Does it support structured data output?

Yes, it can export analysis data as JSON, making it easy to integrate results into other applications or data science pipelines.

What programming language is required for the toolkit?

The toolkit uses Python scripts and requires the pysam and typer libraries to be installed in your local environment.

Can I filter reads by mapping quality?

Absolutely. You can apply filters for minimum mapping quality (MAPQ), base quality, proper pairs, and exclude duplicate reads.

Do I need a BAM index file to use this?

Yes, for efficient random access to specific genomic regions, your BAM files should be indexed (typically using 'samtools index') before running the scripts.

Can this skill process CRAM files?

Yes, the BAM Toolkit supports BAM, SAM, and CRAM alignment formats for comprehensive genomic analysis.

BAM Alignment Toolkit

Name: BAM Alignment Toolkit
Author: dakesan

bydakesan

Ciencia de Datos y ML

Analyzes genomic alignment files to extract reads, identify insertions and deletions, and calculate coverage statistics for WGS and WES data.

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The BAM Toolkit provides a specialized set of utilities for bioinformaticians and researchers to interact with BAM, SAM, and CRAM alignment files within Claude. It streamlines sequence analysis workflows by enabling high-precision read extraction, CIGAR-based indel identification, and detailed coverage calculation, exporting results in structured JSON or standard BAM formats. This tool is essential for quality control in Whole Genome Sequencing (WGS) or Whole Exome Sequencing (WES) projects, offering deep insights into genomic regions of interest directly through AI-driven commands.

Características Principales

0 GitHub stars
Identify insertions and deletions (indels) with automated CIGAR string parsing
Structured JSON output for seamless integration with downstream data analysis
Calculate comprehensive coverage statistics including mean, median, and percent covered
Extract reads from specific genomic regions as JSON or BAM formats
Apply advanced filtering for mapping quality, base quality, and proper pairs

Casos de Uso

Exporting read data to JSON for custom visualization or integration into data science pipelines
Validating variant calls by extracting and grouping high-quality supporting reads for indels
Performing quality control and coverage assessment for specific target regions in WES data

Acerca de

Características Principales

0 GitHub stars
Identify insertions and deletions (indels) with automated CIGAR string parsing
Structured JSON output for seamless integration with downstream data analysis
Calculate comprehensive coverage statistics including mean, median, and percent covered
Extract reads from specific genomic regions as JSON or BAM formats
Apply advanced filtering for mapping quality, base quality, and proper pairs

Casos de Uso

Exporting read data to JSON for custom visualization or integration into data science pipelines
Validating variant calls by extracting and grouping high-quality supporting reads for indels
Performing quality control and coverage assessment for specific target regions in WES data