Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Shimmer: detection of genetic alterations in tumors using next-generation sequence data.

ABSTRACT: Extensive DNA sequencing of tumor and matched normal samples using exome and whole-genome sequencing technologies has enabled the discovery of recurrent genetic alterations in cancer cells, but variability in stromal contamination and subclonal heterogeneity still present a severe challenge to available detection algorithms.Here, we describe publicly available software, Shimmer, which accurately detects somatic single-nucleotide variants using statistical hypothesis testing with multiple testing correction. This program produces somatic single-nucleotide variant predictions with significantly higher sensitivity and accuracy than other available software when run on highly contaminated or heterogeneous samples, and it gives comparable sensitivity and accuracy when run on samples of high purity.http://www.github.com/nhansen/Shimmer

SUBMITTER: Hansen NF

PROVIDER: S-EPMC3673219 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Shimmer: detection of genetic alterations in tumors using next-generation sequence data.

Hansen Nancy F NF Gartner Jared J JJ Mei Lan L Samuels Yardena Y Mullikin James C JC

Bioinformatics (Oxford, England) 20130424 12

<h4>Motivation</h4>Extensive DNA sequencing of tumor and matched normal samples using exome and whole-genome sequencing technologies has enabled the discovery of recurrent genetic alterations in cancer cells, but variability in stromal contamination and subclonal heterogeneity still present a severe challenge to available detection algorithms.<h4>Results</h4>Here, we describe publicly available software, Shimmer, which accurately detects somatic single-nucleotide variants using statistical hypot ...[more]

PMID: 23620360

Similar Datasets

High-throughput detection of clinically targetable alterations using next-generation sequencing.

Project description:Next-generation sequencing (NGS) has revolutionized the therapeutic care of patients by allowing high-throughput and parallel sequencing of large numbers of genes in a single run. However, most of available commercialized cancer panels target a large number of mutations that do not have direct therapeutic implications and that are not fully adapted to low quality formalin-fixed, paraffin-embedded (FFPE) samples. Here, we designed an amplicon-based NGS panel assay of 16 currently actionable genes according to the most recent recommendations of the French National Cancer Institute (NCI). We developed a panel of short amplicons (<150 bp) using dual-strand library preparation. The clinical validation of this panel was performed on well-characterized controls and 140 routine diagnostic samples, including highly degraded and cross-linked genomic DNA extracted from FFPE tumor samples. All mutations were detected with elevated inter-laboratory and inter-run reproducibility. Importantly, we could detect clinically actionable alterations in FFPE samples with variant allele frequencies as low as 1%. In addition, the overall molecular diagnosis rate was increased from 40.7% with conventional techniques to 59.2% with our NGS panel, including 41 novel actionable alterations normally not explored by conventional techniques. Taken together, we believe that this new actionable target panel represents a relevant, highly scalable and robust tool that is easy to implement and is fully adapted to daily clinical practice in hospital and academic laboratories.

| S-EPMC5522202 | biostudies-other

Detection of structural variation using target captured next-generation sequencing data for genetic diagnostic testing.

Project description:PurposeStructural variation (SV) is associated with inherited diseases. Next-generation sequencing (NGS) is an efficient method for SV detection because of its high-throughput, low cost, and base-pair resolution. However, due to lack of standard NGS protocols and a limited number of clinical samples with pathogenic SVs, comprehensive standards for SV detection, interpretation, and reporting are to be established.MethodsWe performed SV assessment on 60,000 clinical samples tested with hereditary cancer NGS panels spanning 48 genes. To evaluate NGS results, NGS and orthogonal methods were used separately in a blinded fashion for SV detection in all samples.ResultsA total of 1,037 SVs in coding sequence (CDS) or untranslated regions (UTRs) and 30,847 SVs in introns were detected and validated. Across all variant types, NGS shows 100% sensitivity and 99.9% specificity. Overall, 64% of CDS/UTR SVs were classified as pathogenic/likely pathogenic, and five deletions/duplications were reclassified as pathogenic using breakpoint information from NGS.ConclusionThe SVs presented here can be used as a valuable resource for clinical research and diagnostics. The data illustrate NGS as a powerful tool for SV detection. Application of NGS and confirmation technologies in genetic testing ensures delivering accurate and reliable results for diagnosis and patient care.

| S-EPMC6752280 | biostudies-literature

Detection of Pathogenic Microbe Composition Using Next-Generation Sequencing Data.

