Next-generation Sequencing Data Analysis Pipelines Research Articles

Evaluation of an optimized germline exomes pipeline using BWA-MEM2 and Dragen-GATK tools.

The next-generation sequencing (NGS) technology represents a significant advance in genomics and medical diagnosis. Nevertheless, the time it takes to perform sequencing, data analysis, and variant interpretation is a bottleneck in using next-generation sequencing in precision medicine. For accurate and efficient performance in clinical diagnostic lab practice, a consistent data analysis pipeline is necessary to avoid false variant calls and achieve optimum accuracy. This study aims to compare the performance of two NGS data analysis pipeline compartments, including short-read mapping (BWA-MEM and BWA-MEM2) and variant calling (GATK-HaplotypeCaller and DRAGEN-GATK). On Whole Exome Sequencing (WES) data, computational performance was assessed using several criteria, including mapping efficiency, variant calling performance, false positive calls rate, and time. We examined four gold-standard WES data sets: Ashkenazim father (NA24149), Ashkenazim mother (NA24143), Ashkenazim son (NA24385), and Asian son (NA25631). In addition, eighteen exome samples were analyzed based on different read counts, and coverage was used precisely in the run-time assessment. By using BWA-MEM 2 and Dragen-GATK, this study achieved faster and more accurate detection for SNVs and indels than the standard GATK Best Practices workflow. This systematic comparison will enable the bioinformatics community to develop a more efficient and faster solution for analyzing NGS data.

A suitable sequence of bioinformatics tools for NGS diagnosis of familial hypercholesterolemia

Background and Aims : Familial hypercholesterolemia (FH) is the most common monogenic congenital metabolic disease with an estimated frequency of 1: 200 - 500 in the population. Aberrations of the LDL receptor gene (LDLR) or mutation in his ligand apolipoprotein B100 (FDB) are the most common cause of FH. To date, over 2000 different mutations have been described in the LDLR. More rarely the pathology could be detected in other genes (APOB, LDLRAP , PCSK9, etc.). Early diagnosis using molecular genetic methods enables appropriate treatment and belongs to the basic pillars of routine practise. Considering various types of possible mutations, next - generation sequencing (NGS) method using hybridization-based enriched libraries can provide a potent solution for required multilevel genetic analysis. Each level of data analysis requires specific bioinformatic tool although the sensitivity to detect certain types of variants may partially overlap.Methods: We present three bioinformatic tools (FreeBayes, Manta and CNVkit) as an example of suitable unique combination of optimal NGS data analysis pipeline - specifically for the custom Hyperlipoproteinemia 1 (HLP) SureSelect library panel (Agilent Technologies) covering 53 HLP genes and 55 SNPs.Results: The spectrum of causal mutations detected in our FH and HLP patients cohort were as follows: 81% of single-nucleotide variants, 5% of minor and 14% of gross (two and more exons) intragenic rearrangements. Mutations in LDLR covered 74,4% of causal variants, 11,6% of mutations were in APOB and 14% in another genes from the HLP panel.Conclusions: The presented combination of bioinformatic tools represents effective diagnostic algorithm with increase diagnostic yield.*This work was supported by the Cooperatio Program, research area "Metabolic Diseases 207037" Background and Aims : Familial hypercholesterolemia (FH) is the most common monogenic congenital metabolic disease with an estimated frequency of 1: 200 - 500 in the population. Aberrations of the LDL receptor gene (LDLR) or mutation in his ligand apolipoprotein B100 (FDB) are the most common cause of FH. To date, over 2000 different mutations have been described in the LDLR. More rarely the pathology could be detected in other genes (APOB, LDLRAP , PCSK9, etc.). Early diagnosis using molecular genetic methods enables appropriate treatment and belongs to the basic pillars of routine practise. Considering various types of possible mutations, next - generation sequencing (NGS) method using hybridization-based enriched libraries can provide a potent solution for required multilevel genetic analysis. Each level of data analysis requires specific bioinformatic tool although the sensitivity to detect certain types of variants may partially overlap. Methods: We present three bioinformatic tools (FreeBayes, Manta and CNVkit) as an example of suitable unique combination of optimal NGS data analysis pipeline - specifically for the custom Hyperlipoproteinemia 1 (HLP) SureSelect library panel (Agilent Technologies) covering 53 HLP genes and 55 SNPs. Results: The spectrum of causal mutations detected in our FH and HLP patients cohort were as follows: 81% of single-nucleotide variants, 5% of minor and 14% of gross (two and more exons) intragenic rearrangements. Mutations in LDLR covered 74,4% of causal variants, 11,6% of mutations were in APOB and 14% in another genes from the HLP panel. Conclusions: The presented combination of bioinformatic tools represents effective diagnostic algorithm with increase diagnostic yield. *This work was supported by the Cooperatio Program, research area "Metabolic Diseases 207037"

