salmon-rna-quantification
Ultra-fast RNA-seq transcript/gene quantification via quasi-mapping (no BAM). Builds a k-mer index from transcriptome FASTA, quantifies in minutes. Outputs TPM/count tables (quant.sf) with optional GC- and sequence-bias correction. Integrates with tximeta/tximport for DESeq2/edgeR. Use STAR when a genome-aligned BAM is needed.
npx skills add BioTender-max/awesome-bio-agent-skills --skill salmon-rna-quantification --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Salmon — Fast RNA-seq Quantification ## Overview Salmon quantifies transcript abundance from RNA-seq reads using quasi-mapping — matching reads to a k-mer index of the transcriptome without full genome alignment. This makes Salmon 20–50× faster than alignment-based tools while producing accurate TPM and estimated count values. Salmon corrects for sequence-specific bias (`--seqBias`), GC-content bias (`--gcBias`), and fragment length distribution automatically. Output `quant.sf` files integrate directly with `tximeta` (R) or `pydeseq2` (Python) for differential expression analysis. For improved accuracy, decoy-aware indexing uses the full genome to identify spurious quasi-mappings. ## When to Use - Performing fast RNA-seq quantification when you do not need a genome-aligned BAM file - Running large-scale RNA-seq studies where alignment speed is a bottleneck (Salmon is 20-50× faster than STAR + featureCounts) - Computing TPM and estimated counts from bulk RNA-seq for differential expression with DESeq2 or edgeR - Correcting for GC bias, fragment length, and sequence context bias with `--gcBias --seqBias` - Estimating transcript-level uncertainty via bootstrap resampling with `--num
- Overview
- When to Use
- Prerequisites
- Quick Start
- Workflow
- Step 1: Download Transcriptome Reference
- Step 2: Build Salmon Index
- Step 3: Quantify Single-End Reads
- Step 4: Quantify Paired-End Reads with Bias Correction
- Step 5: Load and Summarize Quantification Output
- Step 6: Aggregate to Gene Level and Run DESeq2
- Key Parameters
- Common Recipes
- Recipe 1: Batch Quantify All Samples
Install with conda (recommended) conda install -c bioconda salmon Verify salmon --version salmon 1.10.3 Or download pre-compiled binary wget https://github.com/COMBINE-lab/salmon/releases/download/v1.10.0/salmon-1.10.0_linux_x86_64.tar.gz tar xzvf salmon-1.10.0_linux_x86_64.tar.gz export PATH="$PWD/salmon-latest_linux_x86_64/bin:$PATH" salmon index -t transcriptome.fa -i salmon_index/ -p 8
What does the salmon-rna-quantification skill do?
Ultra-fast RNA-seq transcript/gene quantification via quasi-mapping (no BAM). Builds a k-mer index from transcriptome FASTA, quantifies in minutes. Outputs TPM/count tables (quant.sf) with optional GC- and sequence-bias correction. Integrates with tximeta/tximport for DESeq2/edgeR. Use STAR when a genome-aligned BAM is needed.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill salmon-rna-quantification --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 144 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.