HBA-DEALS: accurate and simultaneous identification of differential expression and splicing using hierarchical Bayesian analysis.

We present Hierarchical Bayesian Analysis of Differential Expression and ALternative Splicing (HBA-DEALS), which simultaneously characterizes differential expression and splicing in cohorts. HBA-DEALS attains state of the art or better performance for both expression and splicing and allows genes to be characterized as having differential gene expression, differential alternative splicing, both, or neither. HBA-DEALS analysis of GTEx data demonstrated sets of genes that show predominant DGE or DAST across multiple tissue types. These sets have pervasive differences with respect to gene structure, function, membership in protein complexes, and promoter architecture

Predicting transcriptional regulatory interactions with artificial neural networks applied to E. coli multidrug resistance efflux pumps

BACKGROUND: Little is known about bacterial transcriptional regulatory networks (TRNs). In Escherichia coli, which is the organism with the largest wet-lab validated TRN, its set of interactions involves only ~50% of the repertoire of transcription factors currently known, and ~25% of its genes. Of those, only a small proportion describes the regulation of processes that are clinically relevant, such as drug resistance mechanisms. RESULTS: We designed feed-forward (FF) and bi-fan (BF) motif predictors for E. coli using multi-layer perceptron artificial neural networks (ANNs). The motif predictors were trained using a large dataset of gene expression data; the collection of motifs was extracted from the E. coli TRN. Each network motif was mapped to a vector of correlations which were computed using the gene expression profile of the elements in the motif. Thus, by combining network structural information with transcriptome data, FF and BF predictors were able to classify with a high precision of 83% and 96%, respectively, and with a high recall of 86% and 97%, respectively. These results were found when motifs were represented using different types of correlations together, i.e., Pearson, Spearman, Kendall, and partial correlation. We then applied the best predictors to hypothesize new regulations for 16 operons involved with multidrug resistance (MDR) efflux pumps, which are considered as a major bacterial mechanism to fight antimicrobial agents. As a result, the motif predictors assigned new transcription factors for these MDR proteins, turning them into high-quality candidates to be experimentally tested. CONCLUSION: The motif predictors presented herein can be used to identify novel regulatory interactions by using microarray data. The presentation of an example motif to predictors will make them categorize whether or not the example motif is a BF, or whether or not it is an FF. This approach is useful to find new "pieces" of the TRN, when inspecting the regulation of a small set of operons. Furthermore, it shows that correlations of expression data can be used to discriminate between elements that are arranged in structural motifs and those in random sets of transcripts

Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps-7

Er donut chart (bold green) represents the set of relationships where a putative binding of the regulator to the promoter region of the operon exists. Refer to text for more details.<p><b>Copyright information:</b></p><p>Taken from "Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps"</p><p>http://www.biomedcentral.com/1471-2180/8/101</p><p>BMC Microbiology 2008;8():101-101.</p><p>Published online 19 Jun 2008</p><p>PMCID:PMC2453137.</p><p></p

Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps-9

O their targets that are either operons or isolated genes. (B) Generic examples of true FF/BF motifs and their counterparts. Non-motif samples were generated by modifying one or more targets of the real motif example, as exemplified in the highlighted orange nodes. (C) Procedures for assembling the feature vectors. Here, there is an example of how the BF motifs and BF motifs (illustrated in (B)) are encoded as vectors of correlations. These vectors store the correlations among transcript profiles of motif elements, for all possible pairwise combinations. The k(x, y), s(x, y) and p(x, y), are the Kendall, Spearman and Pearson correlation between x and y, respectively. Also, pc(x, y, z) and pc(x, y, z, t) correspond to the 1and 2order Pearson partial correlation. Therefore, k(1, 2) is the Kendall correlation between the expression profile of TF1 and TF2, k(1,3) is the correlation between TF 1 and its target 3 (an operon or a gene). (D) Learning dataset and the neural network topology used in the study.<p><b>Copyright information:</b></p><p>Taken from "Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps"</p><p>http://www.biomedcentral.com/1471-2180/8/101</p><p>BMC Microbiology 2008;8():101-101.</p><p>Published online 19 Jun 2008</p><p>PMCID:PMC2453137.</p><p></p

Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps-3

Gulator of an MDR efflux pump(s), (c) member of an MDR efflux pump regulator family, (d) local or global regulator of non-MDR efflux pumps, (e) regulator of proteins related to efflux pumps or secretion, (f) regulator of an uptake transport system, and (g) regulator of metabolism. Bar labelled a-e represent the summed proportion of the categories (a) to (e) to the whole set of regulators, and are of special interest in this work because they are associated with efflux systems in bacteria.<p><b>Copyright information:</b></p><p>Taken from "Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps"</p><p>http://www.biomedcentral.com/1471-2180/8/101</p><p>BMC Microbiology 2008;8():101-101.</p><p>Published online 19 Jun 2008</p><p>PMCID:PMC2453137.</p><p></p

Proportion of samples categorized in the motif class (% TP), for each MDR operon, using and a classification threshold of -0

9.<p><b>Copyright information:</b></p><p>Taken from "Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps"</p><p>http://www.biomedcentral.com/1471-2180/8/101</p><p>BMC Microbiology 2008;8():101-101.</p><p>Published online 19 Jun 2008</p><p>PMCID:PMC2453137.</p><p></p

Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps-0

O their targets that are either operons or isolated genes. (B) Generic examples of true FF/BF motifs and their counterparts. Non-motif samples were generated by modifying one or more targets of the real motif example, as exemplified in the highlighted orange nodes. (C) Procedures for assembling the feature vectors. Here, there is an example of how the BF motifs and BF motifs (illustrated in (B)) are encoded as vectors of correlations. These vectors store the correlations among transcript profiles of motif elements, for all possible pairwise combinations. The k(x, y), s(x, y) and p(x, y), are the Kendall, Spearman and Pearson correlation between x and y, respectively. Also, pc(x, y, z) and pc(x, y, z, t) correspond to the 1and 2order Pearson partial correlation. Therefore, k(1, 2) is the Kendall correlation between the expression profile of TF1 and TF2, k(1,3) is the correlation between TF 1 and its target 3 (an operon or a gene). (D) Learning dataset and the neural network topology used in the study.<p><b>Copyright information:</b></p><p>Taken from "Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps"</p><p>http://www.biomedcentral.com/1471-2180/8/101</p><p>BMC Microbiology 2008;8():101-101.</p><p>Published online 19 Jun 2008</p><p>PMCID:PMC2453137.</p><p></p

Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps-1

Man (s), Kendall (k), partial correlation (pc), Spearman/Kendall/Pearson (skp), and another type containing all previous measures (all). Hybrid models (skp and all) outperformed configurations using only one type of correlation (see analysis in the text). All rates represent the average value over the 100 iterations of the 10 × 10-fold cross-validation procedure.<p><b>Copyright information:</b></p><p>Taken from "Predicting transcriptional regulatory interactions with artificial neural networks applied to multidrug resistance efflux pumps"</p><p>http://www.biomedcentral.com/1471-2180/8/101</p><p>BMC Microbiology 2008;8():101-101.</p><p>Published online 19 Jun 2008</p><p>PMCID:PMC2453137.</p><p></p