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High-throughput RNA sequencing (RNA-seq) is a standard technique for transcript discovery and differential gene expression analysis in life science laboratories (Stark 2019). This powerful technique is now within the reach of most scientists thanks to innovations in next generation sequencing (NGS) technologies which have dramatically lowered the cost of sequencing. Data analysis is the last step in an RNA-seq experiment. Here, the right choice of software toolkit is critical for performing the necessary quality checks, addressing biases, and ultimately answering the questions posed by the study. This article will provide a quick overview of basic data analysis strategies for coding and non-coding RNAs, as well as important considerations during the experiment planning phase (Babarinde 2019; Conesa 2016).
The basic steps of an RNA-seq experiment involve RNA extraction, RNA fragmentation, cDNA generation, library amplification, and sequencing on an NGS platform to get strings of continuous sequence data in “reads”. The most common approach is short-read sequencing (read lengths ≤ 300 bp; RNA-seqlopedia). Careful consideration is necessary at each of these steps to address biases and other factors that can affect the reliability of data and abundance estimates.
One important question is whether a sequenced genome is available for the organism of interest. With a well-annotated genome like the human genome, reference transcript sequences are available for RNA-seq analysis. Otherwise, the quantification strategy involves de novo assembly of transcripts followed by mapping onto the transcriptome.
RNA-Seq Library Preparation
The preparation of a sequencing library from RNA is similar to the workflow for genomic DNA samples (see NGS Overview) but requires reverse transcription of the RNA into cDNA. Additional steps are needed to enrich, purify, and amplify the library prior to sequencing. Care must be taken to capture all types and sizes of RNA species to preserve the diversity and quality of RNA libraries, especially with limited or degraded RNA samples.
It is critical that the RNA sample being sequenced is enriched for the RNA type of interest, such as messenger RNA (mRNA) or micro RNA (miRNA). Without enrichment, the majority of sequencing reads will correspond to ribosomal RNA (rRNA), which makes up over 90% of the cell’s RNA (Lodish 2000). Without enrichment, the sequencing read would be biased toward the ribosomal RNA. The two methods used to achieve this in eukaryotic cells are rRNA removal and/or poly(A) selection of mRNA using oligo (dT) primers, which requires high-quality, high-abundance mRNA. Short RNAs like miRNA can be isolated by gel electrophoresis.
The RNA-Seq library prep workflow can be streamlined by performing ribosomal depletion using the SEQuoia RiboDepletion Kit, featuring a novel post-library preparation depletion technology to effectively eliminate ribosomal and mitochondrial RNA fragments from an RNA-Seq library while retaining rare transcripts commonly lost during pre-library rRNA depletion.Learn more about SEQuoia RiboDepletion Kit »
The basic RNA-seq protocol is non-stranded; it does not distinguish between the first and second cDNA strands during cDNA generation. As a result, the link to the original mRNA is lost prior to adapter ligation and subsequent sequencing. Losing this source information can complicate data analysis, especially when studying overlapping transcripts. Stranded (also called directional) RNA-seq first accomplished this by using a modified dUTP nucleotides in place of dTTPs for second-strand cDNA synthesis (Parkhomchuk 2009). This allows for the second strand to be removed by digestion via uracil-N-glycosylase prior to PCR amplification.
Some commercial kits for library generation, like SEQuoia Complete Stranded RNA Library Prep Kit, preserve strandedness without using modified dUTP. The engineered enzyme used in the SEQuoia kit couples cDNA synthesis with adaptor addition at both ends in one continuous reaction. The resulting library is over 99% stranded and captures both long and short RNA types.Learn more about the SEQuoia RNA Library Prep Kit »
RNA-Seq Library Quantitation
Precise quantification of all next-generation sequencing (NGS) libraries is critical for the efficient use of NGS platforms. NGS library quantification provides information about library quality, such as adapter dimers, and indicates library insert size, and enables more efficient and consistent loading of libraries for sequencing runs as well as balancing of pooled library samples.
