Analysis of Alternative Splicing Events in Custom Gene Datasets by AStalavista

  • Sylvain Foissac
  • Michael Sammeth
Part of the Methods in Molecular Biology book series (MIMB, volume 1269)


Alternative splicing (AS) is a eukaryotic principle to derive more than one RNA product from transcribed genes by removing distinct subsets of introns from a premature polymer. We know today that this process is highly regulated and makes up a large part of the differences between species, cell types, and states. The key to compare AS across different genes or organisms is to tokenize the AS phenomenon into atomary units, so-called AS events. These events then usually are grouped by common patterns to investigate the underlying molecular mechanisms that drive their regulation. However, attempts to decompose loci with AS observations into events are often hampered by applying a limited set of a priori defined event patterns which are not capable to describe all AS configurations and therefore cannot decompose the phenomenon exhaustively.

In this chapter, we describe working scenarios of AStalavista, a computational method that reports all AS events reflected by transcript annotations. We show how to practically employ AStalavista to study AS variation in complex transcriptomes, as characterized by the human GENCODE annotation. Our examples demonstrate how the inherent and universal AStalavista paradigm allows for an automatic delineation of AS events in custom gene datasets. Additionally, we sketch an example of an AStalavista use case including next-generation sequencing data (RNA-Seq) to enrich the landscape of discovered AS events.

Key words

Gene expression RNA processing Alternative splicing AS event Definition of alternative splicing Transcriptome annotation RNA-seq Splicing nomenclature AS code Bioinformatics 


  1. 1.
    Kornblihtt AR, Schor IE, Alló M (2013) Alternative splicing: a pivotal step between eukaryotic transcription and translation. Nat Rev Mol Cell Biol 14:153–165PubMedCrossRefGoogle Scholar
  2. 2.
    Foissac S, Sammeth M (2007) ASTALAVISTA: dynamic and flexible analysis of alternative splicing events in custom gene datasets. Nucleic Acids Res 35:W297PubMedCentralPubMedCrossRefGoogle Scholar
  3. 3.
    Harrow J, Frankish A, Gonzalez JM (2012) GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 22:1760PubMedCentralPubMedCrossRefGoogle Scholar
  4. 4.
    Dunham I, Birney E, Lajoie BR, Sanyal A (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489:57CrossRefGoogle Scholar
  5. 5.
    Dobin A, Davis CA, Schlesinger F (2013) STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29:15PubMedCentralPubMedCrossRefGoogle Scholar
  6. 6.
    Mironov AA, Fickett JW, Gelfand MS (1999) Frequent alternative splicing of human genes. Genome Res 9:1288–1293. doi: 10.1101/gr.9.12.1288 PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Tilgner H, Knowles DG, Johnson R (2012) Deep sequencing of subcellular RNA fractions shows splicing to be predominantly co-transcriptional in the human genome but inefficient for lncRNAs. Genome Res 22:1616PubMedCentralPubMedCrossRefGoogle Scholar
  8. 8.
    Boireau S, Maiuri P, Basyuk E, de la Mata M, Knezevich A, Pradet-Balade B, Bäcker V, Kornblihtt A, Marcello A, Bertrand E (2007) The transcriptional cycle of HIV-1 in real-time and live cells. J Cell Biol 179:291–304. doi: 10.1083/jcb.200706018 PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Sammeth M (2009) Complete alternative splicing events are bubbles in splicing graphs. J Comput Biol 16:1117PubMedCrossRefGoogle Scholar
  10. 10.
    Sammeth M, Foissac S, Guigó R (2008) A general definition and nomenclature for alternative splicing events. PLoS Comput Biol 4:e1000147. doi: 10.1371/journal.pcbi.1000147 PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Djebali S, Davis CA, Merkel A, Dobin A, Lassmann T, Mortazavi A, Tanzer A et al (2012) Landscape of transcription in human cells. Nature 489:101–108. doi: 10.1038/nature11233 PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Marco-Sola S, Sammeth M, Guigó R, Ribeca P (2012) The GEM mapper: fast, accurate and versatile alignment by filtration. Nat Methods 9:1185. doi: 10.1038/nmeth.2221 PubMedCrossRefGoogle Scholar
  13. 13.
    Mundry M, Bornberg-Bauer E (2012) Evaluating characteristics of de novo assembly software on 454 transcriptome data: a simulation approach. PLoS One 7:e31410PubMedCentralPubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.UMR1388 GenPhySEFrench National Institute for Agricultural Research (INRA)Castanet TolosanFrance
  2. 2.BioinformaticsNational Laboratory of Cientific Computing (LNCC)PetropolisBrazil

Personalised recommendations