Detection of somatic structural variants from short-read next-generation sequencing data.
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ABSTRACT:
SUBMITTER: Gong T
PROVIDER: S-EPMC8138798 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:UnlabelledNext-generation sequencing technologies expedited research to develop efficient computational tools for the identification of structural variants (SVs) and their use to study human diseases. As deeper data is obtained, the existence of higher complexity SVs in some genomes becomes more evident, but the detection and definition of most of these complex rearrangements is still in its infancy. The full characterization of SVs is a key aspect for discovering their biological implications. Here we present a pipeline (PeSV-Fisher) for the detection of deletions, gains, intra- and inter-chromosomal translocations, and inversions, at very reasonable computational costs. We further provide comprehensive information on co-localization of SVs in the genome, a crucial aspect for studying their biological consequences. The algorithm uses a combination of methods based on paired-reads and read-depth strategies. PeSV-Fisher has been designed with the aim to facilitate identification of somatic variation, and, as such, it is capable of analysing two or more samples simultaneously, producing a list of non-shared variants between samples. We tested PeSV-Fisher on available sequencing data, and compared its behaviour to that of frequently deployed tools (BreakDancer and VariationHunter). We have also tested this algorithm on our own sequencing data, obtained from a tumour and a normal blood sample of a patient with chronic lymphocytic leukaemia, on which we have also validated the results by targeted re-sequencing of different kinds of predictions. This allowed us to determine confidence parameters that influence the reliability of breakpoint predictions.AvailabilityPeSV-Fisher is available at http://gd.crg.eu/tools.
| S-EPMC3660373 | biostudies-literature
Project description:MotivationComplex structural variants (SVs) are genomic rearrangements that involve multiple segments of DNA. They contribute to human diversity and have been shown to cause Mendelian disease. Nevertheless, our abilities to analyse complex SVs are very limited. As opposed to deletions and other canonical types of SVs, there are no established tools that have explicitly been designed for analysing complex SVs.ResultsHere, we describe a new computational approach that we specifically designed for genotyping complex SVs in short-read sequenced genomes. Given a variant description, our approach computes genotype-specific probability distributions for observing aligned read pairs with a wide range of properties. Subsequently, these distributions can be used to efficiently determine the most likely genotype for any set of aligned read pairs observed in a sequenced genome. In addition, we use these distributions to compute a genotyping difficulty for a given variant, which predicts the amount of data needed to achieve a reliable call. Careful evaluation confirms that our approach outperforms other genotypers by making reliable genotype predictions across both simulated and real data. On up to 7829 human genomes, we achieve high concordance with population-genetic assumptions and expected inheritance patterns. On simulated data, we show that precision correlates well with our prediction of genotyping difficulty. This together with low memory and time requirements makes our approach well-suited for application in biomedical studies involving small to very large numbers of short-read sequenced genomes.Availability and implementationSource code is available at https://github.com/kehrlab/Complex-SV-Genotyping.
| S-EPMC11373317 | biostudies-literature
Project description:RNA viruses within infected individuals exist as a population of evolutionary-related variants. Owing to evolutionary change affecting the constitution of this population, the frequency and/or occurrence of individual viral variants can show marked or subtle fluctuations. Since the development of massively parallel sequencing platforms, such viral populations can now be investigated to unprecedented resolution. A critical problem with such analyses is the presence of sequencing-related errors that obscure the identification of true biological variants present at low frequency. Here, we report the development and assessment of the Quality Assessment of Short Read (QUASR) Pipeline (http://sourceforge.net/projects/quasr) specific for virus genome short read analysis that minimizes sequencing errors from multiple deep-sequencing platforms, and enables post-mapping analysis of the minority variants within the viral population. QUASR significantly reduces the error-related noise in deep-sequencing datasets, resulting in increased mapping accuracy and reduction of erroneous mutations. Using QUASR, we have determined influenza virus genome dynamics in sequential samples from an in vitro evolution of 2009 pandemic H1N1 (A/H1N1/09) influenza from samples sequenced on both the Roche 454 GSFLX and Illumina GAIIx platforms. Importantly, concordance between the 454 and Illumina sequencing allowed unambiguous minority-variant detection and accurate determination of virus population turnover in vitro.
