Alignment-Free Quantification

Quantify transcript abundance directly from FASTQ reads using pseudo-alignment (kallisto) or selective alignment (Salmon).

Salmon Workflow

Build Index

Download transcriptome FASTA

Ensembl: Homo_sapiens.GRCh38.cdna.all.fa.gz

Basic index (fast, less accurate)

salmon index -t transcripts.fa -i salmon_index

Decoy-aware index (recommended for accuracy)

First, create decoys from genome

grep "^>" genome.fa | cut -d " " -f 1 | sed 's/>//g' > decoys.txt cat transcripts.fa genome.fa > gentrome.fa salmon index -t gentrome.fa -d decoys.txt -i salmon_index -p 8

Quantify Samples

Paired-end reads

salmon quant -i salmon_index -l A
-1 sample_R1.fastq.gz -2 sample_R2.fastq.gz
-o sample_quant -p 8

Single-end reads

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

Key flags:

• -l A

• Automatically detect library type

• -p

• Number of threads

• --validateMappings

• More accurate (default in recent versions)

• --gcBias

• Correct for GC bias

• --seqBias

• Correct for sequence-specific bias

Library Types

Code Description

A

Automatic detection (recommended)

ISR

Inward, stranded, read 1 from reverse

ISF

Inward, stranded, read 1 from forward

IU

Inward, unstranded

Batch Processing

for sample in sample1 sample2 sample3; do salmon quant -i salmon_index -l A
-1 ${sample}_R1.fastq.gz -2 ${sample}_R2.fastq.gz
-o ${sample}_quant -p 8 done

Output Files

sample_quant/ ├── quant.sf # Main quantification file ├── aux_info/ # Auxiliary information ├── cmd_info.json # Command used ├── lib_format_counts.json # Library format detection └── logs/ # Log files

quant.sf format:

Name Length EffectiveLength TPM NumReads ENST00000456328.2 1657 1477.000 0.000000 0.000 ENST00000450305.2 632 452.000 12.345678 156.789

kallisto Workflow

Build Index

kallisto index -i kallisto_index transcripts.fa

Quantify Samples

Paired-end

kallisto quant -i kallisto_index -o sample_quant
sample_R1.fastq.gz sample_R2.fastq.gz

Single-end (must specify fragment length)

kallisto quant -i kallisto_index -o sample_quant
--single -l 200 -s 20 sample.fastq.gz

With bootstraps (for sleuth)

kallisto quant -i kallisto_index -o sample_quant -b 100
sample_R1.fastq.gz sample_R2.fastq.gz

Key flags:

• -b

• Number of bootstrap samples

• -t

• Number of threads

• --single

• Single-end mode

• -l

• Estimated fragment length (single-end)

• -s

• Fragment length standard deviation

Output Files

sample_quant/ ├── abundance.tsv # Main quantification (text) ├── abundance.h5 # HDF5 format (for sleuth) └── run_info.json # Run information

abundance.tsv format:

target_id length eff_length est_counts tpm ENST00000456328.2 1657 1477.00 0.00 0.000000 ENST00000450305.2 632 452.00 156.79 12.345678

Salmon vs kallisto

Feature Salmon kallisto

Speed Fast Fastest

Accuracy Higher Good

GC bias correction Yes No

Decoy sequences Yes No

Memory usage Moderate Low

Recommendation: Use Salmon for production, kallisto for quick exploratory analysis.

Combining Results

Salmon: use tximport in R

kallisto: use tximport or sleuth

Quick Python combination

python << 'EOF' import pandas as pd from pathlib import Path

samples = ['sample1', 'sample2', 'sample3'] tpm_data = {} counts_data = {}

for sample in samples: quant_file = Path(f'{sample}_quant/quant.sf') # Salmon # quant_file = Path(f'{sample}_quant/abundance.tsv') # kallisto df = pd.read_csv(quant_file, sep='\t', index_col=0) tpm_data[sample] = df['TPM'] counts_data[sample] = df['NumReads'] # or est_counts for kallisto

tpm_matrix = pd.DataFrame(tpm_data) counts_matrix = pd.DataFrame(counts_data) tpm_matrix.to_csv('tpm_matrix.csv') counts_matrix.to_csv('counts_matrix.csv') EOF

Quality Checks

Check mapping rate from Salmon logs

grep "Mapping rate" sample_quant/logs/salmon_quant.log

Check library type detection

cat sample_quant/lib_format_counts.json

Good metrics:

• Mapping rate > 70%

• Consistent library type across samples

Common Issues

Low mapping rate:

• Wrong transcriptome version

• Contamination in samples

• Wrong library type

Inconsistent library types:

• Mixed library preparations

• Sample swap

Related Skills

• read-qc/fastp-workflow - Upstream preprocessing

• rna-quantification/tximport-workflow - Import results to R

• rna-quantification/count-matrix-qc - QC of quantification

• differential-expression/deseq2-basics - Downstream analysis