is given as table that contains (source, target) pairs, which is a simple edge list. Note that HI-union-minimal.tsv is a reduced version of HI-union.tsv that contains only the first two columns.

[2]:

def load_csv_data(filepath, delimiter=','):
    with open(filepath) as csv_file:
        csv_reader = csv.reader(csv_file, delimiter=delimiter)
        data = list(csv_reader)
    return data


filepath = os.path.join('data', 'HuRI.tsv')
data_huri = load_csv_data(filepath, delimiter='\t')

filepath = os.path.join('data', 'HI-union-minimal.tsv')
data_hi_union = load_csv_data(filepath, delimiter='\t')

Create PPi network as NetworkX graph¶

[3]:

def construct_graph(data, name):
    graph = nx.Graph()
    for source, target in data:
        graph.add_edge(source, target)

    uniprot_template = (
        'Degree: {degree}<br>'
        'Uniprot: <a href="https://www.uniprot.org/uniprot/{id}" target="_blank">{id}</a>')
    ensembl_template = (
        'Degree: {degree}<br>'
        'Ensembl: <a href="https://www.ensembl.org/Homo_sapiens/Gene/Summary?g={id}" target="_blank">{id}</a><br>'
        'NCBI Gene: <a href="https://www.ncbi.nlm.nih.gov/gene/?term={id}" target="_blank">{id}</a>')

    for node_id in graph.nodes:
        node = graph.nodes[node_id]
        template = ensembl_template if node_id.lower().startswith('ens') else uniprot_template
        node['hover'] = template.format(id=node_id, degree=graph.degree[node_id])
        node['click'] = '$hover'
    print('Protein-protein interaction network "{}"'.format(name))
    print('- Number of nodes:', len(graph.nodes))
    print('- Number of edges:', len(graph.edges))
    print()
    return graph


graph_huri = construct_graph(data_huri, 'HuRI')
graph_hi_union = construct_graph(data_hi_union, 'HI-union')

Protein-protein interaction network "HuRI"
- Number of nodes: 8272
- Number of edges: 52548

Protein-protein interaction network "HI-union"
- Number of nodes: 9573
- Number of edges: 65330

Human interactome¶

References¶

Load protein-protein interaction (PPi) data¶

Create PPi network as NetworkX graph¶

Plot filtered versions of the large graph¶

Filter 1: Egocentric network (=neighborhood of a chosen node)¶

1) HuRI data¶

2) HI-union data¶

Filter 2: Only well-connected nodes with degree >= n¶

1) HuRI data¶

2) HI-union data¶