The pattern of substitutions and functional consequences predicted by three bioinformatic programs was significantly different among concurrent CVs, and CVs occurring solely in HGMD or in dbSNP.
In addition to the InterPro domain information, additional results generated by seven bioinformatics programs were imported from CFGP 2.0 to provide broader information for each identified gene.
We considered those predicted to be deleterious by at least two bioinformatic programs and there was no significant difference (37 patients vs. 32 control subjects, P = 0.70).
Six of the variants were predicted likely to be deleterious in at least two bioinformatic programs, with three variants (P99H, E160V and R198C) predicted to be damaging by all three programs.
Two bioinformatics programs were used for substrate prediction, and both predicted that the third module encoded for proline [ 58, 59].
Relatively stringent criteria were used such that pathogenicity had to be predicted by all three Bioinformatics programs (Shanks et al., 2012).
Our data support that miR-130a inhibits neurite outgrowth, at least in part, by regulating MECP2, a predicted target gene by three bioinformatics tools.
Open reading frames (ORFs) were identified using two bioinformatics software programs, GenMarkS [ 40] and fgenesV0 (http://linux1.softberry.com/berry.phtml).phtml
Currently, five different bioinformatics programs using MapReduce and two systems are integrated and have been successfully deployed [ 59].
Moreover, agreement among the various methods are poor: in a deeply sequenced set of exome data, about half of all detected SNPs were predicted to be functional by at least one of seven different bioinformatics programs, but the various programs were in full agreement on only 1%% of variants (Tennessen et al. 2012).
