bioinformatics

name	bioinformatics
description	Use for sequence and omics work: FASTQ/BAM/VCF/GTF/BED handling and their coordinate conventions, reference builds and liftover, read QC and trimming, alignment and variant calling, RNA-seq and single-cell analysis, Snakemake and Nextflow pipelines, and pulling data from SRA/ENA/GEO under the right access tier. Reach for it whenever the task touches genomic files, aligners, differential expression, or a workflow that must rerun the same way twice.
version	1.0.0
author	Remedy
tags	["research","bioinformatics","genomics","rna-seq","pipelines","reproducibility"]
requires	[]
tools	["analysis_env","analysis_run","analysis_ledger","data_profile","data_diff","stats_multiplicity","lit_search","cite_add","skill_activate","bash_exec"]
triggers	["\\b(FASTQ|FASTA|\\bBAM\\b|SAM file|\\bVCF\\b|GTF file|BED file|CRAM file)\\b","\\b(RNA-?seq|scRNA-?seq|ChIP-?seq|ATAC-?seq|\\bWGS\\b|\\bWES\\b|variant calling|read (?:alignment|mapping))\\b","\\b(bowtie2|BWA-?MEM|STAR aligner|salmon (?:quant|index)|kallisto|samtools|bcftools|\\bGATK\\b|Snakefile|nextflow)\\b","\\b(differential expression|DESeq2|edgeR|limma|GO enrichment|KEGG pathway|Ensembl|RefSeq|batch effects?)\\b"]

Bioinformatics (the dry lab)

Call skill_activate(skill="research-method") first — it owns question framing, evidence standards, preregistration, citation honesty and how to say "we do not know". Do not restate it. This pack is what is different about genomic data.

Start here, in this order

1. analysis_env(path) — which of samtools, bcftools, bwa, STAR, salmon, R, snakemake, nextflow, conda/mamba, and a project python with pandas actually exist here. Nothing below runs in the sidecar; every real step goes through analysis_run or bash_exec in the project environment.
2. Establish the reference build before touching a file. GRCh37/hg19, GRCh38/hg38 or T2T-CHM13; Ensembl (1, MT) or UCSC (chr1, chrM) naming; the exact annotation release. Coordinates are meaningless without it, and a build mismatch produces plausible, wrong answers rather than an error. See references/reference-genomes.md.
3. Check coordinate conventions. BED is 0-based half-open; GFF/GTF, SAM, VCF and most genome browsers are 1-based inclusive. An off-by-one that shifts every feature by one base is the commonest silent bug in this field. references/file-formats-and-coordinates.md.
4. QC before anything else — FastQC/MultiQC on the raws, adapter and quality trimming, duplicate rate, contamination. A QC gate you skipped is a result you cannot defend. references/qc-and-preprocessing.md.
5. Write it as a pipeline. Snakemake or Nextflow, with pinned tool versions in a conda env or container. The pipeline, not the notebook, is the reproducibility unit. references/pipelines-snakemake-nextflow.md.

Hard rules

• Pin everything: reference FASTA and its checksum, annotation release, every tool version, every seed. Record them in the run, not in your head — analysis_run puts input hashes and argv into the ledger for you.
• Never mix builds or naming conventions in one analysis. If you must cross them, liftover explicitly, keep the unmapped list, and report how many features were lost.
• Thousands of tests, always. Every gene, peak, window or variant tested needs stats_multiplicity (BH FDR is the field default; state the family). A gene "significant at p < 0.05" out of 20,000 is not a finding.
• Counts, not TPM, for differential expression. DESeq2 and edgeR model raw counts with their own normalisation; feeding them TPM or FPKM breaks the model. .