Clinical Quality Considerations when Using Next-Generation Sequencing (NGS) in Clinical Drug Development.
Ontology highlight
ABSTRACT:
SUBMITTER: Menard T
PROVIDER: S-EPMC8332578 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Next-generation sequencing technologies are increasingly being applied in clinical settings, however the data are characterized by a range of platform-specific artifacts making downstream analysis problematic and error- prone. One major application of NGS is in the profiling of clinically relevant mutations whereby sequences are aligned to a reference genome and potential mutations assessed and scored. Accurate sequence alignment is pivotal in reliable assessment of potential mutations however selection of appropriate alignment tools is a non-trivial task complicated by the availability of multiple solutions each with its own performance characteristics. Using targeted analysis of BRCA1 as an example, we have simulated and mutated a test dataset based on Illumina sequencing technology. Our findings reveal key differences in the abilities of a range of common commercial and open source alignment tools to facilitate accurate downstream detection of a range of mutations. These observations will be of importance to anyone using NGS to profile mutations in clinical or basic research.
| S-EPMC3945748 | biostudies-literature
Project description:Water quality is an emergent property of a complex system comprised of interacting microbial populations and introduced microbial and chemical contaminants. Studies leveraging next-generation sequencing (NGS) technologies are providing new insights into the ecology of microbially mediated processes that influence fresh water quality such as algal blooms, contaminant biodegradation, and pathogen dissemination. In addition, sequencing methods targeting small subunit (SSU) rRNA hypervariable regions have allowed identification of signature microbial species that serve as bioindicators for sewage contamination in these environments. Beyond amplicon sequencing, metagenomic and metatranscriptomic analyses of microbial communities in fresh water environments reveal the genetic capabilities and interplay of waterborne microorganisms, shedding light on the mechanisms for production and biodegradation of toxins and other contaminants. This review discusses the challenges and benefits of applying NGS-based methods to water quality research and assessment. We will consider the suitability and biases inherent in the application of NGS as a screening tool for assessment of biological risks and discuss the potential and limitations for direct quantitative interpretation of NGS data. Secondly, we will examine case studies from recent literature where NGS based methods have been applied to topics in water quality assessment, including development of bioindicators for sewage pollution and microbial source tracking, characterizing the distribution of toxin and antibiotic resistance genes in water samples, and investigating mechanisms of biodegradation of harmful pollutants that threaten water quality. Finally, we provide a short review of emerging NGS platforms and their potential applications to the next generation of water quality assessment tools.
| S-EPMC4585245 | biostudies-other
Project description:Next generation sequencing (NGS) technologies provide a high-throughput means to generate large amount of sequence data. However, quality control (QC) of sequence data generated from these technologies is extremely important for meaningful downstream analysis. Further, highly efficient and fast processing tools are required to handle the large volume of datasets. Here, we have developed an application, NGS QC Toolkit, for quality check and filtering of high-quality data. This toolkit is a standalone and open source application freely available at http://www.nipgr.res.in/ngsqctoolkit.html. All the tools in the application have been implemented in Perl programming language. The toolkit is comprised of user-friendly tools for QC of sequencing data generated using Roche 454 and Illumina platforms, and additional tools to aid QC (sequence format converter and trimming tools) and analysis (statistics tools). A variety of options have been provided to facilitate the QC at user-defined parameters. The toolkit is expected to be very useful for the QC of NGS data to facilitate better downstream analysis.
| S-EPMC3270013 | biostudies-literature
Project description:Identifying resistance to antiretroviral drugs is crucial for ensuring the successful treatment of patients infected with viruses such as human immunodeficiency virus (HIV) or hepatitis C virus (HCV). In contrast to Sanger sequencing, next-generation sequencing (NGS) can detect resistance mutations in minority populations. Thus, genotypic resistance testing based on NGS data can offer novel, treatment-relevant insights. Since existing web services for analyzing resistance in NGS samples are subject to long processing times and follow strictly rules-based approaches, we developed geno2pheno[ngs-freq], a web service for rapidly identifying drug resistance in HIV-1 and HCV samples. By relying on frequency files that provide the read counts of nucleotides or codons along a viral genome, the time-intensive step of processing raw NGS data is eliminated. Once a frequency file has been uploaded, consensus sequences are generated for a set of user-defined prevalence cutoffs, such that the constructed sequences contain only those nucleotides whose codon prevalence exceeds a given cutoff. After locally aligning the sequences to a set of references, resistance is predicted using the well-established approaches of geno2pheno[resistance] and geno2pheno[hcv]. geno2pheno[ngs-freq] can assist clinical decision making by enabling users to explore resistance in viral populations with different abundances and is freely available at http://ngs.geno2pheno.org.
