Variant Detection Tools Research Articles

Next-generation sequencing (NGS)-based identification of induced mutations in a doubly mutagenized tomato (Solanum lycopersicum) population.

The identification of mutations in targeted genes has been significantly simplified by the advent of TILLING (Targeting Induced Local Lesions In Genomes), speeding up the functional genomic analysis of animals and plants. Next-generation sequencing (NGS) is gradually replacing classical TILLING for mutation detection, as it allows the analysis of a large number of amplicons in short durations. The NGS approach was used to identify mutations in a population of Solanum lycopersicum (tomato) that was doubly mutagenized by ethylmethane sulphonate (EMS). Twenty-five genes belonging to carotenoids and folate metabolism were PCR-amplified and screened to identify potentially beneficial alleles. To augment efficiency, the 600-bp amplicons were directly sequenced in a non-overlapping manner in Illumina MiSeq, obviating the need for a fragmentation step before library preparation. A comparison of the different pooling depths revealed that heterozygous mutations could be identified up to 128-fold pooling. An evaluation of six different software programs (camba, crisp, gatk unified genotyper, lofreq, snver and vipr) revealed that no software program was robust enough to predict mutations with high fidelity. Among these, crisp and camba predicted mutations with lower false discovery rates. The false positives were largely eliminated by considering only mutations commonly predicted by two different software programs. The screening of 23.47Mb of tomato genome yielded 75 predicted mutations, 64 of which were confirmed by Sanger sequencing with an average mutation density of 1/367Kb. Our results indicate that NGS combined with multiple variant detection tools can reduce false positives and significantly speed up the mutation discovery rate.

An evaluation of copy number variation detection tools for cancer using whole exome sequencing data

BackgroundRecently copy number variation (CNV) has gained considerable interest as a type of genomic/genetic variation that plays an important role in disease susceptibility. Advances in sequencing technology have created an opportunity for detecting CNVs more accurately. Recently whole exome sequencing (WES) has become primary strategy for sequencing patient samples and study their genomics aberrations. However, compared to whole genome sequencing, WES introduces more biases and noise that make CNV detection very challenging. Additionally, tumors’ complexity makes the detection of cancer specific CNVs even more difficult. Although many CNV detection tools have been developed since introducing NGS data, there are few tools for somatic CNV detection for WES data in cancer.ResultsIn this study, we evaluated the performance of the most recent and commonly used CNV detection tools for WES data in cancer to address their limitations and provide guidelines for developing new ones. We focused on the tools that have been designed or have the ability to detect cancer somatic aberrations. We compared the performance of the tools in terms of sensitivity and false discovery rate (FDR) using real data and simulated data. Comparative analysis of the results of the tools showed that there is a low consensus among the tools in calling CNVs. Using real data, tools show moderate sensitivity (~50% - ~80%), fair specificity (~70% - ~94%) and poor FDRs (~27% - ~60%). Also, using simulated data we observed that increasing the coverage more than 10× in exonic regions does not improve the detection power of the tools significantly.ConclusionsThe limited performance of the current CNV detection tools for WES data in cancer indicates the need for developing more efficient and precise CNV detection methods. Due to the complexity of tumors and high level of noise and biases in WES data, employing advanced novel segmentation, normalization and de-noising techniques that are designed specifically for cancer data is necessary. Also, CNV detection development suffers from the lack of a gold standard for performance evaluation. Finally, developing tools with user-friendly user interfaces and visualization features can enhance CNV studies for a broader range of users.

High Frequency of Somatic Mutations of KMT2D and CARD11 Genes in Cold Agglutinin Disease

