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salmon-rna-quantification

Quantify RNA-seq transcripts and genes via quasi-mapping with bias correction.

298|27|Updated Feb 18, 2026
Search Tags:#bioinformatics#differential expression#rna-seq#quantification#transcriptomics#salmon
One-click install
npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill salmon-rna-quantification
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: salmon-rna-quantification
Source: https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/genomics-bioinformatics/salmon-rna-quantification
Command: npx skills add https://github.com/jaechang-hits/SciAgent-Skills --skill salmon-rna-quantification

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires salmon, pandas, pydeseq2, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill automates the process of quantifying gene and transcript expression levels from RNA-sequencing data, enabling downstream differential expression analysis.

Core Features & Use Cases

  • Ultra-fast Quantification: Utilizes quasi-mapping for rapid transcript abundance estimation, significantly faster than alignment-based methods.
  • Bias Correction: Automatically corrects for sequence-specific and GC-content biases for more accurate quantification.
  • Downstream Integration: Outputs compatible with tools like tximeta and pydeseq2 for differential expression analysis.
  • Use Case: Analyze RNA-seq data from a drug treatment experiment to identify genes that are significantly up- or down-regulated.

Quick Start

Use the salmon-rna-quantification skill to build an index from the transcriptome FASTA file and then quantify paired-end reads from sample_R1.fastq.gz and sample_R2.fastq.gz.

Frequently Asked Questions about salmon-rna-quantification

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I quantify RNA-seq transcripts without a genome-aligned BAM file?▼

RNA-seq transcript quantification without a BAM file is achieved through quasi-mapping, which builds a k-mer index directly from a transcriptome FASTA to estimate transcript abundance rapidly.

What is the best way to prepare RNA-seq counts for differential expression analysis?▼

Preparing RNA-seq counts for differential expression involves generating TPM and count tables compatible with downstream tools like pydeseq2 and tximeta, which this quasi-mapping approach provides.

Does salmon quantification correct for sequence-specific and GC-content biases?▼

Yes, salmon quantification automatically corrects for sequence-specific and GC-content biases during transcript abundance estimation to ensure more accurate RNA-seq quantification results.

Can I use pydeseq2 for differential expression directly after RNA-seq quantification?▼

Yes, you can use pydeseq2 directly after RNA-seq quantification because the quasi-mapping process outputs count tables specifically formatted for seamless integration with differential expression tools.

Why choose quasi-mapping over alignment-based methods for large-scale RNA-seq studies?▼

Quasi-mapping is chosen over alignment-based methods for large-scale RNA-seq studies when processing speed is critical, as it bypasses genome alignment to deliver ultra-fast transcript quantification.

What input files do I need to build a transcriptome index for RNA-seq quantification?▼

To build a transcriptome index for RNA-seq quantification, you need a transcriptome FASTA file and paired-end read files, typically formatted as sample_R1.fastq.gz and sample_R2.fastq.gz.

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