KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data.
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ABSTRACT:
SUBMITTER: Crowl S
PROVIDER: S-EPMC9314348 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:MotivationPhosphoproteomic experiments are increasingly used to study the changes in signaling occurring across different conditions. It has been proposed that changes in phosphorylation of kinase target sites can be used to infer when a kinase activity is under regulation. However, these approaches have not yet been benchmarked due to a lack of appropriate benchmarking strategies.ResultsWe used curated phosphoproteomic experiments and a gold standard dataset containing a total of 184 kinase-condition pairs where regulation is expected to occur to benchmark and compare different kinase activity inference strategies: Z-test, Kolmogorov Smirnov test, Wilcoxon rank sum test, gene set enrichment analysis (GSEA), and a multiple linear regression model. We also tested weighted variants of the Z-test and GSEA that include information on kinase sequence specificity as proxy for affinity. Finally, we tested how the number of known substrates and the type of evidence ( in vivo , in vitro or in silico ) supporting these influence the predictions.ConclusionsMost models performed well with the Z-test and the GSEA performing best as determined by the area under the ROC curve (Mean AUC = 0.722). Weighting kinase targets by the kinase target sequence preference improves the results marginally. However, the number of known substrates and the evidence supporting the interactions has a strong effect on the predictions.Availability and implementationThe KSEA implementation is available in https://github.com/ evocellnet/ksea. Additional data is available in http://phosfate.com.Contactpbeltrao@ebi.ac.uk or ochoa@ebi.ac.uk.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC5870625 | biostudies-literature
Project description:Protein phosphorylation is critical for regulating cellular activities by controlling protein activities, localization and turnover, and by transmitting information within cells through signaling networks. However, predictions of protein phosphorylation and signaling networks remain a significant challenge, lagging behind predictions of transcriptional regulatory networks into which they often feed.We developed PhosphoChain to predict kinases, phosphatases and chains of phosphorylation events in signaling networks by combining mRNA expression levels of regulators and targets with a motif detection algorithm and optional prior information. PhosphoChain correctly reconstructed ?78% of the yeast mitogen-activated protein kinase pathway from publicly available data. When tested on yeast phosphoproteomic data from large-scale mass spectrometry experiments, PhosphoChain correctly identified ?27% more phosphorylation sites than existing motif detection tools (NetPhosYeast and GPS2.0), and predictions of kinase-phosphatase interactions overlapped with ?59% of known interactions present in yeast databases. PhosphoChain provides a valuable framework for predicting condition-specific phosphorylation events from high-throughput data.PhosphoChain is implemented in Java and available at http://virgo.csie.ncku.edu.tw/PhosphoChain/ or http://aitchisonlab.com/PhosphoChain
| S-EPMC3777105 | biostudies-literature
Project description:High-throughput data is providing a comprehensive view of the molecular changes in cancer tissues. New technologies allow for the simultaneous genome-wide assay of the state of genome copy number variation, gene expression, DNA methylation and epigenetics of tumor samples and cancer cell lines. Analyses of current data sets find that genetic alterations between patients can differ but often involve common pathways. It is therefore critical to identify relevant pathways involved in cancer progression and detect how they are altered in different patients.We present a novel method for inferring patient-specific genetic activities incorporating curated pathway interactions among genes. A gene is modeled by a factor graph as a set of interconnected variables encoding the expression and known activity of a gene and its products, allowing the incorporation of many types of omic data as evidence. The method predicts the degree to which a pathway's activities (e.g. internal gene states, interactions or high-level 'outputs') are altered in the patient using probabilistic inference. Compared with a competing pathway activity inference approach called SPIA, our method identifies altered activities in cancer-related pathways with fewer false-positives in both a glioblastoma multiform (GBM) and a breast cancer dataset. PARADIGM identified consistent pathway-level activities for subsets of the GBM patients that are overlooked when genes are considered in isolation. Further, grouping GBM patients based on their significant pathway perturbations divides them into clinically-relevant subgroups having significantly different survival outcomes. These findings suggest that therapeutics might be chosen that target genes at critical points in the commonly perturbed pathway(s) of a group of patients.Source code available at http://sbenz.github.com/Paradigm,.Supplementary data are available at Bioinformatics online.
| S-EPMC2881367 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2014-11-24 | GSE62189 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2014-11-24 | E-GEOD-62189 | biostudies-arrayexpress
Project description:Left atrial flutter (LAFL) occurs in patients after atrial fibrillation ablation. Identification of optimal ablation targets to terminate LAFL remains challenging.The purpose of this study was to use patient-specific models to simulate LAFL and predict optimal ablation targets using a novel approach based on flow network theory.Late gadolinium-enhanced cardiac magnetic resonance scans from 10 patients with LAFL were used to construct atrial models incorporating fibrosis by investigators blinded to procedural findings. Rapid pacing was applied in silico to induce LAFL. In each LAFL, we represented reentrant wave propagation as an electric flow network and identified the "minimum cut" (MC), which was the smallest amount of tissue that separated the flow into 2 discontinuous components. In silico ablation was applied at MCs, and targets were compared to those that terminated LAFL during catheter ablation.Patient-specific atrial models were successfully generated from patient scans. LAFL was induced in 7 of 10 models. Ablation of MCs terminated LAFL in 4 models and produced new, slower LAFL morphologies in the other 3. For the latter cases, flow analysis was repeated to identify MCs of emergent LAFLs. Ablation of these MCs terminated emergent LAFLs. The MC-based ablation lesions in simulations were similar in length and location to ablation targets that terminated LAFL during catheter ablation for these 7 patients.Personalized atrial simulations can predict ablation targets for LAFL. These simulations provide a powerful tool for planning ablation procedures and may reduce procedural times and complications.
