igv-integration

IGV snapshots automate visualization of genomic regions by rendering PNG images from multiple BAM tracks simultaneously. Provide a genome ID (hg38, hg19, mm39), BAM file paths, a region or BED file, and an output directory to generate batch scripts that IGV executes, producing annotated snapshots for quality control and comparative analysis.

Yes. BED-driven batch processing lets you define multiple genomic regions in a single file, then generate IGV snapshots across all regions with your BAM inputs in one run. The Skill iterates through each BED entry and produces region-specific PNGs, streamlining multi-region comparative workflows.

Required inputs are a genome identifier (hg38, hg19, mm39), one or more BAM files, either a single region string or a BED file containing regions, and an output directory. Optional settings include IGV installation path, maximum panel height, and Java heap size to customize rendering and performance.

Yes. The Skill is designed for whole-genome sequencing (WGS) and whole-exome sequencing (WES) visualization, supporting both single-region multi-BAM comparisons for detailed inspection and BED-driven batch processing for large-scale quality assurance across multiple samples.

Performance depends on BAM file size, region count, and Java heap allocation. Rendering large regions or many BAM tracks simultaneously can be memory-intensive. Configure the java-heap and max-panel-height parameters to balance visualization quality with available system resources.

Yes. The Skill accepts optional configuration for igv-path, max-panel-height, and java-heap, allowing you to specify the IGV installation location, adjust track panel dimensions, and allocate memory to the Java process for optimal rendering of your BAM datasets.