AgentSkillsCN

bio-workflows-smrna-pipeline

从 FASTQ 到差异 miRNA 表达的全流程小 RNA-seq 分析工作流。适用于分析 miRNA、piRNA 或其他小 RNA 测序数据时使用。

SKILL.md
--- frontmatter
name: bio-workflows-smrna-pipeline
description: End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data.
tool_type: mixed
primary_tool: miRDeep2

Small RNA-seq Pipeline

Pipeline Overview

code
FASTQ → cutadapt trim → miRDeep2 → Quantification → DESeq2 → Target prediction

Step 1: Preprocessing

bash
# Adapter trimming and size selection
cutadapt -a TGGAATTCTCGGGTGCCAAGG \
    --minimum-length 18 --maximum-length 30 \
    -o trimmed.fastq.gz reads.fastq.gz

Step 2: miRDeep2 Analysis

bash
# Align to genome
mapper.pl trimmed.fastq.gz -e -h -i -j -l 18 \
    -m -p genome_index -s reads_collapsed.fa \
    -t reads_collapsed_vs_genome.arf

# miRNA quantification and novel prediction
miRDeep2.pl reads_collapsed.fa genome.fa \
    reads_collapsed_vs_genome.arf \
    mature_ref.fa none hairpin_ref.fa

Step 3: Differential Expression

r
library(DESeq2)
counts <- read.csv('mirna_counts.csv', row.names = 1)
dds <- DESeqDataSetFromMatrix(counts, colData, ~condition)
dds <- DESeq(dds)
results <- results(dds)

Step 4: Target Prediction

bash
# miRanda for target prediction
miranda mature_mirnas.fa target_3utrs.fa -out targets.txt

QC Checkpoints

  1. •After trimming: Size distribution should peak at 21-23nt
  2. •After alignment: >70% mapping rate expected
  3. •After DE: Check volcano plot and PCA

Related Skills

  • •small-rna-seq/mirdeep2-analysis - Detailed miRDeep2
  • •small-rna-seq/differential-mirna - DE analysis
  • •small-rna-seq/target-prediction - Target analysis