Transcription Factor-DNA Binding Via Machine Learning Ensembles

We present ensemble methods in a machine learning (ML) framework combining predictions from five known motif/binding site exploration algorithms. For a given TF the ensemble starts with position weight matrices (PWM's) for the motif, collected from the component algorithms. Using dimension reduction, we identify significant PWM-based subspaces for analysis. Within each subspace a machine classifier is built for identifying the TF's gene (promoter) targets (Problem 1). These PWM-based subspaces form an ML-based sequence analysis tool. Problem 2 (finding binding motifs) is solved by agglomerating k-mer (string) feature PWM-based subspaces that stand out in identifying gene targets. We approach Problem 3 (binding sites) with a novel machine learning approach that uses promoter string features and ML importance scores in a classification algorithm locating binding sites across the genome. For target gene identification this method improves performance (measured by the F1 score) by about 10 percentage points over the (a) motif scanning method and (b) the coexpression-based association method. Top motif outperformed 5 component algorithms as well as two other common algorithms (BEST and DEME). For identifying individual binding sites on a benchmark cross species database (Tompa et al., 2005) we match the best performer without much human intervention. It also improved the performance on mammalian TFs. The ensemble can integrate orthogonal information from different weak learners (potentially using entirely different types of features) into a machine learner that can perform consistently better for more TFs. The TF gene target identification component (problem 1 above) is useful in constructing a transcriptional regulatory network from known TF-target associations. The ensemble is easily extendable to include more tools as well as future PWM-based information.Comment: 33 page

On Weight Matrix and Free Energy Models for Sequence Motif Detection

The problem of motif detection can be formulated as the construction of a discriminant function to separate sequences of a specific pattern from background. In computational biology, motif detection is used to predict DNA binding sites of a transcription factor (TF), mostly based on the weight matrix (WM) model or the Gibbs free energy (FE) model. However, despite the wide applications, theoretical analysis of these two models and their predictions is still lacking. We derive asymptotic error rates of prediction procedures based on these models under different data generation assumptions. This allows a theoretical comparison between the WM-based and the FE-based predictions in terms of asymptotic efficiency. Applications of the theoretical results are demonstrated with empirical studies on ChIP-seq data and protein binding microarray data. We find that, irrespective of underlying data generation mechanisms, the FE approach shows higher or comparable predictive power relative to the WM approach when the number of observed binding sites used for constructing a discriminant decision is not too small.Comment: 23 pages, 1 figure and 4 table

Transcriptional regulation of protein complexes in yeast

BACKGROUND: Multiprotein complexes play an essential role in many cellular processes. But our knowledge of the mechanism of their formation, regulation and lifetimes is very limited. We investigated transcriptional regulation of protein complexes in yeast using two approaches. First, known regulons, manually curated or identified by genome-wide screens, were mapped onto the components of multiprotein complexes. The complexes comprised manually curated ones and those characterized by high-throughput analyses. Second, putative regulatory sequence motifs were identified in the upstream regions of the genes involved in individual complexes and regulons were predicted on the basis of these motifs. RESULTS: Only a very small fraction of the analyzed complexes (5-6%) have subsets of their components mapping onto known regulons. Likewise, regulatory motifs are detected in only about 8-15% of the complexes, and in those, about half of the components are on average part of predicted regulons. In the manually curated complexes, the so-called 'permanent' assemblies have a larger fraction of their components belonging to putative regulons than 'transient' complexes. For the noisier set of complexes identified by high-throughput screens, valuable insights are obtained into the function and regulation of individual genes. CONCLUSIONS: A small fraction of the known multiprotein complexes in yeast seems to have at least a subset of their components co-regulated on the transcriptional level. Preliminary analysis of the regulatory motifs for these components suggests that the corresponding genes are likely to be co-regulated either together or in smaller subgroups, indicating that transcriptionally regulated modules might exist within complexes

Combining classifiers to predict gene function in Arabidopsis thaliana using large-scale gene expression measurements

<p>Abstract</p> <p>Background</p> <p><it>Arabidopsis thaliana </it>is the model species of current plant genomic research with a genome size of 125 Mb and approximately 28,000 genes. The function of half of these genes is currently unknown. The purpose of this study is to infer gene function in Arabidopsis using machine-learning algorithms applied to large-scale gene expression data sets, with the goal of identifying genes that are potentially involved in plant response to abiotic stress.</p> <p>Results</p> <p>Using in house and publicly available data, we assembled a large set of gene expression measurements for <it>A. thaliana</it>. Using those genes of known function, we first evaluated and compared the ability of basic machine-learning algorithms to predict which genes respond to stress. Predictive accuracy was measured using ROC₅₀and precision curves derived through cross validation. To improve accuracy, we developed a method for combining these classifiers using a weighted-voting scheme. The combined classifier was then trained on genes of known function and applied to genes of unknown function, identifying genes that potentially respond to stress. Visual evidence corroborating the predictions was obtained using electronic Northern analysis. Three of the predicted genes were chosen for biological validation. Gene knockout experiments confirmed that all three are involved in a variety of stress responses. The biological analysis of one of these genes (At1g16850) is presented here, where it is shown to be necessary for the normal response to temperature and NaCl.</p> <p>Conclusion</p> <p>Supervised learning methods applied to large-scale gene expression measurements can be used to predict gene function. However, the ability of basic learning methods to predict stress response varies widely and depends heavily on how much dimensionality reduction is used. Our method of combining classifiers can improve the accuracy of such predictions – in this case, predictions of genes involved in stress response in plants – and it effectively chooses the appropriate amount of dimensionality reduction automatically. The method provides a useful means of identifying genes in <it>A. thaliana </it>that potentially respond to stress, and we expect it would be useful in other organisms and for other gene functions.</p

