scholarly journals PUMA: PANDA Using MicroRNA Associations

2020 ◽  
Vol 36 (18) ◽  
pp. 4765-4773
Author(s):  
Marieke L Kuijjer ◽  
Maud Fagny ◽  
Alessandro Marin ◽  
John Quackenbush ◽  
Kimberly Glass
Keyword(s):  
Messenger Rna ◽  
Message Passing ◽  
Mirna Target ◽  
Target Prediction ◽  
Network Models ◽  
Tissue Expression ◽  
Supplementary Information ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Regulatory Processes

Abstract Motivation Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. Availability and implementation PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/). Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals PUMA: PANDA Using MicroRNA Associations

2019 ◽  
Author(s):  
Marieke L. Kuijjer ◽  
Maud Fagny ◽  
Alessandro Marin ◽  
John Quackenbush ◽  
Kimberly Glass
Keyword(s):  
Message Passing ◽  
Mirna Target ◽  
Target Gene ◽  
Target Prediction ◽  
Network Models ◽  
Tissue Expression ◽  
Tissue Specific ◽  
Link Type ◽  
Regulatory Processes ◽  
Shiny App

Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the mRNA transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with protein-protein interaction and target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression (GTEx) project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA-target gene regulatory interactions in the different network models. Furthermore, tissue-specific functions of miRNAs, which we identified by analyzing their regulatory profiles and which we made available through a Shiny app (https://kuijjer.shinyapps.io/puma_gtex/), are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific regulatory processes of miRNAs. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. PUMA is available in C++, MATLAB, and Python code on GitHub (https://github.com/kuijjerlab/PUMA and https://github.com/kuijjerlab/PyPuma).

Bioinformatics Methods for Studying MicroRNA and ARE-Mediated Regulation of Post-Transcriptional Gene Expression

2010 ◽  
Vol 1 (3) ◽  
pp. 97-112 ◽  
Author(s):  
Richipal Singh Bindra ◽  
Jason T. L. Wang ◽  
Paramjeet Singh Bagga
Keyword(s):  
Mirna Target ◽  
Target Prediction ◽  
Untranslated Regions ◽  
Regulatory Rna ◽  
Rna Motifs ◽  
Protein Coding ◽  
Rna Molecules ◽  
Cellular Processes ◽  
Target Sites ◽  
Transcriptional Gene Regulation

MicroRNAs (miRNAs) are short single-stranded RNA molecules with 21-22 nucleotides known to regulate post-transcriptional expression of protein-coding genes involved in most of the cellular processes. Prediction of miRNA targets is a challenging bioinformatics problem. AU-rich elements (AREs) are regulatory RNA motifs found in the 3’ untranslated regions (UTRs) of mRNAs, and they play dominant roles in the regulated decay of short-lived human mRNAs via specific interactions with proteins. In this paper, the authors review several miRNA target prediction tools and data sources, as well as computational methods used for the prediction of AREs. The authors discuss the connection between miRNA and ARE-mediated post-transcriptional gene regulation. Finally, a data mining method for identifying the co-occurrences of miRNA target sites in ARE containing genes is presented.

Bioinformatics Methods for Studying MicroRNA and ARE-Mediated Regulation of Post-Transcriptional Gene Expression

2013 ◽  
pp. 156-173
Author(s):  
Richipal Singh Bindra ◽  
Jason T. L. Wang ◽  
Paramjeet Singh Bagga
Keyword(s):  
Mirna Target ◽  
Target Prediction ◽  
Specific Interactions ◽  
Rna Motifs ◽  
Protein Coding ◽  
Rna Molecules ◽  
Cellular Processes ◽  
Protein Coding Genes ◽  
Prediction Tools ◽  
Transcriptional Gene Regulation

MicroRNAs (miRNAs) are short single-stranded RNA molecules with 21-22 nucleotides known to regulate post-transcriptional expression of protein-coding genes involved in most of the cellular processes. Prediction of miRNA targets is a challenging bioinformatics problem. AU-rich elements (AREs) are regulatory RNA motifs found in the 3’ untranslated regions (UTRs) of mRNAs, and they play dominant roles in the regulated decay of short-lived human mRNAs via specific interactions with proteins. In this paper, the authors review several miRNA target prediction tools and data sources, as well as computational methods used for the prediction of AREs. The authors discuss the connection between miRNA and ARE-mediated post-transcriptional gene regulation. Finally, a data mining method for identifying the co-occurrences of miRNA target sites in ARE containing genes is presented.

