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How can I analyze my Small RNA sequencing data?

Depending on the focus of the experiment there are different ways to analyze small RNA-Seq data.
An example workflow for the analysis of miRNAs in small RNA-Seq library sequencing data is provided. This workflow was adapted from:
Tam et al., (2015) Optimization of miRNA-seq data preprocessing. Briefings in Bioinformatics 16(6).
This paper also provides an additional comparison of programs for read mapping (alignment), normalization methods, and differential expression analysis.

The general workflow involves trimming of adapters, size filtering of trimmed reads, read mapping:

1. Trimming, adapter-removal, and size filtering (BBDuk,
1.1. Use to remove the adapter sequence from the 3' end of the reads and trim homopolymers.
1.2. Split up the reads into short read and long read fractions.
1.3. Short reads read into the adapter sequence AND are shorter than 31 bp and are processed for miRNA alignment.
1.4. Long reads are kept separate for alignment to the reference genome.

2. Mapping* (BWA,, BBMap,
2.1. Short read fraction
2.2. Filter out reads <= 14 bp -> outputs: "filtered reads: too short" statistic
2.3. Map the remaining 15 – 31 bp reads to the ribosomal RNA sequences using BWA. -> outputs: "mapped reads" and "unmapped reads", mapping statistics
2.4. Use the unmapped reads from the BWA alignment and map these against the mirBase annotation (; mature miRNAs, species-specific) -> output "mapped reads", read count statistics, and miRNA-count table.
2.5. Long read fraction
2.6. Map long reads against the reference genome using STAR ( ) -> output: "mapped reads", read count statistics, gene_biotype statistics (including rRNA sequences), and gene-read table.

Alternative programs for read mapping can also be used. A comparison of some alignment programs for small RNA-Seq data can be found in Tam et al., 2015.

Example: Small RNA analysis pipeline built using modified Nextflow workflow tool: .

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