End-to-end small RNA-seq analysis from FASTQ to differential miRNA expression and target prediction. Use when analyzing miRNA, isomiR, tRF, or piRNA sequencing data through preprocessing, quantification, discovery, DE, and targets.
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Reference examples tested with: cutadapt 4.4+, miRDeep2 2.0.1.3+, miRge3.0 0.1.4+, DESeq2 1.42+
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), quantification (miRge3) or discovery (miRDeep2), differential expression (DESeq2), and target prediction (miRanda), with the small-RNA-specific decisions surfaced at each step.
FASTQ -> cutadapt trim -> miRge3 (known) / miRDeep2 (novel) -> DESeq2 -> target prediction
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+-> tRF / piRNA profiling (MINTmap / unitas)
# The 3' adapter is on EVERY real read (insert ~22 nt << read length), so --discard-untrimmed
# drops adapter-dimer / no-insert junk - the inverse of genomic DNA. For NEXTflex 4N libraries,
# strip the random bases AFTER adapter removal (cutadapt -u 4 -u -4); for QIAseq UMIs, extract
# and dedup with umi_tools. Never position-dedup a non-UMI small-RNA library.
cutadapt -a TGGAATTCTCGGGTGCCAAGG \
--minimum-length 18 --maximum-length 30 --discard-untrimmed \
-o trimmed.fastq.gz reads.fastq.gz
# Known-miRNA + isomiR quantification (the common case) - fast, curated libraries:
miRge3.0 annotate -s trimmed.fastq.gz -lib /path/to/miRge3_Lib -on human -db miRBase \
-a illumina -gff -ai -cpu 8 -o mirge_out
# OR novel discovery (high false-positive; needs a genome and bowtie 1):
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
miRDeep2.pl reads_collapsed.fa genome.fa reads_collapsed_vs_genome.arf \
mature_ref.fa none hairpin_ref.fa -t Human
# Choose the miRDeep2 score cutoff from survey.pl signal-to-noise; it is not a fixed rule.
library(DESeq2)
# Feed RAW counts (miR.Counts.csv), NOT RPM. A few miRNAs can dominate the library, so
# inspect sizeFactors for compositional distortion before trusting the calls.
counts <- read.csv('mirge_out/miR.Counts.csv', row.names = 1)
dds <- DESeqDataSetFromMatrix(round(counts), colData, ~condition)
dds <- dds[rowSums(counts(dds)) >= 10, ] # lower prefilter than mRNA
dds <- DESeq(dds)
print(sizeFactors(dds))
res <- lfcShrink(dds, coef = 'condition_treated_vs_control', type = 'apeglm')
# Prediction is a hypothesis: intersect with anti-correlated mRNA DE before trusting targets.
miranda mature_mirnas.fa target_3utrs.fa -sc 140 -en -20 -strict -out targets.txt
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