isoform-dominance

Is this functional isoform-class comparison measurable by short reads? Answer that first, then quantify and test it — one config away for any gene.

Most isoform analyses stumble on two things: deciding which transcripts form a functional class, and knowing whether short reads can resolve the classes at all. isoform-dominance handles both, then quantifies and tests the comparison end-to-end:

annotate  gene symbol      → proposed isoform groups        (Ensembl, by 3' terminal exon)
identify  config           → is the class contrast estimable, and how precisely?
extract   Salmon quant.sf  → per-donor isoform-group TPM
stats     per-donor TPM    → paired Wilcoxon, per cohort + stratified combination, + figure
qc        marker TPM       → contamination control (is the signal a cell-type artifact?)

Bundled self-test: short LEPR isoform (LepRa) predominates over the long isoform (LepRb) in control human choroid plexus, in each of two independent cohorts (5/5 and 6/6 donors). The pooled figure (n = 11, P = 1×10⁻³) is reported alongside the stratified combinations — see Statistical notes for why the stratified ones are the numbers to quote.

Install

pip install isoform-dominance          # released version
isoform-dominance --version

From a clone, for development:

pip install -e ".[dev]"
pytest -q

Verify it works (no downloads, seconds)

isoform-dominance selftest
# or: pytest -q

reproduces the LEPR reference result (5/5, 6/6, combined n=11 P=9.8e-4) on a clean machine.

What makes it more than a quantifier

1. Auto isoform grouping (annotate). Give a gene symbol; it pulls the gene's protein-coding transcripts from Ensembl, clusters them by their 3' terminal-exon splice acceptor (the alternative last exon that defines functional isoform classes), and proposes a comparison you review and rename.

isoform-dominance annotate --gene LEPR --out config.json
#   iso_896aa : 7 transcripts   (short / LepRa)
#   iso_1165aa: 2 transcripts   (long / LepRb, canonical)

Review it: the proposal is a function of the annotation release too. The alternative class is the one with the most transcripts, and transcript counts are what a new release changes most. Across six Ensembl releases from 110 to 116, 30 of the 84 genes in a 109-gene survey that had two classes at every one were proposed a different pair at some release. LEPR up to release 115 was proposed the 1165 aa class against a 906 aa class of two transcripts, because the 896 aa class also had two and ties go to the longer protein; release 116 added five transcripts to the 896 aa class. When a proposal is decided by such a tie, annotate says so on stderr and lists the tied clusters in the config under _proposal.tied_with; at release 116, 39 of the survey's 100 proposals were ties.

A symbol can name more than one gene on the reference chromosomes, and Ensembl's lookup/symbol returns one of them without saying so: for pseudoautosomal genes such as CD99 and SHOX the chrY copy, for HERC3 the newer of two genes of that name. annotate looks for the others. Of a chrX/chrY pair it takes the chrX gene — Salmon keeps only the first of identical sequences, and a GENCODE FASTA lists chrX first — and says so; on any other set it stops and lists them, and --gene-id picks one. The config records gene_id, and identifiability fetches the gene background by it.

2. Identifiability guardrail (identify). A short-read quantifier apportions fragments by solving a linear inverse problem; a class total is recoverable only when it is an estimable function of that system. This checks the condition directly, on the class-collapsed system, and reports the smallest fold change the stated design can resolve for each class total and for the contrast between them. Ask it for the effect size you need:

isoform-dominance identifiability --config config.json --min-log2fc 0.5
# Identifiability (window=31, canonical k-mers)
#   annotation: Ensembl release 116
#   background: 9 same-gene transcript(s)
#   design: paired 100bp reads, fragments 200+-60, depth 30M, TPM 10, n=1
#   [identifiable] iso_1165aa: min |log2FC| 0.064; class 5369 unique k-mers; best transcript 5399 bp in 1 block(s), ~1072 informative reads; conditioning 3.62
#   [identifiable] iso_896aa: min |log2FC| 0.216; class 356 unique k-mers; best transcript 208 bp in 2 block(s), ~63 informative reads; conditioning 115.05
#   contrast iso_896aa vs iso_1165aa: min |log2FC| 0.234; identifiable, conditioning 117.68
#   effective length: iso_896aa 5103 bp vs iso_1165aa 8072 bp (mean per transcript), log2 ratio -0.66
#   distinguishing windows (0 = 5' end, 1 = 3' end of each transcript): iso_896aa median 0.07 [0.05-0.12], 372 positions over 7 transcript(s); iso_1165aa median 0.67 [0.51-0.84], 10738 positions over 2 transcript(s)
#   unique-read counting floor on log2 ratio: SE 0.187 per donor, min resolvable |log2FC| 0.366 at n=1
#   EFFECT SIZE: |log2FC| 0.500 resolved by both class totals and the contrast at this design
#   VERDICT: identifiable
# exit status 0; with --min-log2fc 0.1 the same run reports NOT resolved and exits 3

