Byte Club, October 18 2017. Using MultiQC to produce consolidated QC Reports. Anna Battenhouse, CSSB & CCBB.
- MultiQC is a tool for aggregating NGS QC reports.
- It does not produce reports, just combines them for unified visualization.
- MultiQC "knows" the report formats of many existing NGS tools:
- FastQC, cutadapt, bowtie2, tophat, STAR, kallisto, HISAT2, samtools, featureCounts, HTSeq, MACS2, Picard, GATK
- … and more!
- MultiQC can also be configured to display other data via two straightforward steps:
- format the data appropriately (e.g. tab-delimited text files)
- create appropriate custom data entries in a multiqc_config.yaml configuration file
- MultiQC produces neat, interactive plots in an HTML file.
- So it can be used as a basic plotting tool for many kinds of reports and data, not just those produced by NGS tools!
Main MultiQC links
- Website: http://multiqc.info/
- Documentation: http://multiqc.info/docs
- MultiQC Github repo: https://github.com/ewels/MultiQC
- MultiQC test data repo: https://github.com/ewels/MultiQC_TestData
MultiQC configuration files
- an example multiqc_config.yaml file: https://github.com/ewels/MultiQC/blob/master/multiqc_config_example.yaml
- all multiqc_config.yaml defaults: https://github.com/ewels/MultiQC/blob/master/multiqc/utils/config_defaults.yaml
MultiQC custom data support
- structure of the custom data area of multiqc_config.yaml:
- available plot types:
- while this section is written for Python programming, the options listed in each plot type's "config" block can be specified declaratively in any plot's pconfig section in the multiqc_config.yaml.
- example custom data files from their test data repo:
Example Reports from Anna
Below are descriptions of two projects I've assisted with lately using MultiQC to help pull together visualizations assessing experiment quality.
I recommend using Chrome to view MultiQC reports.
- These example MultiQC reports below were generated by running the multiqc binary on a command line.
- After inspecting them locally (by just opening them as files in a web browser), they were copied to a web-accessible location to share with others.
- Here, that location is Iyer Lab's web-accessible directory on corral
Igor Ponomarev ATAC-seq data
ATAC-seq is a transposon-insertion sequencing method where an engineered, activate transposon inserts in accessible ("open") chromatin. It is considered to be a much simpler protocol to standard DNase-seq, and requires less starting material as well.
Igor Ponomarev's lab (in WCAAR) performed the ATAC-seq protocol on 5k and 50k cell nuclei from mouse brain.
- has both standard and custom data reports
Marcotte lab amplicon sequencing
The Marcotte lab is working on a deep mutational screening project of a human gene transformed into yeast as an amplicon on a plasmid. Here, the gene is MVK, a gene in the yeast cholesterol biosynthesis pathway. The hsMVK gene is amplified with an error-prone polymerase to produce point mutations. Both the native yeast gene and the human ortholog (with which it shares no sequence similarity) are under on/off promoter control. The idea is to compare the mutations that accumulate in the active hsMVK gene, after many growth cycles, with a background in which the hsMVK gene is present but not active (the yeast MVKis doing the work) to see which mutations are favored or disfavored. As part of this project, Riddhiman Garge produced 19 datasets.
- basic FastQC report
- report on BWA mem alignments of the datasets to hsMVK amplicon and plasmid backbone contigs
- standard reports from samtools flagstat, samtools idxstats, Picard MarkDuplicates
- custom data reports from bedtools genomecov and from insert size distribution data Anna computed
- report using custom data from a specialized deep mutational screening tool from the Jesse Bloom lab
- this tool looks only at the overlapping portions of paired-end R1 and R2 reads