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End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression. Use when analyzing miRNA, piRNA, or other small RNA sequencing data.
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SOC 職業分類に基づく
Map translation initiation sites, including non-AUG and alternative starts, from initiation-drug ribosome profiling (TI-seq). Use when locating start codons, detecting near-cognate or upstream initiation, or analyzing harringtonine, lactimidomycin (GTI-seq/QTI-seq), or retapamulin (Ribo-RET) data.
Detect and quantify translated ORFs from Ribo-seq using 3-nucleotide periodicity, including uORFs, internal ORFs, dORFs, and novel ORFs. Use when finding actively translated regions beyond annotated CDS, classifying ORFs by the 2022 community standard, quantifying ORF-level translation, or choosing between periodicity-based callers.
Preprocess ribosome profiling reads with UMI handling, adapter trimming, contaminant/rRNA depletion, and footprint-aware alignment. Use when preparing Ribo-seq FASTQ for periodicity QC, ORF detection, translation efficiency, or stalling analysis, or when deciding how to deduplicate, which aligner to use, or how to size-select ribosome-protected fragments.
Validate Ribo-seq library quality by measuring 3-nucleotide periodicity and calibrating read-length-specific P-site offsets. Use when checking whether footprints capture genuine translation, determining P-site offsets for downstream ORF/TE/stalling analysis, or deciding which read lengths to keep.
Detect ribosome pausing and stalling at codon resolution from Ribo-seq, using local-relative occupancy metrics and A-site assignment. Use when studying elongation dynamics, codon dwell times, pause motifs, or ribosome collisions, and when judging whether a pause is real biology or a cycloheximide artifact.
Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions. Use when separating translational from transcriptional regulation, distinguishing genuine translational control from buffering, or choosing between riborex, Xtail, anota2seq, and DESeq2 interaction models.
| 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 |
Reference examples tested with: DESeq2 1.42+, cutadapt 4.4+
Before using code patterns, verify installed versions match. If versions differ:
packageVersion('<pkg>') then ?function_name to verify parameters<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Analyze my small RNA-seq data from FASTQ to differential miRNAs" -> Orchestrate adapter trimming (cutadapt), miRNA quantification (miRDeep2/miRge3), novel miRNA discovery, differential expression (DESeq2), and target prediction (miRanda).
FASTQ -> cutadapt trim -> miRDeep2 -> Quantification -> DESeq2 -> Target prediction
# Adapter trimming and size selection. Small-RNA inserts (~22 nt) are shorter than the read,
# so the 3' adapter is on EVERY real read -> --discard-untrimmed drops adapter-dimer / no-insert
# junk (the inverse of genomic DNA, where no-adapter reads are the good ones). See read-qc/adapter-trimming.
cutadapt -a TGGAATTCTCGGGTGCCAAGG \
--minimum-length 18 --maximum-length 30 --discard-untrimmed \
-o trimmed.fastq.gz reads.fastq.gz
# 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
library(DESeq2)
counts <- read.csv('mirna_counts.csv', row.names = 1)
dds <- DESeqDataSetFromMatrix(counts, colData, ~condition)
dds <- DESeq(dds)
results <- results(dds)
# miRanda for target prediction
miranda mature_mirnas.fa target_3utrs.fa -out targets.txt