{"id":17223354,"url":"https://github.com/slowkow/picardmetrics","last_synced_at":"2025-04-14T00:22:11.554Z","repository":{"id":28818497,"uuid":"32341799","full_name":"slowkow/picardmetrics","owner":"slowkow","description":":vertical_traffic_light: Run Picard on BAM files and collate 90 metrics into one file.","archived":false,"fork":false,"pushed_at":"2017-09-27T00:05:48.000Z","size":9661,"stargazers_count":40,"open_issues_count":3,"forks_count":7,"subscribers_count":6,"default_branch":"master","last_synced_at":"2025-03-27T14:21:52.437Z","etag":null,"topics":["bam-files","bioinformatics","picard","quality-control","rna-seq"],"latest_commit_sha":null,"homepage":"https://slowkow.github.io/picardmetrics","language":"Shell","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/slowkow.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2015-03-16T17:35:48.000Z","updated_at":"2025-03-20T02:54:39.000Z","dependencies_parsed_at":"2022-09-05T05:01:26.003Z","dependency_job_id":null,"html_url":"https://github.com/slowkow/picardmetrics","commit_stats":null,"previous_names":[],"tags_count":5,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slowkow%2Fpicardmetrics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slowkow%2Fpicardmetrics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slowkow%2Fpicardmetrics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/slowkow%2Fpicardmetrics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/slowkow","download_url":"https://codeload.github.com/slowkow/picardmetrics/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248800048,"owners_count":21163404,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bam-files","bioinformatics","picard","quality-control","rna-seq"],"created_at":"2024-10-15T04:08:06.587Z","updated_at":"2025-04-14T00:22:11.506Z","avatar_url":"https://github.com/slowkow.png","language":"Shell","funding_links":[],"categories":[],"sub_categories":[],"readme":"# picardmetrics\n\nRun [Picard] tools and collate multiple metrics files. Check the quality of\nyour sequencing data.\n\n[![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.17142.svg)](http://dx.doi.org/10.5281/zenodo.17142)\n[![Build Status](https://travis-ci.org/slowkow/picardmetrics.svg?branch=master)](https://travis-ci.org/slowkow/picardmetrics)\n\n## Summary\n\nRun `picardmetrics` like this:\n\n```bash\nfor bam in data/project1/sample?/sample?.bam\ndo\n  # -k keeps the BAM file with marked duplicate reads\n  # -r runs RNA-seq Picard metrics\n  # -o specifies where to put the output files\n  picardmetrics run -k -r -o out/rnaseq $bam\ndone\n\n# The final output file will be called \"project1-all-metrics.tsv\"\npicardmetrics collate project1 out/rnaseq\n```\n\n`picardmetrics` runs up to [12 Picard tools][manual] on each [BAM] file and\ncollates all of the output files into a single table with up to [90 different\nmetrics][definitions]. It also automatically creates the `.refFlat` and\n`.rRNA.list` files required for [CollectRnaSeqMetrics].\n\nSee the [picardmetrics manual][manual] for more details.\n\nNext, plot and explore the metrics in R:\n\n```r\nlibrary(ggplot2)\n\ndat \u003c- read.delim(\"project1-all-metrics.tsv\", stringsAsFactors = FALSE)\n\nggplot(dat) +\n  geom_point(aes(PF_READS, PF_ALIGNED_BASES))\n```\n\nSee two example BAM files in the [data/][data] folder. The\n[test/test.sh][test] script illustrates the usage of `picardmetrics` and tests\nthat it works correctly. See the outputs in the [out/][out] folder. You can\nalso download the [reference files][reference] used to test `picardmetrics`.\n\n[scripts]: https://github.com/slowkow/picardmetrics/tree/master/scripts\n[data]: https://github.com/slowkow/picardmetrics/tree/master/data\n[test]: https://github.com/slowkow/picardmetrics/tree/master/test/test.sh\n[out]: https://github.com/slowkow/picardmetrics/tree/master/out\n\n[manual]: https://slowkow.github.io/picardmetrics\n[reference]: http://dx.doi.org/10.5281/zenodo.18116\n\n[definitions]: https://broadinstitute.github.io/picard/picard-metric-definitions.html\n[CollectRnaSeqMetrics]: https://broadinstitute.github.io/picard/command-line-overview.html#CollectRnaSeqMetrics\n\n## Example\n\n![Genes detected vs. Mean MAPQ and Percent of bases vs. Sample][example]\n\n[example]: https://github.com/slowkow/picardmetrics/blob/master/man/picardmetrics-banner.png\n\nUse Picard to assess the quality of your sequencing data. This example shows\nRNA-seq data from [hundreds of glioblastoma cells and gliomasphere cell\nlines][Patel2014].