MetaSV: an accurate and integrative structural-variant caller for next generation sequencing.

Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed; however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous works have attempted to combine different methods, but they still suffer from poor accuracy particularly for insertions. We propose MetaSV, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes. Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes. Code in Python is at http://bioinform.github.io/metasv/. rd@bina.com Supplementary data are available at Bioinformatics online.

Mohiyuddin M ，Mu JC ，Li J ，Bani Asadi N ，Gerstein MB ，Abyzov A ，Wong WH ，Lam HY ... -  《-》

Comparison of multiple algorithms to reliably detect structural variants in pears.

Liu Y ，Zhang M ，Sun J ，Chang W ，Sun M ，Zhang S ，Wu J ... -  《BMC GENOMICS》

VarSim: a high-fidelity simulation and validation framework for high-throughput genome sequencing with cancer applications.

VarSim is a framework for assessing alignment and variant calling accuracy in high-throughput genome sequencing through simulation or real data. In contrast to simulating a random mutation spectrum, it synthesizes diploid genomes with germline and somatic mutations based on a realistic model. This model leverages information such as previously reported mutations to make the synthetic genomes biologically relevant. VarSim simulates and validates a wide range of variants, including single nucleotide variants, small indels and large structural variants. It is an automated, comprehensive compute framework supporting parallel computation and multiple read simulators. Furthermore, we developed a novel map data structure to validate read alignments, a strategy to compare variants binned in size ranges and a lightweight, interactive, graphical report to visualize validation results with detailed statistics. Thus far, it is the most comprehensive validation tool for secondary analysis in next generation sequencing. Code in Java and Python along with instructions to download the reads and variants is at http://bioinform.github.io/varsim. rd@bina.com Supplementary data are available at Bioinformatics online.

Mu JC ，Mohiyuddin M ，Li J ，Bani Asadi N ，Gerstein MB ，Abyzov A ，Wong WH ，Lam HY ... -  《-》

svclassify: a method to establish benchmark structural variant calls.

Parikh H ，Mohiyuddin M ，Lam HY ，Iyer H ，Chen D ，Pratt M ，Bartha G ，Spies N ，Losert W ，Zook JM ，Salit M ... -  《BMC GENOMICS》

PopIns: population-scale detection of novel sequence insertions.

The detection of genomic structural variation (SV) has advanced tremendously in recent years due to progress in high-throughput sequencing technologies. Novel sequence insertions, insertions without similarity to a human reference genome, have received less attention than other types of SVs due to the computational challenges in their detection from short read sequencing data, which inherently involves de novo assembly. De novo assembly is not only computationally challenging, but also requires high-quality data. Although the reads from a single individual may not always meet this requirement, using reads from multiple individuals can increase power to detect novel insertions. We have developed the program PopIns, which can discover and characterize non-reference insertions of 100 bp or longer on a population scale. In this article, we describe the approach we implemented in PopIns. It takes as input a reads-to-reference alignment, assembles unaligned reads using a standard assembly tool, merges the contigs of different individuals into high-confidence sequences, anchors the merged sequences into the reference genome, and finally genotypes all individuals for the discovered insertions. Our tests on simulated data indicate that the merging step greatly improves the quality and reliability of predicted insertions and that PopIns shows significantly better recall and precision than the recent tool MindTheGap. Preliminary results on a dataset of 305 Icelanders demonstrate the practicality of the new approach. The source code of PopIns is available from http://github.com/bkehr/popins birte.kehr@decode.is Supplementary data are available at Bioinformatics online.

Kehr B ，Melsted P ，Halldórsson BV 《-》