scholarly journals FilTar: using RNA-Seq data to improve microRNA target prediction accuracy in animals

2020 ◽  
Vol 36 (8) ◽  
pp. 2410-2416
Author(s):  
Thomas Bradley ◽  
Simon Moxon
Keyword(s):  
Programming Languages ◽  
Mirna Target ◽  
Target Prediction ◽  
Supplementary Information ◽  
Mirna Target Prediction ◽  
Rna Seq ◽  
Microrna Target ◽  
R Programming ◽  
High Level ◽  
Transcript Expression Level

Abstract Motivation MicroRNA (miRNA) target prediction algorithms do not generally consider biological context and therefore generic target prediction based on seed binding can lead to a high level of false-positive predictions. Here, we present FilTar, a method that incorporates RNA-Seq data to make miRNA target prediction specific to a given cell type or tissue of interest. Results We demonstrate that FilTar can be used to: (i) provide sample specific 3′-UTR reannotation; extending or truncating default annotations based on RNA-Seq read evidence and (ii) filter putative miRNA target predictions by transcript expression level, thus removing putative interactions where the target transcript is not expressed in the tissue or cell line of interest. We test the method on a variety of miRNA transfection datasets and demonstrate increased accuracy versus generic miRNA target prediction methods. Availability and implementation FilTar is freely available and can be downloaded from https://github.com/TBradley27/FilTar. The tool is implemented using the Python and R programming languages, and is supported on GNU/Linux operating systems. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Systems Biology Inferring edge function in protein-protein interaction networks

2018 ◽  
Author(s):  
Daniel Esposito ◽  
Joseph Cursons ◽  
Melissa Davis
Keyword(s):  
Protein Interaction ◽  
Supplementary Information ◽  
Human Interactome ◽  
High Coverage ◽  
Protein Protein Interaction ◽  
Cellular Processes ◽  
Regulatory Processes ◽  
Ppi Networks ◽  
Biological Phenomena ◽  
Biological Insight

AbstractMotivation: Post-translational modifications (PTMs) regulate many key cellular processes. Numerous studies have linked the topology of protein-protein interaction (PPI) networks to many biological phenomena such as key regulatory processes and disease. However, these methods fail to give insight in the functional nature of these interactions. On the other hand, pathways are commonly used to gain biological insight into the function of PPIs in the context of cascading interactions, sacrificing the coverage of networks for rich functional annotations on each PPI. We present a machine learning approach that uses Gene Ontology, InterPro and Pfam annotations to infer the edge functions in PPI networks, allowing us to combine the high coverage of networks with the information richness of pathways.Results: An ensemble method with a combination Logistic Regression and Random Forest classifiers trained on a high-quality set of annotated interactions, with a total of 18 unique labels, achieves high a average F1 score 0.88 despite not taking advantage of multi-label dependencies. When applied to the human interactome, our method confidently classifies 62% of interactions at a probability of 0.7 or higher.Availability: Software and data are available at https://github.com/DavisLaboratory/pyPPIContact:[email protected] information: Supplementary data are available at Bioinformatics online.

scholarly journals Position-wise binding preference is important for miRNA target site prediction

2020 ◽  
Vol 36 (12) ◽  
pp. 3680-3686
Author(s):  
Amlan Talukder ◽  
Xiaoman Li ◽  
Haiyan Hu
Keyword(s):  
Dynamic Programming ◽  
Markov Model ◽  
Mirna Target ◽  
Dynamic Programming Algorithm ◽  
Target Prediction ◽  
Target Site ◽  
Mirna Target Site ◽  
Supplementary Information ◽  
Target Sites ◽  
Target Binding