\n\nOn the left, each point represents an RNA-seq sample. We see that samples\nwith high mean mapping quality have the greatest number of detected genes.\nFurther, the color reveals variation in the percent of reads per sample\nthat are assigned to exons.\n\nOn the right, each bar represents an RNA-seq sample. Each sample is broken\ndown into the percent of sequenced bases coming from different genomic\nregions. We see that many samples have few sequenced bases coming from\ncoding regions relative to intergenic regions.\n\n[Patel2014]: http://www.ncbi.nlm.nih.gov/bioproject/PRJNA248302\n\n## Installation\n\n```bash\n# Download the code.\ngit clone https://github.com/slowkow/picardmetrics\n\ncd picardmetrics\n\n# Download and install the dependencies.\nmake get-deps PREFIX=~/.local\n\n# Install picardmetrics and the man page.\nmake install PREFIX=~/.local\n\n# Edit the configuration file for your project.\nvim ~/picardmetrics.conf\n```\n\nIf you wish, you can manually install the dependencies:\n\n-   [Picard]\n-   [samtools], which depends on [htslib]\n-   [stats]\n-   [gtfToGenePred]\n\n[BAM]: http://samtools.github.io/hts-specs/SAMv1.pdf\n[Gencode]: http://www.gencodegenes.org/\n\n[Picard]: https://broadinstitute.github.io/picard/\n[samtools]: https://github.com/samtools/samtools\n[htslib]: https://github.com/samtools/htslib\n[stats]: https://github.com/arq5x/filo\n[gtfToGenePred]: http://hgdownload.cse.ucsc.edu/admin/exe/linux.x86_64/\n\n## Contributing\n\nPlease [submit an issue][issues] to report bugs or ask questions.\n\nPlease contribute bug fixes or new features with a [pull request][pull] to this repository.\n\n[issues]: https://github.com/slowkow/picardmetrics/issues\n[pull]: https://help.github.com/articles/using-pull-requests/\n\n## Related work\n\n[RNA-SeQC][rnaseqc]\n\n\u003e RNA-SeQC is a java program which computes a series of quality control\n\u003e metrics for RNA-seq data. The input can be one or more BAM files. The output\n\u003e consists of HTML reports and tab delimited files of metrics data. This\n\u003e program can be valuable for comparing sequencing quality across different\n\u003e samples or experiments to evaluate different experimental parameters. It can\n\u003e also be run on individual samples as a means of quality control before\n\u003e continuing with downstream analysis.\n\n[RSeQC][rseqc]\n\n\u003e RSeQC package provides a number of useful modules that can comprehensively\n\u003e evaluate high throughput sequence data especially RNA-seq data. Some basic\n\u003e modules quickly inspect sequence quality, nucleotide composition bias, PCR\n\u003e bias and GC bias, while RNA-seq specific modules evaluate sequencing\n\u003e saturation, mapped reads distribution, coverage uniformity, strand\n\u003e specificity, etc.\n\n[QoRTs][qorts]\n\n\u003e The QoRTs software package is a fast, efficient, and portable multifunction\n\u003e toolkit designed to assist in the analysis, quality control, and data\n\u003e management of RNA-Seq datasets. Its primary function is to aid in the\n\u003e detection and identification of errors, biases, and artifacts produced by\n\u003e paired-end high-throughput RNA-Seq technology. In addition, it can produce\n\u003e count data designed for use with differential expression and differential\n\u003e exon usage tools 2, as well as individual-sample and/or group-summary\n\u003e genome track files suitable for use with the UCSC genome browser (or any\n\u003e compatible browser).\n\n[rnaseqc]: http://www.broadinstitute.org/cancer/cga/rna-seqc\n[rseqc]: http://rseqc.sourceforge.net/\n[qorts]: https://github.com/hartleys/QoRTs\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fslowkow%2Fpicardmetrics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fslowkow%2Fpicardmetrics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fslowkow%2Fpicardmetrics/lists"}