Bioinformatics Analysis of Small RNA Sequencing

Posted by beauty33 on November 22nd, 2018

Small RNAs are important functional molecules in organisms, which have three main categories: microRNA (miRNA), small interfering RNA (siRNA), and piwi-interacting RNA (piRNA). They are less than 200 nt in length and are often not translated into proteins. Small RNA generally accomplishes RNA interference (RNAi) by forming the core of RNA-protein complex (RNA-induced silencing complex, RISC). Small RNA sequencing is a powerful method for profiling small RNA species and functional genomic analysis. Here, we present the guidelines for bioinformatics analysis of small RNA sequencing.

Figure 1. Workflow of bioinformatics analysis of small RNA sequencing.

Table 1. Crucial steps and tools for small RNA sequencing data analysis (Buschmann et al. 2016).

Step To consider Recommended tools or algorithms

Data preprocessing Trimming adapters

Removing short reads Btrim, FASTX-Toolkit

Quality control Library size and read distribution across samples

Per base / sequence Phred score

Read length distribution

Assess degradation

Check for over-represented sequences Btrim, FASTX-Toolkit, FaQCs

Read alignment Reference database or genome

Annotation

Mismatch rate

Handling of multi-reads Bowtie, BWA, HTSEQ, SAMtools, SOAP2

Normalization Library sizes and sequencing depth

Batch effects

Read distribution

Replication level

Data distribution

Replication level DESeq2, EdgeR, svaseq

*DEG analysis Data distribution

Replication level

False discovery rate DESsq2, EdgeR, SAMSeq, voom limma

Target prediction In silico prediction or experimental validation

Canonical and non-canonical target regulation miRanda, miRTarBase, TarBase

Biomarker identification Sensitivity Specificity Classification rate DESeq2, Simca-Q, Numerous R packages: base, pcaMethods, Mixomics

* DGE, differential gene expression.

Raw data pre-processing and quality control

To facilitate correct alignments, raw data must be trimmed to accommodate adapter artifacts and sequences with inadequate lengths. Reads less than 16-18 nt representing degraded RNA or adapter dimers need to be removed. Tools such as Btrim, FASTX-Toolkit, FaQCs, and cutadapt are used for this purpose. However, this is not enough for high quality datasets and accurate alignments. There are algorithms such as Quake, ALLPATHSLG, which is dedicated to correcting unreliable base callings by superimposing the most frequent and similar patterns on them. Reads of low quality also need to be removed partially or completely based on their Phred scores. Popular quality trimming algorithms include Cutadapt, Btrim, FASTX Toolkit, FaQCs, and SolexaQA.

After data pre-processing and quality control, the remaining reads should be rid of low quality sequences (quality score < 20) and adapter artifacts, and read lengths should exhibit a distinct peak based on small RNA species of interest (e.g. 21-23 nt for miRNA and 30-32 nt for piRNA).

Small RNA read alignment

Read alignment strategies involve mapping to a reference genome or specific small RNA databases such as mirBase and Rfam. In addition to comparison with specific sequences, homologous datasets from well-studied organisms are also useful due to strong conservation of seed sequences between most small RNA species in different species.

Table 2. The common tools for small RNA sequencing.

Small RNA read alignment tools Evaluations or recommendations

algorithm BLAST aligner, suffix / prefix Suffix / prefix based on Burrows-Wheeler Transform is fast and efficient in mapping

software Bowtie, BWE, SOAP2 An evaluation of mapping sensitivity and specificity is strongly recommended.

Researches with large datasets or limited time could try BarraCUDA, SOAP3-dp, or MICA.

Normalization

Systematic variations need to be addressed prior to differential expression analysis. This process is called normalization, which deals with undesired differences between libraries in sequencing depth, GC content, and batch effects. Median normalizing of expression ratios from geometric means has been found to work favorably with diverse kinds of datasets. Zyprich-Walczak et al. (2015) proposed a workflow to determine the most suitable normalization method for a specific dataset.