Read and manipulate SAM, BAM, CRAM, VCF, and BCF genomic files.
npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill pysam-lord1egypt
Please help me install this Agent Skill. Skill: pysam Source: https://github.com/Lord1Egypt/scientific-agent-toolkit/tree/main/scientific-skills/pysam Command: npx skills add https://github.com/Lord1Egypt/scientific-agent-toolkit --skill pysam-lord1egypt
This skill addresses the complexity of working with high-throughput sequencing data formats like SAM, BAM, CRAM, and VCF, which are notoriously difficult to parse and manipulate manually.
Use the pysam skill to open the alignment file example.bam and fetch all reads overlapping the region chr1 from position 1000 to 2000.
High-intent search queries and answers about installing and using this skill.
To extract reads from a BAM file, you use region-based analysis with indexed genomic datasets. This allows efficient coordinate-based queries to fetch all reads overlapping specific genomic coordinates like chr1 positions 1000 to 2000.
Yes, you can parse and manipulate VCF and BCF variant files using a comprehensive Python interface. It facilitates complex bioinformatics workflows including reading, writing, and querying variant calls for variant annotation.
The best way to perform coverage analysis on high-throughput sequencing data is by integrating alignment data and reference sequences. This approach combines BAM files and reference genomes to calculate sequencing depth across genomic coordinates.
Yes, processing CRAM files requires htslib integration. This underlying integration provides the high-performance random access needed to query and manipulate compressed genomic alignment datasets efficiently.
You read and write SAM, BAM, and CRAM alignment formats through a Python interface designed for genomic file manipulation. It seamlessly handles parsing and querying these high-throughput sequencing formats within custom bioinformatics workflows.
Indexed genomic datasets are required for coordinate-based queries because they enable high-performance random access. Without indexing, extracting reads or variants from specific regions in large sequencing files would be computationally inefficient.
SciMate-AIRead and analyze BAM, VCF, and FASTA genomic data in Python.
jaechang-hitsFilter, merge, annotate, and extract genetic variants in VCF and BCF files.
dailycafiBuild genome-scale metabolic models from protein FASTA sequences using CarveMe or gapseq.
jaechang-hitsAccess and manipulate genomic data files via a Pythonic htslib interface.