Bio-Rad's digital PCR platform and library quantification kits can be easily incorporated into the NGS library preparation workflow to precisely quantify and balance sequencing libraries on the Illumina or Ion Torrent Platforms. The ddPCR library quantification assay generates data plots that are rich with qualitative library information, a feature not available with other current methodologies.Learn more about Digital PCR Library Quantification Kits »
Single-Ended or Paired-End Reading
Sequencing can be single-ended (SE) or paired-end (PE). SE reads sequence fragments only from one direction, which may suffice for applications involving well-annotated genomes. The more preferred method uses PE reads, where fragments are sequenced from both directions. This approach is more costly, but provides the best coverage and is ideal for transcript discovery/quantitation, and identifying splicing junctions (Katz 2010). Long-read sequencing technologies can be used with PE reads to improve the specificity of mapping coding and long non-coding RNAs (Łabaj 2011).
Depth and Replicates
The optimal number of reads covering a specific region, or sequencing “depth”, depends on the purpose of the study. In general, deeper sequencing provides better quantification, especially for low-abundance transcripts (Mortazavi 2008). However, the extra coverage in deep sequencing may also amplify noise or off-target reads (Tarazona 2011). The number of experimental replicates in an RNA-seq experiment depend on the inherent variability in the sequencing methodology or the biological system being used. In a gene expression profiling study it is critical that any measured differences in abundance are statistically significant.
RNA Sequence Analysis
Depending on the sequencing technology or application, there are a variety of strategies for processing and analyzing the raw sequencing data. Regardless, the process involves quality control, read alignment (mapping) with or without a reference genome, and other steps for transcript identification, transcript counting, and functional annotation.
In general, there are three steps to the bioinformatics analysis: primary, secondary, and tertiary analysis. Primary analysis is performed on the sequencer itself. For example, Illumina sequencing technology uses cluster generation and sequencing by synthesis chemistry to sequence millions or billions of clusters on a single flow cell, depending on the specific sequencing instrument. During sequencing itself, for each cluster, base calls are made and stored for every cycle of sequencing by software on the instrument. The base call data, which includes the confidence or quality of each base are stored in the form of individual base call (or BCL) files. When the sequencing is completed, the base calls in the BCL files are automatically converted into sequence data. This process is called BCL to FASTQ conversion.
Secondary analysis takes these FASTQ files to either perform an alignment (using a reference genome) or a de novo assembly (without a reference genome). For model organisms, where reference genomes are available, researcher can map individual reads from the FASTQ files to a reference. There are variety of sequence alignment tools available online, including public-domain software such as EMBL-EBI Clustal.
There are two paths for analyzing RNA-seq reads when a reference sequence is available: gene-level or transcript-level mapping. In either case, the reads are mapped to the reference sequence to identify and quantitate the expressed genes. This approach is not used for discovering new transcripts. Reads may also map to one position on the reference sequence, or, in the case of repeating gene sequences, to multiple positions (multiread). Multimapping is more common with a fully annotated reference transcriptome because a read will map to all the annotated isoforms of a gene. Conversely, the same read will map to one loci on the corresponding reference genome. Gene-level mapping is the easiest form of analysis and is suited to straightforward quantification studies of coding transcripts with one dominant isoform.
De novo construction is performed when the reference genome is incomplete or one does not exist. In this scenario, an assembly is constructed: short reads are assembled into larger contigs based on areas of sequence overlap, from which a full-length reference transcript is created for mapping. In order to generate adequate coverage for this purpose, PE strand-specific and long-read RNA-seq is preferred to short-read technologies. The software tools for this application include SOAPdenovo-Trans (Xie 2014), Oases (Schulz 2012), Trans-ABySS (Grabherr 2011) or Trinity (Haas 2013). While do novo assembly allows gene analysis from any system, it is less amenable to low-abundance transcripts or genes with complex splicing patterns (Au 2013; Steijger 2013).
