This may be why they failed to meet the stringent statistical criteria employed: relaxing the criteria yielded more act genes (data not shown; with Rank Product analysis the additional act genes had associated p-values of less than 0.05 but the pfp values were above 0.1).
As more gene expression data and TF binding data become available, we believe that our method will be useful for reconstructing large-scale transcriptional regulatory networks.
As more data become available, especially protist TPX2 genes and more gene-order data, this question may be resolvable.
First, more genome sequences and more complete gene data set have been included; second, multiple tree reconstruction methods including more reliable methods like maximum-likelihood (ML) and Bayesian inference were used (see Methods); third, structural motifs, intron configurations and genomic synteny were integrated into the analyses in addition to sequence-based phylogenetics.
A contribution of this work is the establishment of an automatic process that allows us to efficiently scale-up and update our disease-drug network when more gene expression data are generated, deposited and annotated as GDS datasets in GEO.
Taken together, these results indicate that more gene expression data from different life-stages and sexes is needed to classify sex or tissue specificity.
We commit to update the web tool regularly by incorporating more gene expression data sets and imputing the latest panel of variants from the 1000 Genomes Project when available.
Thus, the generation of more gene expression data is necessary in targeted pathologies such as inflammation, and a phenotypically anchored database should be targeted to specific common pathologies in the first instance so critical data masses of gene expression data can be collected.
The coming years will see many more gene expression data sets from specific cell types and other species, which should dramatically accelerate precise hypothesis generation in plant biology (Usadel et al. 2009; Bordych et al. 2013) and particularly within the TCA cycle.
These resources will be useful in the identification of additional disease-associated cell types as more gene expression data become available, as well as in the development of tools better able to explore the etiology of a disease given its cellular context.