Primary cold agglutinin disease (CAD) is a hemolytic anemia mediated by monoclonal anti-I autoantibodies. CAD is caused by an underlying low grade B-cell lymphoproliferative disease of the bone marrow with a typical histology that is different from lymphoplasmacytic lymphoma and, accordingly, does not display the MYD88 L265P mutation (Randen et al., Haematologica, 2014).Since CAD is a clonal lymphoproliferative disorder, we studied the mutational landscape to further characterize the disease and identify potential novel treatment approaches.We prospectively collected bone marrow samples of CAD patients, enrolled in a clinical trial (CAD5; www.clinicaltrials.gov, NCT02689986). Exome sequencing of six cases was performed and findings were confirmed in ten additional cases using targeted sequencing. For these analyses, clonal B cells and normal T cells, used as control, were purified from bone marrow samples using fluorescent activated cell sorting. All mutations were verified by Sanger sequencing. Whole-exome sequencing was performed at BGI Tech Solutions (Hongkong) using the Agilent SureSelect Human All Exon V4 Reagent Kit and Illumina HiSeq technology. The bioinformatics pipeline consistent of BWA alignment tool (aligned to hg19); Picard tools FixMateInformation and MarkDuplicates; the Genome Analysis Toolkit (GATK) IndelRealigner and BaseRecalibrator; somatic variant detection tools Strelka, MuTect and Pindel; the annotation tool SnpEff.Recurrent somatic mutations were found in KMT2D (11/16 cases, 69%) and CARD11 (5/16, 31%) (Table 1, Figure 1-2). 7/16 (44%) of KMT2D mutations were deemed high impact mutations by SnpEff and to result in inactive protein. 2/16 (12,5%) of KMT2D mutations are missense mutations and are predicted to impair SET domain function. 2/16 (12,5%) of KMT2D mutations are classified by SnpEff as low impact mutations of which functional tests are necessary to demonstrate potential consequences for protein function. Of interest, two additional patients showed rare germline KMT2D variants that have also been seen in Kabuki syndrome patients, although these patients do not have Kabuki syndrome.CARD11 was somatically mutated in 5/16 (31%) cases. Four of those patients had a concurrent KMT2D mutation. All CARD 11 mutations were classified as moderate impact mutations by SnpEff. Mutations were tightly clustered in a 20bp sequence of the coiled-coil domain sequence (Table 1, Figure 2).KMT2D is a histone lysine methyl transferase that represses B cell lymphoma development. Mono-allelic mutations of KMT2D seem to act in a dominant fashion and cause partial loss of protein expression with cell growth advantage. KMT2D is frequently mutated in follicular lymphoma, diffuse large B cell lymphoma and nodal marginal zone lymphoma. Mono-allelic constitutional mutations cause Kabuki syndrome, characterized by distinct facial characteristics and multiple organ malformations. Patients frequently develop immune-mediated thrombocytopenia as well as auto-immune hemolytic anemia.CARD11 coiled-coil domain mutations result in constitutive NF-kB activation and enhanced NF-kB activity upon antigen receptor stimulation. Mutations were previously detected in diffuse large B cell lymphomas of activated B cell origin. Mono-allelic CARD11 coiled coil mutations are not oncogenic per se in mice and humans, but result in B-cell proliferation and auto-antibody production. Since four CAD patients showed concurrent KMT2D and CARD11 mutations, it seems likely that the mutations act in concert with anti-I B-cell receptor stimulation to contribute to CAD-associated lymphoproliferative disease.In conclusion, we demonstrated a high frequency of KMT2D and CARD11 mutations in the bone marrow B cell lymphoproliferative disease of patients with CAD. These results confirm that CAD-associated B cell lymphoproliferative disease is a distinct disease different from other known B cell lymphoproliferative diseases of the bone marrow, most notably lymphoplasmacytic lymphoma. The identification of these recurrent mutations in CAD may allow the design of novel treatment modalities. [Display omitted] [Display omitted] [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

MinVar: A rapid and versatile tool for HIV-1 drug resistance genotyping by deep sequencing

