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.