Assembly of millions of short sequencing reads into contigs is computationally difficult and requires addressing problems such as sequencing errors, coverage gaps, and the presence of splicing variants. One of two general approaches or algorithms are employed for assembly: overlap graphs and de Bruijn graphs. Early assemblers used pairwise overlaps between reads to extend contigs. These contigs are connected to create the reference. However, this approach is not practical when dealing with hundreds of millions of reads. Therefore, de novo transcriptome asssemblers use De Bruijn graphs, which are constructed and extended based upon k-mers, using a shorter subset of a read, where k is the length of the subsequence. A set of k-mers in a species’ genome, in a genomic region, or in a class of sequences can be used as a signature of the underlying sequence. This makes the assembly process less computationally intensive. Most secondary analysis assembly tools use this De Bruijn graph method (Lin 2016; Chaisson 2008).
RNA-seq Data Quality Checks
Once a dataset has been mapped or assembled (as part of the secondary analysis), quality checking tools like FASTQC can be used to understand the quality of reads and analysis as well as normalization (RPKM, TPM) and counting (for example, to characterize coverage and number of genes). The purpose of quality control checks of raw reads is to detect PCR bias, contamination, sequencing errors, and other artifacts. These quality checks look for GC content, adapters, number of reads/fragments (k-mers), and duplicate reads. Further, read quality drops towards the 3’ end and can affect transcript mapping. Software toolkits like the FASTX-Toolkit and Trimmomatic (Bolger 2014) can be used to discard low-quality reads.
The percentage of mapped reads (to a reference genome or transcriptome) is also an indicator of sequencing accuracy. A high percentage of mapped reads is expected when a well-annotated genome is available. Conversely, a low percentage can indicate RNA contamination from rRNA or non-coding RNAs. Software packages like NOISeq (Tarazona 2015) or EDASeq (Risso 2011) can be used for such analyses.
The GC content and clustering of mapped reads close to the 3’ end of poly(A)-enriched samples are important indicators of PCR bias and overall RNA quality, respectively. Tools like Picard, RSeQC and Qualimap are useful in quality checks during mapping. Finally, statistical analysis of replicate experiments should indicate a Spearman R2 value which is > 0.9 (Mortazavi 2008). In cases where biological heterogeneity is expected, other forms of statistical analysis are recommended.
A number of software tools are available for data analysis on different RNA-seq platforms. We recommend the use of Bio-Rad’s SeqSense NGS Data Analysis Software. Based on the STAR ultrafast universal RNA-seq aligner (Dobin 2013), the SeqSense RNA-seq data analysis pipeline was designed to leverage the differentiated capabilities of the SEQuoia Complete Stranded RNA Library Prep Kit.
Coding vs. Non-Coding RNA
It is well established that coding RNA (mRNA) makes up only a small percentage of the total RNA in the cell. Recent advances in NGS continue to shed light on interesting biological roles and therapeutic opportunities involving non-coding RNAs, including long and small non-coding RNAs. Non-coding RNAs are difficult to sequence due to their low abundance and high-repeat regions. Distinguishing between coding and non-coding RNA using RNA-seq is not a straightforward process, because the existence of an open reading frame is not necessarily indicative of coding potential (Clamp 2007). Additionally, some genes are transcribed into both coding and non-coding transcripts. One strategy to look for “coding potential” uses sequence comparison to other coding sequences based on homology or amino acid similarity. Some of the tools for these types of analyses include BEDTools (Quinlan 2010) and glbase (Hutchins 2014), BLAST (Altschul 1990), BLAT (Kent 2002), GMAP (Wu 2005), and AUGUSTUS (Stanke 2004).
The number of reads that map to a specific transcript on a reference sequence are used to estimate expression levels for that gene. Alignment-based tools for this quantification strategy include RSEM (Li 2011), StringTie (Pertea 2015), eXpress (Roberts 2013), TopHat/Cufflinks (Trapnell 2012), rQuant (Bohnert 2010), MMSEq (Turro 2011), and Scallop (Shao 2017). Alignment-free approaches assess gene expression by counting unique k-mers using toolkits like Sailfish (Patro 2014) or Salmon (Patro 2017).
