Bio Rna Quantification Alignment Free Quant

Version Compatibility

Reference examples tested with: Salmon 1.10+, kallisto 0.50+, fastp 0.23+, pandas 2.2+

Before using code patterns, verify installed versions match. If versions differ:

• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Alignment-Free Quantification

"Quantify gene expression without aligning to the genome" -> Estimate transcript abundances directly from FASTQ reads by mapping to a transcriptome, then resolving multi-mapping reads (paralogs, shared isoform sequence) with an EM/variational model.

• CLI: salmon quant -i index -l A -1 R1.fq.gz -2 R2.fq.gz -o quant/, kallisto quant -i index -o out R1.fq.gz R2.fq.gz

These tools do not just count reads; they run probabilistic inference. Most fragments are compatible with several transcripts, so the abundance of each transcript is a latent quantity estimated by EM (Salmon offline phase, RSEM) or variational Bayes (Salmon default). The two load-bearing decisions that determine whether the numbers are trustworthy are the index (decoy-aware or not) and the library type. Get either wrong and the run completes silently with biased output.

Decision 1: the decoy-aware index is not optional

A transcriptome-only index has no target for reads that originate from introns, unannotated transcription, or intergenic DNA. Those reads do not vanish; they are force-fit onto whatever transcript shares enough sequence, inflating its count. Adding the genome as a set of decoy sequences fixes this: if a fragment aligns better to a decoy than to any transcript, all of its mappings are discarded rather than misassigned. Always build the decoy-aware index when the genome is available (Srivastava et al. 2020).

# Decoy names = every genome sequence header
grep "^>" genome.fa | cut -d " " -f 1 | sed 's/>//g' > decoys.txt

# gentrome = transcripts FIRST, then genome
cat transcripts.fa genome.fa > gentrome.fa

# Build (k=31 is right for reads >=75 bp; lower to ~23-25 for ~50 bp reads)
salmon index -t gentrome.fa -d decoys.txt -i salmon_index -k 31 -p 8

--validateMappings is deprecated and has no effect: selective alignment has been the default since Salmon 1.0.0. Do not pass it. This is the accuracy difference from pure pseudoalignment (kallisto): pseudoalignment commits a read to its compatible transcript set without scoring base-level mismatches, so it over-assigns intron-, pseudogene-, and error-derived reads, whereas selective alignment computes an actual alignment score around each candidate and drops low-scoring spurious mappings. kallisto has no equivalent decoy index; its closest analog is --d-list genome.fa (a distinguishing-k-mer filter, different mechanism) to partially compensate.

Decision 2: library type drives strandedness

# Salmon: auto-detect, then VERIFY
salmon quant -i salmon_index -l A \
    -1 sample_R1.fastq.gz -2 sample_R2.fastq.gz \
    -o sample_quant --gcBias --seqBias -p 8

# Single-end
salmon quant -i salmon_index -l A -r sample.fastq.gz -o sample_quant -p 8

-l A auto-detects the type and writes the inferred format to lib_format_counts.json. Inspect it: a library with weak strand signal can be miscalled, and the wrong type makes correctly oriented fragments incompatible, collapsing or randomizing abundances. The dominant modern chemistry (dUTP / Illumina TruSeq Stranded / NEBNext Directional) is ISR for Salmon and maps to featureCounts -s 2 (reverse). Map: unstranded IU <-> -s 0; forward ISF/SF <-> -s 1; reverse ISR/SR <-> -s 2. 3'-tag protocols carry their own strandedness (Lexogen QuantSeq FWD is forward, SF/-s 1; QuantSeq REV is reverse), so rely on -l A and lib_format_counts.json rather than assuming reverse.

Bias correction

--gcBias and --seqBias learn sample-specific fragment-GC and random-hexamer-priming biases and reweight the read-to-transcript probabilities. They cost little and protect against the case that produces false positives: when library-prep batch is confounded with the biological condition, an uncorrected GC bias becomes a condition effect. Enable both as a near-default; reserve --posBias for degraded or visibly 3'-biased libraries.

Inferential replicates: only when transcript-level uncertainty matters

Transcripts that share sequence are not individually identifiable, so their point estimates carry inferential (quantification) uncertainty on top of biological variance. This uncertainty cancels when isoforms are summed to the gene, so gene-level DESeq2/edgeR via tximport needs no replicates. It does not cancel at the transcript level: differential transcript expression (DTE) and usage (DTU) require propagating it.

# Generate inferential replicates ONLY for transcript-level downstream testing
salmon quant -i salmon_index -l A --gcBias --seqBias \
    --numGibbsSamples 20 \
    -1 R1.fq.gz -2 R2.fq.gz -o sample_quant -p 8

# kallisto bootstraps (for sleuth)
kallisto quant -i kallisto_index -o sample_quant -b 100 R1.fq.gz R2.fq.gz

Use --numGibbsSamples 20 (or --numBootstraps 30) for Salmon; ~100 bootstraps for kallisto. Downstream consumers: swish (alternative-splicing/isoform-switching), sleuth (expression-matrix/counts-ingest), edgeR catchSalmon (differential-expression/edger-basics). Do not pay this cost for gene-level work.

kallisto Workflow

kallisto index -i kallisto_index transcripts.fa

# Paired-end learns the fragment-length distribution from mate distances
kallisto quant -i kallisto_index -o sample_quant R1.fastq.gz R2.fastq.gz

# Single-end CANNOT observe fragment length -> must supply mean (-l) and sd (-s),
# which set effective lengths and therefore TPM; wrong values bias every TPM
kallisto quant -i kallisto_index -o sample_quant --single -l 200 -s 20 sample.fastq.gz