| S-EPMC6031006 | biostudies-literature
Project description:Molecular technologies have allowed laboratories to detect and establish the profiles of human cancers by identifying a variety of somatic variants. In order to improve personalized patient care, we have established a next-generation sequencing (NGS) test to screen for somatic variants in primary or advanced cancers. In this study, we describe the laboratory quality management program for NGS testing, and also provide an overview of the somatic variants identified in over 1000 patient samples as well as their implications in clinical practice.Over the past one-and-a-half years, our laboratory received a total of 1028 formalin-fixed, paraffin-embedded (FFPE) tumor tissues, which consisted of non-small-cell lung carcinomas (NSCLCs), colon adenocarcinomas, glioma/glioblastomas, melanomas, breast carcinomas, and other tumor types. During this time period, we implemented a series of quality control (QC) checks that included (1) pre-DNA extraction, (2) DNA quantification, (3) DNA quality, (4) library quantification, (5) post-emulsification PCR, and (6) post-sequencing metrics. At least 10 ng of genomic DNA (gDNA) were used to prepare barcoded libraries using the AmpliSeq CHPv2. Samples were multiplexed and sequenced on Ion Torrent 318 chips using the Ion PGM System. Variants were identified using the Variant Caller Plugin, and annotation and functional predictions were performed using the Golden Helix SVS.A total of 1005 samples passed QC1-3, and following additional library preparation QC checkpoints, 877 samples were sequenced. Samples were classified into two categories: wild-type (127) and positive for somatic variants (750). Somatic variants were classified into clinically actionable (60%) and non-actionable (40%).The use of NGS in routine clinical laboratory practice allowed for the detection of tumor profiles that are essential for the selection of targeted therapies and identification of applicable clinical trials, contributing to the improvement of personalized patient care in oncology.
| S-EPMC5506542 | biostudies-other
Project description:The livestock industry in Mongolia accounts for 24% of national revenue, with one third of the population maintaining a pastoral lifestyle. This close connection between Mongolian population and livestock is a major concern for pathogen transfer, especially given the increase in vector-borne diseases globally. This study examines blood samples from livestock to assess the prevalence of tick-borne bacterial infections across three provinces in Mongolia (Dornogovi, Selenge, Töv). Whole blood samples from 243 livestock (cattle=38, camel=11, goat=85, horse=22, sheep=87) were analyzed with 16S metagenomics next-generation sequencing (NGS) to screen for bacterial pathogens. Positive-NGS samples for Anaplasma, Bartonella, Ehrlichia, Francisella, Leptospira, and Rickettsia were confirmed by conventional PCR and DNA sequencing. Prevalence rates of Anaplasma, Bartonella, and Ehrlichia were 57.6%, 12.8%, and 0.4%, respectively. A significant difference in the prevalence of Anaplasma spp. in livestock by province was observed, with a higher prevalence in Selenge (74.2%, p<0.001) and Töv (64.2% p = 0.006) compared to the semi-arid region of Dornogovi (39.8%). In contrast, no association was observed in Bartonella prevalence by provinces. All Anaplasma sequences (N = 139) were characterized as A. ovis. For Bartonella species characterization, phylogenetic analyses of gltA and rpoB genes identified three Bartonella species including B. bovis, B. melophagi and Candidatus B. ovis. Bartonella bovis was detected in all 22-positive cattle, while B. melophagi and Candidatus B. ovis were found in four and three sheep, respectively. This study identifies a high prevalence of tick-borne pathogens within the livestock population and to our knowledge, is the first time NGS methods have been used to explore tick-borne diseases in Mongolia. Further research is needed in Mongolia to better understand the clinical and economic burdens associated with tick-borne diseases in both livestock and pastoral herder populations.
| S-EPMC8665119 | biostudies-literature
Project description:BACKGROUND:Next-Generation Sequencing (NGS) technology has exceptionally increased the ability to sequence DNA in a massively parallel and cost-effective manner. Nevertheless, NGS data analysis requires bioinformatics skills and computational resources well beyond the possibilities of many "wet biology" laboratories. Moreover, most of projects only require few sequencing cycles and standard tools or workflows to carry out suitable analyses for the identification and annotation of genes, transcripts and splice variants found in the biological samples under investigation. These projects can take benefits from the availability of easy to use systems to automatically analyse sequences and to mine data without the preventive need of strong bioinformatics background and hardware infrastructure. RESULTS:To address this issue we developed an automatic system targeted to the analysis of NGS data obtained from large-scale transcriptome studies. This system, we named NGS-Trex (NGS Transcriptome profile explorer) is available through a simple web interface http://www.ngs-trex.org and allows the user to upload raw sequences and easily obtain an accurate characterization of the transcriptome profile after the setting of few parameters required to tune the analysis procedure. The system is also able to assess differential expression at both gene and transcript level (i.e. splicing isoforms) by comparing the expression profile of different samples.By using simple query forms the user can obtain list of genes, transcripts, splice sites ranked and filtered according to several criteria. Data can be viewed as tables, text files or through a simple genome browser which helps the visual inspection of the data. CONCLUSIONS:NGS-Trex is a simple tool for RNA-Seq data analysis mainly targeted to "wet biology" researchers with limited bioinformatics skills. It offers simple data mining tools to explore transcriptome profiles of samples investigated taking advantage of NGS technologies.
