Name: identification of alternative splicing and polyadenylation in rna-seq data
Uploaded: 2021-06-24T13:00:00
Description: Watch this Scientific Journal Video about Identification of Alternative Splicing and Polyadenylation in RNA-seq Data at JoVE.com

This study was supported by an Australian Research Council (ARC) Future Fellowship (FT16010043) and ANU Futures Scheme.

1. Installation of tools and R packages used in the analysis
<ol>
	<li>Conda is a popular and flexible package manager that allows convenient installation of packages with their dependencies across all platforms. Use &#39;Anaconda&#39; (conda package manager) to install &#39;conda&#39; which can be used to install the tools/packages required for the analysis.</li>
	<li>Download &#39;Anaconda&#39; according to the system requirements from https://www.anaconda.com/products/individual#Downloads and install ...

<ol>
	<li>Katz, Y., Wang, E. T., Airoldi, E. M., Burge, C. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Analysis+and+design+of+RNA+sequencing+experiments+for+identifying+isoform+regulation.">Analysis and design of RNA sequencing experiments for identifying isoform regulation.</a> Nature Methods. 7 (12), 1009-1015 (2010).</li><li>Wang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanism+of+alternative+splicing+and+its+regulation.">Mechanism of alternative splicing and its regulation.</a> Biomedical Reports. 3 (2), 152-158 (2015).</li><li>Mehmood, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Systematic+evaluation+of+differential+splicing+tools+for+RNA-seq+studies.">Systematic evaluation of differential splicing tools for RNA-seq studies.</a> Briefings in Bioinformatics. 21 (6), 2052-2065 (2020).</li><li>Movassat, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Coupling+between+alternative+polyadenylation+and+alternative+splicing+is+limited+to+terminal+introns.">Coupling between alternative polyadenylation and alternative splicing is limited to terminal introns.</a> RNA Biology. 13 (7), 646-655 (2016).</li><li>Tian, B., Manley, J. 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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=edgeR:+a+Bioconductor+package+for+differential+expression+analysis+of+digital+gene+expression+data.">edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.</a> Bioinformatics. 26 (1), 139-140 (2010).</li><li>Robinson, M. D., Oshlack, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+scaling+normalization+method+for+differential+expression+analysis+of+RNA-seq+data.">A scaling normalization method for differential expression analysis of RNA-seq data.</a> Genome Biology. 11 (3), 25 (2010).</li><li>Veiga, D. F. 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In this study, we evaluated exon-based and event-based approaches to detect AS and APA in bulk RNA-Seq and 3&#39; end sequencing data. The exon-based AS approaches produce both a list of differentially expressed exons and a gene-level ranking ordered by the statistical significance of overall gene-level differential splicing activity (Tables 1-2, 4-5). For the diffSplice package, differential usage is determined by fitting weighted linear models at an exon-level to estimate the differential log fold-chan...

