Next-generation sequencing is now an established method in genomics, and massive amounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data is typically performed by lab-specific in-house solutions, but the agreement of results from different facilities is often small. General standards for quality control, reproducibility, and documentation are missing.; We developed NGS-pipe, a flexible, transparent, and easy-to-use framework for the design of pipelines to analyze whole-exome, whole-genome, and transcriptome sequencing data. NGS-pipe facilitates the harmonization of genomic data analysis by supporting quality control, documentation, reproducibility, parallelization, and easy adaptation to other NGS experiments.



https://github.com/cbg-ethz/NGS-pipe



niko.beerenwinkel@bsse.ethz.c

Beerenwinkel, Niko

Beisel, Christian

Christofori, Gerhard

Dummer, Reinhard

Hall, Michael N.

Hofmann, Ariane L.

Krek, Wilhelm

Levesque, Mitchell

Manz, Markus G.

Moch, Holger

Ng, Charlotte K. Y.

Papassotiropoulos, Andreas

Piscuoglio, Salvatore

Rinn, Bernd

Ruscheweyh, Hans-Joachim

Singer, Franziska

Singer, Jochen

Stekhoven, Daniel J.

Thurnherr, Thomas

Toussaint, Nora C.

Wild, Peter

Wüst, Thomas

Motivation: Next-generation sequencing is now an established method in genomics, and massive amounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data is typically performed by lab-specific in-house solutions, but the agreement of results from different facilities is often small. General standards for quality control, reproducibility, and documentation are missing.

Results: We developed NGS-pipe, a flexible, transparent, and easy-to-use framework for the design of pipelines to analyze whole-exome, whole-genome, and transcriptome sequencing data. NGS-pipe facilitates the harmonization of genomic data analysis by supporting quality control, documentation, reproducibility, parallelization, and easy adaptation to other NGS experiments

Hofmann, Ariane L

Toussaint, Nora C

Ng, Charlotte K Y

Hall, Michael N

Levesque, Mitchell P

Manz, Markus G

Stekhoven, Daniel J

ZORA

English

NGS-pipe: a flexible, easily extendable, and highly configurable framework for NGS analysis

Motivation

Next-generation sequencing is now an established method in genomics, and massive amounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data is typically performed by lab-specific in-house solutions, but the agreement of results from different facilities is often small. General standards for quality control, reproducibility and documentation are missing.

Results

We developed NGS-pipe, a flexible, transparent and easy-to-use framework for the design of pipelines to analyze whole-exome, whole-genome and transcriptome sequencing data. NGS-pipe facilitates the harmonization of genomic data analysis by supporting quality control, documentation, reproducibility, parallelization and easy adaptation to other NGS experiments.ISSN:1367-4803ISSN:1460-205

Ng, Charlotte K.Y.

Levesque, Mitchell P.

Repository for Publications and Research Data

NGS-pipe: A flexible, easily extendable and highly configurable framework for NGS analysis

Abstract
               
                  Motivation
                  Next-generation sequencing is now an established method in genomics, and massive amounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data is typically performed by lab-specific in-house solutions, but the agreement of results from different facilities is often small. General standards for quality control, reproducibility and documentation are missing.
               
               
                  Results
                  We developed NGS-pipe, a flexible, transparent and easy-to-use framework for the design of pipelines to analyze whole-exome, whole-genome and transcriptome sequencing data. NGS-pipe facilitates the harmonization of genomic data analysis by supporting quality control, documentation, reproducibility, parallelization and easy adaptation to other NGS experiments.
               
               
                  Availability and implementation
                  https://github.com/cbg-ethz/NGS-pipe
               </jats:sec

Jochen Singer

Hans-Joachim Ruscheweyh

Ariane L Hofmann

Thomas Thurnherr

Franziska Singer

Nora C Toussaint

Charlotte K Y Ng

Salvatore Piscuoglio

Christian Beisel

Gerhard Christofori

Reinhard Dummer

Michael N Hall

Wilhelm Krek

Mitchell P Levesque

Markus G Manz

Holger Moch

Andreas Papassotiropoulos

Daniel J Stekhoven

Peter Wild

Thomas Wüst

Bernd Rinn

Niko Beerenwinkel

Crossref

Bioinformatics

NGS-pipe: a flexible, easily extendable and highly configurable framework for NGS analysis

