clc genomics workbench university of california

Krøigård AB, Thomassen M, Lænkholm AV et al (2016) Evaluation of nine somatic variant callers for detection of somatic mutations in exome and targeted deep sequencing data.Xu H, DiCarlo J, Satya RV et al (2014) Comparison of somatic mutation calling methods in amplicon and whole exome sequence data.Cock PJ, Fields CJ, Goto N et al (2010) The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants.Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration.Robinson JT, Thorvaldsdóttir H, Winckler W et al (2011) Integrative genomics viewer.Li H, Handsaker B, Wysoker A et al (2009) The sequence alignment/Map format and SAMtools.Wilm A, Aw PP, Bertrand D et al (2012) LoFreq: a sequence-quality aware, ultra-sensitive variant caller for uncovering cell-population heterogeneity from high-throughput sequencing datasets.Roth A, Ding J, Morin R et al (2012) JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.Larson DE, Harris CC, Chen K et al (2012) SomaticSniper: identification of somatic point mutations in whole genome sequencing data.Saunders CT, Wong WS, Swamy S et al (2012) Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs.Koboldt DC, Zhang Q, Larson DE et al (2012) VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing.Cibulskis K, Lawrence MS, Carter SL et al (2013) Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples.Van der Auwera GA, Carneiro MO, Hartl C et al (2013) From FastQ data to high confidence variant calls: the genome analysis toolkit best practices pipeline.McKenna A, Hanna M, Banks E et al (2010) The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.Fonseca NA, Rung J, Brazma A et al (2012) Tools for mapping high-throughput sequencing data.Ye H, Meehan J, Tong W et al (2015) Alignment of short reads: a crucial step for application of next-generation sequencing data in precision medicine.Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform.

Wright ES, Vetsigian KH (2016) Quality filtering of Illumina index reads mitigates sample cross-talk.Renaud G, Stenzel U, Maricic T et al (2015) deML: robust demultiplexing of Illumina sequences using a likelihood-based approach.Yi H, Li Z, Li T et al (2015) Bayexer: an accurate and fast Bayesian demultiplexer for Illumina sequences.In this chapter we review common tools used to generate reads from Illumina-derived sequence data, align reads, and call variants from hybridization-based targeted NGS panel data generated from tumor FFPE-derived DNA specimens as well as basic quality metrics to assess for each assayed specimen. Throughout these processes, numerous quality control metrics can be assessed at each step to ensure that the resulting called variants are of high quality and are accurate. In addition to single nucleotide changes or small insertions and deletions, high copy gains and losses can also be gleaned from NGS data to call gene amplifications and deletions. Next-generation sequencing (NGS) technologies produce millions of relatively short segments of sequences or reads that require bioinformatics tools to map reads back to a reference genome using various read alignment tools, as well as to determine differences between single bases (single nucleotide variants or SNVs) or multiple bases (insertions and deletions or indels) between the aligned reads and the reference genome to call variants. The use of next-generation sequencing and hybridization-based capture for target enrichment have enabled the interrogation of coding regions of several clinically significant cancer genes in tumor specimens using both targeted panels of a few to hundreds of genes, to whole-exome panels encompassing coding regions of all genes in the genome.

Clc genomics workbench university of california