This example is a counterexample to the skew direction the command states (see below): iso_896aa is the shorter class but is distinguished at its 5' end (median 0.07), and in the simulation its bias ran against that direction under 5 of the 6 skews.

The release-116 sequences behind the 2026-09-11 block this replaces, run offline through --sequences and --background-sequences with the nine same-gene background transcripts; transcript and k-mer counts move as the annotation does. The effective length and distinguishing windows lines were added from a live run against the same release on 2026-09-13, in which every other line reproduced unchanged. The header and the per-class lines are shown as 2.4.1 prints them; the figures are those runs'. Read the min |log2FC| figures first: they are what the verdict and the exit status are built on. conditioning is a diagnostic. Without --min-log2fc the verdict falls back to --tau on the conditioning factor, which has no calibrated value — the same run then reads weakly_identifiable — and the command says on stderr that the verdict is a screening flag, not the exit status.

effective length and distinguishing windows say how exposed the comparison is to positional coverage skew, which none of the figures above models (see It bounds spread, not accuracy below). In simulation, Salmon split ambiguous fragments between the classes by effective length under skew, so the class effective-length ratio tracked the size of the error and, with the direction of the skew, its sign. Positions are fractions of each transcript's own length, 0 at the 5' end, because skew acts on each molecule in its own coordinates. When the classes differ at least 1.23-fold (|log2 ratio| >= 0.3) the command says on stderr which class each direction of skew tends to inflate, with the evidence: in a 49-gene simulation (Salmon, one quantifier, monotone positional skew) the 5' direction held for 35–36 of the 39 genes above that ratio, the 3' direction for 30–32, against 20 of 39 at uniform coverage. No other quantifier, gene panel or form of skew was tested, and below that ratio nothing was measured, so nothing is said.

Exit codes: 0 no --min-log2fc given, or it is resolved · 3 --min-log2fc given and not resolved at the stated design · 2 precondition failure: a transcript shorter than --window has no windows, so the gene total itself is not estimable · 1 config or network error. The structural verdict is never the exit status: it changes with the annotation release the transcripts came from, so it is reported in the output and in the --json report (verdict, with gene_total and effect_resolvable beside it).

Why not just count unique k-mers? Because that proxy — used by this package up to v2.1.1 — is wrong in both directions. A class whose only unique sequence is the ~30 k-mers spanning one splice junction passes while yielding a handful of usable fragments. A class nested inside another owns no unique k-mer at all and fails, yet its total is still an estimable function of the system: the containing class is pinned by its own unique sequence, so the nested class's indicator lies in the row space. That is an algebraic fact about the design matrix — not a claim that "the EM will work it out", since an EM returns numbers for a non-identifiable model just as readily. Both cases are regression-tested in tests/test_identifiability_model.py.

What it checks, in three layers — all from sequence alone, before any read is quantified:

