scholarly journals Causal Inference Engine: a platform for directional gene set enrichment analysis and inference of active transcriptional regulators

2019 ◽  
Author(s):  
Saman Farahmand ◽  
Corey O’Connor ◽  
Jill A Macoska ◽  
Kourosh Zarringhalam
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Expression Data ◽  
Transcriptional Regulators ◽  
Regulatory Mechanisms ◽  
Gene Interactions ◽  
Expression Data ◽  
Inference Algorithms ◽  
Tf Gene ◽  
Mode Of Regulation

Abstract Inference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator–gene interactions. Several commercial platforms provide large and manually curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-seq experiments with gene-expression data from tissue-specific RNA-seq experiments. Our GGM approach identifies high confidence transcription factor (TF)–gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually curated databases of TF–gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.

scholarly journals Causal Inference Engine: A platform for directional gene set enrichment analysis and inference of active transcriptional regulators

2019 ◽  
Author(s):  
Saman Farahmand ◽  
Corey O’Connor ◽  
Jill A. Macoska ◽  
Kourosh Zarringhalam
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Gene Expression Data ◽  
Transcriptional Regulators ◽  
Regulatory Mechanisms ◽  
Gene Interactions ◽  
Expression Data ◽  
Inference Algorithms ◽  
Tf Gene ◽  
Mode Of Regulation

ABSTRACTInference of active regulatory mechanisms underlying specific molecular and environmental perturbations is essential for understanding cellular response. The success of inference algorithms relies on the quality and coverage of the underlying network of regulator-gene interactions. Several commercial platforms provide large and manually-curated regulatory networks and functionality to perform inference on these networks. Adaptation of such platforms for open-source academic applications has been hindered by the lack of availability of accurate, high-coverage networks of regulatory interactions and integration of efficient causal inference algorithms. In this work, we present CIE, an integrated platform for causal inference of active regulatory mechanisms form differential gene expression data. Using a regularized Gaussian Graphical Model, we construct a transcriptional regulatory network by integrating publicly available ChIP-Seq experiments with gene-expression data from tissue-specific RNA-Seq experiments. Our GGM approach identifies high confidence TF-gene interactions and annotates the interactions with information on mode of regulation (activation vs. repression). Benchmarks against manually-curated databases of TF-gene interactions show that our method can accurately detect mode of regulation. We demonstrate the ability of our platform to identify active transcriptional regulators by using controlled in vitro overexpression and stem-cell differentiation studies and utilize our method to investigate transcriptional mechanisms of fibroblast phenotypic plasticity.

scholarly journals Inferring time-lagged causality using the derivative of single-cell expression

2021 ◽  
Author(s):  
Huan-Huan Wei ◽  
Hui Lu ◽  
Hongyu Zhao
Keyword(s):  
Gene Expression ◽  
Causal Inference ◽  
Single Cell ◽  
Causal Relationship ◽  
Gene Expression Data ◽  
Expression Data ◽  
Causal Relationships ◽  
Expression Levels ◽  
Gene Pairs ◽  
Time Lagged

AbstractMany computational methods have been developed for inferring causality among genes using cross-sectional gene expression data, such as single-cell RNA sequencing (scRNA-seq) data. However, due to the limitations of scRNA-seq technologies, time-lagged causal relationships may be missed by existing methods. In this work, we propose a method, called causal inference with time-lagged information (CITL), to infer time-lagged causal relationships from scRNA-seq data by assessing conditional independence between the changing and current expression levels of genes. CITL estimates the changing expression levels of genes by “RNA velocity”. We demonstrate the accuracy and stability of CITL for inferring time-lagged causality on simulation data against other leading approaches. We have applied CITL to real scRNA data and inferred 878 pairs of time-lagged causal relationships, with many of these inferred results supported by the literature.Author summaryComputational causal inference is a promising way to survey causal relationships between genes efficiently. Though many causal inference methods have been applied to gene expression data, none considers the time-lagged causal relationship, which means that some genes may take some time to affect their target genes with several reactions. If relationships between genes are time-lagged, the existing methods’ assumptions will be violated. The relationships will be challenging to recognize. We demonstrate that this is indeed the case through simulation. Therefore, we develop a method for inferring time-lagged causal relationships of single-cell gene expression data. We assume that a time-lagged causal relationship should present a strong association between the cause and the effect’s changing. To calculate such correlation, we first estimate the derivative of gene expression using the information from unspliced transcripts. Then, we use conditional independent tests to search gene pairs satisfying our assumption. Our results suggest that we could accurately infer time-lagged causal gene pairs validated by published literature. This method may complement gene regulatory analysis and provide candidate gene pairs for further controlled experiments.

scholarly journals Identification of gene interactions associated with disease from gene expression data using synergy networks

2008 ◽  
Vol 2 (1) ◽  
pp. 10 ◽  
Author(s):  
John Watkinson ◽  
Xiaodong Wang ◽  
Tian Zheng ◽  
Dimitris Anastassiou
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Interactions ◽  
Expression Data

scholarly journals PEAK Predicts Gene Regulatory Network Linkages during Sea Urchin Development with High Sensitivity from Gene Expression Data Alone

2021 ◽  
Author(s):  
Jingyi Zhang ◽  
Farhan Ibrahim ◽  
Doaa Altarawy ◽  
Lenwood S Heath ◽  
Sarah Tulin
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Sea Urchin ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Gene Networks ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Gene Interactions ◽  
Expression Data

