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

Ewels, Philip; Magnusson, Måns; Lundin, Sverker; Käller, Max

doi:10.1093/bioinformatics/btw354

Cited by 6,167 publications

(4,298 citation statements)

References 7 publications

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“…It has the option to output the same metrics as the .metrics and .recal metrics generated by Picard/GATK 4. The format of the elPrep metrics files is identical to those from Picard/GATK 4 and are compatible with MultiQC [8] for visualization.BAM parsing: elPrep 4 previously relied on calling SAMtools for BAM parsing, but now implements BAM parsing itself using the built-in gzip compression library of Go. The compression is now more efficient in terms of runtime.…”

Section: Methodsmentioning

confidence: 99%

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elPrep 4: A multithreaded framework for sequence analysis

et al. 2019

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We present elPrep 4, a reimplementation from scratch of the elPrep framework for processing sequence alignment map files in the Go programming language. elPrep 4 includes multiple new features allowing us to process all of the preparation steps defined by the GATK Best Practice pipelines for variant calling. This includes new and improved functionality for sorting, (optical) duplicate marking, base quality score recalibration, BED and VCF parsing, and various filtering options. The implementations of these options in elPrep 4 faithfully reproduce the outcomes of their counterparts in GATK 4, SAMtools, and Picard, even though the underlying algorithms are redesigned to take advantage of elPrep’s parallel execution framework to vastly improve the runtime and resource use compared to these tools. Our benchmarks show that elPrep executes the preparation steps of the GATK Best Practices up to 13x faster on WES data, and up to 7.4x faster for WGS data compared to running the same pipeline with GATK 4, while utilizing fewer compute resources.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

elPrep 4: A multithreaded framework for sequence analysis

et al. 2019

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“…The program may be used in conjunction with several common aligners, including Bismark for processing bisulfite libraries. FastQ Screen has been incorporated by other groups into bioinformatics workflows, was reimplemented in the recently released QC tool Aozan 10 , and is compatible with MultiQC 11 , a tool to aid comparison of samples with respect to a large number of QC metrics.…”

Section: Resultsmentioning

confidence: 99%

FastQ Screen: A tool for multi-genome mapping and quality control

2018

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DNA sequencing analysis typically involves mapping reads to just one reference genome. Mapping against multiple genomes is necessary, however, when the genome of origin requires confirmation. Mapping against multiple genomes is also advisable for detecting contamination or for identifying sample swaps which, if left undetected, may lead to incorrect experimental conclusions. Consequently, we present FastQ Screen, a tool to validate the origin of DNA samples by quantifying the proportion of reads that map to a panel of reference genomes. FastQ Screen is intended to be used routinely as a quality control measure and for analysing samples in which the origin of the DNA is uncertain or has multiple sources.

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“…During the run of the workflow, quality control (QC) processes are launched to scan the result files, which can be imported into MultiQC 19 for an overview of quality control metrics. MultiQC modules are available to present relevant statistics and plots for the input FASTQ files by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc), alignment and coverage descriptors by BamQC (https://github.com/s-andrews/BamQC), QualiMap 20 , BCFtools 21 and Samtools 22 , MarkDuplicates statistics using Picard (http://broadinstitute.github.io/picard/), germline and somatic variant analysis results by VCFtools 23 and snpEff 17 .…”

Section: Sample Quality Controlmentioning

confidence: 99%

Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants

Garcia

Juhos

Larsson

et al. 2018

Preprint

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Summary: Whole-genome sequencing (WGS) is a cornerstone of precision medicine, but portable and reproducible open-source workflows for WGS analyses of germline and somatic variants are lacking. We present Sarek, a modular, comprehensive, and easy-to-install workflow, combining a range of software for the identification and annotation of single-nucleotide variants (SNVs), insertion and deletion variants (indels), structural variants, tumor sample heterogeneity, and karyotyping from germline or paired tumor/normal samples. Sarek is implemented in a bioinformatics workflow language (Nextflow) with Docker and Singularity compatible containers, ensuring easy deployment and full reproducibility at any Linux based compute cluster or cloud computing environment. Sarek supports the human reference genomes GRCh37 and GRCh38, and can readily be used both as a core production workflow at sequencing facilities and as a powerful stand-alone tool for individual research groups.Availability: Source code and instructions for local installation are available at GitHub (https://github.com/SciLifeLab/Sarek) under the MIT open-source license, and we invite the research community to contribute additional functionality as a collaborative open-source development project.

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

Cited by 6,167 publications

References 7 publications

elPrep 4: A multithreaded framework for sequence analysis

elPrep 4: A multithreaded framework for sequence analysis

FastQ Screen: A tool for multi-genome mapping and quality control

Sarek: A portable workflow for whole-genome sequencing analysis of germline and somatic variants

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