Abstract

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@scilifelab.se

Keywords

Python (programming language)Computer scienceMIT LicenseData miningDocumentationOutlierSoftwareSample (material)PersonalizationProgramming languageArtificial intelligenceWorld Wide Web

MeSH Terms

Computational BiologyHigh-Throughput Nucleotide SequencingQuality ControlSoftware

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Publication Info

Year
2016
Type
article
Volume
32
Issue
19
Pages
3047-3048
Citations
8917
Access
Closed

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Cite This

Philip Ewels, Måns Magnusson, Sverker Lundin et al. (2016). MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics , 32 (19) , 3047-3048. https://doi.org/10.1093/bioinformatics/btw354

Identifiers

DOI
10.1093/bioinformatics/btw354
PMID
27312411
PMCID
PMC5039924

Data Quality

Data completeness: 90%