| S-EPMC5972526 | biostudies-literature
Project description:DNA methylation is a reaction that results in the formation of 5-methylcytosine when a methyl group is added to the cytosine’s C5 position. As organisms age, DNA methylation patterns change in a reproducible fashion. This phenomenon has established DNA methylation as a valuable biomarker in aging studies. Epigenetic clocks based on weighted combinations of methylation sites have been developed to accurately predict the age of an individual from their methylome. However, many epigenetic clocks, particularly those that utilize penalized regression, model the changes in methylation linearly with age. Moreover, these models, which use methylation levels as features, are not robust to missing data and do not account for the count-based nature of bisulfite sequence data. Additionally, the models are generally not interpretable. To overcome these challenges, we present BayesAge, an extension of the previously developed scAge approach that was developed for the analysis of single cell DNA methylation datasets. BayesAge utilizes maximum likelihood estimation (MLE) to infer ages, models count data using binomial distributions, and uses LOWESS smoothing to capture the non-linear dynamics between methylation and age. Our approach is designed for use with bulk bisulfite sequencing datasets. BayesAge outperforms scAge in several respects. Specifically, BayesAge’s age residuals are not age associated, thus providing a less biased representation of epigenetic age variation across populations. Moreover, BayesAge enables the estimation of error bounds on age inference and, when run on downsampled data, its coefficient of determination between predicted and actual ages surpasses both scAge and penalized regression.
2024-03-20 | GSE261769 | GEO
Project description:In the study of complex diseases, a major challenge is disease heterogeneity, where the dysregulation of different pathways often lead to similar disease phenotypes. As a result, a given pathway could be differentially expressed with respect to controls for some patients, but not for others. Therefore, to develop successful personalized treatment regime, in addition to identifying disease relevant pathways for the entire patient group, it's also important to test if a particular pathway is dysregulated for an individual patient. To this end, we compare pathway gene expression profile for a particular individual in the patient group to the "norm" (or standard) established by a group of control patients. We studied statistical analysis of patient-specific pathway activities under the mixed models framework. Using gene expression dataset with realistic correlation patterns, we showed the proposed hypothesis testing procedure had false positive rate (type I error) as expected. In addition, we illustrated the proposed methodology using a Type 2 Diabetes dataset. Our results showed a previously diabetes associated pathway was only differentially expressed (relative to the control group) in less than 30% of the diabetes patients, which provided an explanation for the moderate group level statistical significance seen in a previous study. This result also suggested targeting this particular pathway would likely be beneficial for only 30% of the patients. In addition to the case-control study we have illustrated, this model can be easily extended to handle more complex designs with additional covariates and multiple sources of variations. Moreover, the proposed model operates within a well-established statistical framework and can be implemented in common statistical packages.
| S-EPMC3793893 | biostudies-literature
Project description:Cascades of phosphorylation between protein kinases comprise a core mechanism in the integration and propagation of intracellular signals. Although we have accumulated a wealth of knowledge around some such pathways, this is subject to study biases and much remains to be uncovered. Phosphoproteomics, the identification and quantification of phosphorylated proteins on a proteomic scale, provides a high-throughput means of interrogating the state of intracellular phosphorylation, both at the pathway level and at the whole-cell level. In this review, we discuss methods for using human quantitative phosphoproteomic data to reconstruct the underlying signalling networks that generated it. We address several challenges imposed by the data on such analyses and we consider promising advances towards reconstructing unbiased, kinome-scale signalling networks.
| S-EPMC6204553 | biostudies-other
Project description:BackgroundProteins are the workforce of the cell and their phosphorylation status tailors specific responses efficiently. One of the main challenges of phosphoproteomic approaches is to deconvolute biological processes that specifically respond to an experimental query from a list of phosphoproteins. Comparison of the frequency distribution of GO (Gene Ontology) terms in a given phosphoproteome set with that observed in the genome reference set (GenRS) is the most widely used tool to infer biological significance. Yet, this comparison assumes that GO term distribution between the phosphoproteome and the genome are identical. However, this hypothesis has not been tested due to the lack of a comprehensive phosphoproteome database.ResultsIn this study, we test this hypothesis by constructing three phosphoproteome databases in Arabidopsis thaliana: one based in experimental data (ExpRS), another based in in silico phosphorylation protein prediction (PredRS) and a third that is the union of both (UnRS). Our results show that the three phosphoproteome reference sets show default enrichment of several GO terms compared to GenRS, indicating that GO term distribution in the phosphoproteomes does not match that of the genome. Moreover, these differences overshadow the identification of GO terms that are specifically enriched in a particular condition. To overcome this limitation, we present an additional comparison of the sample of interest with UnRS to uncover GO terms specifically enriched in a particular phosphoproteome experiment. Using this strategy, we found that mRNA splicing and cytoplasmic microtubule compounds are important processes specifically enriched in the phosphoproteome of dark-grown Arabidopsis seedlings.ConclusionsThis study provides a novel strategy to uncover GO specific terms in phosphoproteome data of Arabidopsis that could be applied to any other organism. We also highlight the importance of specific phosphorylation pathways that take place during dark-grown Arabidopsis development.
| S-EPMC8670200 | biostudies-literature