Genotypic monitoring of drug-resistance mutations (DRMs) in HIV-1 infected individuals is strongly recommended to guide selection of the initial antiretroviral therapy (ART) and changes of drug regimens. Traditionally, mutations conferring drug resistance are detected by population sequencing of the reverse transcribed viral RNA encoding the HIV-1 enzymes target by ART, followed by manual analysis and interpretation of Sanger sequencing traces. This process is labor intensive, relies on subjective interpretation from the operator, and offers limited sensitivity as only mutations above 20% frequency can be reliably detected. Here we present MinVar, a pipeline for the analysis of deep sequencing data, which allows reliable and automated detection of DRMs down to 5%. We evaluated MinVar with data from amplicon sequencing of defined mixtures of molecular virus clones with known DRM and plasma samples of viremic HIV-1 infected individuals and we compared it to VirVarSeq, another virus variant detection tool exclusively working on Illumina deep sequencing data. MinVar was designed to be compatible with a diverse range of sequencing platforms and allows the detection of DRMs and insertions/deletions from deep sequencing data without the need to perform additional bioinformatics analysis, a prerequisite to a widespread implementation of HIV-1 genotyping using deep sequencing in routine diagnostic settings.

Association mapping for floral traits in cultivated Paeonia rockii based on SSR markers.

Tree peony (Paeonia Sect. Moutan) is an economically important ornamental plant, but little is known about the genetic architecture of important ornamental traits. To effectively improve ornamental value, we require a better understanding of genetic architecture in the complex traits of the tree peony. Association mapping is a powerful tool for detection of variation associated with traits. Thus, we examined the genetic diversity and the population structure of 462 unrelated cultivated P. rockii individuals, then performed association mapping to identify simple sequence repeat (SSR) markers associated with 12 floral traits. We observed a moderate level of genetic diversity (PIC=0.459) and low linkage disequilibrium (LD) between markers, demonstrating that the potential value of an LD approach in elucidating the molecular basis of the quantitative variation in this species. An analysis of population structure revealed three subgroups in the association population. Subsequent single-marker association analysis identified 46 significant associations, involving the 11 traits with 37 SSRs. These loci explained a small proportion of the phenotypic variance, ranging from 2.68 to 23.97% (mean 5.50%). We also validated 15 of the 46 associations in a linkage mapping population of 159 individuals. Finally, five associations were further confirmed in the linkage mapping population, involving the four traits with four SSRs. These results can serve as a foundation for further analyses of the genetic architecture of floral traits, and the SSRs associated in this work have potential applications in marker-assisted breeding in tree peony.

Investigation of rare and low-frequency variants using high-throughput sequencing with pooled DNA samples.

High-throughput sequencing using pooled DNA samples can facilitate genome-wide studies on rare and low-frequency variants in a large population. Some major questions concerning the pooling sequencing strategy are whether rare and low-frequency variants can be detected reliably, and whether estimated minor allele frequencies (MAFs) can represent the actual values obtained from individually genotyped samples. In this study, we evaluated MAF estimates using three variant detection tools with two sets of pooled whole exome sequencing (WES) and one set of pooled whole genome sequencing (WGS) data. Both GATK and Freebayes displayed high sensitivity, specificity and accuracy when detecting rare or low-frequency variants. For the WGS study, 56% of the low-frequency variants in Illumina array have identical MAFs and 26% have one allele difference between sequencing and individual genotyping data. The MAF estimates from WGS correlated well (r = 0.94) with those from Illumina arrays. The MAFs from the pooled WES data also showed high concordance (r = 0.88) with those from the individual genotyping data. In conclusion, the MAFs estimated from pooled DNA sequencing data reflect the MAFs in individually genotyped samples well. The pooling strategy can thus be a rapid and cost-effective approach for the initial screening in large-scale association studies.

Identifying micro-inversions using high-throughput sequencing reads.

BackgroundThe identification of inversions of DNA segments shorter than read length (e.g., 100 bp), defined as micro-inversions (MIs), remains challenging for next-generation sequencing reads. It is acknowledged that MIs are important genomic variation and may play roles in causing genetic disease. However, current alignment methods are generally insensitive to detect MIs. Here we develop a novel tool, MID (Micro-Inversion Detector), to identify MIs in human genomes using next-generation sequencing reads.ResultsThe algorithm of MID is designed based on a dynamic programming path-finding approach. What makes MID different from other variant detection tools is that MID can handle small MIs and multiple breakpoints within an unmapped read. Moreover, MID improves reliability in low coverage data by integrating multiple samples. Our evaluation demonstrated that MID outperforms Gustaf, which can currently detect inversions from 30 bp to 500 bp.ConclusionsTo our knowledge, MID is the first method that can efficiently and reliably identify MIs from unmapped short next-generation sequencing reads. MID is reliable on low coverage data, which is suitable for large-scale projects such as the 1000 Genomes Project (1KGP). MID identified previously unknown MIs from the 1KGP that overlap with genes and regulatory elements in the human genome. We also identified MIs in cancer cell lines from Cancer Cell Line Encyclopedia (CCLE). Therefore our tool is expected to be useful to improve the study of MIs as a type of genetic variant in the human genome. The source code can be downloaded from: http://cqb.pku.edu.cn/ZhuLab/MID.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2305-7) contains supplementary material, which is available to authorized users.