Evaluation of the Oncomine Comprehensive Assay v3 panel for the detection of 1p/19q codeletion in oligodendroglial tumours

AimsAccurate assessment of 1p/19q codeletion status in diffuse gliomas is of paramount importance for diagnostic, prognostic and predictive purposes. While targeted next generation sequencing (NGS) has been widely implemented for...

Next-Generation Sequencing Data Analysis on Pool-Seq and Low-Coverage Retinoblastoma Data.

Next-generation sequencing (NGS) is related to massively parallel or deep deoxyribonucleic acid (DNA) sequencing technology which has revolutionized genomic researches in recent years. Although the cost of generating NGS data was decreased compared to the one at the time of emerging this technology, its cost might still be somewhat a problem. Hence, new strategies as pool-seq and low-coverage NGS data have been developed to overcome the cost problem. Despite decreasing cost, it is important to elucidate whether they are efficient in NGS studies. We applied a bioinformatics pipeline on pool-seq and low-coverage retinoblastoma data retrieved from only tumor data. Retinoblastoma is an eye malignancy in childhood that is initiated by RB1 mutation or MYCN amplification and can lead to the loss of vision of eye(s), and even sometimes life. We applied our pipeline on both retinoblastoma disease data and two other particular data to testify the validity and also for comparison purposes in the aspect of performance. High-confidence variant calls from Genome in a Bottle Consortium were used for fulfilling these purposes. We observed that our pipeline successfully called higher number of variants than a standard pipeline for all these three different data. Besides, the recall and F-score values were quite better in our pipeline as being noteworthy. We further presented our results on disease data in the aspects of the variants, variant types and disease-related genes. This study provides a guideline for performing NGS data analysis pipeline on pool-seq and low-coverage sequencing data in conjunction. To get more conclusive outcomes of these two strategies, we recommend using cancer data having higher mutation rates and larger pools.

IRODS metadata management for a cancer genome analysis workflow

BackgroundThe massive amounts of data from next generation sequencing (NGS) methods pose various challenges with respect to data security, storage and metadata management. While there is a broad range of data analysis pipelines, these challenges remain largely unaddressed to date.ResultsWe describe the integration of the open-source metadata management system iRODS (Integrated Rule-Oriented Data System) with a cancer genome analysis pipeline in a high performance computing environment. The system allows for customized metadata attributes as well as fine-grained protection rules and is augmented by a user-friendly front-end for metadata input. This results in a robust, efficient end-to-end workflow under consideration of data security, central storage and unified metadata information.ConclusionsIntegrating iRODS with an NGS data analysis pipeline is a suitable method for addressing the challenges of data security, storage and metadata management in NGS environments.

Bio-Docklets: virtualization containers for single-step execution of NGS pipelines.

