Convert BAM files to normalized bigWig tracks and generate QC plots.
npx skills add https://github.com/gabrielvuksani/wotann --skill deeptools-gabrielvuksani
Please help me install this Agent Skill. Skill: deeptools Source: https://github.com/gabrielvuksani/wotann/tree/main/skills/scientific/deeptools Command: npx skills add https://github.com/gabrielvuksani/wotann --skill deeptools-gabrielvuksani
It helps you analyze high-throughput sequencing (NGS) experiments by converting alignment outputs into normalized tracks, running quality control, and generating publication-ready QC and visualization figures without manual tool juggling.
Use the Skill to validate your BAM, bigWig, and BED inputs first, then generate a ChIP-seq QC workflow template and run it to produce correlation/PCA/coverage/fingerprint outputs.
High-intent search queries and answers about installing and using this skill.
To convert BAM files to normalized bigWig tracks, you can generate coverage outputs suitable for genome browser viewing using normalization methods like RPGC, CPM, RPKM, or BPM. The process validates BAM inputs and applies correct normalization for ChIP-seq and RNA-seq workflows.
Running NGS quality control and sample comparisons involves performing correlation, PCA, fingerprinting, and fragment-size checks on alignment data. This diagnoses dataset quality and replicate consistency across ChIP-seq, RNA-seq, and ATAC-seq experiments without manual tool juggling.
Yes, you can generate heatmaps and profiles around TSS or other genomic features for ATAC-seq data by building computeMatrix outputs. The workflow supports rendering visualization matrices and enrichment plots, including ATAC-seq specific Tn5 offset correction.
When converting BAM to bigWig for RNA-seq coverage, you must select appropriate normalization like RPKM or CPM and avoid extending reads. Applying correct normalization choices ensures accurate strand-specific coverage representation in downstream analysis.
Yes, deepTools supports common RNA-seq workflows including strand-specific coverage generation. It converts BAM alignments into normalized bigWig or bedGraph coverage tracks while providing operational guardrails to prevent incorrect read extension during processing.
Inconsistent correlation in NGS QC plots often indicates variable dataset quality or replicate inconsistency. Running correlation, PCA, and fingerprinting checks diagnoses these issues by assessing coverage and fragment-size distributions across ChIP-seq alignment outputs.
jasrajtulsiValidates input files and generates templates for deepTools workflows.
rubenslivConvert BAM to bigWig and generate QC visualizations for NGS data.
tassiovaleConvert BAM files to bigWig and visualize NGS data with deepTools.
dralkhValidate and process sequencing coverage files for normalization, QC, and visualization.
