title: A computational approach to candidate gene prioritization for X-linked mental retardation using annotation-based binary filtering and motif-based linear discriminatory analysis.
abstract: BACKGROUND
  Several computational candidate gene selection and prioritization methods have recently been developed. These in silico selection and prioritization techniques are usually based on two central approaches--the examination of similarities to known disease genes and/or the evaluation of functional annotation of genes. Each of these approaches has its own caveats. Here we employ a previously described method of candidate gene prioritization based mainly on gene annotation, in accompaniment with a technique based on the evaluation of pertinent sequence motifs or signatures, in an attempt to refine the gene prioritization approach. We apply this approach to X-linked mental retardation (XLMR), a group of heterogeneous disorders for which some of the underlying genetics is known.
  
  
  RESULTS
  The gene annotation-based binary filtering method yielded a ranked list of putative XLMR candidate genes with good plausibility of being associated with the development of mental retardation. In parallel, a motif finding approach based on linear discriminatory analysis (LDA) was employed to identify short sequence patterns that may discriminate XLMR from non-XLMR genes. High rates (>80%) of correct classification was achieved, suggesting that the identification of these motifs effectively captures genomic signals associated with XLMR vs. non-XLMR genes. The computational tools developed for the motif-based LDA is integrated into the freely available genomic analysis portal Galaxy (http://main.g2.bx.psu.edu/). Nine genes (APLN, ZC4H2, MAGED4, MAGED4B, RAP2C, FAM156A, FAM156B, TBL1X, and UXT) were highlighted as highly-ranked XLMR methods.
  
  
  CONCLUSIONS
  The combination of gene annotation information and sequence motif-orientated computational candidate gene prediction methods highlight an added benefit in generating a list of plausible candidate genes, as has been demonstrated for XLMR.
authors.affiliation: Division of Human Genetics, School of Pathology, Faculty of Health Sciences, National Health Laboratory Service & University of the Witwatersrand, Johannesburg, 2000, South Africa. zane.lombard@nhls.ac.za
authors.forename: Zané
authors.initials: Z
authors.lastname: Lombard
authors.forename: Chungoo
authors.initials: C
authors.lastname: Park
authors.forename: Kateryna D
authors.initials: KD
authors.lastname: Makova
authors.forename: Michèle
authors.initials: M
authors.lastname: Ramsay
mesh_terms: D002874:Chromosome Mapping; D041321:Chromosomes, Human, X / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D016002:Discriminant Analysis; D006801:Humans; D038901:X-Linked Intellectual Disability / Q000235:genetics*; D058977:Molecular Sequence Annotation; D012091:Repetitive Sequences, Nucleic Acid