Ultimately, biologists typically want to understand, interpret, and further analyze their data, which are all part of downstream or tertiary analysis. In tertiary analysis, researchers will perform some comparisons or statistical analyses, such as differential expression or variant analysis, to detect significant polymorphisms. Next, researchers typically annotate their findings, using publicly or privately curated databases such as KEGG, Gene Ontology (GO), or dbSNP.
Differential Gene Expression Analysis
One of the most common uses of RNA-seq is to determine how gene expression changes in response to disease pathologies, therapeutic intervention, or other stimuli. Differential gene expression (DGE) analysis compares different experimental samples and uses toolkits to evaluate if the observed difference between normalized read counts of a gene is statistically significant (Dündar 2015). DGE is performed following the initial alignment, quantitation and normalization steps, and has two main goals: 1) Assessing the magnitude of the difference, and 2) assessing the significance of the difference. Popular tools in this space are edgeR (Robinson 2010), DESeq/DESeq2 (Anders and Huber, 2010; Love 2014), and limma-voom (Ritchie 2015).
Small RNAs are a class of non-coding regulatory RNAs that are characterized by lengths shorter than 200 nucleotides. This class includes miRNAs, short-interfering RNAs (siRNAs), and Piwi-interacting RNAs (piRNAs), amongst others. The sequencing strategy for these RNAs are different because most are between 18 and 30 nucleotides. In a standard small RNA-seq experiment, all reads are aligned to a reference genome using tools like Bowtie2 (Langmead 2012), STAR (Dobin 2013), or Burrows-Wheeler Aligner (Li 2009), or aligners like PatMaN (Prüfer 2008) and MicroRazerS (Emde 2010). Contamination from degraded mRNA can skew read data, so it is critical to check for reads that map to high-abundance housekeeping genes.
Differential splicing of mRNA leads to different protein products from a single gene. Many diseases, including cancer have been linked to splicing misregulation, which is why RNA-seq is routinely used to study this phenomena. Alternate splicing is difficult to study using standard short-read RNA-seq; however numerous algorithms are available. The two DGE methods used to analyze alternate splicing are isoform-based (cuffdiff2 and DiffSplice) or count-based, which includes exon-based (DEXSeq, edgeR, JunctionSeq and limma) and event-based (dSpliceType, MAJIQ, rMATS and SUPPA/SUPPA2). Isoform-based methods use sequencing reads to construct the full transcript and measure abundance prior to differential expression analysis (Trapnell 2013; Hu 2013). Count-based methods count different features to determine percentage spliced, exons, and junctions (Anders 2012; Robinson 2010; Hartley 2016; Ritchie 2015; Zhu 2015; Vaquero-Garcia 2016; Shen 2014; Alamancos 2015; Trincado 2018).
Single-cell RNA-seq (scRNA-seq) is an emerging technology that reveals the unique gene expression signature of individual cells, which is otherwise lost in bulk studies. These experiments are inherently different from bulk RNA-seq and employ unique workflows and analysis strategies. A common practice in scRNA-seq is the use of spike-in molecules, like the External RNA Control Consortium (ERCC), as an internal control. The number of molecules corresponding to the spike-in is then used to normalize RNA abundance across all single cells. Unique molecular identifiers (UMIs) may also be included to barcode the molecules of interest as a way to control for PCR bias. One of the main challenges with scRNA-seq continues to be the limits on sequencing depth. Some computational tools for scRNA-seq are single-cell normalization (Brennecke 2014), Monocle (Trapnell 2014), and scLVM (Buettner 2015).
RNA-seq has revolutionized the transcriptomics field and new computational tools continue to help tackle the bottlenecks in data analysis and downstream modeling. Single-cell analysis and long-read RNA sequencing are two areas that are quickly evolving, with future developments expected to address limitations with low-abundance starting RNA and constructing long transcripts.
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