![]() ![]() Internally, plotTranscripts() uses mapTranscriptsToEvents() to identify compatible transcripts by overlapping exons involved in splicing to the gene models provided in the Ensembl GTF. The function uses Gviz for visualization of the genomic locus of splicing event along with involved transcripts. This is a wrapper function for performing both mapping of splicing events to the transcriptome, as well as visualization of transcripts that are compatible with the splice event. The core function for transcripts visualization is plotTranscripts(). Users can use maser to visualize Ensembl transcripts affected by splicing. The table allows to look up event information such as gene names, identifiers and PSI levels.ĥ Genomic visualization of splicing events hypoxia_mib2 A Maser object with 8 splicing events.Įvents in a maser object can be queried using an interactive data table provided by display(). For instance, there are 8 splicing changes affecting MIB2 as seen below. Gene specific events can be selected using geneEvents(). hypoxia_filt A Maser object with 2882 splicing events. We can remove low coverage events using filterB圜overage(), which may signficantly reduced the number of splicing events. Low coverage splicing junctions are commonly found in RNA-seq data and lead to low confidence PSI levels. Access to different data types is done using annotation(), counts(), PSI(), and summary(), which takes as argument the maser object and event type. Splicing events have genomic locations, raw counts, PSI levels and statistics (p-values) regarding their differential splicing between conditions. Path A Maser object with 6022 splicing events. See ?maser for a description of parameters. Newer rMATS versions (>4.0.1) use JCEC or JC nomenclature, while ReadsOnTargetandJunction or JunctionCountOnly are used in rMATS 3.2.5 or lower. Note: The argument ftype indicates which rMATS output files to import. The maser() constructor returns an object containing all events quantified by rMATS. To import splicing events, we use the constructor function maser() indicating the path containing rMATS files, and labels for conditions. The example dataset was reduced because of size constraints. We applied rMATS to detect splicing events using the release 25 GTF (GRCh38 build) from the Gencode website. RNA-seq was collected at 0h and 24h after hypoxia in the HCT116 cell line. We demonstrate the package with data generated to investigate alternative splicing in colorectal cancer cells undergoing hypoxia publication. Throughout the text we use the following abbreviations to describe different types of splicing events: The second vignette describes how to use maser for annotation and visualization of protein features affected by splicing. In this vignette, we describe a basic maser workflow for analyzing alternative splicing, starting with importing splicing events, filtering events based on their coverage and differential expression, and analyzing global or specific spling events with several types of graphics. In this manner, maser can quickly identify splicing affecting known protein domains, extracellular and transmembrane regions, as well as mutation sites in the protein. Visualization of transcripts and protein affected by splicing using custom Gviz plots.Ī key feature of the package is mapping of splicing events to protein features such as topological domains, motifs and mutation sites provided by the UniprotKB database and visualized along side the genomic location of splicing.Integration with UniprotKB for batch annotation of protein features overlapping splicing events.Mapping of splicing events to Ensembl transcripts and UniprotKB proteins.Analysis of global splicing effects using boxplots, principal component analysis and volcano plots.Filtering of rMATS splicing events based on RNA-seq coverage, p-values and differential percent spliced-in (PSI).Overall, maser allows a detailed analysis of splicing events identified by rMATS by implementing the following functionalities: We developed maser ( Mapping Alternative Splicing Events to p Roteins) package to enable functional characterization of splicing in both transcriptomic and proteomic contexts. ![]() However, functional interpretation of splicing requires the integration of heterogeneous data sources such as transcriptomic and proteomic information. ![]() Alternative splicing occurs in most human genes and novel splice isoforms may be associated to disease or tissue specific functions. ![]()
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