gff2parquet 0.2.0

GFF3 cli utils (convert, merge, filter, split, print, extract)

gff2parquet

CLI tool for working with GFF3 genomic annotation files using Polars + Polars-bio for evaluation and processing.
on pypi for convenience. Not meant for serious use, but it works for me...

features

• Convert GFF3 files to Parquet, CSV, or JSON formats
• Merge multiple GFF files with optional column normalization
• Filter features by type, strand, length, sequence ID, and more
• Split annotations into separate files by any column
• Extract sequences from FASTA files based on GFF coordinates
• Translate CDS sequences to proteins with configurable genetic codes
• Lazy evaluation for memory-efficient processing of large datasets
• Glob pattern support for batch processing multiple files

Installation

using (pixi)[https://pixi.sh/latest/] (recommended)

pixi install

Or with pip:

pip install . -e 

Dependencies:

Base:

• python >= 3.9
• polars
• polars-bio
  For the example notebook, you will also need:
• ipykernel
• jupyter
• jupyterlab
• pyarrow (only really used to get the parquets metadata)
• ncbi-datasets (for downloading example datasets, although some are already included)

Quick Start

See example notebook for some more examples...

Convert GFF to Parquet

# Single file
gff2parquet convert annotations.gff3 -o annotations.parquet

# Multiple files with glob pattern
gff2parquet convert "data/*.gff3" -o combined.parquet

# Normalize column names and shift coordinates
gff2parquet convert input.gff3 --normalize --shift-start 1 -o output.parquet

Filter Features

# Extract CDS features longer than 500bp
gff2parquet filter annotations.gff3 --type CDS --min-length 500 -o long_cds.csv

# Filter by strand and sequence
gff2parquet filter input.gff3 --seqid chr1 --strand + -o chr1_plus.parquet

Merge Multiple Files

# Merge all GFF files in a directory
gff2parquet merge "samples/*.gff3" -o merged.parquet

# Merge with normalization
gff2parquet merge file1.gff3 file2.gff3 --normalize -o combined.parquet


### Extract & Translate Sequences
```bash
# Extract CDS sequences as nucleotides
gff2parquet extract annotations.gff3 genome.fasta --type CDS -o cds.fasta

# Extract and translate to proteins (bacterial genetic code)
gff2parquet extract annotations.gff3 genome.fasta --type CDS --outaa amino -o proteins.fasta

# Extract from multiple genomes with custom genetic code
gff2parquet extract "*.gff3" genome*.fasta --outaa amino --genetic-code 2 -o mito_proteins.fasta

Split by Column

# Split by feature type
gff2parquet split annotations.gff3 --column type --output-dir by_type/ -f gff

# Split by chromosome
gff2parquet split annotations.gff3 --column seqid --output-dir by_chr/ -f parquet

Inspect Data

# View first 10 rows
gff2parquet print annotations.gff3 --head 10

# Show statistics
gff2parquet print annotations.gff3 --stats

# Filter and display specific columns
gff2parquet print annotations.gff3 --type gene --columns seqid,start,end,strand -f csv

Common Workflows

Multi-step Analysis Pipeline

# 1. Merge multiple samples
gff2parquet merge sample*.gff3 -o all_samples.parquet

# 2. Filter for long CDS features
gff2parquet filter all_samples.parquet --type CDS --min-length 600 -o long_cds.gff -f gff

# 3. Extract and translate sequences
gff2parquet extract long_cds.gff genome*.fasta --outaa amino -o proteins.fasta

Quality Control

# Check feature distribution
gff2parquet print annotations.gff3 --stats

# Extract short features for inspection
gff2parquet filter annotations.gff3 --max-length 50 -o short_features.csv

Output Formats

• Parquet: The answer to all your problem.
• CSV/TSV: Human-readable, but (polars) csv doesn't support nested data types so the attribute field is smushed together into string.
• GFF3: Standard genomic annotation format. The Attribute field is annoying.
• JSONL: probably not very useful, untested
• FASTA: what most bioinformatics tools use

Genetic Codes

Use --genetic-code with extract command:

• 1 - Standard (default for most organisms)
• 2 - Vertebrate Mitochondrial
• 11 - Bacterial and Plant Plastid (default)

Full list

Advanced Features

Streaming Mode - UNTESTED

For very large files that don't fit in memory:

gff2parquet convert huge_file.gff3 --streaming -o output.parquet

Coordinate Shifting (USE WITH CAUTION)

Convert between 0-based and 1-based coordinates:

gff2parquet convert input.gff3 --shift-start 1 --shift-end 0 -o corrected.parquet

Output to stdout (DOESN'T WORK FOR ALL COMMANDS)

gff2parquet filter input.gff3 --type CDS -o stdout #| grep "gene_id"

Tips

• Use glob patterns for batch processing: "data/*.gff3" or "sample_*.gff"
• Use Parquet format for support of modern stuff.
• Stream large files with --streaming to reduce memory usage (untested)
• Auto-format detection: Output format detected from file extension unless -f specified (not for all commands)
• can be piped to other commands:

[uneri]$ gff2parquet print data/downloaded_gff/groupI_GCA_000859985.2.gff --head 10   | grep "repeat"
Found 1 file(s) matching pattern 'data/downloaded_gff/groupI_GCA_000859985.2.gff'
Scanning: data/downloaded_gff/groupI_GCA_000859985.2.gff
| JN555585.1 | Genbank | inverted_repeat | 1     | 9213   | null  | +      | null  | [{"ID","id-JN555585.1:1..9213"}, {"Note","TRL%3B inverted repeat flanking UL"}, … {"rpt_type","inverted"}] | data/downloaded_gff/groupI_GCA_000859985.2.gff |
| JN555585.1 | Genbank | repeat_region   | 1     | 399    | null  | +      | null  | [{"ID","id-JN555585.1:1..399"}, {"Note","'a' sequence"}, … {"rpt_type","terminal"}]                        | data/downloaded_gff/groupI_GCA_000859985.2.gff |
| JN555585.1 | Genbank | repeat_region   | 98    | 320    | null  | +      | null  | [{"ID","id-JN555585.1:98..320"}, {"Note","'a' sequence reiteration set"}, … {"rpt_unit_range","98..109"}]  | data/downloaded_gff/groupI_GCA_000859985.2.gff |

Development

Using Pixi

Default environment (minimal)

pixi install
pixi shell

Notebook environment (includes Jupyter):

pixi install -e notebook
pixi run -e notebook jupyter lab

License

See LICENSE file.

Citation

Neri and the gang