PETModule: a motif module based approach for enhancer target gene prediction.
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ABSTRACT:
SUBMITTER: Zhao C
PROVIDER: S-EPMC4951774 | biostudies-literature | 2016 Jul
REPOSITORIES: biostudies-literature
Publications
PETModule: a motif module based approach for enhancer target gene prediction.
Zhao Changyong C Li Xiaoman X Hu Haiyan H
Scientific reports 20160720
The identification of enhancer-target gene (ETG) pairs is vital for the understanding of gene transcriptional regulation. Experimental approaches such as Hi-C have generated valuable resources of ETG pairs. Several computational methods have also been developed to successfully predict ETG interactions. Despite these progresses, high-throughput experimental approaches are still costly and existing computational approaches are still suboptimal and not easy to apply. Here we developed a motif modul ...[more]
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Project description:BackgroundMany genome-wide collections of candidate cis-regulatory elements (cCREs) have been defined using genomic and epigenomic data, but it remains a major challenge to connect these elements to their target genes.ResultsTo facilitate the development of computational methods for predicting target genes, we develop a Benchmark of candidate Enhancer-Gene Interactions (BENGI) by integrating the recently developed Registry of cCREs with experimentally derived genomic interactions. We use BENGI to test several published computational methods for linking enhancers with genes, including signal correlation and the TargetFinder and PEP supervised learning methods. We find that while TargetFinder is the best-performing method, it is only modestly better than a baseline distance method for most benchmark datasets when trained and tested with the same cell type and that TargetFinder often does not outperform the distance method when applied across cell types.ConclusionsOur results suggest that current computational methods need to be improved and that BENGI presents a useful framework for method development and testing.
| S-EPMC6977301 | biostudies-literature
Project description:Traditional genomic prediction models based on individual genes suffer from low reproducibility across microarray studies due to the lack of robustness to expression measurement noise and gene missingness when they are matched across platforms. It is common that some of the genes in the prediction model established in a training study cannot be matched to another test study because a different platform is applied. The failure of inter-study predictions has severely hindered the clinical applications of microarray. To overcome the drawbacks of traditional gene-based prediction (GBP) models, we propose a module-based prediction (MBP) strategy via unsupervised gene clustering.K-means clustering is used to group genes sharing similar expression profiles into gene modules, and small modules are merged into their nearest neighbors. Conventional univariate or multivariate feature selection procedure is applied and a representative gene from each selected module is identified to construct the final prediction model. As a result, the prediction model is portable to any test study as long as partial genes in each module exist in the test study. We demonstrate that K-means cluster sizes generally follow a multinomial distribution and the failure probability of inter-study prediction due to missing genes is diminished by merging small clusters into their nearest neighbors. By simulation and applications of real datasets in inter-study predictions, we show that the proposed MBP provides slightly improved accuracy while is considerably more robust than traditional GBP.http://www.biostat.pitt.edu/bioinfo/ctseng@pitt.eduSupplementary data are available at Bioinformatics online.
| S-EPMC2951088 | biostudies-other
Project description:Type II diabetes is a chronic condition that affects the way our body metabolizes sugar. The body's important source of fuel is now becoming a chronic disease all over the world. It is now very necessary to identify the new potential targets for the drugs which not only control the disease but also can treat it. Support vector machines are the classifier which has a potential to make a classification of the discriminatory genes and non-discriminatory genes. SVMRFE a modification of SVM ranks the genes based on their discriminatory power and eliminate the genes which are not involved in causing the disease. A gene regulatory network has been formed with the top ranked coding genes to identify their role in causing diabetes. To further validate the results pathway study was performed to identify the involvement of the coding genes in type II diabetes. The genes obtained from this study showed a significant involvement in causing the disease, which may be used as a potential drug target.
| S-EPMC5331150 | biostudies-literature
Project description:Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power.Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi???5, and 0.87 for pKi???6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi???5, and 0.76 for pKi???6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone. In the case of aloe-emodin's laxative effect, MOST predicted that acetylcholinesterase was the mechanism-of-action target; in vivo studies validated this prediction.Using the MOST approach can result in highly accurate and robust target prediction. Integrated with a FDR control procedure, MOST provides a reliable framework for multiple-target inference. It has prospective applications in drug repurposing and mechanism-of-action target prediction.
| S-EPMC5346209 | biostudies-literature
Project description:An increasingly common method for predicting gene activity is genome-wide chromatin immunoprecipitation of M-bM-^@M-^XactiveM-bM-^@M-^Y chromatin modifications followed by massively parallel sequencing (ChIP-seq). Using a novel ChIP-seq quantification method (cRPKM), we tested the power of such ChIP-seq strategies to predict relative protein and RNA levels at the pre-pro-B and pro-B differentiation stages in early B cell lymphopoiesis. Using a multi-omics approach that compares promoter chromatin status (ChIP-seq; published in GSE:21978) with ongoing active transcription (GRO-seq; published in GSE:40173), steady state mRNA (RNA-seq), inferred mRNA stability, and relative proteome abundance measurements (iTRAQ), we demonstrate that active chromatin modifications at promoters are a good indicator of transcription and steady state mRNA levels. Moreover, we found that promoters with active chromatin modifications exclusively in one of these cell states frequently predicted differentially expressed proteins. However, we found that many genes whose promoters have non-differential but active chromatin modifications also displayed changes in expression of their cognate proteins. This large class of developmentally and differentially regulated proteins that was uncoupled from chromatin status used mostly post-transcriptional mechanisms. Interestingly, the most differentially expressed protein in our B-cell development system, 2410004B18Rik, was regulated by a post-transcriptional mechanism, which further analyses indicated was mediated by an identified miRNA. These data provide a striking example of how our integrated multi-omics data set can be useful in uncovering regulatory mechanisms. Total RNA from mouse pre-pro-B and pro-B cells, depleted of rRNA and small RNAs, was sequenced using a strand specific, single end sequencing strategy.