RNA-seq has been widely used over the years typically for estimating differential gene expression and gene discovery1. In addition, it can also be utilized to estimate varying exon level usage due to gene expressing different isoforms, hence contributing to a better understanding of gene regulation at the post-transcriptional level. The majority of eukaryotic genes generate different isoforms by alternative splicing (AS) to increase the diversity of mRNA expression. AS events can be divided into different patterns: skipping of complete exons (SE) where a (&#34;cassette&#34;) exon is completely removed out of the transcript along with its flanking introns; alternative (donor) 5&#39; splice site selection (A5SS) and alternative 3&#39; (acceptor) splice site selection (A3SS) when two or more splice sites are present on either end of an exon; retention of introns (RI) when an intron is retained within the mature mRNA transcript and mutual exclusion of exon usage (MXE) where only one of the two available exons can be retained at a time2,3. Alternative polyadenylation (APA) also plays an important role in regulating gene expression using alternative poly (A) sites to generate multiple mRNA isoforms from a single transcript4. Most polyadenylation sites (pAs) are located in the 3&#39; untranslated region (3&#39; UTRs), generating mRNA isoforms with diverse 3&#39; UTR lengths. As the 3&#39; UTR is the central hub for recognizing regulatory elements, different 3&#39; UTR lengths can affect mRNA localization, stability and translation5. There are a class of 3&#39; end sequencing assays optimized to detect APA that differ in the details of the protocol6. The pipeline described here is designed for PolyA-seq, but can be adapted for other protocols as described.
In this study, we present a pipeline of differential exon analysis methods7,8 (Figure 1), which can be divided into two broad categories: exon-based (DEXSeq9, diffSplice10) and event-based (replicate Multivariate Analysis of Transcript Splicing (rMATS)11). The exon-based methods compare the fold change across conditions of individual exons, against a measure of overall gene fold change to call differentially expressed exon usage, and from that compute a gene-level measure of AS activity. Event-based methods use exon-intron-spanning junction reads to detect and classify specific splicing events such as exon skipping or retention of introns, and distinguish these AS types in the output3. Thus, these methods provide complementary views for a complete analysis of AS12,13. We selected DEXSeq (based on the DESeq214 DGE package) and diffSplice (based on the Limma10 DGE package) for the study as they are amongst the most widely used packages for differential splicing analysis. rMATS was chosen as a popular method for event-based analysis. Another popular event-based method is MISO (Mixture of Isoforms)1. For APA we adapt the exon-based approach.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62636/62636fig01.jpg" /> 
Figure 1.&#160;Analysis pipeline. Flowchart of the steps used in the analysis. Steps include: obtaining the data, performing quality checks and read alignment followed by counting reads using annotations for known exons, introns and pA sites, filtering to remove low counts and normalization. PolyA-seq data was analysed for alternative pA sites using diffSplice/DEXSeq methods, bulk RNA-Seq was analysed for alternative splicing at the exon level with diffSplice/DEXseq methods, and AS events analysed with rMATS. <a href="https://www.jove.com/files/ftp_upload/62636/62636fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The RNA-seq data used in this survey was acquired from Gene Expression Omnibus (GEO) (GSE138691)15. We used mouse RNA-seq data from this study with two condition groups: wild-type (WT) and Muscleblind-like type 1 knockout (Mbnl1 KO) with three replicates each. To demonstrate differential polyadenylation site usage analysis, we obtained mouse embryo fibroblasts (MEFs) PolyA-seq data (GEO Accession GSE60487)16. The data has four condition groups: Wild-type (WT), Muscleblind-like type1/type 2 double knockout (Mbnl1/2 DKO), Mbnl 1/2 DKO with Mbnl3 knockdown (KD) and Mbnl1/2 DKO with Mbnl3 control (Ctrl). Each condition group consists of two replicates.
<table border="1" fo:font-size="5px" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td></td>
			<td>GEO Accession</td>
			<td>SRA Run number</td>
			<td>Sample name</td>
			<td>Condition</td>
			<td>Replicate</td>
			<td>Tissue</td>
			<td>Sequencing</td>
			<td>Read length</td>
		</tr>
		<tr>
			<td rowspan="6">RNA-Seq</td>
			<td>GSM4116218</td>
			<td>SRR10261601</td>
			<td>Mbnl1KO_Thymus_1</td>
			<td>Mbnl1 knockout</td>
			<td>Rep 1</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116219</td>
			<td>SRR10261602</td>
			<td>Mbnl1KO_Thymus_2</td>
			<td>Mbnl1 knockout</td>
			<td>Rep 2</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116220</td>
			<td>SRR10261603</td>
			<td>Mbnl1KO_Thymus_3</td>
			<td>Mbnl1 knockout</td>
			<td>Rep 3</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116221</td>
			<td>SRR10261604</td>
			<td>WT_Thymus_1</td>
			<td>Wild type</td>
			<td>Rep 1</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116222</td>
			<td>SRR10261605</td>
			<td>WT_Thymus_2</td>
			<td>Wild type</td>
			<td>Rep 2</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td>GSM4116223</td>
			<td>SRR10261606</td>
			<td>WT_Thymus_3</td>
			<td>Wild type</td>
			<td>Rep 3</td>
			<td>Thymus</td>
			<td>Paired-end</td>
			<td>100 bp</td>
		</tr>
		<tr>
			<td rowspan="8">3P-Seq</td>
			<td>GSM1480973</td>
			<td>SRR1553129</td>
			<td>WT_1</td>
			<td>Wild type (WT)</td>
			<td>Rep 1</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480974</td>
			<td>SRR1553130</td>
			<td>WT_2</td>
			<td>Wild type (WT)</td>
			<td>Rep 2</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480975</td>
			<td>SRR1553131</td>
			<td>DKO_1</td>
			<td>Mbnl 1/2 double knockout (DKO)</td>
			<td>Rep 1</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480976</td>
			<td>SRR1553132</td>
			<td>DKO_2</td>
			<td>Mbnl 1/2 double knockout (DKO)</td>
			<td>Rep 2</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480977</td>
			<td>SRR1553133</td>
			<td>DKOsiRNA_1</td>
			<td>Mbnl 1/2 double knockout with Mbnl 3 siRNA (KD)</td>
			<td>Rep 1</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480978</td>
			<td>SRR1553134</td>
			<td>DKOsiRNA_2</td>
			<td>Mbnl 1/2 double knockout with Mbnl 3 siRNA (KD)</td>
			<td>Rep 2</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>36 bp</td>
		</tr>
		<tr>
			<td>GSM1480979</td>
			<td>SRR1553135</td>
			<td>DKONTsiRNA_1</td>
			<td>Mbnl 1/2 double knockout with non-targeting siRNA (Ctrl)</td>
			<td>Rep 1</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
		<tr>
			<td>GSM1480980</td>
			<td>SRR1553136</td>
			<td>DKONTsiRNA_2</td>
			<td>Mbnl 1/2 double knockout with non-targeting siRNA (Ctrl)</td>
			<td>Rep 2</td>
			<td>Mouse embryonic Fibroblasts (MEFs)</td>
			<td>Single-end</td>
			<td>40 bp</td>
		</tr>
	</tbody>
</table>
Table 1. Summary of RNA-Seq and PolyA-seq datasets used for the analysis.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Not relevent for computational study</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>