Abstract 4856: ITD Assembler: An algorithm for internal tandem duplication discovery from short-read sequencing data

Abstract Tandem duplication of coding sequence is an important mechanism of somatic mutation capable of activating oncogenes such as fms-related tyrosine kinase 3 (FLT3) as well as the potential for underlying constitutional or germline disease. Detection of internal tandem duplication (ITD) in short-read sequencing data remains challenging due to deficiencies in short-read alignment where reads carrying signatures of ITD are often unmapped. Assembly based approaches collapse repetitive sequences, resulting in reduced or lost genomic complexity. The majority of structural variation detection tools are limited to finding insertions that are contained entirely within individual read alignments, and are unable to detect read-spanning duplications. Tools designed to detect large structural variation from paired-end reads that map discordantly based on expected fragment size and orientation do not provide the precision needed to detect ITDs. Only tools that consider the characteristic alignment features that signal the presence of ITDs will be able to detect them; these alignment features include: an excess of reads with unaligned, soft-clipped bases, read pairs where one end is unmapped located near the site of insertion, and read pairs where both ends are unmapped. We introduce ITD Assembler, a novel approach that searches all unmapped reads from exome capture or whole genome sequencing data to identify duplications. From the entire set of unmapped reads plus subset of mapped reads harboring soft-clipped bases, de Bruijn graph assembly is applied to select reads that form cycles, indicative of duplicated sequence structures. Reads from de Bruijn graph cycles are then assembled using an Overlap Layout Consensus (OLC) methodology thereby alleviating the collapse of repeat sequences affecting de Bruijn graph assembly approaches. Resulting OLC assembled contigs are locally aligned to the reference sequence to annotate the position of ITDs. ITD Assembler was run on The Cancer Genome Atlas (TCGA) acute myeloid leukemia (AML) dataset, and FLT3-ITD detection rates were compared against orthogonal algorithms. ITD Assembler identified the highest percentage of TCGA FLT3-ITDs, reported significantly higher allele fractions, and discovered additional ITDs in the KIT, CEBPA and WT1 cancer genes. Citation Format: Oliver A. Hampton, Navin Rustagi, Jie Li, Liu Xi, Richard A. Gibbs, Sharon E. Plon, Marek Kimmel, David A. Wheeler. ITD Assembler: An algorithm for internal tandem duplication discovery from short-read sequencing data. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4856. doi:10.1158/1538-7445.AM2015-4856

NGS-eval: NGS Error analysis and novel sequence VAriant detection tooL.

Massively parallel sequencing of microbial genetic markers (MGMs) is used to uncover the species composition in a multitude of ecological niches. These sequencing runs often contain a sample with known composition that can be used to evaluate the sequencing quality or to detect novel sequence variants. With NGS-eval, the reads from such (mock) samples can be used to (i) explore the differences between the reads and their references and to (ii) estimate the sequencing error rate. This tool maps these reads to references and calculates as well as visualizes the different types of sequencing errors. Clearly, sequencing errors can only be accurately calculated if the reference sequences are correct. However, even with known strains, it is not straightforward to select the correct references from databases. We previously analysed a pyrosequencing dataset from a mock sample to estimate sequencing error rates and detected sequence variants in our mock community, allowing us to obtain an accurate error estimation. Here, we demonstrate the variant detection and error analysis capability of NGS-eval with Illumina MiSeq reads from the same mock community. While tailored towards the field of metagenomics, this server can be used for any type of MGM-based reads. NGS-eval is available at http://www.ibi.vu.nl/programs/ngsevalwww/.