| S-EPMC3678329 | biostudies-literature
Project description:Short tandem repeats (STRs), also known as microsatellites, are commonly defined as consisting of tandemly repeated nucleotide motifs of 2-6 base pairs in length. STRs appear throughout the human genome, and about 239,000 are documented in the Simple Repeats Track available from the UCSC (University of California, Santa Cruz) genome browser. STRs vary in size, producing highly polymorphic markers commonly used as genetic markers. A small fraction of STRs (about 30 loci) have been associated with human disease whereby one or both alleles exceed an STR-specific threshold in size, leading to disease. Detection of repeat expansions is currently performed with polymerase chain reaction-based assays or with Southern blots for large expansions. The tests are expensive and time-consuming and are not always conclusive, leading to lengthy diagnostic journeys for patients, potentially including missed diagnoses. The advent of whole exome and whole genome sequencing has identified the genetic cause of many genetic disorders; however, analysis pipelines are focused primarily on the detection of short nucleotide variations and short insertions and deletions (indels). Until recently, repeat expansions, with the exception of the smallest expansion (SCA6), were not detectable in next-generation short-read sequencing datasets and would have been ignored in most analyses. In the last two years, four analysis methods with accompanying software (ExpansionHunter, exSTRa, STRetch, and TREDPARSE) have been released. Although a comprehensive comparative analysis of the performance of these methods across all known repeat expansions is still lacking, it is clear that these methods are a valuable addition to any existing analysis pipeline. Here, we detail how to assess short-read data for evidence of expansions, reviewing all four methods and outlining their strengths and weaknesses. Implementation of these methods should lead to increased diagnostic yield of repeat expansion disorders for known STR loci and has the potential to detect novel repeat expansions.
| S-EPMC6008857 | biostudies-literature
Project description:PurposeTargeted next-generation sequencing (NGS) panels for solid tumors have been useful in clinical framework for accurate tumor diagnosis and identifying essential molecular aberrations. However, most cancer panels have been designed to address a wide spectrum of pan-cancer models, lacking integral prognostic markers that are highly specific to gliomas.Materials and methodsTo address such challenges, we have developed a glioma-specific NGS panel, termed "GliomaSCAN," that is capable of capturing single nucleotide variations and insertion/deletion, copy number variation, and selected promoter mutations and structural variations that cover a subset of intron regions in 232 essential glioma-associated genes. We confirmed clinical concordance rate using pairwise comparison of the identified variants from whole exome sequencing (WES), immunohistochemical analysis, and fluorescence in situ hybridization.ResultsOur panel demonstrated high sensitivity in detecting potential genomic variants that were present in the standard materials. To ensure the accuracy of our targeted sequencing panel, we compared our targeted panel to WES. The comparison results demonstrated a high correlation. Furthermore, we evaluated clinical utility of our panel in 46 glioma patients to assess the detection capacity of potential actionable mutations. Thirty-two patients harbored at least one recurrent somatic mutation in clinically actionable gene.ConclusionWe have established a glioma-specific cancer panel. GliomaSCAN highly excelled in capturing somatic variations in terms of both sensitivity and specificity and provided potential clinical implication in facilitating genome-based clinical trials. Our results could provide conceptual advance towards improving the response of genomically guided molecularly targeted therapy in glioma patients.
| S-EPMC6962483 | biostudies-literature
Project description:Of hundreds to thousands of somatic mutations that exist in each cancer genome, a large number are unique and non-recurrent variants. Prioritizing genetic variants identified via next generation sequencing technologies remains a major challenge. Many such variants occur in tumor genes that have well-established biological and clinical relevance and are putative targets of molecular therapy, however, most variants are still of unknown significance. With large amounts of data being generated as high throughput sequencing assays enter the clinical realm, there is a growing need to better communicate relevant findings in a timely manner while remaining cognizant of the potential consequences of misuse or overinterpretation of genomic information. Herein we describe a systematic framework for variant annotation and prioritization, and we propose a structured molecular pathology report using standardized terminology in order to best inform oncology clinical practice. We hope that our experience developing a comprehensive knowledge database of emerging predictive markers matched to targeted therapies will help other institutions implement similar programs.
