/bio-comparative-genomics-ortholog-inference

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--- name: bio-comparative-genomics-ortholog-inference description: Infer orthologous gene groups across species using OrthoFinder and ProteinOrtho. Identify orthologs, paralogs, and co-orthologs for comparative genomics and functional annotation transfer. Use when identifying gene orthologs across species or building orthogroups for evolutionary analysis. tool_type: cli primary_tool: OrthoFinder measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

• read_file
• run_shell_command

---

Ortholog Inference

OrthoFinder Workflow

'''Ortholog inference with OrthoFinder'''

import subprocess
import pandas as pd
import os


def run_orthofinder(proteome_dir, output_dir=None, threads=4):
    '''Run OrthoFinder on directory of proteomes

    Input: Directory with one FASTA file per species
    File naming: Species name derived from filename

    OrthoFinder performs:
    1. All-vs-all DIAMOND/BLAST
    2. Gene tree inference
    3. Species tree inference
    4. Ortholog/paralog classification
    '''
    cmd = f'orthofinder -f {proteome_dir} -t {threads}'

    if output_dir:
        cmd += f' -o {output_dir}'

    # -M msa: Use MSA for gene trees (more accurate but slower)
    # -S diamond: Fast search (default)
    # -S blast: More sensitive search
    result = subprocess.run(cmd, shell=True, capture_output=True, text=True)

    # Output location
    if output_dir:
        results_dir = output_dir
    else:
        # OrthoFinder creates Results_MonDD in proteome_dir
        results_dir = None
        for d in os.listdir(proteome_dir):
            if d.startswith('OrthoFinder/Results_'):
                results_dir = os.path.join(proteome_dir, d)
                break

    return results_dir


def parse_orthogroups(orthogroups_file):
    '''Parse OrthoFinder Orthogroups.tsv

    Columns: Orthogroup, Species1, Species2, ...
    Values: Gene IDs (comma-separated if multiple)

    Orthogroup types:
    - Single-copy: One gene per species (ideal for phylogenomics)
    - Multi-copy: Duplications in some lineages
    - Species-specific: Genes unique to one species
    '''
    df = pd.read_csv(orthogroups_file, sep='\t')
    df = df.set_index('Orthogroup')

    orthogroups = {}
    for og_id, row in df.iterrows():
        genes = {}
        for species in df.columns:
            cell = row[species]
            if pd.notna(cell) and cell:
                genes[species] = cell.split(', ')
            else:
                genes[species] = []
        orthogroups[og_id] = genes

    return orthogroups


def classify_orthogroups(orthogroups, species_list):
    '''Classify orthogroups by copy number pattern

    Categories:
    - single_copy: Exactly one gene per species (best for phylogenomics)
    - universal: Present in all species (possibly multicopy)
    - partial: Missing from some species
    - species_specific: Only in one species
    '''
    classification = {
        'single_copy': [],
        'universal': [],
        'partial': [],
        'species_specific': []
    }

    for og_id, genes in orthogroups.items():
        present_in = [sp for sp in species_list if genes.get(sp)]
        copy_counts = [len(genes.get(sp, [])) for sp in species_list]

        if len(present_in) == 1:
            classification['species_specific'].append(og_id)
        elif len(present_in) == len(species_list):
            if all(c == 1 for c in copy_counts):
                classification['single_copy'].append(og_id)
            else:
                classification['universal'].append(og_id)
        else:
            classification['partial'].append(og_id)

    return classification


def get_single_copy_orthologs(orthogroups_file):
    '''Extract single-copy orthologs for phylogenomics

    Single-copy orthologs are ideal because:
    - Clear 1:1 relationships
    - No paralogy complications
    - Suitable for concatenated alignments
    '''
    df = pd.read_csv(orthogroups_file, sep='\t')
    df = df.set_index('Orthogroup')

    single_copy = []
    for og_id, row in df.iterrows():
        is_single = True
        for species in df.columns:
            cell = row[species]
            if pd.isna(cell) or cell == '':
                is_single = False
                break
            if ',' in str(cell):
                is_single = False
                break
        if is_single:
            single_copy.append(og_id)

    return df.loc[single_copy]

Gene Trees and Reconciliation

def parse_gene_trees(gene_trees_dir):
    '''Load gene trees from OrthoFinder

    Gene trees show evolutionary history within orthogroups
    Duplication/loss events inferred by species tree reconciliation
    '''
    from Bio import Phylo
    import glob

    trees = {}
    for tree_file in glob.glob(f'{gene_trees_dir}/*.txt'):
        og_id = os.path.basename(tree_file).replace('_tree.txt', '')
        trees[og_id] = Phylo.read(tree_file, 'newick')

    return trees


def identify_paralogs(orthogroup, species):
    '''Identify in-paralogs within an orthogroup

    In-paralogs: Duplications after speciation (within-species)
    Out-paralogs: Duplications before speciation (between-species)

    Multiple genes from same species in an orthogroup are in-paralogs
    '''
    genes = orthogroup.get(species, [])
    if len(genes) > 1:
        return {
            'species': species,
            'paralogs': genes,
            'count': len(genes)
        }
    return None


def find_co_orthologs(orthogroups, gene_id, species):
    '''Find co-orthologs of a gene

    Co-orthologs: Multiple genes in one species that are