Project description:Next-generation sequencing (NGS) technologies have provided great opportunities to analyze pathogenic microbes with high-resolution data. The main goal is to accurately detect microbial composition and abundances in a sample. However, high similarity among sequences from different species and the existence of sequencing errors pose various challenges. Numerous methods have been developed for quantifying microbial composition and abundance, but they are not versatile enough for the analysis of samples with mixtures of noise. In this paper, we propose a new computational method, PGMicroD, for the detection of pathogenic microbial composition in a sample using NGS data. The method first filters the potentially mistakenly mapped reads and extracts multiple species-related features from the sequencing reads of 16S rRNA. Then it trains an Support Vector Machine classifier to predict the microbial composition. Finally, it groups all multiple-mapped sequencing reads into the references of the predicted species to estimate the abundance for each kind of species. The performance of PGMicroD is evaluated based on both simulation and real sequencing data and is compared with several existing methods. The results demonstrate that our proposed method achieves superior performance. The software package of PGMicroD is available at https://github.com/BDanalysis/PGMicroD.

| S-EPMC7734255 | biostudies-literature

Detection and quantification of mitochondrial DNA deletions from next-generation sequence data.

Project description:BackgroundChromosomal deletions represent an important class of human genetic variation. Various methods have been developed to mine "next-generation" sequencing (NGS) data to detect deletions and quantify their clonal abundances. These methods have focused almost exclusively on the nuclear genome, ignoring the mitochondrial chromosome (mtDNA). Detecting mtDNA deletions requires special care. First, the chromosome's relatively small size (16,569 bp) necessitates the ability to detect extremely focal events. Second, the chromosome can be present at thousands of copies in a single cell (in contrast to two copies of nuclear chromosomes), and mtDNA deletions may be present on only a very small percentage of chromosomes. Here we present a method, termed MitoDel, to detect mtDNA deletions from NGS data.ResultsWe validate the method on simulated and real data, and show that MitoDel can detect novel and previously-reported mtDNA deletions. We establish that MitoDel can find deletions such as the "common deletion" at heteroplasmy levels well below 1%.ConclusionsMitoDel is a tool for detecting large mitochondrial deletions at low heteroplasmy levels. The tool can be downloaded at http://mendel.gene.cwru.edu/laframboiselab/ .

| S-EPMC5657046 | biostudies-literature

Offline Next Generation Metagenomics Sequence Analysis Using MinION Detection Software (MINDS).

Project description:Field laboratories interested in using the MinION often need the internet to perform sample analysis. Thus, the lack of internet connectivity in resource-limited or remote locations renders downstream analysis problematic, resulting in a lack of sample identification in the field. Due to this dependency, field samples are generally transported back to the lab for analysis where internet availability for downstream analysis is available. These logistics problems and the time lost in sample characterization and identification, pose a significant problem for field scientists. To address this limitation, we have developed a stand-alone data analysis packet using open source tools developed by the Nanopore community that does not depend on internet availability. Like Oxford Nanopore Technologies' (ONT) cloud-based What's In My Pot (WIMP) software, we developed the offline MinION Detection Software (MINDS) based on the Centrifuge classification engine for rapid species identification. Several online bioinformatics applications have been developed surrounding ONT's framework for analysis of long reads. We have developed and evaluated an offline real time classification application pipeline using open source tools developed by the Nanopore community that does not depend on internet availability. Our application has been tested on ATCC's 20 strain even mix whole cell (ATCC MSA-2002) sample. Using the Rapid Sequencing Kit (SQK-RAD004), we were able to identify all 20 organisms at species level. The analysis was performed in 15 min using a Dell Precision 7720 laptop. Our offline downstream bioinformatics application provides a cost-effective option as well as quick turn-around time when analyzing samples in the field, thus enabling researchers to fully utilize ONT's MinION portability, ease-of-use, and identification capability in remote locations.

| S-EPMC6723491 | biostudies-literature

Targeted molecular profiling of genetic alterations in colorectal cancer using next-generation sequencing.

Project description:Colorectal cancer (CRC) is a major contributor to cancer-associated mortality in China and remains a vast challenge worldwide. Although the genetic basis of CRC has been investigated, the uncommonly mutated genes in CRC remain unknown, in particular in the Asian population. In the present study, targeted region sequencing on 22 CRC and 10 paired non-cancerous tissues was performed to determine the genetic pattern of CRC samples in the Chinese population. Driver genes were detected by three distinct softwares, including MutSigCV, oncodriveFM and iCAGES. A total of 1,335 reliable somatic mutations were identified in tumour samples compared with normal samples. Furthermore, mismatch repair (MMR) mutant patients presented significantly higher mutation density compared with MMR wild-type patients. The results from MutSigCV, oncodriveFM and iCAGES analyses simultaneously detected 29 unique driver genes. In addition, the genes APC regulator of WNT signaling pathway, SMAD family member 4, neurofibromin 1, AT-rich interaction domain 5B and nuclear receptor corepressor 1 were the top five most frequently mutated genes in CRC samples, with mutation rates of 68, 36, 36, 32 and 27%, respectively. The findings from the present study may therefore serve as a basis for future investigation on the diagnosis and oncogenesis of CRC.

| S-EPMC6955650 | biostudies-literature

Characterization of within-host Plasmodium falciparum diversity using next-generation sequence data.

