Quantifies bulk RNA-seq transcript abundance with salmon (v1.11.4 selective alignment) and kallisto (v0.52.0, kb-python workflow), builds a decoy-aware gentrome index, runs quant with --validateMappings --gcBias -l A, then imports estimates via tximport/tximeta with a tx2gene map and hands differential expression to alterlab-pydeseq2. Warns that salmon's index format changed to SSHash (rebuild pre-v1.11.2 indices) and that 'salmon alevin' was REMOVED (single-cell now uses piscem + alevin-fry). Use when quantifying RNA-seq transcript abundance, running salmon or kallisto, building a decoy-aware index, or wiring tximport to DESeq2; for differential expression use alterlab-pydeseq2, for FASTQ-to-VCF variant calling use alterlab-nf-core-sarek. Part of the AlterLab Academic Skills suite.
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name
alterlab-rnaseq-quant
description
Quantifies bulk RNA-seq transcript abundance with salmon (v1.11.4 selective alignment) and kallisto (v0.52.0, kb-python workflow), builds a decoy-aware gentrome index, runs quant with --validateMappings --gcBias -l A, then imports estimates via tximport/tximeta with a tx2gene map and hands differential expression to alterlab-pydeseq2. Warns that salmon's index format changed to SSHash (rebuild pre-v1.11.2 indices) and that 'salmon alevin' was REMOVED (single-cell now uses piscem + alevin-fry). Use when quantifying RNA-seq transcript abundance, running salmon or kallisto, building a decoy-aware index, or wiring tximport to DESeq2; for differential expression use alterlab-pydeseq2, for FASTQ-to-VCF variant calling use alterlab-nf-core-sarek. Part of the AlterLab Academic Skills suite.
Requires the salmon and/or kallisto CLI on PATH (conda/bioconda or a container). salmon v1.11.4 and kallisto v0.52.0 are the versions this skill targets; the tximport/tx2gene helper runs under `uv run python` with pure stdlib (no pandas needed). No API key or account required; all work is local.
The command-line quantification entry point for bulk RNA-seq: take raw FASTQ
reads plus a reference transcriptome and produce transcript-level abundance
estimates (counts + TPM) with salmon (selective alignment) or kallisto
(pseudoalignment via kb-python), then aggregate to the gene level with
tximport/tximeta and hand off to alterlab-pydeseq2 for differential
expression. It is the raw-data-to-count-matrix pipeline that the repo's Python
analysis skills assume already ran.
Quick Start
Quantify these RNA-seq FASTQs with salmon and a decoy-aware index
Build a salmon gentrome index from this transcriptome + genome
Run kallisto / kb count on my paired-end reads
Turn my salmon quant.sf files into a gene-level count matrix for DESeq2
→ Build a decoy-aware index once, run salmon quant (or kb count) per
sample, then run scripts/build_tx2gene.py + scripts/import_quant.py to make
the tximport gene matrix and route it to alterlab-pydeseq2.
When to Use This Skill
Use this skill when the request is about getting from FASTQ to transcript or
gene abundance with a lightweight quantifier:
"Quantify my RNA-seq with salmon / kallisto."
"Build a decoy-aware salmon index (gentrome + decoys.txt)."
"Run selective alignment with --validateMappings --gcBias."
"I have quant.sf files — make me a gene-level count matrix for DESeq2."
"Set up tximport / tximeta with a tx2gene map."
"Use kb-python / kb count to pseudoalign these reads."
Does NOT Trigger — route these to the right sibling
The request is really about…
Route to
Differential expression on a count matrix (DESeq2 Wald tests, FDR, volcano)
Loading/manipulating the resulting matrix as an AnnData object
alterlab-anndata
BLAST/DIAMOND sequence similarity search
alterlab-blast
Quick gene/transcript ID lookups & reference fetch (Ensembl/RefSeq)
alterlab-gget
Aligned BAM manipulation, coverage, read counting from alignments
alterlab-pysam
This skill stops at the count/abundance matrix. It does not call DEGs, does
not handle single-cell barcodes, and does not align to a genome for variant
calling.
Two Critical Correctness Traps (read before quantifying)
These are the two failures most outdated RNA-seq instructions get wrong as of
salmon v1.11.4 (released 2026-03-11). Both are confirmed in the upstream
release notes (see references/tool_versions.md).
The salmon index format changed to SSHash. salmon switched from the
colored compacted de Bruijn graph index to a new SSHash-based k-mer index.
The release notes state all previously built indices must be rebuilt
before using v1.11.2+. If you reuse a pre-v1.11.2 index you will get an error
or silently wrong results — always rebuild the index with the same salmon
version you quantify with.
salmon alevin was REMOVED. Single-cell quantification is no longer part
of salmon. The release notes direct former alevin users to the
piscem + alevin-fry pipeline. Do not write salmon alevin commands.
If the user has single-cell / droplet data, route per the table above and see
references/single_cell_alevin.md.
Pipeline (salmon, the default path)
1. Build a decoy-aware gentrome index (once per reference)
A decoy-aware index lets salmon distinguish reads that align better to the
genome than the transcriptome, reducing spurious assignments. You build a
"gentrome" = transcripts FASTA concatenated with the genome FASTA, plus a
decoys.txt listing the genome sequence names as decoys.
# 1. decoys.txt = the genome's sequence (chromosome) names, one per line
grep "^>" genome.fa | sed 's/^>//; s/ .*//' > decoys.txt
# 2. gentrome = transcripts FIRST, then genome (order matters)cat transcripts.fa genome.fa > gentrome.fa
# 3. build the index (rebuild for v1.11.4 — see trap #1)
salmon index \
-t gentrome.fa \
-d decoys.txt \
-i salmon_index \
-k 31 \
-p 8
-k 31 is the default k-mer; lower it only for very short reads.
