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
Check for over-represented sequences Btrim, FASTX-Toolkit, FaQCs
Read alignment Reference database or genome
Handling of multi-reads Bowtie, BWA, HTSEQ, SAMtools, SOAP2
Normalization Library sizes and sequencing depth
Replication level DESeq2, EdgeR, svaseq
*DEG analysis Data distribution
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.
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.
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About the Authorbeauty33
Joined: July 10th, 2017
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