Abstract Motivation It is a fundamental task to identify microRNAs (miRNAs) targets and accurately locate their target sites. Genome-scale experiments for miRNA target site detection are still costly. The prediction accuracies of existing computational algorithms and tools are often not up to the expectation due to a large number of false positives. One major obstacle to achieve a higher accuracy is the lack of knowledge of the target binding features of miRNAs. The published high-throughput experimental data provide an opportunity to analyze position-wise preference of miRNAs in terms of target binding, which can be an important feature in miRNA target prediction algorithms. Results We developed a Markov model to characterize position-wise pairing patterns of miRNA–target interactions. We further integrated this model as a scoring method and developed a dynamic programming (DP) algorithm, MDPS (Markov model-scored Dynamic Programming algorithm for miRNA target site Selection) that can screen putative target sites of miRNA-target binding. The MDPS algorithm thus can take into account both the dependency of neighboring pairing positions and the global pairing information. Based on the trained Markov models from both miRNA-specific and general datasets, we discovered that the position-wise binding information specific to a given miRNA would benefit its target prediction. We also found that miRNAs maintain region-wise similarity in their target binding patterns. Combining MDPS with existing methods significantly improves their precision while only slightly reduces their recall. Therefore, position-wise pairing patterns have the promise to improve target prediction if incorporated into existing software tools. Availability and implementation The source code and tool to calculate MDPS score is available at http://hulab.ucf.edu/research/projects/MDPS/index.html. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Evaluating the Effect of 3′-UTR Variants in DICER1 and DROSHA on Their Tissue-Specific Expression by miRNA Target Prediction

2021 ◽  
Vol 43 (2) ◽  
pp. 605-617
Author(s):  
Dmitrii S. Bug ◽  
Artem V. Tishkov ◽  
Ivan S. Moiseev ◽  
Natalia V. Petukhova
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Target Prediction ◽  
Specific Gene ◽  
Phenotypic Effect ◽  
Specific Expression ◽  
Form Complex ◽  
Tissue Specific

Untranslated gene regions (UTRs) play an important role in controlling gene expression. 3′-UTRs are primarily targeted by microRNA (miRNA) molecules that form complex gene regulatory networks. Cancer genomes are replete with non-coding mutations, many of which are connected to changes in tumor gene expression that accompany the development of cancer and are associated with resistance to therapy. Therefore, variants that occurred in 3′-UTR under cancer progression should be analysed to predict their phenotypic effect on gene expression, e.g., by evaluating their impact on miRNA target sites. Here, we analyze 3′-UTR variants in DICER1 and DROSHA genes in the context of myelodysplastic syndrome (MDS) development. The key features of this analysis include an assessment of both “canonical” and “non-canonical” types of mRNA-miRNA binding and tissue-specific profiling of miRNA interactions with wild-type and mutated genes. As a result, we obtained a list of DICER1 and DROSHA variants likely altering the miRNA sites and, therefore, potentially leading to the observed tissue-specific gene downregulation. All identified variants have low population frequency consistent with their potential association with pathology progression.

scholarly journals miRDRN—miRNA disease regulatory network: a tool for exploring disease and tissue-specific microRNA regulatory networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7309
Author(s):  
Hsueh-Chuan Liu ◽  
Yi-Shian Peng ◽  
Hoong-Chien Lee
Keyword(s):  
Web Service ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Target Genes ◽  
Gene Annotation ◽  
Biological Pathways ◽  
Tissue Specific ◽  
Cellular Processes ◽  
Disease Pair

Background MicroRNA (miRNA) regulates cellular processes by acting on specific target genes, and cellular processes proceed through multiple interactions often organized into pathways among genes and gene products. Hundreds of miRNAs and their target genes have been identified, as are many miRNA-disease associations. These, together with huge amounts of data on gene annotation, biological pathways, and protein–protein interactions are available in public databases. Here, using such data we built a database and web service platform, miRNA disease regulatory network (miRDRN), for users to construct disease and tissue-specific miRNA-protein regulatory networks, with which they may explore disease related molecular and pathway associations, or find new ones, and possibly discover new modes of drug action. Methods Data on disease-miRNA association, miRNA-target association and validation, gene-tissue association, gene-tumor association, biological pathways, human protein interaction, gene ID, gene ontology, gene annotation, and product were collected from publicly available databases and integrated. A large set of miRNA target-specific regulatory sub-pathways (RSPs) having the form (T, G1, G2) was built from the integrated data and stored, where T is a miRNA-associated target gene, G1 (G2) is a gene/protein interacting with T (G1). Each sequence (T, G1, G2) was assigned a p-value weighted by the participation of the three genes in molecular interactions and reaction pathways. Results A web service platform, miRDRN (http://mirdrn.ncu.edu.tw/mirdrn/), was built. The database part of miRDRN currently stores 6,973,875 p-valued RSPs associated with 116 diseases in 78 tissue types built from 207 diseases-associated miRNA regulating 389 genes. miRDRN also provides facilities for the user to construct disease and tissue-specific miRNA regulatory networks from RSPs it stores, and to download and/or visualize parts or all of the product. User may use miRDRN to explore a single disease, or a disease-pair to gain insights on comorbidity. As demonstrations, miRDRN was applied: to explore the single disease colorectal cancer (CRC), in which 26 novel potential CRC target genes were identified; to study the comorbidity of the disease-pair Alzheimer’s disease-Type 2 diabetes, in which 18 novel potential comorbid genes were identified; and, to explore possible causes that may shed light on recent failures of late-phase trials of anti-AD, BACE1 inhibitor drugs, in which genes downstream to BACE1 whose suppression may affect signal transduction were identified.