Making the difference: integrating structural variation detection tools.

From prokaryotes to eukaryotes, phenotypic variation, adaptation and speciation has been associated with structural variation between genomes of individuals within the same species. Many computer algorithms detecting such variations (callers) have recently been developed, spurred by the advent of the next-generation sequencing technology. Such callers mainly exploit split-read mapping or paired-end read mapping. However, as different callers are geared towards different types of structural variation, there is still no single caller that can be considered a community standard; instead, increasingly the various callers are combined in integrated pipelines. In this article, we review a wide range of callers, discuss challenges in the integration step and present a survey of pipelines used in population genomics studies. Based on our findings, we provide general recommendations on how to set-up such pipelines. Finally, we present an outlook on future challenges in structural variation detection.

Cancer genomics: new software tools making sequencing more accessible.

Next-generation sequencing transforms today’s cancer research Next-generation sequencing (NGS) technologies can parallelize sequencing processes and produce millions of short-read sequences concurrently. The advent of NGS technologies has transformed today’s cancer genome research by providing an unbiased and comprehensive method of detecting somatic genome alterations [1]. The application of NGS technologies, through whole-genome, -exome and -transcriptome approaches has led to the discovery of mutated genes that drive oncogenic phenotypes in tumors. In a recent The Cancer Genome Atlas (TCGA) Pan-Cancer effort [2], 127 mutated driver genes were identified from 3281 tumors across 12 tumor types. These cancer drivers converged on several well-known pathways such as cell cycle and MAPK pathways. Interestingly, these drivers are also involved in cellular processes in cancers that have previously been less well characterized such as histone modification, RNA splicing, cellular metabolism and proteolysis. Mutations in transcription factors are more tissue specific, whereas mutations in histone modifiers are shared among multiple cancer types. The average number of driver mutations varies across tumor types and most individual tumors have two to six mutations [2]. Generally, larger numbers of driver mutations are involved in tumors with high levels of background mutations [3]. For example, most squamous cell lung carcinomas are attributed to life-long tobacco exposure. Carcinogens from tobacco exposure can cause a broad spectrum of DNA lesions on the genome, and this increases the chance that mutations conferring a small growth advantage to cells are selected in the lung microenvironment. Many such mutations with small effects can accumulate in a specific group of cells over time and collectively lead to carcinogenesis. This is in contrast to hematologic cancers, where fewer driver mutations (with potentially large effects) are observed in tumors with low background mutations. Driver genes are promising drug targets and their discovery has the potential to revolutionize personalized gene-targeted treatments that guide patient therapy according to the genomic profile of the tumor. The battle of Dr Lukas Wartman at Washington University in St Louis (WUSTL; MO, USA) against leukemia is a prime example of the promise of personalized medicine [4]. When Wartman had relapsed a second time, his colleagues at WUSTL sequenced the entire genome of DNA and RNA from his cancerous cells. His RNA sequencing showed that a gene called FLT3 was unexpectedly overproduced by his cancer cells. It so happens that the drug Sutent, previously approved for treating advanced kidney cancer, effectively inhibits FLT3. Sutent was prescribed to Wartman, and his leukemia was in remission 2 weeks later. In next decade, we will witness many such cases come to the forefront of cancer treatment and soon realize the promise of personalized medicines purred by advances in cancer genomics and associated drug development. part of Editorial

Socrates: identification of genomic rearrangements in tumour genomes by re-aligning soft clipped reads

Motivation: Methods for detecting somatic genome rearrangements in tumours using next-generation sequencing are vital in cancer genomics. Available algorithms use one or more sources of evidence, such as read depth, paired-end reads or split reads to predict structural variants. However, the problem remains challenging due to the significant computational burden and high false-positive or false-negative rates.Results: In this article, we present Socrates (SOft Clip re-alignment To idEntify Structural variants), a highly efficient and effective method for detecting genomic rearrangements in tumours that uses only split-read data. Socrates has single-nucleotide resolution, identifies micro-homologies and untemplated sequence at break points, has high sensitivity and high specificity and takes advantage of parallelism for efficient use of resources. We demonstrate using simulated and real data that Socrates performs well compared with a number of existing structural variant detection tools.Availability and implementation: Socrates is released as open source and available from http://bioinf.wehi.edu.au/socrates.Contact: papenfuss@wehi.edu.auSupplementary information: Supplementary data are available at Bioinformatics online.

