SplAdder: Identification, quantification and testing of alternative splicing events from RNA-Seq data

Abstract

Motivation: Understanding the occurrence and regulation of alternative splicing (AS) is a key task towards explaining the regulatory processes that shape the complex transcriptomes of higher eukaryotes. With the advent of high-throughput sequencing of RNA (RNA-Seq), the diversity of AS transcripts could be measured at an unprecedented depth. Although the catalog of known AS events has grown ever since, novel transcripts are commonly observed when working with less well annotated organisms, in the context of disease, or within large populations. Whereas an identification of complete transcripts is technically challenging and computationally expensive, focusing on single splicing events as a proxy for transcriptome characteristics is fruitful and sufficient for a wide range of analyses. Results: We present SplAdder, an alternative splicing toolbox, that takes RNA-Seq alignments and an annotation file as input to (i) augment the annotation based on RNA-Seq evidence, (ii) identify alternative splicing events present in the augmented annotation graph, (iii) quantify and confirm these events based on the RNA-Seq data and (iv) test for significant quantitative differences between samples. Thereby, our main focus lies on performance, accuracy and usability. Availability: Source code and documentation are available for download at http://github.com/rats chlab/spladder. Example data, introductory information and a small tutorial are accessible via

McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data

“…SplAdder was used in a large-scale study [22] to detect and quantify alternative splicing events in nearly 9,000 tumor RNA-seq samples. In a comparative benchmark analysis performed in [23], it showed a better performance than competing methods JuncBase [6], rMATS [42], and SpliceGrapher [40]. Compared to SplAdder, which is limited to the detection of simple types of splicing events, MAJIQ introduced a novel approach that additionally captures more complex transcript variations.…”

“…Event-based methods differ in the complexity of the units of AS they quantify. In the simplest case, methods such as MISO [24], SUPPA, ASGAL [9], SpliceGrapher [40], and SplAdder [23] identify one of the canonical types of AS, such as exon skipping, alternative 5' and 3' splice sites, and intron retentions (see Supplementary Figure S1). These simple types of splicing events describe two possible splicing outcomes that differ in a single exon or splice site.…”

Bisbee: A proteomics validated analysis package for detecting differential splicing, identifying splice outliers, and predicting splice event protein effects

“…Here we leverage an existing tool for detection and implement new methods for statistical analysis and effect prediction steps. Software packages for detecting splicing alterations may be broadly broken down into two categories: those that only identify events found in annotated transcripts such as ballgown [9], MISO [10], rMATs [11], and SUPPA2 [12], and those that additionally detect novel splice events, such as ASPLI [13], SplAdder [14], SGSeq [15], LeafCutter [16], and MAJIQ [17]. As aberrant splicing in disease states may result in novel transcripts, we sought to identify and extend the capability of a tool that can identify novel splice events.…”

“…Novel splice events may reflect a well-annotated gene exhibiting previously unknown alternative splicing that occurs in diseased-tissue as opposed to normal tissue. We chose SplAdder for basic splice event detection because it has demonstrated utility in a large pan-cancer study and can enable comparisons to large sets of normal tissues from GTEx without requiring access to raw GTex data [14]. SplAdder also has advantages in terms of its modularity, facilitating analysis of large datasets in a cluster computing environment, and it also reports splice event coordinates in a straightforward manner.…”

“…Several splicing analysis packages include functions for testing differential splicing between two groups (e.g., diseased and non-diseased tissues), including ballgown [9], ASPLI [13], and SplAdder [14]. These typically use a generalized linear model and treat the overall expression level of the gene as a covariate to normalize expression differences that may confound the detection of splice isoform frequency differences.…”