A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> We describe an algorithm, SSAHA (Sequence Search and Alignment by Hashing Algorithm), for performing fast searches on databases containing multiple gigabases of DNA. Sequences in the database are preprocessed by breaking them into consecutive k-tuples of k contiguous bases and then using a hash table to store the position of each occurrence of each k-tuple. Searching for a query sequence in the database is done by obtaining from the hash table the “hits” for each k-tuple in the query sequence and then performing a sort on the results. We discuss the effect of the tuple length k on the search speed, memory usage, and sensitivity of the algorithm and present the results of computational experiments which show that SSAHA can be three to four orders of magnitude faster than BLAST or FASTA, while requiring less memory than suffix tree methods. The SSAHA algorithm is used for high-throughput single nucleotide polymorphism (SNP) detection and very large scale sequence assembly. Also, it provides Web-based sequence search facilities for Ensembl projects. <s> BIB001 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <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> BIB002 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> Motivation: There is a strong demand in the genomic community to develop effective algorithms to reliably identify genomic variants. Indel detection using next-gen data is difficult and identification of long structural variations is extremely challenging. Results: We present Pindel, a pattern growth approach, to detect breakpoints of large deletions and medium-sized insertions from paired-end short reads. We use both simulated reads and real data to demonstrate the efficiency of the computer program and accuracy of the results. Availability: The binary code and a short user manual can be freely downloaded from http://www.ebi.ac.uk/∼kye/pindel/. <s> BIB003 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> Evan Eichler and colleagues have developed an algorithm called mrFAST to map short, next-generation sequence reads across the genome that allows for the accurate prediction of copy-number variation. <s> BIB004 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> SOAP2 is a significantly improved version of the short oligonucleotide alignment program that both reduces computer memory usage and increases alignment speed at an unprecedented rate. We used a Burrows Wheeler Transformation (BWT) compression index to substitute the seed strategy for indexing the reference sequence in the main memory. We tested it on the whole human genome and found that this new algorithm reduced memory usage from 14.7 to 5.4 GB and improved alignment speed by 20-30 times. SOAP2 is compatible with both single- and paired-end reads. Additionally, this tool now supports multiple text and compressed file formats. A consensus builder has also been developed for consensus assembly and SNP detection from alignment of short reads on a reference genome. <s> BIB005 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm <s> BIB006 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <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> BIB007 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <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> BIB008 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> High-volume sequencing of DNA and RNA is now within reach of any research laboratory and is quickly becoming established as a key research tool. In many workflows, each of the short sequences (“reads”) resulting from a sequencing run are first “mapped” (aligned) to a reference sequence to infer the read from which the genomic location derived, a challenging task because of the high data volumes and often large genomes. Existing read mapping software excel in either speed (e.g., BWA, Bowtie, ELAND) or sensitivity (e.g., Novoalign), but not in both. In addition, performance often deteriorates in the presence of sequence variation, particularly so for short insertions and deletions (indels). Here, we present a read mapper, Stampy, which uses a hybrid mapping algorithm and a detailed statistical model to achieve both speed and sensitivity, particularly when reads include sequence variation. This results in a higher useable sequence yield and improved accuracy compared to that of existing software. <s> BIB009 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> The direct detection of haplotypes from short-read DNA sequencing data requires changes to existing small-variant detection methods. Here, we develop a Bayesian statistical framework which is capable of modeling multiallelic loci in sets of individuals with non-uniform copy number. We then describe our implementation of this framework in a haplotype-based variant detector, FreeBayes. <s> BIB010 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> The Splitread algorithm uses a split-read strategy to detect structural variants and small insertions and deletions (indels) in whole-exome and whole-genome sequence datasets at high sensitivity. It maps the breakpoints at single-base-pair resolution, even in low-complexity regions, and can detect novel processed pseudogenes. <s> BIB011 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> 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. <s> BIB012 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <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> BIB013 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> BackgroundWhole exome sequencing studies identify hundreds to thousands of rare protein coding variants of ambiguous significance for human health. Computational tools are needed to accelerate the identification of specific variants and genes that contribute to human disease.ResultsWe have developed the Variant Effect Scoring Tool (VEST), a supervised machine learning-based classifier, to prioritize rare missense variants with likely involvement in human disease. The VEST classifier training set comprised ~ 45,000 disease mutations from the latest