Abstract
Motivation. Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background.Results. A software suite named Splicing Express was created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills. Splicing Express performs automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool named Splooce. As a final result, Splicing Express creates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show that Splicing Express is able to perform all tasks in a straightforward way, identifying well-known specific events.Availability and Implementation.Splicing Express is written in Perl and is suitable to run only in UNIX-like systems. More details can be found at: http://www.bioinformatics-brazil.org/splicingexpress.
Highlights
Alternative splicing is involved in many biological phenomena and has been better evidenced due to the development of next-generation sequencing (NGS) technologies (Trapnell, Pachter & Salzberg, 2009)
By using RNA-Seq data from the Illumina Human Body Map Project and the Rat Body Map (Yu et al, 2014), we show that Splicing Express is able to extract meaningful information from deep transcriptome data
GTF files created by Cufflinks are recommended to be used as input due to some annotation standards referring to sequence names and FPKM expression
Summary
Alternative splicing is involved in many biological phenomena and has been better evidenced due to the development of next-generation sequencing (NGS) technologies (Trapnell, Pachter & Salzberg, 2009). The huge amount of data generated in each experiment brings challenges for the analysis and interpretation of alternative splicing. This is critical for researchers or research groups that lack bioinformatics expertise. Despite the fact that some bioinformatics tools have already been developed for the identification of ASEs in NGS-derived data (Liu et al, 2012; Seok et al, 2012; Florea, Song & Salzberg, 2013), challenges remain. There is a lack of user-friendly tools that are suitable for genome-wide ASE analysis, especially when several samples need to be compared
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