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

TLDR

Fast and accurate quality control is essential for next‑generation sequencing studies, yet no common approach exists to flexibly integrate QC metrics across tools and large sample sets. The study introduces MultiQC to generate a single report visualising output from multiple tools across many samples, facilitating rapid identification of global trends and biases. MultiQC aggregates QC outputs from numerous bioinformatics tools, plots them, and supports easy extension and customization. MultiQC provides comprehensive visualisation of QC metrics across tools and samples, enabling efficient detection of batch effects and outliers. MultiQC is released under GNU GPLv3 and is available on GitHub, PyPI, and Bioconda, with documentation and example reports hosted online.

Abstract

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.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.MultiQC is available with an GNU GPLv3 license on GitHub, the Python Package Index and Bioconda. Documentation and example reports are available at http://[e-mail].