Filtered functional variants 18 of 19 (94.7%) were further confirmed using conventional Sanger sequencing.
These results suggest that integrative analyses of epigenomic and genomic data provide an efficient way to filter functional biomarkers for future epidemiological studies in human cancers.
We have found that setting the aa-IDc < 40% can include TRs from the same protein family that are not functional homologs; while setting aa-IDc > 40% can filter functional homologs and limit the cross organism correlations.
The originality of the work reported here consists in the specific screen of a set of 714 kinases and, using integrative data-mining analyses to filter functional kinase groups, constructing kinase interaction networks that successfully identify new biologically relevant targets.
Significantly enriched GO terms (FDR corrected p-value < 0.001) related to cell fusion (GO 0000747, conjugation with cellular fusion) and mating (GO 0019236, response to pheromone) were identified in the filtered functional TF-gene binding targets but not in the original target list (Table 4).
All csv-files were imported into the data processing software Guineu (1.0.3 VTT; Espoo, Finland) (Castillo et al., 2011) for alignment, normalization (with internal standards), filtering and functional group identification.
Panseq, however, is not able to provide pangenomic profile and functional enrichment analysis that is important for the biologists to filter out functional relevance of the pangenomic elements.
To identify genes that function in specific biologic pathways, these 3,538 genes were further filtered using functional annotations derived from the NetAffx database‚ Analysis Center (Liu et al. 2003; http://www.affymetrix.com/analysis/index.affx), together with manual annotations from published literature, before hierarchical clustering using GeneSpring.
We filtered 50 functional partners in NorA, aacA-aphD (aac6ie), aad9ib (ant), aadd (knt), baca (uppP), bl2a_pc (blaZ), ble, ermA, SAV0052 (ermb), ermc, fosB, mecA (mecI), mecR (mecr1), mepA, msrA1, qacA, vraR (str), tet38 and tetM while 40 functional partners are identified in tet and aphA-3 (aph3iiia).
To identify differentially regulated processes the dataset was filtered for functional categories enriched (p<0.005) in one or two depots while being less enriched (p>0.1) in the other depot(s).