Human Gene Mutation Database release and another ~45,000 high frequency (allele frequency > 1%) putatively neutral missense variants from the Exome Sequencing Project. VEST outperforms some of the most popular methods for prioritizing missense variants in carefully designed holdout benchmarking experiments (VEST ROC AUC = 0.91, PolyPhen2 ROC AUC = 0.86, SIFT4.0 ROC AUC = 0.84). VEST estimates variant score p-values against a null distribution of VEST scores for neutral variants not included in the VEST training set. These p-values can be aggregated at the gene level across multiple disease exomes to rank genes for probable disease involvement. We tested the ability of an aggregate VEST gene score to identify candidate Mendelian disease genes, based on whole-exome sequencing of a small number of disease cases. We used whole-exome data for two Mendelian disorders for which the causal gene is known. Considering only genes that contained variants in all cases, the VEST gene score ranked dihydroorotate dehydrogenase (DHODH) number 2 of 2253 genes in four cases of Miller syndrome, and myosin-3 (MYH3) number 2 of 2313 genes in three cases of Freeman Sheldon syndrome.ConclusionsOur results demonstrate the potential power gain of aggregating bioinformatics variant scores into gene-level scores and the general utility of bioinformatics in assisting the search for disease genes in large-scale exome sequencing studies. VEST is available as a stand-alone software package at http://wiki.chasmsoftware.org and is hosted by the CRAVAT web server at http://www.cravat.us <s> BIB014 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> High-throughput DNA sequencing technology has transformed genetic research and is starting to make an impact on clinical practice. However, analyzing high-throughput sequencing data remains challenging, particularly in clinical settings where accuracy and turnaround times are critical. We present a new approach to this problem, implemented in a software package called Platypus. Platypus achieves high sensitivity and specificity for SNPs, indels and complex polymorphisms by using local de novo assembly to generate candidate variants, followed by local realignment and probabilistic haplotype estimation. It is an order of magnitude faster than existing tools and generates calls from raw aligned read data without preprocessing. We demonstrate the performance of Platypus in clinically relevant experimental designs by comparing with SAMtools and GATK on whole-genome and exome-capture data, by identifying de novo variation in 15 parent-offspring trios with high sensitivity and specificity, and by estimating human leukocyte antigen genotypes directly from variant calls. <s> BIB015 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> Accurate deleteriousness prediction for nonsynonymous variants is crucial for distinguishing pathogenic mutations from background polymorphisms in whole exome sequencing (WES) studies. Although many deleteriousness prediction methods have been developed, their prediction results are sometimes inconsistent with each other and their relative merits are still unclear in practical applications. To address these issues, we comprehensively evaluated the predictive performance of 18 current deleteriousness-scoring methods, including 11 function prediction scores (PolyPhen-2, SIFT, MutationTaster, Mutation Assessor, FATHMM, LRT, PANTHER, PhD-SNP, SNAP, SNPs&GO and MutPred), 3 conservation scores (GERP++, SiPhy and PhyloP) and 4 ensemble scores (CADD, PON-P, KGGSeq and CONDEL). We found that FATHMM and KGGSeq had the highest discriminative power among independent scores and ensemble scores, respectively. Moreover, to ensure unbiased performance evaluation of these prediction scores, we manually collected three distinct testing datasets, on which no current prediction scores were tuned. In addition, we developed two new ensemble scores that integrate nine independent scores and allele frequency. Our scores achieved the highest discriminative power compared with all the deleteriousness prediction scores tested and showed low false-positive prediction rate for benign yet rare nonsynonymous variants, which demonstrated the value of combining information from multiple orthologous approaches. Finally, to facilitate variant prioritization in WES studies, we have pre-computed our ensemble scores for 87 347 044 possible variants in the whole-exome and made them publicly available through the ANNOVAR software and the dbNSFP database. <s> BIB016 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> SUMMARY ::: Whole-exome sequencing (WES) has extensively been used in cancer genome studies; however, the use of WES data in the study of loss of heterozygosity or more generally allelic imbalance (AI) has so far been very limited, which highlights the need for user-friendly and flexible software that can handle low-quality datasets. We have developed a statistical approach, ExomeAI, for the detection of recurrent AI events using WES datasets, specifically where matched normal samples are not available. ::: ::: ::: AVAILABILITY ::: ExomeAI is a web-based application, publicly available at: http://genomequebec.mcgill.ca/exomeai. ::: ::: ::: CONTACT ::: JavadNadaf@gmail.com or somayyeh.fahiminiya@mcgill.ca ::: ::: ::: SUPPLEMENTARY INFORMATION ::: Supplementary data are available at Bioinformatics online. <s> BIB017 </s> A Survey of Computational Tools to Analyze and Interpret Whole Exome Sequencing Data <s> Methods for Structural Variants (SVs) Identification. <s> Motivation: Advances of next generation sequencing technologies and availability of short read data enable the detection of structural variations (SVs). Deletions, an important type of SVs, have been suggested in association with genetic diseases. There are three types of deletions: blunt deletions, <s> BIB018
