YCRD: Yeast Combinatorial Regulation Database

YQFC: a web tool to compare quantitative biological features between two yeast gene lists

Nowadays high-throughput omics technologies are routinely used in biological research. From the omics data, researchers can easily get two gene lists (e.g. stress-induced genes vs. stress-repressed genes) related to their biological question. The next step would be to apply enrichment analysis tools to identify distinct functional/regulatory features between these two gene lists for further investigation. Although various enrichment analysis tools are already available, two challenges remain to be addressed. First, most existing tools are designed to analyze only one gene list, so they cannot directly compare two gene lists. Second, almost all existing tools focus on identifying the enriched qualitative features (e.g. gene ontology [GO] terms, pathways, domains, etc.). Many quantitative features (e.g. number of mRNA isoforms of a gene, mRNA half-life, protein half-life, transcriptional plasticity, translational efficiency, etc.) are available in the yeast, but no existing tools provide analyses on these quantitative features. To address these two challenges, here we present Yeast Quantitative Features Comparator (YQFC) that can directly compare various quantitative features between two yeast gene lists. In YQFC, we comprehensively collected and processed 85 quantitative features from the yeast literature and yeast databases. For each quantitative feature, YQFC provides three statistical tests (t-test, U test and KS test) to test whether this quantitative feature is statistically different between the two input yeast gene lists. The distinct quantitative features identified by YQFC may help researchers to study the underlying molecular mechanisms that differentiate the two input yeast gene lists. We believe that YQFC is a useful tool to expedite the biological research that uses high-throughput omics technologies.

DATABASE URL

http://cosbi2.ee.ncku.edu.tw/YQFC/.

YGMD: a repository for yeast cooperative transcription factor sets and their target gene modules

By organizing the genome into gene modules (GMs), a living cell coordinates the activities of a set of genes to properly respond to environmental changes. The transcriptional regulation of the expression of a GM is usually carried out by a cooperative transcription factor set (CoopTFS) consisting of several cooperative transcription factors (TFs). Therefore, a database which provides CoopTFSs and their target GMs is useful for studying the cellular responses to internal or external stimuli. To address this need, here we constructed YGMD (Yeast Gene Module Database) to provide 34120 CoopTFSs, each of which consists of two to five cooperative TFs, and their target GMs. The cooperativity between TFs in a CoopTFS is suggested by physical/genetic interaction evidence or/and predicted by existing algorithms. The target GM regulated by a CoopTFS is defined as the common target genes of all the TFs in that CoopTFS. The regulatory association between any TF in a CoopTFS and any gene in the target GM is supported by experimental evidence in the literature. In YGMD, users can (i) search the GM regulated by a specific CoopTFS of interest or (ii) search all possible CoopTFSs whose target GMs contain a specific gene of interest. The biological relevance of YGMD is shown by a case study which demonstrates that YGMD can provide a GM enriched with genes known to be regulated by the query CoopTFS (Cbf1-Met4-Met32). We believe that YGMD provides a valuable resource for yeast biologists to study the transcriptional regulation of GMs.

Database URL:http://cosbi4.ee.ncku.edu.tw/YGMD/, http://cosbi5.ee.ncku.edu.tw/YGMD/ or http://cosbi.ee.ncku.edu.tw/YGMD/

Detecting Cooperativity between Transcription Factors Based on Functional Coherence and Similarity of Their Target Gene Sets

In eukaryotic cells, transcriptional regulation of gene expression is usually achieved by cooperative transcription factors (TFs). Therefore, knowing cooperative TFs is the first step toward uncovering the molecular mechanisms of gene expression regulation. Many algorithms based on different rationales have been proposed to predict cooperative TF pairs in yeast. Although various types of rationales have been used in the existing algorithms, functional coherence is not yet used. This prompts us to develop a new algorithm based on functional coherence and similarity of the target gene sets to identify cooperative TF pairs in yeast. The proposed algorithm predicted 40 cooperative TF pairs. Among them, three (Pdc2-Thi2, Hot1-Msn1 and Leu3-Met28) are novel predictions, which have not been predicted by any existing algorithms. Strikingly, two (Pdc2-Thi2 and Hot1-Msn1) of the three novel predictions have been experimentally validated, demonstrating the power of the proposed algorithm. Moreover, we show that the predictions of the proposed algorithm are more biologically meaningful than the predictions of 17 existing algorithms under four evaluation indices. In summary, our study suggests that new algorithms based on novel rationales are worthy of developing for detecting previously unidentifiable cooperative TF pairs.