Processing of next-generation sequencing (NGS) data requires significant technical skills, involving installation, configuration, and execution of bioinformatics data pipelines, in addition to specialized postanalysis visualization and data mining software. In order to address some of these challenges, developers have leveraged virtualization containers toward seamless deployment of preconfigured bioinformatics software and pipelines on any computational platform. We present an approach for abstracting the complex data operations of multistep, bioinformatics pipelines for NGS data analysis. As examples, we have deployed 2 pipelines for RNA sequencing and chromatin immunoprecipitation sequencing, preconfigured within Docker virtualization containers we call Bio-Docklets. Each Bio-Docklet exposes a single data input and output endpoint and from a user perspective, running the pipelines as simply as running a single bioinformatics tool. This is achieved using a “meta-script” that automatically starts the Bio-Docklets and controls the pipeline execution through the BioBlend software library and the Galaxy Application Programming Interface. The pipeline output is postprocessed by integration with the Visual Omics Explorer framework, providing interactive data visualizations that users can access through a web browser. Our goal is to enable easy access to NGS data analysis pipelines for nonbioinformatics experts on any computing environment, whether a laboratory workstation, university computer cluster, or a cloud service provider. Beyond end users, the Bio-Docklets also enables developers to programmatically deploy and run a large number of pipeline instances for concurrent analysis of multiple datasets.

Erratum to: A metagenomics study for the identification of respiratory viruses in mixed clinical specimens: an application of the iterative mapping approach.

Metagenomic approaches to detect viral genomes and variants in clinical samples have various challenges, including low viral titers and bacterial and human genome contamination. To address these limitations, we examined a next-generation sequencing (NGS) and iterative mapping approach for virus detection in clinical samples. We analyzed 40 clinical specimens from hospitalized children diagnosed with acute bronchiolitis, croup, or respiratory tract infections in which virus identification by viral culture or polymerase chain reaction (PCR) was unsuccessful. For our NGS data analysis pipeline, clinical samples were pooled into two NGS groups to reduce sequencing costs, and the depth and coverage of assembled contigs were effectively increased using an iterative mapping approach. PCR was individually performed for each specimen according to the NGS-predicted viral type. We successfully detected previously unidentified respiratory viruses in 26 of 40 specimens using our proposed NGS pipeline. Two dominant populations within the detected viruses were human rhinoviruses (HRVs; n = 14) and human coronavirus NL63 (n = 8), followed by human parainfluenza virus (HPIV), human parechovirus, influenza A virus, respiratory syncytial virus (RSV), and human metapneumovirus. This is the first study reporting the complete genome sequences of HRV-A101, HRV-C3, HPIV-4a, and RSV, as well as an analysis of their genetic variants, in Taiwan. These results demonstrate that this NGS pipeline allows to detect viruses which were not identified by routine diagnostic assays, directly from clinical samples.

A metagenomics study for the identification of respiratory viruses in mixed clinical specimens: an application of the iterative mapping approach.

Metagenomic approaches to detect viral genomes and variants in clinical samples have various challenges, including low viral titers and bacterial and human genome contamination. To address these limitations, we examined a next-generation sequencing (NGS) and iterative mapping approach for virus detection in clinical samples. We analyzed 40 clinical specimens from hospitalized children diagnosed with acute bronchiolitis, croup, or respiratory tract infections in which virus identification by viral culture or polymerase chain reaction (PCR) was unsuccessful. For our NGS data analysis pipeline, clinical samples were pooled into two NGS groups to reduce sequencing costs, and the depth and coverage of assembled contigs were effectively increased using an iterative mapping approach. PCR was individually performed for each specimen according to the NGS-predicted viral type. We successfully detected previously unidentified respiratory viruses in 26 of 40 specimens using our proposed NGS pipeline. Two dominant populations within the detected viruses were human rhinoviruses (HRVs; n = 14) and human coronavirus NL63 (n = 8), followed by human parainfluenza virus (HPIV), human parechovirus, influenza A virus, respiratory syncytial virus (RSV), and human metapneumovirus. This is the first study reporting the complete genome sequences of HRV-A101, HRV-C3, HPIV-4a, and RSV, as well as an analysis of their genetic variants, in Taiwan. These results demonstrate that this NGS pipeline allows to detect viruses which were not identified by routine diagnostic assays, directly from clinical samples.Electronic supplementary materialThe online version of this article (doi:10.1007/s00705-017-3367-4) contains supplementary material, which is available to authorized users.

