Features of sequence composition and population genetical measures of selection to analyse alternatively spliced exons and introns

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Vukusic:2006:ISMB,
  author =       "Ivana Vukusic and Sushma-Nagaraja Grellscheid and 
                 Thomas Wiehe",
  title =        "Features of sequence composition and population
                 genetical measures of selection to analyse
                 alternatively spliced exons and introns",
  booktitle =    "14th Annual International Conference on Intelligent
                 Systems For Molecular Biology",
  year =         "2006",
  editor =       "Goran Neshich",
  pages =        "L-30",
  address =      "Fortaleza, Brazil",
  month =        "6-10 " # aug,
  organisation = "International Society for Computational Biology",
  keywords =     "genetic algorithms, genetic programming, Discipulus,
                 Poster",
  URL =          "http://www.iscb.org/cms_addon/conferences/ismb2006/archive/ismb2006.cbi.cnptia.embrapa.br/posters_list.php",
  abstract =     "Short Abstract: We have developed a binary classifier
                 based on Genetic Programming (GP) to predict whether a
                 given gene sequence is spliced constitutively or
                 alternatively. The prediction accuracies are greater
                 than 85percent on the dataset of retained introns.
                 Furthermore we showed that skipped exons show traces of
                 positive selection.",
  abstract =     "Long Abstract: Features of sequence composition and
                 population genetical measures of selection to analyse
                 alternatively spliced exons and introns

                 Alternative pre-mRNA splicing is a major source of
                 mammalian transcriptome and proteome diversity.
                 Aberrant splicing is an important cause for genetic
                 diseases and cancer. Until a few years ago it was
                 believed that almost 95percent of all genes undergo
                 constitutive splicing, which always proceeds in a
                 removal of introns which is followed by a merge of
                 exons. It is now widely believed that alternative
                 splicing is the rule rather than the exception and that
                 up to 74 percent of all human genes are alternatively
                 spliced. Whether an exon or an intron will be included
                 or excluded in the transcripts of a gene of a certain
                 cell type is influenced by the information contained in
                 the sequence of the exon and the flanking intronic
                 region. It is commonly accepted that no single factor
                 dictates whether or not an exon will be spliced into a
                 transcript. Instead it is probably a combinatorial
                 effect of various factors that include cis-acting
                 sequences and trans-acting splicing factors.

                 To predict whether a given gene sequence is spliced
                 constitutively or alternatively we used the technique
                 of Genetic Programming (GP). GP is a sub-discipline of
                 Machine Learning. Basic ideas of GP are inspired by the
                 paradigm of Darwinian evolution. New programs are
                 'bred' from a population of existing programs and
                 subject to selection, mutation and recombination. We
                 used the GP system 'Discipulus', a supervised learning
                 system, which generates programs on data that describe
                 a certain problem. The features provided to this system
                 are in form of a 'feature-matrix', containing e.g.
                 nucleotide composition, length, motifs etc.

                 After each GP run Discipulus collects the information,
                 of how often each feature was used in the thirty best
                 programs, in a so-called 'input-impact'-table. This
                 table can be used to reveal the 'best features' for a
                 certain classification problem.

                 The system has been tested on extended version of the
                 AltSplice data base. Here, we concentrated on cassette
                 exons (SCE) and retained introns (SIR) and analysed
                 27,519 constitutively spliced exons and 9641 cassette
                 exons including their upstream and downstream introns;
                 in addition we focused on the analysis of the
                 difference of 33,316 constitutively spliced introns
                 compared to 2712 retained introns. The classifier shows
                 very high prediction accuracy on the SIR data:
                 sensitivity is 91.4percent and specificity is
                 81.9percent. In contrast, on the SCE data the
                 prediction accuracy is lower: sensitivity is
                 48.2percent and specificity is 70.3percent. This
                 suggests that sequence properties, such as those
                 collected in the GP feature matrix, are better suited
                 to detect alternative splicing of introns than that of
                 exons. A possible biological reason is that the
                 constraints imposed by the genetic code affect (coding)
                 exons but not introns.",
  abstract =     "During cross-validation we have collected and analysed
                 the five input-impact-tables resulting from each GP
                 run. A frequency value of 5 of a certain feature means
                 that in all 5 GP runs the 30 best programs contained
                 this feature. The most frequently used features of the
                 SCE data are: Number of A residues (frequency value:
                 5), GGG sequences (frequency value: 3,6) and the number
                 of C residues (frequency value: 1). Although every
                 single run starts with a new population of randomly
                 generated programs, a similar pattern occurred in all
                 other runs performed during cross-validation. The best
                 feature on the SIR data set is the number of A residues
                 (frequency value: 4,1), followed by GC divided by
                 length (frequency value: 1.8) and the number of T
                 residues (frequency value: 1.4) in accordance with the
                 fact that exonic splicing enhancer tend to be purine
                 rich sequences.

                 To see whether selection, positive or negative, acts
                 differently in alternatively than constitutively
                 spliced exons we extracted for our lists of exons all
                 annotated sequence polymorphisms from the latest
                 release of the HapMap database. A common measure to
                 test for the presence of positive selection is Tajima's
                 D. We find that Tajima's D is smaller in the European
                 population compared to Africa. Also, Tajima's D is
                 smaller in the skipped compared to the constitutive
                 exon dataset in both populations, indicating an
                 elevated level of positive directional selection in
                 alternatively spliced genes. Linkage disequilibrium is
                 higher in derived populations and in alternatively
                 spliced genes in all populations. We also find a
                 slightly elevated level of genetic diversity close to
                 the splice boundaries in alternative exons. However,
                 while these features indicate a general trend, the
                 sequence polymorphism data are too sparse in order to
                 be used as a predictor of alternative versus
                 constitutively spliced exons in particular cases.",
  notes =        "http://ismb2006.cbi.cnptia.embrapa.br/
                 http://www.iscb.org/cms_addon/conferences/ismb2006/archive/ismb2006.cbi.cnptia.embrapa.br/posters_list.php",
}

Genetic Programming entries for Ivana Vukusic Sushma-Nagaraja Grellscheid Thomas Wiehe

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