Dataset Information

Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models.

ABSTRACT: An obstacle to validating and benchmarking methods for genome analysis is that there are few reference datasets available for which the "ground truth" about the mutational landscape of the sample genome is known and fully validated. Additionally, the free and public availability of real human genome datasets is incompatible with the preservation of donor privacy. In order to better analyze and understand genomic data, we need test datasets that model all variants, reflecting known biology as well as sequencing artifacts. Read simulators can fulfill this requirement, but are often criticized for limited resemblance to true data and overall inflexibility. We present NEAT (NExt-generation sequencing Analysis Toolkit), a set of tools that not only includes an easy-to-use read simulator, but also scripts to facilitate variant comparison and tool evaluation. NEAT has a wide variety of tunable parameters which can be set manually on the default model or parameterized using real datasets. The software is freely available at github.com/zstephens/neat-genreads.

SUBMITTER: Stephens ZD

PROVIDER: S-EPMC5125660 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models.

Stephens Zachary D ZD Hudson Matthew E ME Mainzer Liudmila S LS Taschuk Morgan M Weber Matthew R MR Iyer Ravishankar K RK

PloS one 20161128 11

An obstacle to validating and benchmarking methods for genome analysis is that there are few reference datasets available for which the "ground truth" about the mutational landscape of the sample genome is known and fully validated. Additionally, the free and public availability of real human genome datasets is incompatible with the preservation of donor privacy. In order to better analyze and understand genomic data, we need test datasets that model all variants, reflecting known biology as wel ...[more]

PMID: 27893777

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Project description:BACKGROUND:A strong focus of the post-genomic era is mining of the non-coding regulatory genome in order to unravel the function of regulatory elements that coordinate gene expression (Nat 489:57-74, 2012; Nat 507:462-70, 2014; Nat 507:455-61, 2014; Nat 518:317-30, 2015). Whole-genome approaches based on next-generation sequencing (NGS) have provided insight into the genomic location of regulatory elements throughout different cell types, organs and organisms. These technologies are now widespread and commonly used in laboratories from various fields of research. This highlights the need for fast and user-friendly software tools dedicated to extracting cis-regulatory information contained in these regulatory regions; for instance transcription factor binding site (TFBS) composition. Ideally, such tools should not require prior programming knowledge to ensure they are accessible for all users. RESULTS:We present TrawlerWeb, a web-based version of the Trawler_standalone tool (Nat Methods 4:563-5, 2007; Nat Protoc 5:323-34, 2010), to allow for the identification of enriched motifs in DNA sequences obtained from next-generation sequencing experiments in order to predict their TFBS composition. TrawlerWeb is designed for online queries with standard options common to web-based motif discovery tools. In addition, TrawlerWeb provides three unique new features: 1) TrawlerWeb allows the input of BED files directly generated from NGS experiments, 2) it automatically generates an input-matched biologically relevant background, and 3) it displays resulting conservation scores for each instance of the motif found in the input sequences, which assists the researcher in prioritising the motifs to validate experimentally. Finally, to date, this web-based version of Trawler_standalone remains the fastest online de novo motif discovery tool compared to other popular web-based software, while generating predictions with high accuracy. CONCLUSIONS:TrawlerWeb provides users with a fast, simple and easy-to-use web interface for de novo motif discovery. This will assist in rapidly analysing NGS datasets that are now being routinely generated. TrawlerWeb is freely available and accessible at: http://trawler.erc.monash.edu.au .

Project description:IntroductionCurrent clinical detection of Human immunodeficiency virus 1 (HIV-1) is used to target viral genes and proteins. However, the immunoassay, such as viral culture or Polymerase Chain Reaction (PCR), lacks accuracy in the diagnosis, as these conventional assays rely on the stable genome and HIV-1 is a highly-mutated virus. Next generation sequencing (NGS) promises to be transformative for the practice of infectious disease, and the rapidly reducing cost and processing time mean that this will become a feasible technology in diagnostic and research laboratories in the near future. The technology offers the superior sensitivity to detect the pathogenic viruses, including unknown and unexpected strains.AimTo leverage the NGS technology in order to improve current HIV-1 diagnosis and genotyping methods.Materials and methodsTen blood samples were collected from HIV-1 infected patients which were diagnosed by RT PCR at Singapore Communicable Disease Centre, Tan Tock Seng Hospital from October 2014 to March 2015. Viral RNAs were extracted from blood plasma and reversed into cDNA. The HIV-1 cDNA samples were cleaned up using a PCR purification kit and the sequencing library was prepared and identified through MiSeq.ResultsTwo common mutations were observed in all ten samples. The common mutations were identified at genome locations 1908 and 2104 as missense and silent mutations respectively, conferring S37N and S3S found on aspartic protease and reverse transcriptase subunits.ConclusionThe common mutations identified in this study were not previously reported, therefore suggesting the potential for them to be used for identification of viral infection, disease transmission and drug resistance. This was especially the case for, missense mutation S37N which could cause an amino acid change in viral proteases thus reducing the binding affinity of some protease inhibitors. Thus, the unique common mutations identified in this study could be used as diagnostic biomarkers to indicate the origin of infection as being from Singapore.

Dataset Information

Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models.

Publications

Simulating Next-Generation Sequencing Datasets from Empirical Mutation and Sequencing Models.

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