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Summary: We report on a new single-cell DNA sequence simulator, SimSCSnTree, which generates an evolutionary tree of cells and evolves single nucleotide variants (SNVs) and copy number aberrations (CNAs) along its branches. Data generated by the simulator can be used to benchmark tools for single-cell genomic analyses, particularly in cancer where SNVs and CNAs are ubiquitous., Availability and Implementation: SimSCSnTree is now on BioConda and also is freely available for download at https://github.com/compbiofan/SimSCSnTree.git with detailed documentation., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Multi-locus species phylogeny inference is based on models of sequence evolution on gene trees as well as models of gene tree evolution within the branches of species phylogenies. Almost all statistical methods for this inference task assume a common mechanism across all loci as captured by a single value of each branch length of the species phylogeny., Results: In this paper, we pursue a "no common mechanism" (NCM) model, where every gene tree evolves according to its own parameters of the species phylogeny. Based on this model, we derive an analytically integrated likelihood of both species trees and networks given the gene trees of multiple loci under an NCM model. We demonstrate the performance of inference under this integrated likelihood on both simulated and biological data., Conclusions: The model presented here will afford opportunities for exploring connections among various criteria for estimating species phylogenies from multiple, independent loci. Furthermore, further development of this model could potentially result in more efficient methods for searching the space of species phylogenies by focusing solely on the topology of the phylogeny.

Summary: The evolutionary histories of individual regions across a genomic alignment-called 'local genealogies'-can differ from each other, due to processes such as recombination. Elucidating and analyzing these local genealogies are important for a large number of inference tasks, including those pertaining to species phylogenies, evolutionary processes and trait mapping. In this paper, we present a toolkit for automated local phylogenomic analyses, or ALPHA. The purpose of this toolkit is to provide a wide array of functionalities for automated inference of local genealogies as well as analyses based on these local genealogies. The toolkit uses sliding windows to construct local genealogies and can compute a wide array of local phylogeny based statistics, such as the D-statistic. The toolkit comes with a graphical user interface and several import/export functionalities. Over the last few decades, much emphasis in phylogenomics has been put on developing tools for inferring species phylogenies. This toolkit complements those efforts by emphasizing the 'local' aspect of phylogenomics., Availability and Implementation: ALPHA is freely available for installation and use, including source code, at https://github.com/chilleo/ALPHA.

Achieving complete, accurate, and cost-effective assembly of human genomes is of great importance for realizing the promise of precision medicine. The abundance of repeats and genetic variations in human genomes and the limitations of existing sequencing technologies call for the development of novel assembly methods that can leverage the complementary strengths of multiple technologies. We propose a Hybrid Structural variant Assembly (HySA) approach that integrates sequencing reads from next-generation sequencing and single-molecule sequencing technologies to accurately assemble and detect structural variants (SVs) in human genomes. By identifying homologous SV-containing reads from different technologies through a bipartite-graph-based clustering algorithm, our approach turns a whole genome assembly problem into a set of independent SV assembly problems, each of which can be effectively solved to enhance the assembly of structurally altered regions in human genomes. We used data generated from a haploid hydatidiform mole genome (CHM1) and a diploid human genome (NA12878) to test our approach. The result showed that, compared with existing methods, our approach had a low false discovery rate and substantially improved the detection of many types of SVs, particularly novel large insertions, small indels (10-50 bp), and short tandem repeat expansions and contractions. Our work highlights the strengths and limitations of current approaches and provides an effective solution for extending the power of existing sequencing technologies for SV discovery., (© 2017 Fan et al.; Published by Cold Spring Harbor Laboratory Press.)

Background: Recent advances in deep digital sequencing have unveiled an unprecedented degree of clonal heterogeneity within a single tumor DNA sample. Resolving such heterogeneity depends on accurate estimation of fractions of alleles that harbor somatic mutations. Unlike substitutions or small indels, structural variants such as deletions, duplications, inversions and translocations involve segments of DNAs and are potentially more accurate for allele fraction estimations. However, no systematic method exists that can support such analysis., Results: In this paper, we present a novel maximum-likelihood method that estimates allele fractions of structural variants integratively from various forms of alignment signals. We develop a tool, BreakDown, to estimate the allele fractions of most structural variants including medium size (from 1 kilobase to 1 megabase) deletions and duplications, and balanced inversions and translocations., Conclusions: Evaluation based on both simulated and real data indicates that our method systematically enables structural variants for clonal heterogeneity analysis and can greatly enhance the characterization of genomically instable tumors.

Background: Phylogenomic analyses involving whole-genome or multi-locus data often entail dealing with incongruent gene trees. In this paper, we consider two causes of such incongruence, namely, incomplete lineage sorting (ILS) and hybridization, and consider both parsimony and probabilistic criteria for dealing with them., Results: Under the assumption of ILS, computing the probability of a gene tree given a species tree is a very hard problem. We present a heuristic for speeding up the computation, and demonstrate how it scales up computations to data sizes that are not feasible to analyze using current techniques, while achieving very good accuracy. Further, under the assumption of both ILS and hybridization, computing the probability of a gene tree and parsimoniously reconciling it with a phylogenetic network are both very hard problems. We present two exact algorithms for these two problems that speed up existing techniques significantly and enable analyses of much larger data sets than is currently feasible., Conclusion: Our heuristics and algorithms enable phylogenomic analyses of larger (in terms of numbers of taxa) data sets than is currently feasible. Further, our methods account for ILS and hybridization, thus allowing analyses of reticulate evolutionary histories.