The helper scripts/make_decoys.py writes decoys.txt and gentrome.fa for
you and refuses to proceed if the genome names are absent from the transcript
FASTA (a common silent mistake). See references/decoy_index.md.
-l A — auto-detect library type (strandedness). Let salmon infer it
unless you have a documented protocol; verify the inferred type in
lib_format_counts.json.
--validateMappings — enables selective alignment (the accurate default
mode; scores mappings rather than trusting raw pseudo-mappings).
--gcBias — corrects fragment-level GC bias; recommended for DE and cheap
to enable. Add --seqBias for 5'/3' sequence-specific bias if needed.
For single-end reads, pass -r reads.fastq.gz instead of -1/-2.
Each sample produces quants/<sample>/quant.sf (transcript-level estimates) and
quants/<sample>/lib_format_counts.json (the inferred library type). See
references/salmon_quant.md for the full flag map
and per-sample QC checks.
3. Aggregate to gene level with tximport
Build a transcript→gene map (tx2gene) from your annotation, then summarize the
per-sample quant.sf files into a gene-level matrix that pydeseq2 consumes.
# tx2gene from a GTF/GFF3 (transcript_id -> gene_id)
uv run python scripts/build_tx2gene.py annotation.gtf --out tx2gene.tsv
# import + summarize to gene level (tximport "lengthScaledTPM" counts)
uv run python scripts/import_quant.py \
--quants quants \
--tx2gene tx2gene.tsv \
--out-counts gene_counts.tsv \
--out-tpm gene_tpm.tsv
import_quant.py produces an integer-rounded gene × sample count matrix plus a
gene × sample TPM matrix, the inputs alterlab-pydeseq2 expects. It implements
tximport's makeCountsFromAbundance(..., "lengthScaledTPM") at the transcript
level (scale each transcript's TPM by its sample-averaged effective length, then
rescale each sample column back to its mapped-read library size) and sums to
genes — so the counts are length-corrected and library-size-scaled, not raw
summed NumReads. The canonical R route is the tximport / tximeta
Bioconductor packages with countsFromAbundance = "lengthScaledTPM"; the Python
helper here reproduces that computation so you can stay in uv (differing only
in integer rounding and the absence of tximeta provenance). See
references/tximport_handoff.md for the exact
semantics, the tximeta linkedTxome metadata option, and when to prefer the R
path.
4. Hand off to differential expression
Pass gene_counts.tsv (+ a sample/condition sheet) to alterlab-pydeseq2.
This skill does not call DEGs — that is pydeseq2's job (size-factor
normalization, dispersion, Wald tests, BH-FDR, volcano/MA plots).
Pipeline (kallisto, the pseudoalignment path)
kallisto (standalone v0.52.0) and the kb-python wrapper (kb) give a
faster pseudoalignment route. kb-python drives kallisto | bustools and writes
tidy outputs.
# build a kallisto index from the transcriptome
kallisto index -i kallisto_index.idx transcripts.fa
# quantify a paired-end sample
kallisto quant -i kallisto_index.idx -o quants_kallisto/sampleA \
sampleA_R1.fastq.gz sampleA_R2.fastq.gz
# OR the kb-python workflow (bulk)# -f1 is the cDNA FASTA kb WRITES; trailing positionals are genome FASTA THEN GTF
kb ref -i index.idx -g t2g.txt -f1 cdna.fa genome.fa annotation.gtf
kb count -i index.idx -g t2g.txt -x bulk -o quants_kb/sampleA \
sampleA_R1.fastq.gz sampleA_R2.fastq.gz
kallisto outputs abundance.tsv / abundance.h5; feed these to tximport
(type="kallisto") the same way as salmon's quant.sf.
Long reads: kb-python exposes lr-kallisto via the --long flag (and
k>31 k-mers) — use it for ONT/PacBio cDNA. See
references/kallisto_kb.md.
kallisto does not use the decoy/gentrome construction; that is salmon-specific.
Turnkey alternative — nf-core/rnaseq
For an end-to-end, provenance-tracked pipeline (trimming → alignment →
quantification → QC), nf-core/rnaseq v3.26.0 runs --aligner star_salmon by
default: STAR maps to the genome, projects onto the transcriptome, and Salmon
does the quantification. Reach for it when the user wants a reproducible
Nextflow pipeline rather than hand-run commands; this skill covers the
direct-salmon/kallisto path and the tximport handoff. See
references/tool_versions.md.
Offload note
Indexing and per-sample quantification are CPU/IO-heavy but fully offline. On a
local workstation these are good candidates to run directly (e.g. overnight)
rather than streaming large FASTQs through an API session. Build the index once;
quantify samples in a loop.
Self-Check Before Reporting
Did you rebuild the salmon index with the v1.11.4 you quantified with
(SSHash format — trap #1)? Never reuse a pre-v1.11.2 index.
Is the index decoy-aware (gentrome + decoys.txt) for salmon? Confirm the
genome names made it into decoys.txt.
Did you let -l A infer strandedness, and did you sanity-check the inferred
type in lib_format_counts.json?
Is the data actually single-cell? If so you must NOT use this path —
salmon alevin is gone; route to piscem + alevin-fry (trap #2).
Did you stop at the count matrix and hand DE off to alterlab-pydeseq2
rather than calling DEGs here?
References
references/tool_versions.md — pinned versions
(salmon v1.11.4, kallisto v0.52.0, kb-python, nf-core/rnaseq v3.26.0) and the
upstream release-note facts (SSHash index change, alevin removal).
references/decoy_index.md — decoy-aware gentrome
index construction, gotchas, and the make_decoys.py helper.
