In recent years, a number of recognizing disease gene approaches and computer tools have been developed through building mathematic models based on functional annotation, sequence-based features, protein interaction, and disease phenotype [ 7– 16], such as sequence features [ 15], functional annotation [ 7, 8, 10, 13], and physical interactions [ 12, 13, 17].
Okazaki, Y. et al. Analysis of the mouse transcriptome based on functional annotation of 60,770 full-length cDNAs.
Further the most likely candidate genes for each important QTL were also discussed based on functional annotation.
In addition, the most likely candidate genes for three new QTL (qLVN6, qSVN7, and qSVA8.1) were identified based on functional annotation.
Pathway analysis based on functional annotation of these estrogen-regulated genes identified gene signatures with known or predicted roles in cell cycle control, cell growth (i.e. ribosome biogenesis and protein synthesis), cell death/survival signaling and transcriptional regulation.
By analysis based on functional annotation of acutely estrogen-regulated genes we have identified gene signatures that encompass four different aspects of estrogen action, and contain different proportions of c-Myc-responsive genes: cell cycle control, cell growth, cell death and transcriptional regulation.
For those C. albicans and zebrafish proteins that were selected based on functional annotations, one-way analysis of variance (ANOVA) was applied on the gene expression profiles in our time-course microarray data that correspond to these proteins to further select differentially expressed proteins.
Functional gene classification analysis generates gene-to-gene similarity matrix based on functional annotations associated with each gene and classifies genes into functionally related groups.
We assigned the functions to 95.6% of the protein-coding genes based on functional annotations from five different public databases (Supplementary information, Table S10).
For doing that, we constructed a sequence alignment of 35 distinct Kunitz type proteins (Fig. 8) which are categorized into five classes; body trypsin inhibitor (BT), chymotrypsin inhibitor in venoms (CTI), trypsin inhibitor in venoms (TI), double functional toxin (DFT) as well as K+ channel blocker (KB) of various species based on functional annotations of Swissprot database.
