Agent skill

bio-genome-intervals-gtf-gff-handling

View SKILL.md on GitHub Repository
Stars 2,009
Forks 275

Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-genome-intervals-gtf-gff-handling

SKILL.md

name: bio-genome-intervals-gtf-gff-handling description: Parse, query, and convert GTF and GFF3 annotation files. Extract gene, transcript, and exon coordinates using gffread, gtfparse, and gffutils. Use when extracting specific features from gene annotations or converting between annotation formats. tool_type: mixed primary_tool: gffread measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

GTF/GFF Handling

GTF and GFF3 are standard gene annotation formats. Both use 1-based coordinates.

Format Comparison

Feature GTF GFF3
Coordinate system 1-based, inclusive 1-based, inclusive
Hierarchy Implicit (gene_id, transcript_id) Explicit (Parent attribute)
Attribute format key "value"; key=value;
Comments # #
Fasta sequences Not standard ##FASTA directive

GTF Format

chr1	HAVANA	gene	11869	14409	.	+	.	gene_id "ENSG00000223972"; gene_name "DDX11L1";
chr1	HAVANA	transcript	11869	14409	.	+	.	gene_id "ENSG00000223972"; transcript_id "ENST00000456328";
chr1	HAVANA	exon	11869	12227	.	+	.	gene_id "ENSG00000223972"; transcript_id "ENST00000456328"; exon_number "1";

GFF3 Format

chr1	HAVANA	gene	11869	14409	.	+	.	ID=ENSG00000223972;Name=DDX11L1
chr1	HAVANA	mRNA	11869	14409	.	+	.	ID=ENST00000456328;Parent=ENSG00000223972
chr1	HAVANA	exon	11869	12227	.	+	.	ID=exon1;Parent=ENST00000456328

Parse GTF with gtfparse (Python)

Installation

bash
pip install gtfparse

Basic Parsing

python
import gtfparse

# Load entire GTF
df = gtfparse.read_gtf('annotation.gtf')

# View columns
print(df.columns)
# ['seqname', 'source', 'feature', 'start', 'end', 'score', 'strand', 'frame',
#  'gene_id', 'transcript_id', 'gene_name', ...]

# Filter by feature type
genes = df[df['feature'] == 'gene']
transcripts = df[df['feature'] == 'transcript']
exons = df[df['feature'] == 'exon']

# Get specific gene
gene_df = df[df['gene_name'] == 'TP53']

Extract Gene Coordinates

python
import gtfparse

df = gtfparse.read_gtf('annotation.gtf')

# All genes
genes = df[df['feature'] == 'gene'][['seqname', 'start', 'end', 'strand', 'gene_id', 'gene_name']]

# Convert to BED format (0-based)
genes_bed = genes.copy()
genes_bed['start'] = genes_bed['start'] - 1  # GTF is 1-based, BED is 0-based
genes_bed = genes_bed[['seqname', 'start', 'end', 'gene_name', 'gene_id', 'strand']]
genes_bed.to_csv('genes.bed', sep='\t', header=False, index=False)

Get Exons for Gene

python
import gtfparse

df = gtfparse.read_gtf('annotation.gtf')

# Get all exons for TP53
tp53_exons = df[(df['gene_name'] == 'TP53') & (df['feature'] == 'exon')]
tp53_exons = tp53_exons[['seqname', 'start', 'end', 'transcript_id', 'exon_number']]
print(tp53_exons)

Parse GFF with gffutils (Python)

Installation

bash
pip install gffutils

Create Database

python
import gffutils

# Create database (slow first time, fast for subsequent queries)
db = gffutils.create_db('annotation.gff3', 'annotation.db',
                         force=True, merge_strategy='create_unique')

# Or load existing database
db = gffutils.FeatureDB('annotation.db')

Query Features

python
import gffutils

db = gffutils.FeatureDB('annotation.db')

# Count features by type
for featuretype in db.featuretypes():
    count = db.count_features_of_type(featuretype)
    print(f'{featuretype}: {count}')

# Get all genes
for gene in db.features_of_type('gene'):
    print(f'{gene.id}: {gene.seqid}:{gene.start}-{gene.end}')

# Get gene by ID
gene = db['ENSG00000141510']  # TP53
print(f'{gene.attributes["Name"][0]}: {gene.seqid}:{gene.start}-{gene.end}')

# Get children (transcripts, exons)
for transcript in db.children(gene, featuretype='mRNA'):
    print(f'  Transcript: {transcript.id}')
    for exon in db.children(transcript, featuretype='exon'):
        print(f'    Exon: {exon.start}-{exon.end}')

Get Introns

python
import gffutils

db = gffutils.FeatureDB('annotation.db')

# Get introns for a transcript
transcript = db['ENST00000269305']
introns = list(db.interfeatures(db.children(transcript, featuretype='exon'),
                                 new_featuretype='intron'))
for intron in introns:
    print(f'Intron: {intron.start}-{intron.end}')

Convert Formats with gffread (CLI)

Installation

bash
conda install -c bioconda gffread

GTF to GFF3

bash
gffread annotation.gtf -o annotation.gff3

GFF3 to GTF

bash
gffread annotation.gff3 -T -o annotation.gtf

Extract Sequences

bash
# Extract transcript sequences
gffread -w transcripts.fa -g genome.fa annotation.gtf

