Scripts to reproduce results reported in FamPlex manuscript

This repository is organized into six folders corresponding to each stage of constructing and evaluating the FamPlex resource.

Step 1: Get list of genes and corresponding PMIDs

• Download Reactome signaling proteins list (see step1_genes_pmids/signaling_proteins_readme.txt)
  • output: signaling_proteins.tsv
• Run process_proteins.py to get a gene list for literature search
  • For each protein, get gene name.
  • output: signaling_genes.txt
  • Get genes identified in famplex/relations.csv (which included families from BEL and from the Ras family).
  • Combine the two gene lists
  • output: combined_genes.txt.
• Run get_pmids.py to get a list of PMIDs from gene list
  • For each gene in combined_genes.txt, get papers curated from Entrez gene.
  • Save dict mapping gene to list of papers
  • output: combined_genes_to_pmids.pkl
  • Make set of unique PMIDs, save
  • output: combined_pids.txt

Step 2: Build REACH without FamPlex and read PMIDs

• Build REACH without FamPlex
• output: combined_genes_no_famplex_stmts.pkl (the pickle file contains a dictionary mapping each PMID to a list of Statements)

Step 3: Shuffle the PMIDs and subsample the data into training and test sets

• 80% of papers to training set, 20% of papers to test set
• get_training_test_stmts.py
  • output: training_pmid_stmts.pkl
  • output: test_pmid_stmts.pkl
  • output: training_pmids.txt
  • output: test_pmids.txt

Step 4: stmt_entity_stats

• Run get_agents_for_evaluation.py. Creates random sample of agents from training (or test) set for evaluation.
  • output: training_agents_sample.csv
  • output: training_agents.csv
  • output: training_ungrounded.csv
  • output: training_agent_distribution.pdf
  • output: training_ungrounded_distribution.pdf
• Open training_agents_sample_curated.csv in Excel to curate
  • Curate agents. Code:
    • P: protein
    • F: family
    • C: complex
    • X: complex of families
    • S: small molecule
    • B: biological process
    • U: unknown/other
    • M: microRNA
• For ease of curation, run generate_agent_links.py to create an HTML table to check groundings in different databases.
• Same for test set.

Step 5: Curate grounding map

• texts_for_gene.py helps in finding lexical synonyms for unmapped families.
• Fraction of most frequent agents that in clude family or complex
  • Fraction of mentions
• Fraction of most frequent ungrounded that include family or complex
  • Fraction of ungrounded mentions
• Table with 3 columns Entity, Frequency, Category (F, C, X, or empty), Curator

Step 6: Evaluate FamPlex resource

• Creates a plot of frequency of groundings to each entity