Pass --background-fasta. The default background is the gene's own remaining transcripts, which is better than comparing the configured classes alone but still blind to pseudogenes, paralogues and homologous loci elsewhere in the index. A quantifier resolves fragments against the whole index, so any claim about what it can separate should be judged against the transcript FASTA the index was built from (without the genome decoys). Whether the default should require it was settled by a rule posted before the code (#3): on the 100 two-class configs annotate proposes at Ensembl 116 for a 109-gene panel, the whole-index background, GENCODE 50's 644,292 reference-chromosome transcripts, changed the exit status at --min-log2fc 0.5 of none (10 would have made the flag required), the verdict of one (SDHA, to not_identifiable), and the contrast's min |log2FC| by a median 0.003 (ACO2 the most, 3.8 to 8.6). A close paralogue can still decide the answer: SMN1's classes are weakly_identifiable against the gene's own transcripts and not_identifiable once SMN2's are columns. A record that holds a configured transcript's window found in at most M records of the FASTA (--max-window-records M, default 20) becomes a column of the compatibility system, as a transcript of the gene background is, so a competitor found once gives one answer whether it comes from the FASTA or from --background-sequences; a record longer than 1 Mb never does. Every window of the system that a record left out holds — a repeat's, a low-complexity stretch's, a genome record's — is dropped from every layer, so the system is the one with every record a column less the rows that touch a left-out record, and its answer is never more optimistic than that one's. The scan is streamed one record at a time, so its memory is set by the longest record, not by the file: one 20 Mb record took about 125 MB resident, 10,000 records of 2 kb about 47 MB (the whole process, Python and numpy included). It reads a window only where a 16-nt seed of it matches the configured transcripts, which gives the answer reading every window gives: a synthetic 210 Mb FASTA took 3.0 s plain and 4.7 s gzipped, against 132 s for the 2.4.1 scan (measured 2026-10-02); the FASTA is read twice, once for the columns' windows and once for the windows of the records added. M bounds the system: on synthetic FASTAs in which 1,000 and 3,000 records carried a diverged copy of a repeat in the configured transcripts, 35 and 25 records joined, 840 and 2,501 were left out, 270 and 272 of 7,262 configured windows were dropped, and each run took under 2 s and 90 MB, where one column per record that shares a window had taken 6.7 s and 0.5 GB, and 56 s and 2.7 GB. On the panel above a run added a median 56.5 records (at most 928), dropped a median 0.4% of the configured transcripts' windows (at most 21%, SDHA), and took a median 46 s and at most 1.3 GB, four runs at once on four cores. A NOTE says how many configured windows were dropped. Salmon's default index keeps one of identical sequences, so a background sequence — the gene's, --background-sequences or a FASTA record — with a configured transcript's sequence is not counted, and one with the sequence of another background sequence is counted once: in GENCODE v50's reference-chromosome FASTA, all 382 chrY transcripts of the 18 protein-coding genes on both chrX and chrY are such records. Where the index keeps the copy's name rather than the configured transcript's, extract cannot count those reads, and the command says how to avoid that. For an index built with Salmon's --keepDuplicates, pass --keep-duplicates. For a decoy-aware index, pass its transcript FASTA, or its gentrome with --decoys decoys.txt: Salmon sets aside only the reads that map better to a decoy than to any transcript, so a decoy competes for no read the transcripts explain as well, while judged against the genome every window inside an exon would lose its uniqueness. A record longer than 1 Mb read without --decoys is named in a NOTE and left out, so every exon window it holds is dropped: a gentrome without --decoys gives the most conservative answer, not a wrong one.

Pin the release. The verdict is a function of the annotation release. Rebuilt with --ensembl-release against six Ensembl releases from 110 (GENCODE 44, July 2023) to 116, a 109-gene survey went from 2,005 transcripts to 4,396, nearly all of it in the last two releases, where GENCODE added transcripts from long-read data through its TAGENE pipeline: protein-coding ones at 115, protein-coding and nonsense-mediated-decay ones at 116. Of the 100 comparisons annotate proposes at release 116, 38 could not be posed at 110 at all, because one of the two classes had no protein-coding transcript there; of the 62 that could, 11 change verdict between the two. NTRK3's is identifiable at every one of those releases before 116 and not_identifiable at 116. The exit status moves too: at --min-log2fc 1.0 it changes across the six for 13 of the 62. annotate records the release it fetched from as ensembl_release, and identifiability prints it in its header and carries it in the --json report, beside fetched_release, the release any sequence was fetched from in that run. Both commands take --ensembl-release N: without it they read the release rest.ensembl.org serves — 116, the last one Ensembl publishes on its REST API; later releases are on the new Ensembl platform only, which has none — and with it they read release N from Ensembl's REST archive, after checking that the server reports N. A config annotated against release 110 is re-run against 110 with identifiability --config config.json --ensembl-release 110, and the command names that flag when the two differ. Archives do not last: none from release 104 or earlier answered on 2026-09-24, and 111's timed out for most of that day. For a verdict that has to outlive them, add --save-inputs run.json, which writes the sequence the run used, the release it came from and the k, window and k-mer convention it was built at; identifiability --config config.json --inputs run.json then repeats the run with no request at all. The file holds the gene, not one grouping of it: a rerun pools its class and background sequence and splits it again by the config in hand, so a class the config has narrowed still has the transcripts it dropped in the background, where a live run puts them, and a transcript moved the other way is used rather than refused. --background-fasta and --decoys are not copied into that file, but their SHA-256 is, and a rerun given neither or another file says so, as does one at another k or window. The --json report names both files by SHA-256 too. A verdict quoted without a release is not a reproducible claim.