Identification of BAP1-associated MicroRNAs and Implications in Cancer Development

2019 ◽  
pp. 1-4
Author(s):  
Tikam Chand ◽  
Tikam Chand
Keyword(s):  
Gene Regulation ◽  
In Silico ◽  
Mirna Target ◽  
Target Prediction ◽  
Differential Regulation ◽  
Cancer Development ◽  
Mirna Target Prediction ◽  
Cancer Types ◽  
In Silico Tools

Having role in gene regulation and silencing, miRNAs have been implicated in development and progression of a number of diseases, including cancer. Herein, I present potential miRNAs associated with BAP1 gene identified using in-silico tools such as TargetScan and Exiqon miRNA Target Prediction. I identified fifteen highly conserved miRNA (hsa-miR-423-5p, hsa-miR-3184-5p, hsa-miR-4319, hsa-miR125b-5p, hsa-miR-125a-5p, hsa-miR-6893-3p, hsa-miR-200b-3p, hsa-miR-200c-3p, hsa-miR-505-3p.1, hsa-miR-429, hsa-miR-370-3p, hsa-miR-125a-5p, hsa-miR-141-3p, hsa-miR-200a-3p, and hsa-miR-429) associated with BAP1 gene. We also predicted the differential regulation of these twelve miRNAs in different cancer types.

scholarly journals E-MAGMA: an eQTL-informed method to identify risk genes using genome-wide association study summary statistics

2021 ◽  
Author(s):  
Zachary F Gerring ◽  
Angela Mina-Vargas ◽  
Eric R Gamazon ◽  
Eske M Derks
Keyword(s):  
Genome Wide Association ◽  
Chromosome 1 ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Tissue Specific ◽  
Complex Disorders ◽  
Risk Variants ◽  
Genome Wide ◽  
Causal Genes

Abstract Motivation Genome-wide association studies have successfully identified multiple independent genetic loci that harbour variants associated with human traits and diseases, but the exact causal genes are largely unknown. Common genetic risk variants are enriched in non-protein-coding regions of the genome and often affect gene expression (expression quantitative trait loci, eQTL) in a tissue-specific manner. To address this challenge, we developed a methodological framework, E-MAGMA, which converts genome-wide association summary statistics into gene-level statistics by assigning risk variants to their putative genes based on tissue-specific eQTL information. Results We compared E-MAGMA to three eQTL informed gene-based approaches using simulated phenotype data. Phenotypes were simulated based on eQTL reference data using GCTA for all genes with at least one eQTL at chromosome 1. We performed 10 simulations per gene. The eQTL-h2 (i.e., the proportion of variation explained by the eQTLs) was set at 1%, 2%, and 5%. We found E-MAGMA outperforms other gene-based approaches across a range of simulated parameters (e.g. the number of identified causal genes). When applied to genome-wide association summary statistics for five neuropsychiatric disorders, E-MAGMA identified more putative candidate causal genes compared to other eQTL-based approaches. By integrating tissue-specific eQTL information, these results show E-MAGMA will help to identify novel candidate causal genes from genome-wide association summary statistics and thereby improve the understanding of the biological basis of complex disorders. Availability A tutorial and input files are made available in a github repository: https://github.com/eskederks/eMAGMA-tutorial. Supplementary information Supplementary data are available at Bioinformatics online.

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