Exome sequencing of case-unaffected-parents trios reveals recessive and de novo genetic variants in sporadic ALS

The contribution of genetic variants to sporadic amyotrophic lateral sclerosis (ALS) remains largely unknown. Either recessive or de novo variants could result in an apparently sporadic occurrence of ALS. In an attempt to find such variants we sequenced the exomes of 44 ALS-unaffected-parents trios. Rare and potentially damaging compound heterozygous variants were found in 27% of ALS patients, homozygous recessive variants in 14% and coding de novo variants in 27%. In 20% of patients more than one of the above variants was present. Genes with recessive variants were enriched in nucleotide binding capacity, ATPase activity, and the dynein heavy chain. Genes with de novo variants were enriched in transcription regulation and cell cycle processes. This trio study indicates that rare private recessive variants could be a mechanism underlying some case of sporadic ALS, and that de novo mutations are also likely to play a part in the disease

GenomeVIP: A cloud platform for genomic variant discovery and interpretation

Identifying genomic variants is a fundamental first step toward the understanding of the role of inherited and acquired variation in disease. The accelerating growth in the corpus of sequencing data that underpins such analysis is making the data-download bottleneck more evident, placing substantial burdens on the research community to keep pace. As a result, the search for alternative approaches to the traditional “download and analyze” paradigm on local computing resources has led to a rapidly growing demand for cloud-computing solutions for genomics analysis. Here, we introduce the Genome Variant Investigation Platform (GenomeVIP), an open-source framework for performing genomics variant discovery and annotation using cloud- or local high-performance computing infrastructure. GenomeVIP orchestrates the analysis of whole-genome and exome sequence data using a set of robust and popular task-specific tools, including VarScan, GATK, Pindel, BreakDancer, Strelka, and Genome STRiP, through a web interface. GenomeVIP has been used for genomic analysis in large-data projects such as the TCGA PanCanAtlas and in other projects, such as the ICGC Pilots, CPTAC, ICGC-TCGA DREAM Challenges, and the 1000 Genomes SV Project. Here, we demonstrate GenomeVIP's ability to provide high-confidence annotated somatic, germline, and de novo variants of potential biological significance using publicly available data sets.</jats:p

A toolkit for rapid gene mapping in the nematode Caenorhabditis briggsae

<p>Abstract</p> <p>Background</p> <p>The nematode <it>C. briggsae </it>serves as a useful model organism for comparative analysis of developmental and behavioral processes. The amenability of <it>C. briggsae </it>to genetic manipulations and the availability of its genome sequence have prompted researchers to study evolutionary changes in gene function and signaling pathways. These studies rely on the availability of forward genetic tools such as mutants and mapping markers.</p> <p>Results</p> <p>We have computationally identified more than 30,000 polymorphisms (SNPs and indels) in <it>C. briggsae </it>strains AF16 and HK104. These include 1,363 SNPs that change restriction enzyme recognition sites (snip-SNPs) and 638 indels that range between 7 bp and 2 kb. We established bulk segregant and single animal-based PCR assay conditions and used these to test 107 polymorphisms. A total of 75 polymorphisms, consisting of 14 snip-SNPs and 61 indels, were experimentally confirmed with an overall success rate of 83%. The utility of polymorphisms in genetic studies was demonstrated by successful mapping of 12 mutations, including 5 that were localized to sub-chromosomal regions. Our mapping experiments have also revealed one case of a misassembled contig on chromosome 3.</p> <p>Conclusions</p> <p>We report a comprehensive set of polymorphisms in <it>C. briggsae </it>wild-type strains and demonstrate their use in mapping mutations. We also show that molecular markers can be useful tools to improve the <it>C. briggsae </it>genome sequence assembly. Our polymorphism resource promises to accelerate genetic and functional studies of <it>C. briggsae </it>genes.</p

VarScan 2: Somatic mutation and copy number alteration discovery in cancer by exome sequencing

Cancer is a disease driven by genetic variation and mutation. Exome sequencing can be utilized for discovering these variants and mutations across hundreds of tumors. Here we present an analysis tool, VarScan 2, for the detection of somatic mutations and copy number alterations (CNAs) in exome data from tumor–normal pairs. Unlike most current approaches, our algorithm reads data from both samples simultaneously; a heuristic and statistical algorithm detects sequence variants and classifies them by somatic status (germline, somatic, or LOH); while a comparison of normalized read depth delineates relative copy number changes. We apply these methods to the analysis of exome sequence data from 151 high-grade ovarian tumors characterized as part of the Cancer Genome Atlas (TCGA). We validated some 7790 somatic coding mutations, achieving 93% sensitivity and 85% precision for single nucleotide variant (SNV) detection. Exome-based CNA analysis identified 29 large-scale alterations and 619 focal events per tumor on average. As in our previous analysis of these data, we observed frequent amplification of oncogenes (e.g., CCNE1, MYC) and deletion of tumor suppressors (NF1, PTEN, and CDKN2A). We searched for additional recurrent focal CNAs using the correlation matrix diagonal segmentation (CMDS) algorithm, which identified 424 significant events affecting 582 genes. Taken together, our results demonstrate the robust performance of VarScan 2 for somatic mutation and CNA detection and shed new light on the landscape of genetic alterations in ovarian cancer

An ensemble approach to accurately detect somatic mutations using SomaticSeq

SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses. We validate our results with both synthetic and real data. We report that SomaticSeq is able to achieve better overall accuracy than any individual tool incorporated. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-015-0758-2) contains supplementary material, which is available to authorized users