# Extract CDS sequences
gffread -x cds.fa -g genome.fa annotation.gtf

# Extract protein sequences
gffread -y proteins.fa -g genome.fa annotation.gtf

Filter Features

bash
# Keep only protein-coding genes
gffread annotation.gtf -C -o coding.gtf

# Keep specific gene types
gffread annotation.gtf --keep-genes=protein_coding -o coding.gtf

Extract Regions with bedtools

Get Promoters

bash
# Extract TSS (transcript start sites)
awk '$3 == "transcript"' annotation.gtf | \
    awk -v OFS='\t' '{
        if ($7 == "+") print $1, $4-1, $4, ".", ".", $7;
        else print $1, $5-1, $5, ".", ".", $7;
    }' > tss.bed

# Get promoter regions (2kb upstream of TSS)
bedtools flank -i tss.bed -g genome.txt -l 2000 -r 0 -s > promoters.bed

Get Gene Bodies

bash
# Extract gene coordinates to BED
awk '$3 == "gene"' annotation.gtf | \
    awk -v OFS='\t' '{
        split($0, a, "gene_id \""); split(a[2], b, "\"");
        print $1, $4-1, $5, b[1], ".", $7;
    }' > genes.bed

Get Exons

bash
# Extract unique exons
awk '$3 == "exon"' annotation.gtf | \
    awk -v OFS='\t' '{print $1, $4-1, $5, ".", ".", $7}' | \
    sort -k1,1 -k2,2n | uniq > exons.bed

Python: GTF to BED Conversion

python
import gtfparse
import pandas as pd

def gtf_to_bed(gtf_path, feature_type='gene', output_path=None):
    '''Convert GTF features to BED format.'''
    df = gtfparse.read_gtf(gtf_path)
    features = df[df['feature'] == feature_type].copy()

    # Convert to 0-based coordinates
    bed = pd.DataFrame({
        'chrom': features['seqname'],
        'start': features['start'] - 1,
        'end': features['end'],
        'name': features.get('gene_name', features.get('gene_id', '.')),
        'score': 0,
        'strand': features['strand']
    })

    if output_path:
        bed.to_csv(output_path, sep='\t', header=False, index=False)
    return bed

# Usage
genes_bed = gtf_to_bed('annotation.gtf', 'gene', 'genes.bed')
exons_bed = gtf_to_bed('annotation.gtf', 'exon', 'exons.bed')

Validate GTF/GFF

bash
# Check GTF format
gffread -E annotation.gtf

# Check GFF3 format
gffread -E annotation.gff3

# Detailed validation
gt gff3validator annotation.gff3  # requires genometools

Common Attributes

GTF Attributes

Attribute Description
gene_id Ensembl gene ID
gene_name Gene symbol
gene_biotype protein_coding, lncRNA, etc.
transcript_id Ensembl transcript ID
transcript_name Transcript symbol
exon_number Exon position in transcript
exon_id Ensembl exon ID

GFF3 Attributes

Attribute Description
ID Unique feature identifier
Name Display name
Parent Parent feature ID
Dbxref Database cross-references
gene_biotype Gene type

Memory-Efficient Processing

python
import gtfparse

# Process large files in chunks (gtfparse loads all into memory)
# For very large files, use gffutils database approach

# Or filter during parsing
df = gtfparse.read_gtf('annotation.gtf',
                        features=['gene', 'exon'])  # Only load specific features

Related Skills

  • bed-file-basics - BED format and conversion
  • interval-arithmetic - Gene/exon overlap analysis
  • proximity-operations - TSS proximity analysis
  • differential-expression/de-results - Gene coordinate mapping

Recommended Agent Skills

Expand your agent's capabilities with these related and highly-rated skills.

FreedomIntelligence/OpenClaw-Medical-Skills

vcf-annotator

Annotate VCF variants with VEP, ClinVar, gnomAD frequencies, and ancestry-aware context. Generates prioritised variant reports.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

chemist-analyst

Analyzes events through chemistry lens using molecular structure, reaction mechanisms, thermodynamics, kinetics, and analytical techniques (spectroscopy, chromatography, mass spectrometry). Provides insights on chemical processes, material properties, reaction pathways, synthesis, and analytical methods. Use when: Chemical reactions, material analysis, synthesis planning, process optimization, environmental chemistry. Evaluates: Molecular structure, reaction mechanisms, yield, selectivity, safety, environmental impact.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-alignment-io

Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

sleep-analyzer

分析睡眠数据、识别睡眠模式、评估睡眠质量，并提供个性化睡眠改善建议。支持与其他健康数据的关联分析。

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

metabolomics-workbench-database

Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.

2,009 275
Explore
FreedomIntelligence/OpenClaw-Medical-Skills

bio-hi-c-analysis-matrix-operations

Balance, normalize, and transform Hi-C contact matrices using cooler and cooltools. Apply iterative correction (ICE), compute expected values, and generate observed/expected matrices. Use when normalizing or transforming Hi-C matrices.

2,009 275
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results