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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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
まだレビューはありません。使ってみた感想をお寄せください。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。
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日本語の概要は準備中です。原文の説明を表示しています。