Structural Variants (SVs) such as insertions and deletions (INDELs) in high-throughput sequencing data are more challenging to identify than single nucleotide variants because they could involve an undefined number of nucleotides. The majority of WES studies follow SAMtools BIB002 or GATK BIB007 BIB008 BIB013 workflows which will identify INDELs in the data. However, other software has been developed to increase the sensitivity of INDEL discovery while simultaneously decreasing the false discovery rate. Platypus BIB015 was developed to find SNVs, INDELs, and complex polymorphisms using local de novo assembly. When compared to SAMtools and GATK, Platypus had the lowest Fosmid false discovery rate for both SNVs and INDELs in whole genome sequencing of 15 samples. It also had the shortest runtime of these tools. However, GATK and SAMtools had lower Fosmid false discovery rates than Platypus when finding SNVs and INDELs in WES data BIB015 . Therefore, Platypus seems to be appropriate for whole genome sequencing but caution should be used when utilizing this tool with WES data. FreeBayes uses a unique approach to INDEL detection compared to other tools. The method utilizes haplotypebased variant detection under a Bayesian statistics framework BIB010 . This method has been used in several studies in combination with other approaches for the identifying of unique INDELs   . Pindel was one of the first programs developed to address the issue of unidentified large INDELs due to the short length of WGS reads BIB003 . In brief, after alignment of the reads to the reference genome, Pindel identifies reads where one end was mapped and the other was not BIB003 . Then, Pindel searches the reference genome for the unmapped portion of this read over a user defined area of the genome BIB003 . This splitread algorithm successfully identified large INDELs. Other computational tools developed after Pindel still utilize this algorithm as the foundation in their methods for detecting INDELs. Splitread BIB011 was developed to specifically identify structural variants and INDELs in WES data from 1 bp to 1 Mbp building on the split-read approach of Pindel BIB003 . The algorithms used by SAMtools and GATK limit the size of Illumina generated data. Paired-end mapping using a one end anchored algorithm allows for detection of novel insertions. http://mrfast.sourceforge.net/ BIB004 Novoalign Alignment done on paired-end or single-end sequences, also capable of doing methylation studies. Allows for a mismatch up to 50% of a read length and has built-in adapter and base quality trimming. http://www.novocraft.com/products/novoalign/ http://www .novocraft.com/ SOAP (1 & 2) SOAP2 improved speed by an order of magnitude over SOAP1 and can align a wide range of read lengths at the speed of 2 minutes for one million single-end reads using a two-way BWT algorithm. http://soap.genomics.org.cn/  BIB005 SSAHA Uses a hashing algorithm to find exact or close to exact matching in DNA and protein databases, analogous to doing a BLAST search for each read. https://www.vectorbase.org/glossary/ ssaha-sequence-search-and-alignment-hashing-algorithm/ BIB001 Stampy Alignment done using a hashing algorithm and statistical model, to align Illumina reads for genome, RNA, and Chip sequencing allowing for a large number or variations including insertions and deletions. http://www.well.ox.ac.uk/project-stampy BIB009 International Journal of Genomics 5 International Journal of Genomics SIFT By using PSI-BLAST, a prediction can be made on the effect of a nonsynonymous mutation within a protein. http://sift.jcvi.org/ BIB006 VEST Machine learning approach to determine the probability that a missense mutation will impair the functionality of a protein. http://karchinlab.org/apps/appVest.html BIB014 MetaSVM & MetaLR Integration of a Support Vector Machine and Logistic Regression to integrate nine deleterious prediction scores of missense mutations. https://sites.google.com/site/jpopgen/dbNSFP BIB016 International Journal of Genomics 7 VarScan2 Determines copy number changes in matched or unmatched samples using read ratios and then postprocessed with a circular binary segmentation algorithm. http://dkoboldt.github.io/varscan/using-varscan.html BIB012 ExomeAI Detects allele imbalance including LOH in unmatched tumor samples using a statistical approach that is capable of handling low-quality datasets. http://gqinnovationcenter.com/index.aspx BIB017 CNVseeqer Exon coverage between matched sequences was calculated using log 2 ratios followed by the circular binary segmentation algorithm. International Journal of Genomics 9 Recently developed indelMINER is a compilation of tools that takes the strengths of split-read and de novo assembly to determine INDELs from paired-end reads of WGS data  . Comparisons were done between SAMtools, Pindel, and indelMINER on a simulated dataset with 7,500 INDELs  . SAMtools found the least INDELs with 6,491, followed by Pindel with 7,239 and indelMINER with 7,365 INDELs identified. However, indelMINER's false-positive percentage (3.57%) was higher than SAMtools (2.65%) but lower than Pindel (4.53%). Conversely, indelMINER did have the lowest number of false-negatives with 398 compared to 589 and 1,181 for Pindel and SAMtools, respectively. Each of these tools has their own strengths and weaknesses as demonstrated by the authors of indelMINER  . Therefore, it can be predicted that future tools developed for SV detection will take an approach similar to indelMINER in trying to incorporate the best methods that have been developed thus far. Most of the recent SV detection tools rely on realigning split-reads for detecting deletions. Instead of a more universal approach like indelMINER, Sprites BIB018 aims to solve the problem of deletions with microhomologies and deletions with microinsertions. Sprites algorithm realigns soft-clipping reads to find the longest prefix or suffix that has a match in the target sequence. In terms of the -score, Sprites performed better than Pindel using real and simulated data BIB018 . All of these tools use different algorithms to address the problem of structural variants, which are common in human genomes. Each of these tools has strengths and weaknesses in detecting SVs. Therefore, it is suggested to use several of these tools in combination to detect SVs in WES.