HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data.
Matthew A MyersBrian J ArnoldVineet BansalMetin BalabanKatelyn M MullenSimone ZaccariaBenjamin J RaphaelPublished in: Genome biology (2024)
Bulk DNA sequencing of multiple samples from the same tumor is becoming common, yet most methods to infer copy-number aberrations (CNAs) from this data analyze individual samples independently. We introduce HATCHet2, an algorithm to identify haplotype- and clone-specific CNAs simultaneously from multiple bulk samples. HATCHet2 extends the earlier HATCHet method by improving identification of focal CNAs and introducing a novel statistic, the minor haplotype B-allele frequency (mhBAF), that enables identification of mirrored-subclonal CNAs. We demonstrate HATCHet2's improved accuracy using simulations and a single-cell sequencing dataset. HATCHet2 analysis of 10 prostate cancer patients reveals previously unreported mirrored-subclonal CNAs affecting cancer genes.
Keyphrases
- copy number
- single cell
- mitochondrial dna
- genome wide
- rna seq
- dna methylation
- bioinformatics analysis
- prostate cancer
- high throughput
- electronic health record
- big data
- machine learning
- papillary thyroid
- molecular dynamics
- gene expression
- deep learning
- squamous cell
- young adults
- transcription factor
- childhood cancer
- lymph node metastasis