The headline figure is min_resolvable_log2fc: the smallest |log2 fold change| a 95% interval excludes zero for at the stated design, under Poisson counting error only.

It bounds spread, not accuracy. The figure is built from the delta-method standard error of the log class ratio, so it describes replicate scatter under uniform coverage and a correctly specified compatibility model. It says nothing about bias when coverage is not uniform. In simulation (Salmon, 49 genes, 30 replicates each; 39 genes estimable with a finite predicted SE in all seven coverage models, each a monotone positional skew), no gene's |bias| reaches its own predicted SE at uniform coverage: 0 of 39. At a 2.3× ratio between first- and last-decile gene-body coverage, a routine mildly degraded sample, 20–24 of 39 exceed it, depending on the direction of the skew. The replicate SD meanwhile stays below the predicted SE in 34–37 of 39, as it did in 35 of 39 at uniform coverage, so the scatter gives no sign of the bias. NTRK3 holds a replicate SD of 0.016–0.025 while its bias runs from 0.002 to 2.82 log2. Replicate agreement does not detect this failure. Measure your own libraries' gene-body coverage (RSeQC geneBody_coverage.py) before reading the figure as an error bar.

The conditioning factor is kept as a diagnostic, not a headline — a geometry proxy, not a standard error — it assumes Var(y) = sigma^2 I, which a quantifier does not satisfy, and it moves by up to two orders of magnitude with the annotation release. Its thresholds are provisional. The three layers answer different questions and can disagree. Structural estimability is not precision, and neither is a promise that a quantifier's optimiser will land on the right answer — an EM returns numbers for a non-identifiable model too. What the estimability layer asserts is narrower and checkable: whether the class contrast is a linear functional of the observable fragment-class expectations.

Full workflow (real data)

# 0) build a Salmon index once -- the commands are under "Index scope" below:
#    - from the GENCODE release that matches `annotate --ensembl-release`
#      (https://www.gencodegenes.org/human/releases.html), and
#    - from reference-chromosome transcripts only
# 1) quantify on an HPC cluster, one output directory per cohort (both example sample maps
#    name their donors ctrl1-ctrl5, and the script skips a donor whose quant.sf exists):
sbatch --export=ALL,SAMPLE_MAP=example/sample_map_GSE228458.csv,OUTDIR=quant/GSE228458 \
    scripts/01_salmon_quant.sbatch                       # -> quant/GSE228458/<donor>/quant.sf
sbatch --export=ALL,SAMPLE_MAP=example/sample_map_GSE137619.csv,OUTDIR=quant/GSE137619 \
    scripts/01_salmon_quant.sbatch
# 2) extract per cohort:
isoform-dominance extract --config config.json --quantdir quant/GSE228458 \
    --samplemap example/sample_map_GSE228458.csv --cohort GSE228458 --out perdonor_GSE228458.csv
isoform-dominance extract --config config.json --quantdir quant/GSE137619 \
    --samplemap example/sample_map_GSE137619.csv --cohort GSE137619 --out perdonor_GSE137619.csv
# 3) stats + figure:
isoform-dominance stats --config config.json --condition control \
    --perdonor GSE228458=perdonor_GSE228458.csv --perdonor GSE137619=perdonor_GSE137619.csv \
    --out results/dominance
# 4) optional contamination control:
isoform-dominance qc --config config.json \
    --markers GSE228458=markers_228.csv --target GSE228458=perdonor_GSE228458.csv --out results/qc

Index scope

From GENCODE release 48, gencode.vX.transcripts.fa.gz holds the transcripts on scaffolds, patches and alternate loci as well as those on the reference chromosomes; releases 44–47 hold the reference chromosomes only. The change is in the release-48 entry of the changelog in GENCODE's FTP _README.TXT; the file description in the same README still calls the file reference-chromosome only. Ensembl's cdna.all.fa.gz holds both too (checked for release 116). A gene with a copy on such a region (SMN1, HLA-A) is in that FASTA twice, under its own name and another gene id. An index built from it lets the quantifier split the gene's reads with the copy — Salmon folds an identical copy into the reference transcript, but splits reads with a copy that differs, and kallisto splits them either way — and extract counts only the configured transcript ids, so the class totals are biased. Build the index from reference-chromosome transcripts only. For GENCODE, keep the transcripts of the reference-chromosome GTF (gencode.vX.annotation.gtf.gz):

gzip -dc gencode.v50.annotation.gtf.gz | awk -F'\t' '$3=="transcript"' \
  | grep -o 'transcript_id "[^"]*"' | cut -d'"' -f2 | sort -u > chr_ids.txt
gzip -dc gencode.v50.transcripts.fa.gz \
  | awk 'NR==FNR {keep[$1]; next} /^>/ {split(substr($0,2), h, "|"); p = (h[1] in keep)} p' chr_ids.txt - \
  > gencode.v50.transcripts.chr.fa
grep -c '^>' gencode.v50.transcripts.chr.fa    # = wc -l < chr_ids.txt  (644,292 for v50)

