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miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships.

Le TD, Zhang J, Liu L, Liu H, Li J - PLoS ONE (2015)

Bottom Line: microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miRNA-mRNA regulatory relationships is a crucial problem.Consequently, several computational methods of predicting miRNA targets have been proposed using expression data with or without sequence based miRNA target information.Here, we present an R package, miRLAB, for automating the procedure of inferring and validating miRNA-mRNA regulatory relationships.

View Article: PubMed Central - PubMed

Affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia.

ABSTRACT
microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miRNA-mRNA regulatory relationships is a crucial problem. Consequently, several computational methods of predicting miRNA targets have been proposed using expression data with or without sequence based miRNA target information. A typical procedure for applying and evaluating such a method is i) collecting matched miRNA and mRNA expression profiles in a specific condition, e.g. a cancer dataset from The Cancer Genome Atlas (TCGA), ii) applying the new computational method to the selected dataset, iii) validating the predictions against knowledge from literature and third-party databases, and comparing the performance of the method with some existing methods. This procedure is time consuming given the time elapsed when collecting and processing data, repeating the work from existing methods, searching for knowledge from literature and third-party databases to validate the results, and comparing the results from different methods. The time consuming procedure prevents researchers from quickly testing new computational models, analysing new datasets, and selecting suitable methods for assisting with the experiment design. Here, we present an R package, miRLAB, for automating the procedure of inferring and validating miRNA-mRNA regulatory relationships. The package provides a complete set of pipelines for testing new methods and analysing new datasets. miRLAB includes a pipeline to obtain matched miRNA and mRNA expression datasets directly from TCGA, 12 benchmark computational methods for inferring miRNA-mRNA regulatory relationships, the functions for validating the predictions using experimentally validated miRNA target data and miRNA perturbation data, and the tools for comparing the results from different computational methods.

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The workflow of miRLAB package.The workflow mainly includes three components: Datasets and pre-processing, Computaional methods for exploration, and Validation and post-processing. It is optional to integrate miRNA target predictions and/or miRNA target binding information from CLIP-seq experiments to the computational models. Users can provide their own datasets/methods in each step of the workflow.
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pone.0145386.g001: The workflow of miRLAB package.The workflow mainly includes three components: Datasets and pre-processing, Computaional methods for exploration, and Validation and post-processing. It is optional to integrate miRNA target predictions and/or miRNA target binding information from CLIP-seq experiments to the computational models. Users can provide their own datasets/methods in each step of the workflow.

Mentions: As illustrated in Fig 1, miRLAB package provides a pipeline consisting of three components to identify and validate miRNA-mRNA regulatory relationships. In the following, we describe the workflow of miRLAB package in detail.


miRLAB: An R Based Dry Lab for Exploring miRNA-mRNA Regulatory Relationships.

Le TD, Zhang J, Liu L, Liu H, Li J - PLoS ONE (2015)

The workflow of miRLAB package.The workflow mainly includes three components: Datasets and pre-processing, Computaional methods for exploration, and Validation and post-processing. It is optional to integrate miRNA target predictions and/or miRNA target binding information from CLIP-seq experiments to the computational models. Users can provide their own datasets/methods in each step of the workflow.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4696828&req=5

pone.0145386.g001: The workflow of miRLAB package.The workflow mainly includes three components: Datasets and pre-processing, Computaional methods for exploration, and Validation and post-processing. It is optional to integrate miRNA target predictions and/or miRNA target binding information from CLIP-seq experiments to the computational models. Users can provide their own datasets/methods in each step of the workflow.
Mentions: As illustrated in Fig 1, miRLAB package provides a pipeline consisting of three components to identify and validate miRNA-mRNA regulatory relationships. In the following, we describe the workflow of miRLAB package in detail.

Bottom Line: microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miRNA-mRNA regulatory relationships is a crucial problem.Consequently, several computational methods of predicting miRNA targets have been proposed using expression data with or without sequence based miRNA target information.Here, we present an R package, miRLAB, for automating the procedure of inferring and validating miRNA-mRNA regulatory relationships.

View Article: PubMed Central - PubMed

Affiliation: School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia, Australia.

ABSTRACT
microRNAs (miRNAs) are important gene regulators at post-transcriptional level, and inferring miRNA-mRNA regulatory relationships is a crucial problem. Consequently, several computational methods of predicting miRNA targets have been proposed using expression data with or without sequence based miRNA target information. A typical procedure for applying and evaluating such a method is i) collecting matched miRNA and mRNA expression profiles in a specific condition, e.g. a cancer dataset from The Cancer Genome Atlas (TCGA), ii) applying the new computational method to the selected dataset, iii) validating the predictions against knowledge from literature and third-party databases, and comparing the performance of the method with some existing methods. This procedure is time consuming given the time elapsed when collecting and processing data, repeating the work from existing methods, searching for knowledge from literature and third-party databases to validate the results, and comparing the results from different methods. The time consuming procedure prevents researchers from quickly testing new computational models, analysing new datasets, and selecting suitable methods for assisting with the experiment design. Here, we present an R package, miRLAB, for automating the procedure of inferring and validating miRNA-mRNA regulatory relationships. The package provides a complete set of pipelines for testing new methods and analysing new datasets. miRLAB includes a pipeline to obtain matched miRNA and mRNA expression datasets directly from TCGA, 12 benchmark computational methods for inferring miRNA-mRNA regulatory relationships, the functions for validating the predictions using experimentally validated miRNA target data and miRNA perturbation data, and the tools for comparing the results from different computational methods.

Show MeSH
Related in: MedlinePlus