For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
39
views
6
references
 Top references
cited by
2,019
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      258
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      MultiQC: summarize analysis results for multiple tools and samples in a single report

      brief-report

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Motivation: Fast and accurate quality control is essential for studies involving next-generation sequencing data. Whilst numerous tools exist to quantify QC metrics, there is no common approach to flexibly integrate these across tools and large sample sets. Assessing analysis results across an entire project can be time consuming and error prone; batch effects and outlier samples can easily be missed in the early stages of analysis.

          Results: We present MultiQC, a tool to create a single report visualising output from multiple tools across many samples, enabling global trends and biases to be quickly identified. MultiQC can plot data from many common bioinformatics tools and is built to allow easy extension and customization.

          Availability and implementation: MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://multiqc.info

          Contact: phil.ewels@ 123456scilifelab.se

          Related collections

          Most cited references6

          • Record: found
          • Abstract: not found
          • Article: not found

          Matplotlib: A 2D Graphics Environment

          John Hunter (2007)
          Article 0 comments Cited 5386 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            RSeQC: quality control of RNA-seq experiments.

            Liguo Wang, Shengqin Wang, Wei Li (2012)
            RNA-seq has been extensively used for transcriptome study. Quality control (QC) is critical to ensure that RNA-seq data are of high quality and suitable for subsequent analyses. However, QC is a time-consuming and complex task, due to the massive size and versatile nature of RNA-seq data. Therefore, a convenient and comprehensive QC tool to assess RNA-seq quality is sorely needed. We developed the RSeQC package to comprehensively evaluate different aspects of RNA-seq experiments, such as sequence quality, GC bias, polymerase chain reaction bias, nucleotide composition bias, sequencing depth, strand specificity, coverage uniformity and read distribution over the genome structure. RSeQC takes both SAM and BAM files as input, which can be produced by most RNA-seq mapping tools as well as BED files, which are widely used for gene models. Most modules in RSeQC take advantage of R scripts for visualization, and they are notably efficient in dealing with large BAM/SAM files containing hundreds of millions of alignments. RSeQC is written in Python and C. Source code and a comprehensive user's manual are freely available at: http://code.google.com/p/rseqc/.
            Article 0 comments Cited 650 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Qualimap 2: advanced multi-sample quality control for high-throughput sequencing data

              Konstantin Okonechnikov, Ana Conesa, Fernando García-Alcalde (2015)
              Motivation: Detection of random errors and systematic biases is a crucial step of a robust pipeline for processing high-throughput sequencing (HTS) data. Bioinformatics software tools capable of performing this task are available, either for general analysis of HTS data or targeted to a specific sequencing technology. However, most of the existing QC instruments only allow processing of one sample at a time. Results: Qualimap 2 represents a next step in the QC analysis of HTS data. Along with comprehensive single-sample analysis of alignment data, it includes new modes that allow simultaneous processing and comparison of multiple samples. As with the first version, the new features are available via both graphical and command line interface. Additionally, it includes a large number of improvements proposed by the user community. Availability and implementation: The implementation of the software along with documentation is freely available at http://www.qualimap.org. Contact: meyer@mpiib-berlin.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online.
              Article 0 comments Cited 508 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Journal
                Journal ID (nlm-ta): Bioinformatics
                Journal ID (iso-abbrev): Bioinformatics
                Journal ID (publisher-id): bioinformatics
                Journal ID (hwp): bioinfo
                Title: Bioinformatics
                Publisher: Oxford University Press
                ISSN (Print): 1367-4803
                ISSN (Electronic): 1367-4811
                Publication date (Print): 01 October 2016
                Publication date (Electronic): 16 June 2016
                Publication date PMC-release: 16 June 2016
                Volume: 32
                Issue: 19
                Pages: 3047-3048
                Affiliations
                1Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm 106 91, Sweden,
                2Department of Molecular Medicine and Surgery, Science for Life Laboratory, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
                3Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, Royal Institute of Technology, Stockholm, Sweden
                Author notes
                *To whom correspondence should be addressed.

                Associate Editor: Jonathan Wren

                Article
                Publisher ID: btw354
                DOI: 10.1093/bioinformatics/btw354
                PMC ID: 5039924
                PubMed ID: 27312411
                SO-VID: 61596899-5f72-4f44-b2db-57bbf3fdf33f
                Copyright © © The Author 2016. Published by Oxford University Press.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 30 April 2016
                Date revision received : 30 April 2016
                Date accepted : 29 May 2016
                Page count
                Pages: 2
                Categories
                Subject: Applications Notes
                Subject: Data and Text Mining

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology

                Comments

                Comment on this article

                scite_

                Similar content258

                See all similar

                Cited by2,353

                See all cited by

                Most referenced authors23

                See all reference authors