MultiQC: summarize analysis results for multiple tools and samples in a single report
Open accessAbstract
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
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The quality of RNA-seq data was verified using FastQC v0.11.5 and MultiQC v1.10.
“The quality of the data was verified using the FastQC v0.11.5 and MultiQC v1.10 applications54.”
From Integrative genomic analyses identify candidate causal genes for calcific aortic valve stenosis involving tissue-specific regulation (2024) · cites this paper as [54]
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- Authors
- Ewels, P., Magnusson, M., Lundin, S., & Käller, M.
- Affiliations
- Stockholm University (SE)
- Science for Life Laboratory (SE)
- Corresponding author
- Ewels, Philip
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-06-16
- Volume
- 32
- Issue
- 19
- Pages
- 3047-3048
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC5039924
- OpenAlex
- W2432815617
- Cited by
- 10,143
- References
- 2
- Citations by year
- 2026: 2080 · 2025: 2138 · 2024: 1814 · 2023: 1216 · 2022: 1089 · 2021: 840 · 2020: 481 · 2019: 302 · 2018: 130 · 2017: 42 · 2016: 7
- FWCI
- 83.65
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HighWire Press Open Archive (submittedVersion)
- Funders
- Science for Life Laboratory
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- MultiQC, batch effects, outlier detection, Python
- MeSH terms
- Quality Control, Software, Computational Biology, High-Throughput Nucleotide Sequencing
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