Biology subjects

Valecha, M.

Publications and source records attributed to Valecha, M..

3 recordsLinked to original sources

bioRxiv2022

Single-cell phylogenies reveal deviations from clock-like, neutral evolution in cancer and healthy tissues

How tumors evolve affects cancer progression, therapy response, and relapse. However, whether tumor evolution is driven primarily by selectively advantageous or neutral mutations remains under debate. Resolving this controversy has so far been limited by the use of bulk sequencing data. Here, we leverage the high resolution of single-cell DNA sequencing (scDNA-seq) to test for clock-like, neutral evolution. Under neutrality, different cell lineages evolve at a similar rate, accumulating mutations according to a molecular clock. We developed and benchmarked a test of the somatic clock based on single-cell phylogenies and applied it to 22 scDNA-seq datasets. We rejected the clock in 10/13 cancer and 5/9 healthy datasets. The clock rejection in seven cancer datasets could be related to known driver mutations. Our findings demonstrate the power of scDNA-seq for studying somatic evolution and suggest that some cancer and healthy cell populations are driven by selection while others seem to evolve under neutrality.

evolutionary biology↗

bioRxiv2022

SIEVE: joint inference of single-nucleotide variants and cell phylogeny from single-cell DNA sequencing data

Single-cell DNA sequencing (scDNA-seq) has enabled the identification of single nucleotide somatic variants and the reconstruction of cell phylogenies. However, statistical phylogenetic models for cell phylogeny reconstruction from raw sequencing data are still in their infancy. Here we present SIEVE (SIngle-cell EVolution Explorer), a statistical method for the joint inference of somatic variants and cell phylogeny under the finite-sites assumption from scDNA-seq reads. SIEVE leverages raw read counts for all nucleotides at candidate variant sites, and corrects the acquisition bias of branch lengths. In our simulations, SIEVE outperforms other methods both in phylogenetic accuracy and variant calling accuracy. We apply SIEVE to three scDNA-seq datasets, for colorectal (CRC) and triple-negative breast cancer (TNBC), one of them generated by us. On simulated data, SIEVE reliably infers homo-and heterozygous somatic variants. The analysis of real data uncovers that double mutant genotypes are rare in CRC but unexpectedly frequent in TNBC samples.

bioinformatics↗

bioRxiv2022

Phylovar: Towards scalable phylogeny-aware inference of single-nucleotide variations from single-cell DNA sequencing data

Single-nucleotide variants (SNVs) are the most common variations in the human genome. Recently developed methods for SNV detection from single-cell DNA sequencing (scDNAseq) data, such as SCI{Phi} and scVILP, leverage the evolutionary history of the cells to overcome the technical errors associated with single-cell sequencing protocols. Despite being accurate, these methods are not scalable to the extensive genomic breadth of single-cell whole-genome (scWGS) and whole-exome sequencing (scWES) data. Here we report on a new scalable method, Phylovar, which extends the phylogeny-guided variant calling approach to sequencing datasets containing millions of loci. Through benchmarking on simulated datasets under different settings, we show that, Phylovar outperforms SCI{Phi} in terms of running time while being more accurate than Monovar (which is not phylogeny-aware) in terms of SNV detection. Furthermore, we applied Phylovar to two real biological datasets: an scWES triple-negative breast cancer data consisting of 32 cells and 3375 loci as well as an scWGS data of neuron cells from a normal human brain containing 16 cells and approximately 2.5 million loci. For the cancer data, Phylovar detected somatic SNVs with high or moderate functional impact that were also supported by bulk sequencing dataset and for the neuron dataset, Phylovar identified 5745 SNVs with non-synonymous effects some of which were associated with neurodegenerative diseases. We implemented Phylovar and made it publicly available at https://github.com/mae6/Phylovar.git.

cancer biology↗