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gatk4-genome-processing-pipeline-azure
Workflows used for processing whole genome sequence data + germline variant calling.
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WorkOS
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rnaseq
RNA sequencing analysis pipeline using STAR, RSEM, HISAT2 or Salmon with gene/isoform counts and extensive quality control.
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InfluxDB
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Seqkit - thoroughly maintained with extensive tutorials and benchmarking info - https://github.com/shenwei356/seqkit
You could check out https://github.com/lazear/sage - it's a near comprehensive program/pipeline for analyzing DDA/shotgun proteomics data. Most proteomics pipelines consist of running multiple, separate tools in sequence (search, spectrum rescoring, retention time prediction, quantification), but sage performs all of these. This cuts down on the need for disk space for storing intermediate results (none required), the need for IO (files are read once), and results in a proteomics pipeline that is >10-1000x faster than anything else, including commercial solutions
Related posts
- Why does it feels impossible to set up github nextflow pipeline without a root?
- R pipelines for bulk RNA-seq analyses
- Does anyone know a great guide/documentation explaining how to implement Percolator?
- Point of using Hisat2 build to index reference genomes when working with known genomes mouse/human?
- I used featureCounts to quantify RNA-seq reads and got a low successful alignment percentage. Is this a problem?