| S-EPMC3633008 | biostudies-literature
Project description:DNA analysis by NGS has become important to direct the clinical care of cancer patients. However, NGS is not successful in all cases, and the factors responsible for test failures have not been systematically evaluated.A series of 1528 solid and hematolymphoid tumor specimens was tested by an NGS comprehensive cancer panel during 2012-2014. DNA was extracted and 2×101 bp paired-end sequence reads were generated on cancer-related genes utilizing Illumina HiSeq and MiSeq platforms.Testing was unsuccessful in 343 (22.5%) specimens. The failure was due to insufficient tissue (INST) in 223/343 (65%) cases, insufficient DNA (INS-DNA) in 99/343 (28.9%) cases, and failed library (FL) in 21/343 (6.1%) cases. 87/99 (88%) of the INS-DNA cases had below 10 ng DNA available for testing. Factors associated with INST and INS-DNA failures were site of biopsy (SOB) and type of biopsy (TOB) (both p < 0.0001), and clinical setting of biopsy (CSB, initial diagnosis or recurrence) (p < 0.0001). Factors common to INST and FL were age of specimen (p ≤ 0.006) and tumor viability (p ≤ 0.05). Factors common to INS-DNA and FL were DNA purity and DNA degradation (all p ≤ 0.005). In multivariate analysis, common predictors for INST and INS-DNA included CSB (p = 0.048 and p < 0.0001) and TOB (both p ≤ 0.003), respectively. SOB (p = 0.004) and number of cores (p = 0.001) were specific for INS-DNA, whereas TOB and DNA degradation were associated with FL (p = 0.04 and 0.02, respectively).Pre-analytical causes (INST and INS-DNA) accounted for about 90% of all failed cases; independent of test design. Clinical setting; site and type of biopsy; and number of cores used for testing all correlated with failure. Accounting for these factors at the time of tissue biopsy acquisition could improve the analytic success rate.
| S-EPMC5528718 | biostudies-other
Project description:Based on immunohistochemistry (IHC) and in situ hybridization (ISH), HER2-low breast cancers (BC) subtype-defined as IHC1+ or IHC2+/ISH- tumors-emerged and represent more than half of all BC. We evaluated the performance of NGS for integrated molecular characterization of HER2-low BC, including identification of actionable molecular targets, copy number variation (CNV), and microsatellite instability (MSI) analysis. Thirty-one BC specimens (11 HER2+, 10 HER2-, and 10 HER2-low) were routinely analyzed using IHC and ISH, and were selected and analyzed using NGS for gene mutations including ESR1, PIK3CA, AKT1, ERBB2, TP53, BRCA1, and BRCA2, CNV, and MSI. CNV values for the ERBB2 gene were significantly (p < 0.001) different between HER2+, and either HER2-low or HER2- tumors with mean values of 7.8 (SD = 6.8), 1.9 (SD = 0.3), and 2.0 (SD = 0.3), respectively. Using 3.25 as the cutoff value, 96.8% overall concordance of HER2 status was achieved between IHC and NGS compared to IHC and ISH. Using NGS, gene mutations and amplifications were detected in 68% (21/31) and 19% (6/31) of the cases, respectively. One case of MSI was detected in a HER2-negative and ISH unamplified case. Beside IHC, NGS allows the identification of HER2-low subtype simultaneously, with the detection of multiple actionable gene mutations being helpful for molecular board treatment selection.
| S-EPMC10740754 | biostudies-literature
Project description:BackgroundIn recent years more than 20 assemblers have been proposed to tackle the hard task of assembling NGS data. A common heuristic when assembling a genome is to use several assemblers and then select the best assembly according to some criteria. However, recent results clearly show that some assemblers lead to better statistics than others on specific regions but are outperformed on other regions or on different evaluation measures. To limit these problems we developed GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic locus (called blocks) are identified through reads' alignments and stored in a weighted graph. The merging phase is carried out with the help of this weighted graph that allows an optimal resolution of local problematic regions.ResultsGAM-NGS has been tested on six different datasets and compared to other assembly reconciliation tools. The availability of a reference sequence for three of them allowed us to show how GAM-NGS is a tool able to output an improved reliable set of sequences. GAM-NGS is also a very efficient tool able to merge assemblies using substantially less computational resources than comparable tools. In order to achieve such goals, GAM-NGS avoids global alignment between contigs, making its strategy unique among other assembly reconciliation tools.ConclusionsThe difficulty to obtain correct and reliable assemblies using a single assembler is forcing the introduction of new algorithms able to enhance de novo assemblies. GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.
| S-EPMC3633056 | biostudies-literature