Review of current methods, applications, and data management for the bioinformatics analysis of whole exome sequencing.

The advent of next-generation sequencing technologies has greatly promoted advances in the study of human diseases at the genomic, transcriptomic, and epigenetic levels. Exome sequencing, where the coding region of the genome is captured and sequenced at a deep level, has proven to be a cost-effective method to detect disease-causing variants and discover gene targets. In this review, we outline the general framework of whole exome sequence data analysis. We focus on established bioinformatics tools and applications that support five analytical steps: raw data quality assessment, pre-processing, alignment, post-processing, and variant analysis (detection, annotation, and prioritization). We evaluate the performance of open-source alignment programs and variant calling tools using simulated and benchmark datasets, and highlight the challenges posed by the lack of concordance among variant detection tools. Based on these results, we recommend adopting multiple tools and resources to reduce false positives and increase the sensitivity of variant calling. In addition, we briefly discuss the current status and solutions for big data management, analysis, and summarization in the field of bioinformatics.

SSW Library: An SIMD Smith-Waterman C/C++ Library for Use in Genomic Applications

BackgroundThe Smith-Waterman algorithm, which produces the optimal pairwise alignment between two sequences, is frequently used as a key component of fast heuristic read mapping and variation detection tools for next-generation sequencing data. Though various fast Smith-Waterman implementations are developed, they are either designed as monolithic protein database searching tools, which do not return detailed alignment, or are embedded into other tools. These issues make reusing these efficient Smith-Waterman implementations impractical.ResultsTo facilitate easy integration of the fast Single-Instruction-Multiple-Data Smith-Waterman algorithm into third-party software, we wrote a C/C++ library, which extends Farrar’s Striped Smith-Waterman (SSW) to return alignment information in addition to the optimal Smith-Waterman score. In this library we developed a new method to generate the full optimal alignment results and a suboptimal score in linear space at little cost of efficiency. This improvement makes the fast Single-Instruction-Multiple-Data Smith-Waterman become really useful in genomic applications. SSW is available both as a C/C++ software library, as well as a stand-alone alignment tool at: https://github.com/mengyao/Complete-Striped-Smith-Waterman-Library.ConclusionsThe SSW library has been used in the primary read mapping tool MOSAIK, the split-read mapping program SCISSORS, the MEI detector TANGRAM, and the read-overlap graph generation program RZMBLR. The speeds of the mentioned software are improved significantly by replacing their ordinary Smith-Waterman or banded Smith-Waterman module with the SSW Library.

Using Genome Query Language to uncover genetic variation.

With high-throughput DNA sequencing costs dropping <$1000 for human genomes, data storage, retrieval and analysis are the major bottlenecks in biological studies. To address the large-data challenges, we advocate a clean separation between the evidence collection and the inference in variant calling. We define and implement a Genome Query Language (GQL) that allows for the rapid collection of evidence needed for calling variants. We provide a number of cases to showcase the use of GQL for complex evidence collection, such as the evidence for large structural variations. Specifically, typical GQL queries can be written in 5-10 lines of high-level code and search large datasets (100 GB) in minutes. We also demonstrate its complementarity with other variant calling tools. Popular variant calling tools can achieve one order of magnitude speed-up by using GQL to retrieve evidence. Finally, we show how GQL can be used to query and compare multiple datasets. By separating the evidence and inference for variant calling, it frees all variant detection tools from the data intensive evidence collection and focuses on statistical inference. GQL can be downloaded from http://cseweb.ucsd.edu/~ckozanit/gql.