Abstract BackgroundGene regulatory network (GRN) inference can now take advantage of powerful machine learning algorithms to predict the entire landscape of gene-to-gene interactions with the potential to complement traditional experimental methods in building gene networks. However, the dynamical nature of embryonic development -- representing the time-dependent interactions between thousands of transcription factors, signaling molecules, and effector genes -- is one of the most challenging arenas for GRN prediction. ResultsIn this work, we show that successful GRN predictions for developmental systems from gene expression data alone can be obtained with the Priors Enriched Absent Knowledge (PEAK) network inference algorithm. PEAK is a noise-robust method that models gene expression dynamics via ordinary differential equations and selects the best network based on information-theoretic criteria coupled with the machine learning algorithm Elastic net. We test our GRN prediction methodology using two gene expression data sets for the purple sea urchin (S. purpuratus) and cross-check our results against existing GRN models that have been constructed and validated by over 30 years of experimental results. Our results found a remarkably high degree of sensitivity in identifying known gene interactions in the network (maximum 76.32%). We also generated 838 novel predictions for interactions that have not yet been described, which provide a resource for researchers to use to further complete the sea urchin GRN. ConclusionsGRN predictions that match known gene interactions can be produced using gene expression data alone from developmental time series experiments.

scholarly journals EXPath 2.0: An Updated Database for Integrating High-Throughput Gene Expression Data with Biological Pathways

2020 ◽  
Vol 61 (10) ◽  
pp. 1818-1827
Author(s):  
Kuan-Chieh Tseng ◽  
Guan-Zhen Li ◽  
Yu-Cheng Hung ◽  
Chi-Nga Chow ◽  
Nai-Yun Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Metabolic Pathways ◽  
Expression Profiles ◽  
Regulatory Mechanisms ◽  
Biological Processes ◽  
Expression Data ◽  
Rna Seq ◽  
Correlation Networks ◽  
Transcriptional Regulatory Mechanisms

Abstract Co-expressed genes tend to have regulatory relationships and participate in similar biological processes. Construction of gene correlation networks from microarray or RNA-seq expression data has been widely applied to study transcriptional regulatory mechanisms and metabolic pathways under specific conditions. Furthermore, since transcription factors (TFs) are critical regulators of gene expression, it is worth investigating TFs on the promoters of co-expressed genes. Although co-expressed genes and their related metabolic pathways can be easily identified from previous resources, such as EXPath and EXPath Tool, this information is not simultaneously available to identify their regulatory TFs. EXPath 2.0 is an updated database for the investigation of regulatory mechanisms in various plant metabolic pathways with 1,881 microarray and 978 RNA-seq samples. There are six significant improvements in EXPath 2.0: (i) the number of species has been extended from three to six to include Arabidopsis, rice, maize, Medicago, soybean and tomato; (ii) gene expression at various developmental stages have been added; (iii) construction of correlation networks according to a group of genes is available; (iv) hierarchical figures of the enriched Gene Ontology (GO) terms are accessible; (v) promoter analysis of genes in a metabolic pathway or correlation network is provided; and (vi) user’s gene expression data can be uploaded and analyzed. Thus, EXPath 2.0 is an updated platform for investigating gene expression profiles and metabolic pathways under specific conditions. It facilitates users to access the regulatory mechanisms of plant biological processes. The new version is available at http://EXPath.itps.ncku.edu.tw.

scholarly journals ModEx: A text mining system for extracting mode of regulation of Transcription Factor-gene regulatory interaction

2019 ◽  
Author(s):  
Saman Farahmand ◽  
Todd Riley ◽  
Kourosh Zarringhalam
Keyword(s):  
Gene Expression ◽  
Text Mining ◽  
Gene Interactions ◽  
Mining System ◽  
Link Type ◽  
Gene Regulatory ◽  
Rest Api ◽  
Tf Gene ◽  
Text Mining System ◽  
Mode Of Regulation

ABSTRACTBackgroundTranscription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TF-gene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine.MethodsIn this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases.ResultsOur results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage.AvailibilitySource code and datasets are available for download on GitHub: https://github.com/samanfrm/modex HTTP REST API: https://watson.math.umb.edu/modex/[type query]

scholarly journals Network Insights into Improving Drug Target Inference Algorithms

2020 ◽  
Author(s):  
Muying Wang ◽  
Heeju Noh ◽  
Ericka Mochan ◽  
Jason E. Shoemaker
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Protein Interactions ◽  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Expression Data ◽  
Protein Protein Interactions ◽  
Cell Tissue ◽  
Inference Algorithms

AbstractTo improve the efficacy of drug research and development (R&D), a better understanding of drug mechanisms of action (MoA) is needed to improve drug discovery. Computational algorithms, such as ProTINA, that integrate protein-protein interactions (PPIs), protein-gene interactions (PGIs) and gene expression data have shown promising performance on drug target inference. In this work, we evaluated how network and gene expression data affect ProTINA’s accuracy. Network data predominantly determines the accuracy of ProTINA instead of gene expression, while the size of an interaction network or selecting cell/tissue-specific networks have limited effects on the accuracy. However, we found that protein network betweenness values showed high accuracy in predicting drug targets. Therefore, we suggested a new algorithm, TREAP (https://github.com/ImmuSystems-Lab/TREAP), that combines betweenness values and adjusted p-values for target inference. This algorithm has resulted in higher accuracy than ProTINA using the same datasets.

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