The Challenge and Workflow of Small RNA Sequencing

What is small RNA sequencing?

Small RNA sequencing represents an increasingly popular approach to address the biological issues of miRNAs and other small non-coding RNAs (sncRNAs), such as piwi-interacting RNAs (piRNAs), small interfering RNAs (siRNAs), transcription initiation RNA (tiRNAs). MiRNAs, piRNAs, and siRNA are the three most focused sncRNAs and are widely studied by sequencing since they have been reported to play a vital role in post-translational regulation of gene expression. Small RNA sequencing has become a good standard for both small RNA discovery and small RNA profiling since they can sequence the entire complement of small RNA with high throughput and high sensitivity. Compared with microarrays and qPCR, small RNA sequencing doesn’t require a priori knowledge of sequence. In addition to the acquirement of the complete range of miRNA and small RNA species, small RNA sequencing also helps understand how post-transcriptional regulation affect phenotype and identify novel biomarkers. It has been widely applied in cancer and complex disease research.

• The challenge: small RNA ligation bias

Researchers have gradually found that the sequencing data containing differential expression of small RNAs is sometimes inconsistent with microarray, qPCR, and Northern blot results. This has been primarily attributed to the RNA-ligase-dependent bias for particular adapter sequences introduced during small RNA library construction. Studies have suggested that randomizing the adaptor sequences close to the ligation junction can reduce ligation bias and optimize sequencing results. Bioo Scientific’s NEXTflex Small RNA-seq kit is currently the only kit commercially available that reduces ligation associated bias. This kit utilizes adapters with 4nt random ends which provides a more even coverage of individual small RNA species. A work by Baran-Gale et al., (2015) has suggested that the NEXTflex protocol is the least biased kit for small RNA sequencing.

Table 1. The commercially available kits for small RNA library construction.

The workflow of small RNA sequencing

The workflow of small RNA sequencing generally includes total RNA isolation, small RNA library construction, deep sequencing, and bioinformatics analysis.

• Small RNA library construction

The 5’ phosphate and 3’ hydroxyl groups on the small RNA allow ligases to selectively target and capture these small RNA species. For small RNA sequencing, library construction typically starts with the ligated of pre-adenylated DNA adaptor to the 3’-end of the sRNAs by using a truncated version of T4 RNA ligase 2, following by the ligation of an RNA adaptor to their 5’-end using T4 RNA ligase 1. After this ligation, the products can be easily reversed transcribed into cDNA and amplified by PCR before high-throughput sequencing. There are many commercially available kits that generate small RNA libraries directly from total RNA sample (Table 1). The products are suitable for Illumina sequencing. The general principles of small RNA library construction are outlined in Figure 1, but different kits may have some different protocols. The process of gel purification is dedicated to quality control and size selection. According to your research interests, the appropriate size of sequences is recovered by bead-based size selection or PAGE purification for deep sequencing. 18-40 bp insert cDNA library is a typical choice which covers miRNA, siRNA, and piRNA. Please note that sequencing miRNA and mRNA require two separate library protocols, even with the same total RNA sample. After the quality control of small RNA libraries, high-throughput sequencing is then performed. Illumina platforms are the most popular instruments for small RNA sequencing, such as MiSeq and HiSeq.

Figure 1. The principles of small RNA library construction.

• Bioinformatics analysis

Small RNA sequencing can be used for small RNA clustering, novel small RNA discovery, miRNA target prediction, differential expression of small RNA, evolutionary analysis, and functional analysis. But prior to these, raw sequencing data need to be preprocessed and normalized. The data preprocessing involves removal of adapter and barcode, size selection, removal of complex reads, and the generation of unique reads. Normalization is the process to make expression levels comparable across libraries. Rfam and miRBase are two common databases for small RNA analysis. Rfam is an open-access database providing information about tRNA, rRNA, and snoRNA, etc. miRBase contains sequences and annotations of all known miRNAs across species.

Figure 2. The bioinformatics analysis workflow of small RNA sequencing data (Buschmann et al. 2016).

For more detailed bioinformatics pipeline for small RNA sequencing, please refer to this page. In addition to small RNA sequencing, we further provide other transcriptomic sequencing services, including RNA-seq, bacterial RNA sequencing, lncRNA sequencing, circRNA sequencing and degradome sequencing.

References:

1. Baran-Gale J, Kurtz C L, Erdos M R, et al. Addressing Bias in Small RNA Library Preparation for Sequencing: A New Protocol Recovers MicroRNAs that Evade Capture by Current Methods. Frontiers in Genetics, 2015, 6(e73240).
2. Buschmann D, Haberberger A, Kirchner B, et al. Toward reliable biomarker signatures in the age of liquid biopsies-how to standardize the small RNA-Seq workflow. Nucleic acids research, 2016, 44(13): 5995-6018.
3. Fuchs R T, Sun Z, Zhuang F, et al. Bias in ligation-based small RNA sequencing library construction is determined by adaptor and RNA structure. Plos One, 2015, 10(5):e0126049.