2013-11-19 | E-GEOD-52450 | biostudies-arrayexpress
Project description:An increasingly common method for predicting gene activity is genome-wide chromatin immunoprecipitation of ‘active’ chromatin modifications followed by massively parallel sequencing (ChIP-seq). Using a novel ChIP-seq quantification method (cRPKM), we tested the power of such ChIP-seq strategies to predict relative protein and RNA levels at the pre-pro-B and pro-B differentiation stages in early B cell lymphopoiesis. Using a multi-omics approach that compares promoter chromatin status (ChIP-seq; published in GSE:21978) with ongoing active transcription (GRO-seq; published in GSE:40173), steady state mRNA (RNA-seq), inferred mRNA stability, and relative proteome abundance measurements (iTRAQ), we demonstrate that active chromatin modifications at promoters are a good indicator of transcription and steady state mRNA levels. Moreover, we found that promoters with active chromatin modifications exclusively in one of these cell states frequently predicted differentially expressed proteins. However, we found that many genes whose promoters have non-differential but active chromatin modifications also displayed changes in expression of their cognate proteins. This large class of developmentally and differentially regulated proteins that was uncoupled from chromatin status used mostly post-transcriptional mechanisms. Interestingly, the most differentially expressed protein in our B-cell development system, 2410004B18Rik, was regulated by a post-transcriptional mechanism, which further analyses indicated was mediated by an identified miRNA. These data provide a striking example of how our integrated multi-omics data set can be useful in uncovering regulatory mechanisms.
2013-11-19 | GSE52450 | GEO
Project description:The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example.
| S-EPMC3617101 | biostudies-literature
Project description:BackgroundThe availability of large collections of microarray datasets (compendia), or knowledge about grouping of genes into pathways (gene sets), is typically not exploited when training predictors of disease outcome. These can be useful since a compendium increases the number of samples, while gene sets reduce the size of the feature space. This should be favorable from a machine learning perspective and result in more robust predictors.MethodologyWe extracted modules of regulated genes from gene sets, and compendia. Through supervised analysis, we constructed predictors which employ modules predictive of breast cancer outcome. To validate these predictors we applied them to independent data, from the same institution (intra-dataset), and other institutions (inter-dataset).ConclusionsWe show that modules derived from single breast cancer datasets achieve better performance on the validation data compared to gene-based predictors. We also show that there is a trend in compendium specificity and predictive performance: modules derived from a single breast cancer dataset, and a breast cancer specific compendium perform better compared to those derived from a human cancer compendium. Additionally, the module-based predictor provides a much richer insight into the underlying biology. Frequently selected gene sets are associated with processes such as cell cycle, E2F regulation, DNA damage response, proteasome and glycolysis. We analyzed two modules related to cell cycle, and the OCT1 transcription factor, respectively. On an individual basis, these modules provide a significant separation in survival subgroups on the training and independent validation data.
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Project description:BackgroundIt is necessary to evaluate the efficacy of individual drugs on patients to realize personalized medicine. Testing drugs on patients in clinical trial is the only way to evaluate the efficacy of drugs. The approach is labour intensive and requires overwhelming costs and a number of experiments. Therefore, preclinical model system has been intensively investigated for predicting the efficacy of drugs. Current computational drug sensitivity prediction approaches use general biological network modules as their prediction features. Therefore, they miss indirect effectors or the effects from tissue-specific interactions.ResultsWe developed cell line specific functional modules. Enriched scores of functional modules are utilized as cell line specific features to predict the efficacy of drugs. Cell line specific functional modules are clusters of genes, which have similar biological functions in cell line specific networks. We used linear regression for drug efficacy prediction. We assessed the prediction performance in leave-one-out cross-validation (LOOCV). Our method was compared with elastic net model, which is a popular model for drug efficacy prediction. In addition, we analysed drug sensitivity-associated functions of five drugs - lapatinib, erlotinib, raloxifene, tamoxifen and gefitinib- by our model.ConclusionsOur model can provide cell line specific drug efficacy prediction and also provide functions which are associated with drug sensitivity. Therefore, we could utilize drug sensitivity associated functions for drug repositioning or for suggesting secondary drugs for overcoming drug resistance.
| S-EPMC4965733 | biostudies-literature
Project description:Drug targets are biomacromolecules or biomolecular structures that bind to specific drugs and produce therapeutic effects. Therefore, the prediction of drug-target interactions (DTIs) is important for disease therapy. Incorporating multiple similarity measures for drugs and targets is of essence for improving the accuracy of prediction of DTIs. However, existing studies with multiple similarity measures ignored the global structure information of similarity measures, and required manual extraction features of drug-target pairs, ignoring the non-linear relationship among features. In this paper, we proposed a novel approach MDADTI for DTIs prediction based on MDA. MDADTI applied random walk with restart method and positive pointwise mutual information to calculate the topological similarity matrices of drugs and targets, capturing the global structure information of similarity measures. Then, MDADTI applied multimodal deep autoencoder to fuse multiple topological similarity matrices of drugs and targets, automatically learned the low-dimensional features of drugs and targets, and applied deep neural network to predict DTIs. The results of 5-repeats of 10-fold cross-validation under three different cross-validation settings indicated that MDADTI is superior to the other four baseline methods. In addition, we validated the predictions of the MDADTI in six drug-target interactions reference databases, and the results showed that MDADTI can effectively identify unknown DTIs.
| S-EPMC6997437 | biostudies-literature