VirusSeeker, a computational pipeline for virus discovery and virome composition analysis

The advent of Next Generation Sequencing (NGS) has vastly increased our ability to discover novel viruses and to systematically define the spectrum of viruses present in a given specimen. Such studies have led to the discovery of novel viral pathogens as well as broader associations of the virome with diverse diseases including inflammatory bowel disease, severe acute malnutrition and HIV/AIDS. Critical to the success of these efforts are robust bioinformatic pipelines for rapid classification of microbial sequences. Existing computational tools are typically focused on either eukaryotic virus discovery or virome composition analysis but not both. Here we present VirusSeeker, a BLAST-based NGS data analysis pipeline designed for both purposes. VirusSeeker has been successfully applied in several previously published virome studies. Here we demonstrate the functionality of VirusSeeker in both novel virus discovery and virome composition analysis.

A Next-Generation Sequencing Data Analysis Pipeline for Detecting Unknown Pathogens from Mixed Clinical Samples and Revealing Their Genetic Diversity.

Forty-two cytopathic effect (CPE)-positive isolates were collected from 2008 to 2012. All isolates could not be identified for known viral pathogens by routine diagnostic assays. They were pooled into 8 groups of 5–6 isolates to reduce the sequencing cost. Next-generation sequencing (NGS) was conducted for each group of mixed samples, and the proposed data analysis pipeline was used to identify viral pathogens in these mixed samples. Polymerase chain reaction (PCR) or enzyme-linked immunosorbent assay (ELISA) was individually conducted for each of these 42 isolates depending on the predicted viral types in each group. Two isolates remained unknown after these tests. Moreover, iteration mapping was implemented for each of these 2 isolates, and predicted human parechovirus (HPeV) in both. In summary, our NGS pipeline detected the following viruses among the 42 isolates: 29 human rhinoviruses (HRVs), 10 HPeVs, 1 human adenovirus (HAdV), 1 echovirus and 1 rotavirus. We then focused on the 10 identified Taiwanese HPeVs because of their reported clinical significance over HRVs. Their genomes were assembled and their genetic diversity was explored. One novel 6-bp deletion was found in one HPeV-1 virus. In terms of nucleotide heterogeneity, 64 genetic variants were detected from these HPeVs using the mapped NGS reads. Most importantly, a recombination event was found between our HPeV-3 and a known HPeV-4 strain in the database. Similar event was detected in the other HPeV-3 strains in the same clade of the phylogenetic tree. These findings demonstrated that the proposed NGS data analysis pipeline identified unknown viruses from the mixed clinical samples, revealed their genetic identity and variants, and characterized their genetic features in terms of viral evolution.

SNVerGUI: a desktop tool for variant analysis of next-generation sequencing data

BackgroundAdvances in next generation sequencing (NGS) technology have made it possible to interrogate comprehensively genome-wide genetic variations. However, most existing tools for variation detection are based on command-line interface, which...

Customisation of the Exome Data Analysis Pipeline Using a Combinatorial Approach

The advent of next generation sequencing (NGS) technologies have revolutionised the way biologists produce, analyse and interpret data. Although NGS platforms provide a cost-effective way to discover genome-wide variants from a single experiment, variants discovered by NGS need follow up validation due to the high error rates associated with various sequencing chemistries. Recently, whole exome sequencing has been proposed as an affordable option compared to whole genome runs but it still requires follow up validation of all the novel exomic variants. Customarily, a consensus approach is used to overcome the systematic errors inherent to the sequencing technology, alignment and post alignment variant detection algorithms. However, the aforementioned approach warrants the use of multiple sequencing chemistry, multiple alignment tools, multiple variant callers which may not be viable in terms of time and money for individual investigators with limited informatics know-how. Biologists often lack the requisite training to deal with the huge amount of data produced by NGS runs and face difficulty in choosing from the list of freely available analytical tools for NGS data analysis. Hence, there is a need to customise the NGS data analysis pipeline to preferentially retain true variants by minimising the incidence of false positives and make the choice of right analytical tools easier. To this end, we have sampled different freely available tools used at the alignment and post alignment stage suggesting the use of the most suitable combination determined by a simple framework of pre-existing metrics to create significant datasets.

Targeted Next-Generation Sequencing (NGS) Enables for the First Time the Detection of Point Mutations, Molecular Insertions and Deletions, as Well as Leukemia-Specific Fusion Genes in AML in a Single Procedure.