| S-EPMC5528527 | biostudies-other
Project description:BACKGROUND:When compared to Sanger sequencing technology, next-generation sequencing (NGS) technologies are hindered by shorter sequence read length, higher base-call error rate, non-uniform coverage, and platform-specific sequencing artifacts. These characteristics lower the quality of their downstream analyses, e.g. de novo and reference-based assembly, by introducing sequencing artifacts and errors that may contribute to incorrect interpretation of data. Although many tools have been developed for quality control and pre-processing of NGS data, none of them provide flexible and comprehensive trimming options in conjunction with parallel processing to expedite pre-processing of large NGS datasets. METHODS:We developed ngsShoRT (next-generation sequencing Short Reads Trimmer), a flexible and comprehensive open-source software package written in Perl that provides a set of algorithms commonly used for pre-processing NGS short read sequences. We compared the features and performance of ngsShoRT with existing tools: CutAdapt, NGS QC Toolkit and Trimmomatic. We also compared the effects of using pre-processed short read sequences generated by different algorithms on de novo and reference-based assembly for three different genomes: Caenorhabditis elegans, Saccharomyces cerevisiae S288c, and Escherichia coli O157 H7. RESULTS:Several combinations of ngsShoRT algorithms were tested on publicly available Illumina GA II, HiSeq 2000, and MiSeq eukaryotic and bacteria genomic short read sequences with the focus on removing sequencing artifacts and low-quality reads and/or bases. Our results show that across three organisms and three sequencing platforms, trimming improved the mean quality scores of trimmed sequences. Using trimmed sequences for de novo and reference-based assembly improved assembly quality as well as assembler performance. In general, ngsShoRT outperformed comparable trimming tools in terms of trimming speed and improvement of de novo and reference-based assembly as measured by assembly contiguity and correctness. CONCLUSIONS:Trimming of short read sequences can improve the quality of de novo and reference-based assembly and assembler performance. The parallel processing capability of ngsShoRT reduces trimming time and improves the memory efficiency when dealing with large datasets. We recommend combining sequencing artifacts removal, and quality score based read filtering and base trimming as the most consistent method for improving sequence quality and downstream assemblies. ngsShoRT source code, user guide and tutorial are available at http://research.bioinformatics.udel.edu/genomics/ngsShoRT/. ngsShoRT can be incorporated as a pre-processing step in genome and transcriptome assembly projects.
| S-EPMC4064128 | biostudies-literature
Project description:BackgroundA copy number variation (CNV) is a difference between genotypes in the number of copies of a genomic region. Next generation sequencing (NGS) technologies provide sensitive and accurate tools for detecting genomic variations that include CNVs. However, statistical approaches for CNV identification using NGS are limited. We propose a new methodology for detecting CNVs using NGS data. This method (henceforth denoted by m-HMM) is based on a hidden Markov model with emission probabilities that are governed by mixture distributions. We use the Expectation-Maximization (EM) algorithm to estimate the parameters in the model.ResultsA simulation study demonstrates that our proposed m-HMM approach has greater power for detecting copy number gains and losses relative to existing methods. Furthermore, application of our m-HMM to DNA sequencing data from the two maize inbred lines B73 and Mo17 to identify CNVs that may play a role in creating phenotypic differences between these inbred lines provides results concordant with previous array-based efforts to identify CNVs.ConclusionsThe new m-HMM method is a powerful and practical approach for identifying CNVs from NGS data.
| S-EPMC4021345 | biostudies-literature
Project description:A primary component of next-generation sequencing analysis is to align short reads to a reference genome, with each read aligned independently. However, reads that observe the same non-reference DNA sequence are highly correlated and can be used to better model the true variation in the target genome. A novel short-read micro realigner, SRMA, that leverages this correlation to better resolve a consensus of the underlying DNA sequence of the targeted genome is described here.
| S-EPMC3218665 | biostudies-literature
Project description:BACKGROUND: Renewable energy production is currently a major issue worldwide. Biogas is a promising renewable energy carrier as the technology of its production combines the elimination of organic waste with the formation of a versatile energy carrier, methane. In consequence of the complexity of the microbial communities and metabolic pathways involved the biotechnology of the microbiological process leading to biogas production is poorly understood. Metagenomic approaches are suitable means of addressing related questions. In the present work a novel high-throughput technique was tested for its benefits in resolving the functional and taxonomical complexity of such microbial consortia. RESULTS: It was demonstrated that the extremely parallel SOLiD™ short-read DNA sequencing platform is capable of providing sufficient useful information to decipher the systematic and functional contexts within a biogas-producing community. Although this technology has not been employed to address such problems previously, the data obtained compare well with those from similar high-throughput approaches such as 454-pyrosequencing GS FLX or Titanium. The predominant microbes contributing to the decomposition of organic matter include members of the Eubacteria, class Clostridia, order Clostridiales, family Clostridiaceae. Bacteria belonging in other systematic groups contribute to the diversity of the microbial consortium. Archaea comprise a remarkably small minority in this community, given their crucial role in biogas production. Among the Archaea, the predominant order is the Methanomicrobiales and the most abundant species is Methanoculleus marisnigri. The Methanomicrobiales are hydrogenotrophic methanogens. Besides corroborating earlier findings on the significance of the contribution of the Clostridia to organic substrate decomposition, the results demonstrate the importance of the metabolism of hydrogen within the biogas producing microbial community. CONCLUSIONS: Both microbiological diversity and the regulatory role of the hydrogen metabolism appear to be the driving forces optimizing biogas-producing microbial communities. The findings may allow a rational design of these communities to promote greater efficacy in large-scale practical systems. The composition of an optimal biogas-producing consortium can be determined through the use of this approach, and this systematic methodology allows the design of the optimal microbial community structure for any biogas plant. In this way, metagenomic studies can contribute to significant progress in the efficacy and economic improvement of biogas production.
| S-EPMC3395570 | biostudies-other