jsrc

Python library for bioinformatics and scientific computing.

Installation

Install from PyPI:

pip install jsrc

From source (development):

git clone https://github.com/imjiaoyuan/jsrc.git
cd jsrc
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"

Run jsrc --help to get started.

For detailed usage, see the Documentation. 中文文档请参阅 文档。

Quick Start

jsrc --help
jsrc <module> --help
jsrc <module> <subcommand> --help

Examples:

jsrc seq --help
jsrc analyze phylo --help
jsrc genome stats --help

Paths like test/... in the examples below are illustrative — substitute your own input files.

Module Overview

Error Output Conventions

• Input and validation failures are reported in unified format: Error: <message>.
• Missing files, invalid parameters, and incompatible inputs follow the same style across subcommands.
• Use --help on the target module/subcommand first when argument combinations are unclear.

A Glance of jsrc's Functions

grn module

Generate and launch a 1000-gene random network viewer:

jsrc grn net2json -i test/grn/network.tsv -o test/grn/grn.json
jsrc grn anno2json -i test/grn/annotation.tsv -o test/grn/annotation.json
jsrc grn build -d test/grn/public -g test/grn/grn.json -n test/grn/annotation.json -z test/grn/grn-viewer.zip -a -t 200
jsrc grn serve -d test/grn/public -g test/grn/public/json/grn.json -n test/grn/public/json/annotation.json -p 8000 -a -t 200

Centrality ranking (top 5):

jsrc grn centrality -i test/grn/network.tsv --top 5

genome module

jsrc genome stats -fa genome.fa
jsrc genome cpg -fa genome.fa --window 200 --min-len 500
jsrc genome ani -fa1 genome1.fa -fa2 genome2.fa -k 21
jsrc genome codon -fa cds.fa --cai reference.fa --enc

Genome statistics: N50 2,450,000 bp, L50 3, GC 45.2%, 15 gaps.

CpG islands: 42 islands found, longest 1,250 bp.

ANI: 96.8% (Jaccard 0.85, Mash distance 0.032).

Codon usage: CAI 0.78, ENC 52.3, top codon CTG (RSCU 1.85).

seq module

jsrc seq extract -fa test/seq/test.fa -gff test/seq/test.gff -ids test/seq/ids.txt -o test/seq/extracted.fa -feature gene -match ID

Extract sequences by gene ID from FASTA+GFF, rename via CSV map, run QC stats, k-mer counting, and sliding-window analysis.

QC: 2 sequences, 268 bp total, GC 56.7%, N50 160.

k-mer (k=3): top ATC (40), TCG (40), CGA (39).

analyze module

jsrc analyze msa_consensus -fa test/analyze/aln.fa --json
jsrc analyze snpindel -fa test/analyze/aln.fa
jsrc analyze motif -fa test/analyze/aln.fa -o test/analyze/motif_out -minw 3 -maxw 5 -nmotifs 3
jsrc analyze phylo -fa test/analyze/aln.fa -o test/analyze/tree.nwk

• Consensus: ATGCTAGCTAGCTAGCTAGC, mean conservation 0.983
• SNP: seq1 vs seq3 has 1 SNP (alignment score 19/20)
• Motif (top): GCT (12), CTA (12), TAG (12)
• Phylogeny: (seq1:0.00000,seq2:0.00000,seq3:0.05000)Inner1:0.00000;