Biology subjects

Posada, D.

Publications and source records attributed to Posada, D..

5 recordsLinked to original sources

bioRxiv2018

Comparison of single-cell whole-genome amplification strategies

Single-cell genomics is an alluring area that holds the potential to change the way we understand cell populations. Due to the small amount of DNA within a single cell, whole-genome amplification becomes a mandatory step in many single-cell applications. Unfortunately, single-cell whole-genome amplification (scWGA) strategies suffer from several technical biases that complicate the posterior interpretation of the data. Here we compared the performance of six different scWGA methods (GenomiPhi, REPLIg, TruePrime, Ampli1, MALBAC, and PicoPLEX) after amplifying and low-pass sequencing the complete genome of 230 healthy/tumoral human cells. Overall, REPLIg outperformed competing methods regarding DNA yield, amplicon size, amplification breadth, amplification uniformity -being the only method with a random amplification bias-, and false single-nucleotide variant calls. On the other hand, non-MDA methods, and in particular Ampli1, showed less allelic imbalance and ADO, more reliable copy-number profiles and less chimeric amplicons. While no single scWGA method showed optimal performance for every aspect, they clearly have distinct advantages. Our results provide a convenient guide for selecting a scWGA method depending on the question of interest while revealing relevant weaknesses that should be considered during the analysis and interpretation of single-cell sequencing data.

genomics↗

bioRxiv2018

Selective pressures on human cancer genes along the evolution of mammals

Cancer is a disease of the genome caused by somatic mutation and subsequent clonal selection. Several genes associated to cancer in humans, hereafter cancer genes, also show evidence of (germline) positive selection among species. Taking advantage of a large collection of mammalian genomes, we systematically looked for statistically significant signatures of positive selection using dN/dS models in a list of 430 cancer genes. Among these, we identified 63 genes under putative positive selection in mammals, which are significantly enriched in processes like crosslinking DNA repair. We also found evidence of a higher incidence of positive selection in cancer genes bearing germline mutations, like BRCA2, where positively selected residues are physically linked with known pathogenic variants, suggesting a potential association between germline positive selection and risk of hereditary cancer. Overall, our results suggest that genes associated with hereditary cancer have less selective constraints than genes related to sporadic cancer. Also, that the adaptive evolution of human cancer genes in mammals has been most likely driven by adaptive changes in important traits not directly related to cancer.

evolutionary biology↗

bioRxiv2017

Sensitivity to sequencing depth in single-cell cancer genomics

BackgroundQuerying cancer genomes at single-cell resolution is expected to provide a powerful framework to understand in detail the dynamics of cancer evolution. However, given the high costs currently associated with single-cell sequencing, together with the inevitable technical noise arising from single-cell genome amplification, cost-effective strategies that maximize the quality of single-cell data are critically needed. Taking advantage of five published single-cell whole-genome and whole-exome cancer datasets, we studied the impact of sequencing depth and sampling effort towards single-cell variant detection, including structural and driver mutations, genotyping accuracy, clonal inference and phylogenetic reconstruction, using recent tools specifically designed for single-cell data.\n\nResultsAltogether, our results suggest that, for relatively large sample sizes (25 or more cells), sequencing single tumor cells at depths >5x does not drastically improve somatic variant discovery, the characterization of clonal genotypes or the estimation of phylogenies from single tumor cells.\n\nConclusionsWe demonstrate that sequencing many individual tumor cells at a modest depth represents an effective alternative to explore the mutational landscape and clonal evolutionary patterns of cancer genomes, without the excessively high costs associated with high-coverage genome sequencing.

genomics↗

bioRxiv2017

NGSphy: phylogenomic simulation of next-generation sequencing data

MotivationAdvances in sequencing technologies have made it feasible to obtain massive datasets for phylogenomic inference, often consisting of large numbers of loci from multiple species and individuals. The phylogenomic analysis of next-generation sequencing (NGS) data implies a complex computational pipeline where multiple technical and methodological decisions are necessary that can influence the final tree obtained, like those related to coverage, assembly, mapping, variant calling and/or phasing.\n\nResultsTo assess the influence of these variables we introduce NGSphy, an open-source tool for the simulation of Illumina reads/read counts obtained from haploid/diploid individual genomes with thousands of independent gene families evolving under a common species tree. In order to resemble real NGS experiments, NGSphy includes multiple options to model sequencing coverage (depth) heterogeneity across species, individuals and loci, including off-target or uncaptured loci. For comprehensive simulations covering multiple evolutionary scenarios, parameter values for the different replicates can be sampled from user-defined statistical distributions.\n\nAvailabilitySource code, full documentation and tutorials including a quick start guide are available at http://github.com/merlyescalona/ngsphy.\n\nContactmerlyescalona@uvigo.es. dposada@uvigo.es

bioinformatics↗

bioRxiv2016

Biased evolutionary inferences from bulk tumor samples

It is generally agreed that tumors are composed of multiple cell clones defined by different somatic mutations. Characterizing the evolutionary mechanisms driving this intratumor genetic heterogeneity (ITH) is crucial to improve both cancer diagnosis and therapeutic strategies. For that purpose, recent ITH studies have focused on qualitative comparisons of mutational profiles derived from bulk sequencing of multiple tumor samples extracted from the same patient. Here, we show some examples where the naive use of bulk data in multiregional studies may lead to erroneous inferences of the evolutionary trajectories that underlie tumor progression, including biased timing of somatic mutations, spurious parallel mutation events, and/or incorrect chronological ordering of metastatic events. In addition, we analyze three real datasets to highlight how the use of bulk mutational profiles instead of inferred clones can lead to different conclusions about mutational recurrence and population structure.

evolutionary biology↗