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Genome analysisNGS-pipe: a flexible, easily extendable andhighly configurable framework for NGS analysisJochen Singer1,2,†, Hans-Joachim Ruscheweyh1,2,3,†,Ariane L. Hofmann1,2, Thomas Thurnherr1,2, Franziska Singer2,4,Nora C. Toussaint2,4, Charlotte K. Y. Ng5,6,7, Salvatore Piscuoglio6,Christian Beisel1, Gerhard Christofori5, Reinhard Dummer8,Michael N. Hall9, Wilhelm Krek10, Mitchell P. Levesque8,Markus G. Manz11, Holger Moch12, Andreas Papassotiropoulos13,14,15,16,Daniel J. Stekhoven2,4, Peter Wild12, Thomas Wu¨st2,3, Bernd Rinn2,3 andNiko Beerenwinkel1,2,*1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland, 2SIB Swiss Institute ofBioinformatics, Basel, Switzerland, 3Scientific IT Services, ETH Zurich, Basel, Switzerland, 4NEXUS PersonalizedHealth Technologies, Zurich, Switzerland, 5Department of Biomedicine, University of Basel, Basel, Switzerland,6Institute of Pathology, 7Division of Gastroenterology and Hepatology, University Hospital Basel, Basel,Switzerland, 8Department of Dermatology, University Hospital Zurich, Zurich, Switzerland, 9Biozentrum, Universityof Basel, Basel, Switzerland, 10Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland, 11Divisionof Hematology, 12Institute of Pathology and Molecular Pathology, University Hospital Zurich, Zurich, Switzerland,13Division of Molecular Neuroscience, Department of Psychology, 14Transfaculty Research Platform Molecularand Cognitive Neurosciences, 15Psychiatric University Clinics University of Basel, Basel, Switzerland and16Department Biozentrum, Life Sciences Training Facility, University of Basel, Basel, Switzerland*To whom correspondence should be addressed.†The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors.Associate Editor: Bonnie BergerReceived on March 30, 2017; revised on August 18, 2017; editorial decision on August 24, 2017; accepted on August 26, 2017AbstractMotivation: Next-generation sequencing is now an established method in genomics, and massiveamounts of sequencing data are being generated on a regular basis. Analysis of the sequencing data istypically performed by lab-specific in-house solutions, but the agreement of results from different facili-ties is often small. General standards for quality control, reproducibility and documentation are missing.Results: We developed NGS-pipe, a flexible, transparent and easy-to-use framework for the designof pipelines to analyze whole-exome, whole-genome and transcriptome sequencing data. NGS-pipe facilitates the harmonization of genomic data analysis by supporting quality control, docu-mentation, reproducibility, parallelization and easy adaptation to other NGS experiments.Availability and implementation: https://github.com/cbg-ethz/NGS-pipeContact: niko.beerenwinkel@bsse.ethz.ch1 IntroductionAdvances in next-generation sequencing (NGS) have led to technologiescapable of producing massive amounts of data at low costs. However,the analysis of these data is usually carried out using lab-specific in-house solutions. As a consequence, many different workflows are imple-mented for the same type of data, such that results are not easilyVC The Author 2017. Published by Oxford University Press. 1This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/),which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contactjournals.permissions@oup.comBioinformatics, 2017, 1–2doi: 10.1093/bioinformatics/btx540Advance Access Publication Date: 28 August 2017Applications Notecomparable and are often hard to reproduce. Several studies haveshown that individual pipelines often have limited overlap in their re-sults (Alioto et al., 2015; Denroche et al., 2015; Hofmann et al., 2017),which impedes the potential of identifying true biological signals and ofclinical applications. The developers of the Genome Analysis Toolkit at-tempt to stratify genome analysis by providing best practices (https://software.broadinstitute.org/gatk/best-practices/), but these recommenda-tions are currently not fully implemented computationally.Here, we introduce NGS-pipe, an automated and user friendlyframework for the design of pipelines for the analysis of large-scalesequencing data, such as cancer genomics data. NGS-pipe allows toeasily develop tailored workflows for the analysis of whole-exome(WES), whole-genome (WGS) and transcriptome (RNA-seq)sequencing data by providing building blocks to execute state-of-the-art tools, as well as appropriate error handling. An importantgoal of NGS-pipe is to overcome the common lack of automatedprocedures to ensure reproducibility. This is particularly importantfor clinical applications, where well documented and standardizedprotocols are a requirement (Aziz et al., 2015).2 Features of NGS-pipeNGS-pipe incorporates tools for detecting single nucleotide variants(SNVs), insertions and deletions (indels) and copy number variants(CNVs), as well as for estimating gene expression levels. In addition tothe primary read data analysis, NGS-pipe also