SUMMARY: We present a new version of the popular somatic variant caller, Lancet, that supports the analysis of linked-reads sequencing data. By seamlessly integrating barcodes and haplotype read assignments within the colored De Bruijn graph local-assembly framework, Lancet computes a barcode-aware coverage and identifies variants that disagree with the local haplotype structure. AVAILABILITY AND IMPLEMENTATION: Lancet is implemented in C++ and available for academic and non-commercial research purposes as an open-source package at https://github.com/nygenome/lancet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Sequencing of matched tumor and normal samples is the standard study design for reliable detection of somatic alterations. However, even very low levels of cross-sample contamination significantly impact calling of somatic mutations, because contaminant germline variants can be incorrectly interpreted as somatic. There are currently no sequence-only based methods that reliably estimate contamination levels in tumor samples, which frequently display copy number changes. As a solution, we developed Conpair, a tool for detection of sample swaps and cross-individual contamination in whole-genome and whole-exome tumor-normal sequencing experiments. RESULTS: On a ladder of in silico contaminated samples, we demonstrated that Conpair reliably measures contamination levels as low as 0.1%, even in presence of copy number changes. We also estimated contamination levels in glioblastoma WGS and WXS tumor-normal datasets from TCGA and showed that they strongly correlate with tumor-normal concordance, as well as with the number of germline variants called as somatic by several widely-used somatic callers. AVAILABILITY AND IMPLEMENTATION: The method is available at: https://github.com/nygenome/conpair CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

Viruses diversify over time within hosts, often undercutting the effectiveness of host defenses and therapeutic interventions. To design successful vaccines and therapeutics, it is critical to better understand viral diversification, including comprehensively characterizing the genetic variants in viral intra-host populations and modeling changes from transmission through the course of infection. Massively parallel sequencing technologies can overcome the cost constraints of older sequencing methods and obtain the high sequence coverage needed to detect rare genetic variants (< 1%) within an infected host, and to assay variants without prior knowledge. Critical to interpreting deep sequence data sets is the ability to distinguish biological variants from process errors with high sensitivity and specificity. To address this challenge, we describe V-Phaser, an algorithm able to recognize rare biological variants in mixed populations. V-Phaser uses covariation (i.e. phasing) between observed variants to increase sensitivity and an expectation maximization algorithm that iteratively recalibrates base quality scores to increase specificity. Overall, V-Phaser achieved > 97% sensitivity and > 97% specificity on control read sets. On data derived from a patient after four years of HIV-1 infection, V-Phaser detected 2,015 variants across the -10 kb genome, including 603 rare variants (< 1% frequency) detected only using phase information. V-Phaser identified variants at frequencies down to 0.2%, comparable to the detection threshold of allele-specific PCR, a method that requires prior knowledge of the variants. The high sensitivity and specificity of V-Phaser enables identifying and tracking changes in low frequency variants in mixed populations such as RNA viruses.

MOTIVATION: Comparing the genomes from closely related species provides a powerful tool to identify functional elements in a reference genome. Many methods have been developed to identify conserved sequences across species; however, existing methods only model conservation as a decrease in the rate of mutation and have ignored selection acting on the pattern of mutations. RESULTS: We present a new approach that takes advantage of deeply sequenced clades to identify evolutionary selection by uncovering not only signatures of rate-based conservation but also substitution patterns characteristic of sequence undergoing natural selection. We describe a new statistical method for modeling biased nucleotide substitutions, a learning algorithm for inferring site-specific substitution biases directly from sequence alignments and a hidden Markov model for detecting constrained elements characterized by biased substitutions. We show that the new approach can identify significantly more degenerate constrained sequences than rate-based methods. Applying it to the ENCODE regions, we identify as much as 10.2% of these regions are under selection. AVAILABILITY: The algorithms are implemented in a Java software package, called SiPhy, freely available at http://www.broadinstitute.org/science/software/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Motivation: During the process of high-throughput genome sequencing there are opportunities for mixups of reagents and data associated with particular projects. The sequencing templates or sequence data generated for an assembly may become contaminated with reagents or sequences from another project, resulting in poorer quality and inaccurate assemblies. Results: We have developed a system to assess sequence assemblies and monitor for laboratory mixups. We describe several methods for testing the consistency of assemblies and resolving mixed ones. We use statistical tests to evaluate the distribution of sequencing reads from different plates into contigs, and a graph-based approach to resolve situations where data has been inappropriately combined. While these methods have been designed for use in a high-throughput DNA sequencing environment processing thousands of clones, they can be applied in any situation where distinct sequencing projects are performed at redundant coverage. Availability: Our software is available for downloading from: ftp-genome.wi.mit.edu/distribution/mixups_detection Contact: [email protected] * To whom correspondence should be addressed.