Transcription factor-DNA binding via machine learning ensembles

The network of interactions between transcription factors (TFs) and their regulatory gene targets governs many of the behaviors and responses of cells. Construction of a transcriptional regulatory network involves three interrelated problems, defined for any regulator: finding (1) its target genes, (2) its binding motif and (3) its DNA binding sites. Many tools have been developed in the last decade to solve these problems. However, performance of algorithms for these has not been consistent for all transcription factors. Because machine learning algorithms have shown advantages in integrating information of different types, we investigate a machine-based approach to integrating predictions from an ensemble of commonly used motif exploration algorithms.Published versio

Incorporating genome-scale tools for studying energy homeostasis

Mammals have evolved complex regulatory systems that enable them to maintain energy homeostasis despite constant environmental challenges that limit the availability of energy inputs and their composition. Biological control relies upon intricate systems composed of multiple organs and specialized cell types that regulate energy up-take, storage, and expenditure. Because these systems simultaneously perform diverse functions and are highly integrated, they are extremely difficult to understand in terms of their individual component contributions to energy homeostasis. In order to provide improved treatments and clinical options, it is important to identify the principle genetic and molecular components, as well as the systemic features of regulation. To begin, many of these features can be discovered by integrating experimental technologies with advanced methods of analysis. This review focuses on the analysis of transcriptional data derived from microarrays and how it can complement other experimental techniques to study energy homeostasis

Androgen-responsive non-coding small RNAs extend the potential of HCG stimulation to act as a bioassay of androgen sufficiency

Background: It is unclear whether a short-term change in circulating androgens is associated with changes in the transcriptome of the peripheral blood mononuclear cells (PBMC). Aims &amp; Methods: To explore the effect of hCG-stimulation on the PBMC-transcriptome, 12 boys with a median age (range) of 0.7yrs (0.3, 11.2) who received intramuscular hCG 1500u on 3 consecutive days as part of their investigations underwent transcriptomic array analysis on RNA extracted from peripheral blood mononuclear cells before and after hCG stimulation. Results: Median pre and post hCG testosterone for the overall group was 0.7nmol/l (&lt;0.5,6) and 7.9nmol/l (&lt;0.5, 31.5), respectively. Of the 12 boys, 3 (25%) did not respond to hCG stimulation with a pre and post median serum testosterone of &lt;0.5nmol/l and &lt;0.5nmol/l, respectively. When corrected for gene expression changes in the non-responders to exclude hCG effects, all 9 of the hCG responders consistently demonstrated a 20% or greater increase in the expression of piR-37153 and piR-39248, non-coding PIWI-interacting RNAs (piRNAs). In addition, of the 9 responders, 8, 6 and 4 demonstrated a 30%, 40% and 50% rise, respectively in a total of 2 further piRNAs. In addition, 3 of the responders showed a 50% or greater rise in the expression of another small RNA, SNORD5. On comparing fold change in serum testosterone with fold change in the above transcripts, a positive correlation was detected for SNORD5 (p=0.01). Conclusions: The identification of a dynamic and androgen-responsive PBMC-transcriptome extends the potential value of the hCG test for assessment of androgen sufficiency

The functional landscape of mouse gene expression

BACKGROUND: Large-scale quantitative analysis of transcriptional co-expression has been used to dissect regulatory networks and to predict the functions of new genes discovered by genome sequencing in model organisms such as yeast. Although the idea that tissue-specific expression is indicative of gene function in mammals is widely accepted, it has not been objectively tested nor compared with the related but distinct strategy of correlating gene co-expression as a means to predict gene function. RESULTS: We generated microarray expression data for nearly 40,000 known and predicted mRNAs in 55 mouse tissues, using custom-built oligonucleotide arrays. We show that quantitative transcriptional co-expression is a powerful predictor of gene function. Hundreds of functional categories, as defined by Gene Ontology 'Biological Processes', are associated with characteristic expression patterns across all tissues, including categories that bear no overt relationship to the tissue of origin. In contrast, simple tissue-specific restriction of expression is a poor predictor of which genes are in which functional categories. As an example, the highly conserved mouse gene PWP1 is widely expressed across different tissues but is co-expressed with many RNA-processing genes; we show that the uncharacterized yeast homolog of PWP1 is required for rRNA biogenesis. CONCLUSIONS: We conclude that 'functional genomics' strategies based on quantitative transcriptional co-expression will be as fruitful in mammals as they have been in simpler organisms, and that transcriptional control of mammalian physiology is more modular than is generally appreciated. Our data and analyses provide a public resource for mammalian functional genomics