LongPhase-TO co-phases germline and somatic alleles in a unified graph instead of mapping somatic variants onto germline haplotypes, and resolves LOH and tumor DNA fraction internally from heterozygosity depletion and haplotype imbalance without a copy-number and ploidy model.
Abstract
Separating somatic from germline variants and reconstructing somatic haplotypes are the two central problems of tumor-only cancer genome analysis. Long reads carry the linkage needed to solve both, but chromosome-scale loss of heterozygosity (LOH) and an unknown degree of normal-cell admixture blur the distinction between somatic and germline haplotypes. Here we present LongPhase-TO, the first method to reconstruct somatic haplotypes from a tumor sample alone. LongPhase-TO co-phases germline and somatic alleles in a unified graph instead of mapping somatic variants onto germline haplotypes, and resolves LOH and tumor DNA fraction internally from heterozygosity depletion and haplotype imbalance without a copy-number and ploidy model. Across eight datasets from six cancer cell lines, LongPhase-TO increased haplotype block N50 by a median of 2.9-fold relative to germline phasers. It also consistently improved somatic single-nucleotide variant (SNV) and indel calls from ClairS-TO and DeepSomatic-TO, raising mean F1 from 0.55 to 0.62 and 0.65 for SNVs and from 0.19 to 0.23 for indels, with the largest gains at low tumor DNA fraction. Across breast, melanoma and lung cancer cell lines, LongPhase-TO improves the accuracy of existing somatic callers and reconstructs megabase-scale somatic haplotypes.
Small insertions and deletions (Indels) provide critical information for cancer genomics and clonal evolution, yet their detection from single-cell sequencing (SCS) such as scRNA-seq and scATAC-seq remains challenging due to sparse coverage, alignment artifacts, and RNA editing. Here, we present Monopogen-Indel, a bioi...
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The first integrated analysis of the Somatic Mosaicism across Human Tissues (SMaHT) Network’s production resource is presented, profiling up to 20 tissues from 25 donors using short- and long-read, duplex, single-cell, transcriptomic, and epigenomic sequencing, alongside donor-specific near-telomere-to-telomere assembl...
F. Sedlazeck, Tim H. H. Coorens, Peter J. Park et al.· bioRxiv· 2 citations
Evaluating multiple callers showed that telomere-to-telomere CHM13 alignment improves structural variant detection and offers advantages in difficult-to-map regions compared to GRCh38, and a binomial-based model was developed to estimate coverage requirements for variant detection.
Nahyun Kong, Zi-Tian Tang, Andrew Ruttenberg et al.· Cell Genomics· 1 citation
Short-read sequencing cannot determine whether co-occurring variants within a cancer gene lie on the same allele (cis) or opposing alleles (trans), a distinction with direct therapeutic consequences: trans configurations confirm biallelic tumor suppressor inactivation, whereas cis configurations generate compound oncog...
Josh N. Vo, Yi-Mi Wu, Rui Wang et al.· Cancer Discovery· 0 citations
Single-cell eQTL studies can reveal heterogeneous genetic effects on gene expression that vary with cell type, but their wider application has been constrained by the frequent absence of matched genotype data. Here, we present single-cell Transcriptome Allelic Prediction and Association Studies (scTAPAS), a bioinformat...
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Somatic mutations accumulate independently in the two parental genome copies of our cells throughout life and shape cancer evolution. Although local mutation rates are influenced by allele-specific features such as DNA sequence, epigenetic marks, and chromatin structure, whether these translate into genome-wide differe...
Maxime Lefèbvre, Arnaud Cléris, Mathieu Parmentier et al.· bioRxiv· 0 citations
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