A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Introduction <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> Introduction <s> Demand has never been greater for revolutionary technologies that deliver fast, inexpensive and accurate genome information. This challenge has catalysed the development of next-generation sequencing (NGS) technologies. The inexpensive production of large volumes of sequence data is the primary advantage over conventional methods. Here, I present a technical review of template preparation, sequencing and imaging, genome alignment and assembly approaches, and recent advances in current and near-term commercially available NGS instruments. I also outline the broad range of applications for NGS technologies, in addition to providing guidelines for platform selection to address biological questions of interest. <s> BIB002 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Introduction <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> BIB003 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Introduction <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> BIB004 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Introduction <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> Introduction <s> Advances in DNA sequencing technologies have led to vast increases in the diversity of sequencing-based applications and in the amount of data generated. This Review discusses the current state-of-the-art technologies in both short-read and long-read DNA sequencing, their underlying mechanisms, relative strengths and limitations, and emerging applications. <s> BIB006 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Introduction <s> Large-scale reference data sets of human genetic variation are critical for the medical and functional interpretation of DNA sequence changes. Here we describe the aggregation and analysis of high-quality exome (protein-coding region) sequence data for 60,706 individuals of diverse ethnicities generated as part of the Exome Aggregation Consortium (ExAC). The resulting catalogue of human genetic diversity contains an average of one variant every eight bases of the exome, and provides direct evidence for the presence of widespread mutational recurrence. We show that this catalogue can be used to calculate objective metrics of pathogenicity for sequence variants, and to identify genes subject to strong selection against various classes of mutation; we identify 3,230 genes with near-complete depletion of truncating variants, 72% of which have no currently established human disease phenotype. Finally, we demonstrate that these data can be used for the efficient filtering of candidate disease-causing variants, and for the discovery of human “knockout” variants in protein-coding genes. <s> BIB007
Recent advances in next-generation sequencing technologies provide revolutionary opportunities to characterize the genomic landscapes of individuals at single base resolution for identifying actionable mutations for disease treatment and management BIB002 BIB006 . Whole Exome Sequencing (WES) is the application of the next-generation technology to determine the variations in the exome, that is, all coding regions of known genes in a genome. For example, more than 85% of disease-causing mutations in Mendelian diseases are found in the exome, and WES provides an unbiased approach to detect these variants in the era of personalized and precision medicine. Next-generation sequencing technologies have shifted the bottleneck in experimental data production to computationally intensive informatics-based data analysis. For example, the Exome Aggregation Consortium (ExAC) has assembled and reanalyzed WES data of 60,706 unrelated individuals from various disease-specific and population genetic studies BIB007 . To gain insights in WES, novel computational algorithms and bioinformatics methods represent a critical component in modern biomedical research to analyze and interpret these massive datasets. Genomic studies that employ WES have increased over the years, and new bioinformatics methods and computational tools have developed to assist the analysis and interpretation of this data ( Figure 1 ). The majority of WES computational tools are centered on the generation of a Variant Calling Format (VCF) file from raw sequencing data. Once the VCF files have been generated, further downstream analyses can be performed by other computational methods. Therefore, in this review we have classified bioinformatics methods and computational tools into Pre-VCF and Post-VCF categories. Pre-VCF workflows include tools for aligning the raw sequencing reads to a reference genome, variant detection, and annotation. Post-VCF workflows include methods for somatic mutation detection, pathway analysis, copy number alterations, INDEL identification, and driver prediction. Depending on the nature of the hypothesis, beyond VCF analysis can also include methods that link variants to clinical data as well as potential therapeutics ( Figure 2 ). Computational tools developed to align raw sequencing data to an annotated VCF file have been well established. Most studies tend to follow workflows associated with GATK BIB003 BIB004 BIB005 , SAMtools BIB001 , or a combination of these. In general, workflows start with aligning WES reads to a reference genome and noting reads that vary. The most common of these variants are single nucleotide variants (SNVs) but also include insertions, deletions, and rearrangements. The location of these variants is used to annotate them to a specific gene. After annotation, the SNVs found can be compared to databases of SNVs found in other studies. This allows for the determination of frequency of a particular SNV in a given population. In some studies, such as those relating to cancer, rare somatic mutations are of interest. However, in Mendelian studies, the germline mutational landscape will be of more interest than somatic mutations. Before a final VCF file is produced for a given sample, software can be used to predict if the variant will be functionally damaging to the protein for prioritizing candidate genes for further study. Bioinformatics methods developed beyond the establishment of annotated VCF files are far less established. In cancer research, the most established types of beyond VCF tools are focused on the detection of somatic mutations. However, there are strides being made to develop other computational tools including pathway analysis, copy number alteration, INDEL identification, driver mutation predictions, and linking candidate genes to clinical data and actionable targets. Here, we will review recent computational tools in the analysis and interpretation of WES data, with special focus on the applications of these methods in cancer research. We have surveyed the current trends in next-generation sequencing analysis tools and compared their methodology so that researches can better determine which tools are the best for their WES study and the advancement of precision medicine. In addition, we include a list of publicly available bioinformatics and computational tools as a reference for WES studies (Table 1) .