A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Single Nucleotide Variants (SNVs) Calling. <s> Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. ::: ::: Availability: http://samtools.sourceforge.net ::: ::: Contact: [email protected] <s> BIB001 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Single Nucleotide Variants (SNVs) Calling. <s> Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas. <s> BIB002 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Single Nucleotide Variants (SNVs) Calling. <s> Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets. <s> BIB003 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Single Nucleotide Variants (SNVs) Calling. <s> Recent advances in genome sequencing technologies provide unprecedented opportunities to characterize individual genomic landscapes and identify mutations relevant for diagnosis and therapy. Specifically, whole-exome sequencing using next-generation sequencing (NGS) technologies is gaining popularity in the human genetics community due to the moderate costs, manageable data amounts and straightforward interpretation of analysis results. While whole-exome and, in the near future, whole-genome sequencing are becoming commodities, data analysis still poses significant challenges and led to the development of a plethora of tools supporting specific parts of the analysis workflow or providing a complete solution. Here, we surveyed 205 tools for whole-genome/whole-exome sequencing data analysis supporting five distinct analytical steps: quality assessment, alignment, variant identification, variant annotation and visualization. We report an overview of the functionality, features and specific requirements of the individual tools. We then selected 32 programs for variant identification, variant annotation and visualization, which were subjected to hands-on evaluation using four data sets: one set of exome data from two patients with a rare disease for testing identification of germline mutations, two cancer data sets for testing variant callers for somatic mutations, copy number variations and structural variations, and one semi-synthetic data set for testing identification of copy number variations. Our comprehensive survey and evaluation of NGS tools provides a valuable guideline for human geneticists working on Mendelian disorders, complex diseases and cancers. <s> BIB004 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Single Nucleotide Variants (SNVs) Calling. <s> This unit describes how to use BWA and the Genome Analysis Toolkit (GATK) to map genome sequencing data to a reference and produce high-quality variant calls that can be used in downstream analyses. The complete workflow includes the core NGS data processing steps that are necessary to make the raw data suitable for analysis by the GATK, as well as the key methods involved in variant discovery using the GATK. <s> BIB005 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Single Nucleotide Variants (SNVs) Calling. <s> The recent development of massively parallel sequencing technology has allowed the creation of comprehensive catalogs of genetic variation. However, due to the relatively high sequencing error rate for short read sequence data, sophisticated analysis methods are required to obtain high-quality variant calls. Here, we developed a probabilistic multinomial method for the detection of single nucleotide variants (SNVs) as well as short insertions and deletions (indels) in whole genome sequencing (WGS) and whole exome sequencing (WES) data for single sample calling. Evaluation with DNA genotyping arrays revealed a concordance rate of 99.98% for WGS calls and 99.99% for WES calls. Sanger sequencing of the discordant calls determined the false positive and false negative rates for the WGS (0.0068% and 0.17%) and WES (0.0036% and 0.0084%) datasets. Furthermore, short indels were identified with high accuracy (WGS: 94.7%, WES: 97.3%). We believe our method can contribute to the greater understanding of human diseases. <s> BIB006
After sequences have been aligned to the reference genome, the next step is to perform variant detection in the WES data. There are four general categories of variant calling strategies: germline variants, somatic variants, copy number variations, and structural variants. Multiple tools that perform one or more of these variant calling techniques were recently compared to each other BIB004 . Some common SNV calling programs are GATK BIB002 BIB003 BIB005 , SAMtools BIB001 , and VCMM BIB006 . The actual SNV calling mechanisms of GATK and SAMtools are very similar. However, the context before and after SNV calling represents the differences between these tools. GATK assumes each sequencing error is independent while SAMtools believes a secondary error carries more weight. After SNV calling GATK learns from data while SAMtools relies on options of the user, Variant Caller with Multinomial probabilistic Model (VCMM) is another tool developed to detect SNVs and INDELs from WES and Whole Genome Sequencing (WGS) studies using a multinomial probabilistic model with quality score and a strand bias filter BIB006 . VCMM suppressed the false-positive and false-negative variant calls when compared to GATK and SAMtools. However, the number of variant calls was similar to previous studies. The comparison done by the authors of VCMM demonstrated that while all three methods call a large number of common SNVs, each tool also identifies SNVs not found by the other methods BIB006 . The ability of each method to call SNVs not found by the others should be taken into account when choosing a SNV variant calling tool(s).