MultiQC：在一份报告中汇总多个工具和样本的分析结果。
MultiQC: summarize analysis results for multiple tools and samples in a single report.
作者信息
Ewels Philip, Magnusson Måns, Lundin Sverker, Käller Max
机构信息
Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Stockholm 106 91, Sweden.
Department of Molecular Medicine and Surgery, Science for Life Laboratory, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden.
出版信息
Bioinformatics. 2016 Oct 1;32(19):3047-8. doi: 10.1093/bioinformatics/btw354. Epub 2016 Jun 16.
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
动机
对于涉及下一代测序数据的研究而言，快速且准确的质量控制至关重要。虽然存在众多用于量化质量控制指标的工具，但尚无通用方法可灵活地将这些工具整合到跨工具和大样本集的分析中。评估整个项目的分析结果可能既耗时又容易出错；批次效应和异常样本在分析的早期阶段很容易被遗漏。
结果
我们展示了MultiQC，这是一种用于创建单个报告的工具，可可视化来自多个工具针对众多样本的输出，从而能够快速识别全局趋势和偏差。MultiQC可以绘制来自许多常见生物信息学工具的数据，并且其设计便于轻松扩展和定制。
可用性与实现方式
MultiQC在GitHub、Python软件包索引和Bioconda上以GNU GPLv3许可提供。文档和示例报告可在http://multiqc.info获取。
联系方式
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