For Ensembl cdna.all, keep the records whose header region (chromosome:GRCh38:<name>:…) is 1–22, X, Y or MT:

gzip -dc Homo_sapiens.GRCh38.cdna.all.fa.gz \
  | awk '/^>/ {split($3, r, ":"); p = (r[3] ~ /^([1-9]|1[0-9]|2[0-2]|X|Y|MT)$/)} p' \
  > Homo_sapiens.GRCh38.cdna.primary.fa

Then build the index from the filtered FASTA, once, and quantify every donor of a cohort against it (the sbatch script's INDEX):

salmon index -t gencode.v50.transcripts.chr.fa -i salmon_index_v50chr -k 31 -p 12

Without --gencode, quant.sf names keep the whole GENCODE header, which is what lets extract check them for same-name copies; with it, names are cut to the transcript id and extract reads them just the same.

The package checks for such an index where it can. identifiability --background-fasta warns when the FASTA holds a transcript with the configured gene's name under another gene id, unless its sequence is a configured or a background transcript's (of identical sequences the index keeps one, so such a copy splits no read), and so does extract when quant.sf names carry the whole GENCODE header (an index built without Salmon's --gencode). Only an Ensembl header says where a gene lies, so a same-name gene on a reference chromosome — the chrY copy of a pseudoautosomal gene such as CD99 or SHOX, or a distinct gene sharing the name — is left out there, and reported from a GENCODE header as a possible copy. extract also writes <out>.index.json, which records the index each donor was quantified against (from Salmon's aux_info/meta_info.json), and it stops when one cohort mixes indexes, unless --allow-mixed-index is given. It also counts the configured transcripts in each quant.sf: a cohort with none of them is refused, and some missing is a warning. Salmon drops all but the first of identical sequences when it builds an index (the index's duplicate_clusters.tsv lists them), so a config that names a dropped copy finds nothing.

How this relates to existing tools

Differential transcript usage (DTU) is a mature area, and for genome-wide discovery you should use the established tools — this one does not replace them:

• DEXSeq — differential exon usage, from exon-bin counts, with its own counting scripts.
• DRIMSeq, satuRn — genome-wide transcript-level DTU testing. They assume you already have a transcript-by-sample count matrix and defined transcript groups.
• IsoformSwitchAnalyzeR — genome-wide isoform-switch testing with rich functional annotation of the switches (domains, NMD, coding potential) in R/Bioconductor; grouping and import are configured by the analyst.
• Kmerator (doi:10.1093/nargab/lqab058) — builds gene- and transcript-specific k-mer signatures against a reference, the uniqueness question this package's first layer asks, without a verdict on a contrast.
• fishpond / swish — rigorously propagates quantification uncertainty using Salmon inferential replicates.

On identifiability specifically, this package builds on existing work rather than claiming the idea:

• Hiller et al. 2009 (doi:10.1093/bioinformatics/btp544) — criteria under which isoform abundances are uniquely determined by RNA-seq observations.
• Ferrer-Bonsoms et al. 2022 (doi:10.1093/bioinformatics/btab873) — a read- and fragment-length-aware identifiability criterion, applied genome wide.
• terminus (doi:10.1093/bioinformatics/btaa448) — groups transcripts post hoc, from the data, by inferential uncertainty.

The first two address transcript-level identifiability of the full deconvolution; terminus chooses its groups after seeing the data. Neither answers the question a biologist arrives with: is the contrast between two classes I defined on biological grounds estimable?

Two different questions hide under the same word, and keeping them apart is the whole reason this package exists. A contrast is estimable when it lies in the row space of the compatibility system — a yes/no in exact arithmetic. It is measurable when it can actually be recovered at a finite sequencing depth. Transcript-level identifiability settles the first in one direction only, and is silent on the second.

