Agent skill

bio-rna-quantification-alignment-free-quant

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Install this agent skill to your Project

npx add-skill https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-rna-quantification-alignment-free-quant

SKILL.md

name: bio-rna-quantification-alignment-free-quant description: Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto. tool_type: cli primary_tool: salmon measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:

  • read_file
  • run_shell_command

Alignment-Free Quantification

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

Salmon Workflow

Build Index

bash
# 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

bash
# 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

bash
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

bash
kallisto index -i kallisto_index transcripts.fa

Quantify Samples

bash
# 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

bash
# 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

bash
# 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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