generates runtime statis-tics and quality reports. It can be launched on a single computer or acluster, where independent steps are executed in parallel. A practicalintroduction and examples can be found in the GitHub repository.Modularity. NGS-pipe is implemented using the workflow man-agement system Snakemake (Koster and Rahmann, 2012). In com-bination with a modular backbone, where the execution of eachanalysis step is controlled by a rule, NGS-pipe is a flexible, easily ex-tendable and highly configurable framework for NGS analysis. Bymodifying a configuration file, users can easily adjust the parametersfor each rule without changing its implementation and include or ex-clude complete analysis steps in order to adapt the pre-configuredworkflows to the specific needs of their own experiment.Workflows for WES, WGS and RNA-seq data. To illustrate NGS-pipe, we have implemented and tested predefined workflows for theautomated analysis for cancer WES, WGS and RNA-seq data (Fig. 1)to assist users inexperienced in data analysis or pipeline design. A de-scription of these workflows, including the computational tools theyintegrate, can be found in the GitHub repository. Similar workflowscan be implemented using NGS-pipe for other NGS applications.Quality control and statistics. NGS-pipe supports quality controland provides statistics on each step of the analysis. Users can assessthe quality of each sequencing file in the output of FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) or Qualimap2(Okonechnikov et al., 2016), and inspect basic statistics such as howmany reads passed the individual analysis steps.Performance and scalability. With NGS-pipe, samples can be ana-lyzed independently of each other, providing full parallelization. For in-stance, we analyzed WES data from a tumor and matched normalsample comprising 60 million paired-end reads in 20 h and 10 such pairsin 22h on a compute cluster [HPE ProLiant BL460c Gen9 – Two 12-core Intel Xeon E5-2680v3 processors (2.5–3.3 GHz)], where the two-hour overhead is due to waiting times of the local batch queuing system.Similarly, one RNA-seq dataset consisting of 80 million single-end readsand 10 such datasets were analyzed in 2.5 and 3 h, respectively.Reproducibility, documentation and error handling. A high level ofautomation, a clear documentation of the pipeline and strict errorhandling facilitate reproducibility, a major goal of NGS-pipe. All param-eters for all tools included in the analysis of an NGS experiment aredocumented in a configuration file. Using Snakemake functionality, thereare several additional layers of documentation within NGS-pipe, e.g. log-ging the executed commands and generating graphical representations ofthe workflows. As NGS-pipe has been designed to analyze a large num-ber of datasets in parallel, automatized error handling is a fundamentalrequirement. If one of the steps of the pipeline failed and produced in-complete or no results, the computation of all depending steps is haltedand an error message is thrown, using Snakemake intrinsics. After theissue is resolved the pipeline independently resumes the analysis.3 ConclusionNGS has become a standard genomics method in research labs and iscurrently implemented in clinical settings to aid patient diagnostics andtreatment. NGS-pipe provides a Snakemake-based framework for ana-lyzing such NGS data in a transparent and reproducible manner. Thepre-configured workflows are easy to extend and adapt, extending therange of possible applications, including beyond cancer genomics.FundingThis work was supported by the European Research Council [ERC Synergy GrantNo. 609883]; SystemsX.ch [RTD Grant 2013/150, IPhD Grant SXPHI0_142005and SyBIT]; the Swiss Cancer League [KLS-2892-02-2012]; the Swiss NationalScience Foundation [Ambizione grant number PZ00P3_168165 to S.P.].Conflict of Interest: none declared.ReferencesAlioto,T.S. et al. (2015) A comprehensive assessment of somatic mutation detec-tion in cancer using whole-genome sequencing.Nat. Commun., 6, 10001.Aziz,N. et al. (2015) College of american pathologists’ laboratory standards fornext-generation sequencing clinical tests.Arch. Pathol. Lab.Med., 139, 481–493.Denroche,R.E. et al. (2015) A cancer cell-line titration series for evaluatingsomatic classification. BMC Res. Notes, 8, 823.Hofmann,A.L. et al. (2017) Detailed simulation of cancer exome sequencingdata reveals differences and common limitations of variant callers. BMCBioinformatics, 18, 8.Koster,J. and Rahmann,S. (2012) Snakemake–a scalable bioinformatics work-flow engine. Bioinformatics, 28, 2520–2522.Okonechnikov,K. et al. (2016) Qualimap 2: advanced multi-sample quality con-trol for high-throughput sequencing data.Bioinformatics, 32, 292–294.Fig. 1. Schematic overview of the different pre-configured pipelines availablein NGS-pipe2 J.Singer et al.

NGS-pipe: a flexible, easily extendable, and highly configurable framework for NGS analysis

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