• It is not necessary for estimability. Two transcripts with identical sequence make each abundance unidentifiable while their sum is perfectly estimable. A transcript-level verdict can reject a comparison that is in fact sound.
• It is sufficient for estimability — and that is the problem. If every transcript is identifiable the design matrix has full column rank, so every contrast is estimable. The row-space condition is then satisfied trivially and stops discriminating exactly where you need it to.
• It is not sufficient for measurability. Full rank says nothing about finite depth: the class direction can be so weakly observed that a formally estimable contrast is unrecoverable. identifiability therefore reports the smallest resolvable effect size beside the row-space verdict, and neither on its own.

isoform-dominance targets that question: for one gene, which functional isoform class predominates? The contribution is evaluating the estimability of the user's own class contrast, before quantification — a check none of the tools above performs. Taking a gene name as input is not new (Kmerator does), and is not the claim. Around that sit two conveniences rather than claims: annotate goes from a gene symbol to a reviewed isoform-group proposal (presented for review, not treated as final), and the whole thing is a scriptable Python CLI with a download-free self-test, meant to ship alongside a manuscript.

More documentation: a step-by-step NTRK2 walkthrough, a gallery of further example genes (NTRK2/NTRK3 kinase truncations and the FLT1 soluble-decoy receptor), and an API reference.

Statistical notes

• Donor-level two-sided Wilcoxon signed-rank (scipy.stats.wilcoxon, method="auto"), per cohort. It is exact only with neither a zero difference nor a tie among the absolute differences (and n ≤ 50); otherwise SciPy runs an exhaustive permutation test at n ≤ 13 and the normal approximation above that. Which one produced each p-value is reported (wilcoxon_method) — at n = 14 with one tie the approximation can sit well above the exact figure.
• Small-n floor is computed, not just documented. signed_rank_resolution_floor(n) returns 2^(1-n) — under the sign-permutation null exactly one assignment puts every difference on the same side. At n = 5 that is 0.0625, so no arrangement of five donors reaches 0.05. Ties among the absolute differences do not raise it. Each per-cohort test and the donor-pooled one is reported with its floor and flagged when the floor exceeds 0.05; the Stouffer and stratified rows of the stats table carry none yet.
• Cohorts are combined three ways, all reported: donor-pooled (what v2.1 reported alone), a weighted Stouffer combination of the per-cohort exact tests, and a weighted stratified signed-rank combination. The latter borrows van Elteren's design-free 1/(n+1) stratum weights but is not van Elteren's test, which is a stratified two-sample rank-sum procedure; these data are paired within donor. Stouffer is the default, because at these sample sizes each stratum's exact p-value is trustworthy and the normal approximation behind a combined rank statistic is not. Pooling donors from independent studies ranks one cohort's differences against another's and lets depth or tissue handling drive the result — quote the stratified figures when the cohorts are genuinely independent.
• Effect size = median fold-change with a donor-bootstrap 95% interval (seeded, so it reproduces).

Layout

src/isoform_dominance/   annotate · identifiability · extract · stats · contamination · cli · _selftest
tests/                   pytest (offline; reproduces the reference result + unit tests)
scripts/01_salmon_quant.sbatch
example/                 config + sample maps
.github/workflows/ci.yml docs/  pyproject.toml  CITATION.cff  LICENSE

Citation

Version provenance. Version 2.1.1 was used for the LEPR isoform quantification and aggregation in a leptin-receptor/LRP1 choroid-plexus manuscript (under revision), and is archived at 10.5281/zenodo.20738150. Releasing a new version does not alter that record: Zenodo mints a separate version DOI and leaves the old one in place, and the v2.1.1 tag and release are left untouched by policy — not because they are technically immutable, but because the manuscript under revision cites them. The extract aggregation behaviour those results rest on — transcript-to-group mapping and per-donor TPM summation — is unchanged through 2.5.0, and the bundled self-test still reproduces the same reference numbers. 2.4.0 adds two refusals to extract where 2.3.0 wrote a table: a cohort quantified against more than one Salmon index, and a config none of whose transcripts is in any donor's quant.sf. 2.4.1 counts an index with Salmon's decoys and one without as two, and writes the rows in 2.1.1's order again (2.4.0 sorted them by donor name).

Cite this repository (see CITATION.cff, DOI 10.5281/zenodo.20672051) and Salmon: Patro, R. et al. Nat. Methods 14, 417–419 (2017). https://doi.org/10.1038/nmeth.4197

License

MIT (see LICENSE).