Population-scale analysis of human microsatellites reveals novel sources of exonic variation

Using our microsatellite specific genotyping method, we analyzed tandem repeats, which are known to be highly variable with some recognized as biomarkers causative of disease, in over 500 individuals who were exon sequenced in a 1000 Genomes Project pilot study. We were able to genotype over 97% of the microsatellite loci in the targeted regions. A total of 25,115 variations were observed, including repeat length and single nucleotide polymorphisms, corresponding to an average of 45.6 variations per individual and a density of 1.1 variations per kilobase. Standard variant detection did not report 94.2% of the exonic repeat length variations in part because the alignment techniques are not ideal for repetitive regions. Additionally some standard variation detection tools rely on a database of known variations, making them less likely to call repeat length variations as only a small percent of these loci (~6000) have been accurately characterized. A subset of the hundreds of non-synonymous variations we identified was experimentally validated, indicating an accuracy of 96.5% for our microsatellite-based genotyping method, with some novel variants identified in genes associated with cancer. We propose that microsatellite-based genotyping be used as a part of large scale sequencing studies to identify novel variants.

A Survey of Copy‐Number Variation Detection Tools Based on High‐Throughput Sequencing Data

Copy-number variation (CNV) is a major class of genomic variation with potentially important functional consequences in both normal and diseased populations. Remarkable advances in development of next-generation sequencing (NGS) platforms provide an unprecedented opportunity for accurate, high-resolution characterization of CNVs. In this unit, we give an overview of available computational tools for detection of CNVs and discuss comparative advantages and disadvantages of different approaches.

Improving Indel Detection Specificity of the Ion Torrent PGM Benchtop Sequencer

The emergence of benchtop sequencers has made clinical genetic testing using next-generation sequencing more feasible. Ion Torrent's PGMTM is one such benchtop sequencer that shows clinical promise in detecting single nucleotide variations (SNVs) and microindel variations (indels). However, the large number of false positive indels caused by the high frequency of homopolymer sequencing errors has impeded PGMTM's usage for clinical genetic testing. An extensive analysis of PGMTM data from the sequencing reads of the well-characterized genome of the Escherichia coli DH10B strain and sequences of the BRCA1 and BRCA2 genes from six germline samples was done. Three commonly used variant detection tools, SAMtools, Dindel, and GATK's Unified Genotyper, all had substantial false positive rates for indels. By incorporating filters on two major measures we could dramatically improve false positive rates without sacrificing sensitivity. The two measures were: B-Allele Frequency (BAF) and VARiation of the Width of gaps and inserts (VARW) per indel position. A BAF threshold applied to indels detected by UnifiedGenotyper removed ∼99% of the indel errors detected in both the DH10B and BRCA sequences. The optimum BAF threshold for BRCA sequences was determined by requiring 100% detection sensitivity and minimum false discovery rate, using variants detected from Sanger sequencing as reference. This resulted in 15 indel errors remaining, of which 7 indel errors were removed by selecting a VARW threshold of zero. VARW specific errors increased in frequency with higher read depth in the BRCA datasets, suggesting that homopolymer-associated indel errors cannot be reduced by increasing the depth of coverage. Thus, using a VARW threshold is likely to be important in reducing indel errors from data with higher coverage. In conclusion, BAF and VARW thresholds provide simple and effective filtering criteria that can improve the specificity of indel detection in PGMTM data without compromising sensitivity.

Cerebrospinal fluid findings after treatment of early syphilis with penicillin.

<h3>Abstract</h3> Most structural variant detection tools use clusters of discordant read-pair and split-read alignments to identify variants, yet do not integrate depth of sequence coverage as an additional means to support or refute putative events. Here, we present <i>duphold</i>, as a new method to efficiently annotate structural variant calls with sequence depth information that can add (or remove) confidence to SV predicted to affect copy number. It indicates not only the change in depth across the event, but also the presence of a rapid change in depth relative to the regions surrounding the breakpoints. It uses a unique algorithm that allows the run time to be nearly independent of the number of variants. This performance is important for large, jointly-called projects with many samples, each of which must be evaluated at thousands of sites. We show that filtering on <i>duphold</i> annotations can greatly improve the specificity of deletion calls and that its annotations match visual inspection. Duphold can annotate structural variant predictions made from both short-read and long-read data. It is available under the MIT license at: https://github.com/brentp/duphold.