| Summary: | <p>Abstract</p> <p>Background</p> <p>Several computational candidate gene selection and prioritization methods have recently been developed. These <it>in silico </it>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.</p> <p>Results</p> <p>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 (<url>http://main.g2.bx.psu.edu/</url>). Nine genes (<it>APLN</it>, <it>ZC4H2</it>, <it>MAGED4</it>, <it>MAGED4B</it>, <it>RAP2C</it>, <it>FAM156A</it>, <it>FAM156B</it>, <it>TBL1X</it>, and <it>UXT</it>) were highlighted as highly-ranked XLMR methods.</p> <p>Conclusions</p> <p>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.</p> <p><it>Reviewers: This article was reviewed by Dr Barbara Bardoni (nominated by Prof Juergen Brosius); Prof Neil Smalheiser and Dr Dustin Holloway (nominated by Prof Charles DeLisi).</it></p>
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