Agent skill
bio-rna-quantification-alignment-free-quant
Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/alignment-free-quant
SKILL.md
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
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