HMMcopy

HMMcopy corrects GC content and mappability biases in sequencing read counts across non-overlapping fixed-length genomic windows in single high-coverage whole-genome samples to enable more accurate copy number variation estimates.

Key Features:

• Bias Correction: Corrects biases introduced by GC content and mappability that distort read counts (coverage).
• Window-Based Analysis: Operates on non-overlapping windows of fixed length across the genome for per-window coverage correction.
• Copy Number Estimation: Produces corrected read counts that support rough estimates of copy number variations.
• Statistical Adjustment: Leverages statistical methods to adjust read counts by accounting for GC content and mappability.
• R/Bioconductor Implementation: Implemented in R and distributed via the Bioconductor project.

Scientific Applications:

• Cancer Genomics: Applicable to high-coverage whole-genome tumour and normal samples for analysis of genomic alterations and copy number differences.
• Genomic Research: Improves reliability of downstream analyses such as identification of copy number variations as biomarkers.

Methodology:

Per-sample statistical adjustment of read counts for GC content and mappability, implemented in R within the Bioconductor ecosystem.