Project description:Our understanding of the composition of multi-clonal malarial infections and the epidemiological factors which shape their diversity remain poorly understood. Traditionally within-host diversity has been defined in terms of the multiplicity of infection (MOI) derived by PCR-based genotyping. Massively parallel, single molecule sequencing technologies now enable individual read counts to be derived on genome-wide datasets facilitating the development of new statistical approaches to describe within-host diversity. In this class of measures the F(WS) metric characterizes within-host diversity and its relationship to population level diversity. Utilizing P. falciparum field isolates from patients in West Africa we here explore the relationship between the traditional MOI and F(WS) approaches. F(WS) statistics were derived from read count data at 86,158 SNPs in 64 samples sequenced on the Illumina GA platform. MOI estimates were derived by PCR at the msp-1 and -2 loci. Significant correlations were observed between the two measures, particularly with the msp-1 locus (P?=?5.92×10(-5)). The F(WS) metric should be more robust than the PCR-based approach owing to reduced sensitivity to potential locus-specific artifacts. Furthermore the F(WS) metric captures information on a range of parameters which influence out-crossing risk including the number of clones (MOI), their relative proportions and genetic divergence. This approach should provide novel insights into the factors which correlate with, and shape within-host diversity.

| S-EPMC3290604 | biostudies-literature

Beginner's guide to comparative bacterial genome analysis using next-generation sequence data.

Project description:High throughput sequencing is now fast and cheap enough to be considered part of the toolbox for investigating bacteria, and there are thousands of bacterial genome sequences available for comparison in the public domain. Bacterial genome analysis is increasingly being performed by diverse groups in research, clinical and public health labs alike, who are interested in a wide array of topics related to bacterial genetics and evolution. Examples include outbreak analysis and the study of pathogenicity and antimicrobial resistance. In this beginner's guide, we aim to provide an entry point for individuals with a biology background who want to perform their own bioinformatics analysis of bacterial genome data, to enable them to answer their own research questions. We assume readers will be familiar with genetics and the basic nature of sequence data, but do not assume any computer programming skills. The main topics covered are assembly, ordering of contigs, annotation, genome comparison and extracting common typing information. Each section includes worked examples using publicly available E. coli data and free software tools, all which can be performed on a desktop computer.

| S-EPMC3630013 | biostudies-literature

Detection and characterization of novel sequence insertions using paired-end next-generation sequencing.

Project description:In the past few years, human genome structural variation discovery has enjoyed increased attention from the genomics research community. Many studies were published to characterize short insertions, deletions, duplications and inversions, and associate copy number variants (CNVs) with disease. Detection of new sequence insertions requires sequence data, however, the 'detectable' sequence length with read-pair analysis is limited by the insert size. Thus, longer sequence insertions that contribute to our genetic makeup are not extensively researched.We present NovelSeq: a computational framework to discover the content and location of long novel sequence insertions using paired-end sequencing data generated by the next-generation sequencing platforms. Our framework can be built as part of a general sequence analysis pipeline to discover multiple types of genetic variation (SNPs, structural variation, etc.), thus it requires significantly less-computational resources than de novo sequence assembly. We apply our methods to detect novel sequence insertions in the genome of an anonymous donor and validate our results by comparing with the insertions discovered in the same genome using various sources of sequence data.The implementation of the NovelSeq pipeline is available at http://compbio.cs.sfu.ca/strvar.htmeee@gs.washington.edu; cenk@cs.sfu.ca

| S-EPMC2865866 | biostudies-literature

Validation of Variant Assembly Using HAPHPIPE with Next-Generation Sequence Data from Viruses.

Project description:Next-generation sequencing (NGS) offers a powerful opportunity to identify low-abundance, intra-host viral sequence variants, yet the focus of many bioinformatic tools on consensus sequence construction has precluded a thorough analysis of intra-host diversity. To take full advantage of the resolution of NGS data, we developed HAplotype PHylodynamics PIPEline (HAPHPIPE), an open-source tool for the de novo and reference-based assembly of viral NGS data, with both consensus sequence assembly and a focus on the quantification of intra-host variation through haplotype reconstruction. We validate and compare the consensus sequence assembly methods of HAPHPIPE to those of two alternative software packages, HyDRA and Geneious, using simulated HIV and empirical HIV, HCV, and SARS-CoV-2 datasets. Our validation methods included read mapping, genetic distance, and genetic diversity metrics. In simulated NGS data, HAPHPIPE generated pol consensus sequences significantly closer to the true consensus sequence than those produced by HyDRA and Geneious and performed comparably to Geneious for HIV gp120 sequences. Furthermore, using empirical data from multiple viruses, we demonstrate that HAPHPIPE can analyze larger sequence datasets due to its greater computational speed. Therefore, we contend that HAPHPIPE provides a more user-friendly platform for users with and without bioinformatics experience to implement current best practices for viral NGS assembly than other currently available options.

| S-EPMC7412389 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data