Abstract 706Today, the genetic characterization necessary for optimal treatment of acute myeloid leukemia (AML) requires a combination of different labor-intensive methods such as chromosome banding analysis, sequencing for the detection of molecular mutations, and RT-PCR for the confirmation of characteristic fusion genes. DNA sequence enrichment from complex genomic samples using microarrays has recently been proposed to enable a targeted Next-Generation Sequencing (NGS) approach. Here, we combined 454 PicoTiterPlate (PTP) pyrosequencing with long-oligonucleotide sequence capture arrays to evaluate whether this technique allows a comprehensive genetic characterization in a one-step procedure (Roche Diagnostics Corporation, Branford, CT). 6 AML cases were analyzed with either known chromosomal aberrations–inversions and translocations–leading to fusion genes (CBFB-MYH11, RUNX1-RUNX1T1, MLL-MLLT3, MLL-unidentified fusion partner) or molecular mutations (KIT, FLT3-ITD, FLT3-TKD, and KRAS). A custom 1.91 Mb microarray was designed to contain capture probes for all coding regions of 92 target genes with relevance in leukemia, including e.g. KIT, NF1, KRAS, CEBPA, NPM1, FLT3, IKZF1, or TP53 (1559 exons). In addition, the complete genomic regions were targeted for the genes CBFB, RUNX1, and MLL (NimbleGen 385K format; Madison, WI). Starting with 20 μg of genomic DNA, this array design allowed a median 207-fold DNA enrichment of the targeted genomic loci. For sequencing, 454 Titanium chemistry was applied and in median 56.1 Mb of sequence data were generated per patient (median number of reads: 178.146). In median, 66.0% of reads were mapped to the original sequence capture array design, resulting in 18.7-fold median coverage per patient. The applied NGS data analysis pipeline used algorithms to map the obtained reads both exactly against the human genome, but also searched for chimeric sequences mapping to different regions in the genome. By this approach all corresponding fusion genes were detected as RUNX1-RUNX1T1 as well as the reciprocal RUNX1T1-RUNX1; CBFB-MYH11 and MYH11-CBFB; and MLL-MLLT3 and MLLT3-MLL, respectively. Interestingly, in one case a translocation t(11;19)(q23;p13) had been observed in chromosome banding analysis and the involvement of the MLL gene had been proven by FISH. However, using RT-PCR neither MLL-MLLT1 nor MLL-ELL fusion transcripts could be amplified. In contrast, the NGS approach identified chimeric reads containing both MLL and ELL sequences and, in addition, chimeric reads which were composed of SFRS14 (splicing factor, arginine/serine-rich 14; also located on 19p13 centromeric of ELL) and MLL. This suggested that a deletion had occurred in the breakpoint area and thus prevented the formation of a reciprocal ELL-MLL fusion gene. To confirm this assumption we performed a SNP array analysis (Affymetrix genome-wide human SNP array 6.0) and data from the SNP microarrays demonstrated a 615 kb deletion on 19p13, flanked by ELL and SFRS14, spanning from chr19: 18,346,048 - 18,961,490. Furthermore, with NGS it was possible to detect all molecular mutations identified by conventional methods including point mutations (KRAS G12C, FLT3-TKD D835Y), deletions (KIT D419X), and insertions (FLT3-ITD: 63 base pair length mutation). In conclusion, we demonstrated for the first time that fusion genes, point mutations, as well as deletions and insertions can be detected in a one-step methodological approach using the combination of a targeted DNA sequence enrichment assay followed by NGS technology. Furthermore, the genomic representation of only one of the partner genes of a chimeric fusion on this capture platform is sufficient to identify also any potentially unknown partner gene. As such, this novel assay has a strong potential to become an important method for a comprehensive genetic characterization of leukemias and other malignancies. Disclosures:Kohlmann:MLL Munich Leukemia Laboratory: Employment. Grossmann:MLL Munich Leukemia Laboratory: Employment. Schindela:MLL Munich Leukemia Laboratory: Employment. Schnittger:MLL Munich Leukemia Laboratory: Equity Ownership. Kern:MLL Munich Leukemia Laboratory: Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Equity Ownership.