RESUMEN
Bam-readcount is a utility for generating low-level information about sequencing data at specific nucleotide positions. Originally designed to help filter genomic mutation calls, the metrics it outputs are useful as input for variant detection tools and for resolving ambiguity between variant callers1,2. In addition, it has found broad applicability in diverse fields including tumor evolution, single-cell genomics, climate change ecology, and tracking community spread of SARS-CoV-2.3-6.
RESUMEN
In this work, we present the Genome Modeling System (GMS), an analysis information management system capable of executing automated genome analysis pipelines at a massive scale. The GMS framework provides detailed tracking of samples and data coupled with reliable and repeatable analysis pipelines. The GMS also serves as a platform for bioinformatics development, allowing a large team to collaborate on data analysis, or an individual researcher to leverage the work of others effectively within its data management system. Rather than separating ad-hoc analysis from rigorous, reproducible pipelines, the GMS promotes systematic integration between the two. As a demonstration of the GMS, we performed an integrated analysis of whole genome, exome and transcriptome sequencing data from a breast cancer cell line (HCC1395) and matched lymphoblastoid line (HCC1395BL). These data are available for users to test the software, complete tutorials and develop novel GMS pipeline configurations. The GMS is available at https://github.com/genome/gms.
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Mapeo Cromosómico/métodos , Genoma Humano/genética , Bases del Conocimiento , Modelos Genéticos , Análisis de Secuencia de ADN/métodos , Interfaz Usuario-Computador , Algoritmos , Simulación por Computador , Sistemas de Administración de Bases de Datos , Bases de Datos Genéticas , Humanos , Alineación de Secuencia/métodosRESUMEN
Detecting somatic single nucleotide variants (SNVs) is an essential component of cancer research with next generation sequencing data. This protocol describes how to run the SomaticSniper somatic SNV detector and then filter the output to eliminate most false positives. It also includes support protocols detailing the compilation of the software.
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Biología Computacional , Polimorfismo de Nucleótido Simple , InternetRESUMEN
The advent of the next-generation sequencing data has made it possible to cost-effectively detect and characterize genomic variation in human genomes. Structural variation, including deletion, duplication, insertion, inversion and translocation, is of great importance to human genetics due to its association with many genetic diseases. BreakDancer is a bioinformatics tool that relates paired-end read alignments from a test genome to the reference genome for the purpose of comprehensively and accurately detecting various types of structural variation.
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Genoma Humano , Variación Estructural del Genoma , HumanosRESUMEN
MOTIVATION: The sequencing of tumors and their matched normals is frequently used to study the genetic composition of cancer. Despite this fact, there remains a dearth of available software tools designed to compare sequences in pairs of samples and identify sites that are likely to be unique to one sample. RESULTS: In this article, we describe the mathematical basis of our SomaticSniper software for comparing tumor and normal pairs. We estimate its sensitivity and precision, and present several common sources of error resulting in miscalls. AVAILABILITY AND IMPLEMENTATION: Binaries are freely available for download at http://gmt.genome.wustl.edu/somatic-sniper/current/, implemented in C and supported on Linux and Mac OS X. CONTACT: delarson@wustl.edu; lding@wustl.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