identification of alternative splicing and polyadenylation in rna-seq data

As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also be utilized to explore other complex regulatory mechanisms at the exon level. Alternative splicing and polyadenylation play a crucial role in the functional diversity of a gene by generating different isoforms to regulate gene expression at the post-transcriptional level, and limiting analyses to the whole gene level can miss this important regulatory layer. Here, we demonstrate detailed step by step analyses for identification and visualization of differential exon and polyadenylation site usage across conditions, using Bioconductor and other packages and functions, including DEXSeq, diffSplice from the Limma package, and rMATS.

After running the above step-by-step workflow, the AS and APA&#160;analysis outputs and representative results are in the form of tables and data plots, generated as follows.
AS: 
The main output of the AS analysis (Supplementary Table 1 for diffSplice; Table 2 for DEXSeq) is a list of exons showing differential usage across conditions, and a list of genes showing significant overall splicing activity of one or more of its constituent exo...

Watch this Scientific Journal Video about Identification of Alternative Splicing and Polyadenylation in RNA-seq Data at JoVE.com

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Alternative splicing (AS) and alternative polyadenylation (APA) expand the diversity of transcript isoforms and their products. Here, we describe bioinformatic protocols to analyze bulk RNA-seq and 3' end sequencing assays to detect and visualize AS and APA varying across experimental conditions.

As well as the typical analysis of RNA-Seq to measure differential gene expression (DGE) across experimental/biological conditions, RNA-seq data can also ...

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Biology

RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells

The characterization of gene expression in cells via measurement of mRNA levels is a useful tool in determining how the transcriptional machinery of the ...

Depletion of Ribosomal RNA for Mosquito Gut Metagenomic RNA-seq

The mosquito gut accommodates dynamic microbial communities across different stages of the insect's life cycle. Characterization of the genetic capacity ...

Alternative Cultures for Human Pluripotent Stem Cell Production, Maintenance, and Genetic Analysis

Human pluripotent stem cells (hPSCs) hold great promise for regenerative medicine and biopharmaceutical applications. Currently, optimal culture and ...

Detection of Alternative Splicing During Epithelial-Mesenchymal Transition

Alternative splicing plays a critical role in the epithelial-mesenchymal transition (EMT), an essential cellular program that occurs in various ...

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, ...

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Rapid Identification of Chemical Genetic Interactions in Saccharomyces cerevisiae

Determining the mode of action of bioactive chemicals is of interest to a broad range of academic, pharmaceutical, and industrial scientists. ...

Methods to Discover Alternative Promoter Usage and Transcriptional Regulation of Murine Bcrp1

Gene expression in different tissues is often controlled by alternative promoters that result in the synthesis of mRNA with unique &#8212; usually ...

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

Identification of Novel Regulators of Plant Transpiration by Large-Scale Thermal Imaging Screening in Helianthus Annuus

Plant adaptation to biotic and abiotic stresses is governed by a variety of factors, among which the regulation of stomatal aperture in response to water ...

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Distilling and reporting large datasets, such as whole genome or transcriptome data, is often a daunting task. One way to break down results is to focus ...