Predicting Alternative Splicing events
Next-Generation Sequencing (NGS) technologies need new methodologies for Alternative Splicing (AS) analysis. We have developed a combinatorial structure that gives a compact representation of gene structures—called splicing graph—and investigated the computational problem of building a splicing graph that is compatible with NGS data. More precisely, a fundamental question we have investigated is: under which conditions can the reconstruction of a gene structure be efficiently accomplished using only information provided by RNA-Seq data?
In order to partially answer this question, we have introduced the formal definition of the computational problem of reconstructing the gene structure from RNA-Seq data when the solution is represented by a splicing graph and we have given some necessary conditions under which the reconstructed splicing graph represents the real (unknown) gene structure. Finally we have described an efficient algorithm that, under some conditions, is able to exactly solve our problem but that is also able to achieve good accuracy on real genes violating such conditions.
Alternative splicing and gene structures
Our research activity on this topic includes the development of efficient algorithms for predicting the gene structure into exons and introns from a cluster of transcript data (EST, mRNA, RNA-seq) .
Our research on this project have also produced a WEBtool for AS prediction ASPIc-WEB and a database collecting data on AS ASPIc-DB and Pintron, the most recent software used for computing gene annotations stored in ASPIc-DB.
Transcriptome Assembly and Alternative Splicing Analysis
Paola Bonizzoni, Gianluca Della Vedova, Graziano Pesole, Ernesto Picardi, Yuri Pirola, and Raffaella Rizzi, in RNA Bioinformatics, Methods in Molecular Biology, vol. 1269, Springer-Verlag 2015
Modeling Alternative Splicing Variants from RNA-Seq Data with Isoform Graphs Journal of Computational Biology Journal of Computational Biology 21(1): 16-40 (2014) S. Beretta, P. Bonizzoni, G.D. Vedova, Y. Pirola, and R. Rizzi
PIntron is a novel pipeline for gene-structure prediction based on spliced alignment of transcript sequences (ESTs and mRNAs) against a genomic sequence.
PIntron has been used to compute several predictions of AspicDB.
Yuri Pirola, Raffaella Rizzi, Ernesto Picardi, Graziano Pesole, Gianluca Della Vedova, and Paola Bonizzoni.
PIntron: A fast method for gene structure prediction via maximal pairings of a pattern and a text.
BMC Bioinformatics, 13(S5), S2, (2012). doi:10.1186/1471-2105-13-S5-S2
Downloads and support
Please refer to the main project page for download, installation, and usage instructions.