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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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Related in: MedlinePlus

The number of confirmed miRNA-mRNA interactions by experimentally confirmed data and perturbation data.The top 100, 200, 300, and 400 interactions for each miRNA are selected for the validation in the PRAD dataset from TCGA.
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pone.0145386.g004: The number of confirmed miRNA-mRNA interactions by experimentally confirmed data and perturbation data.The top 100, 200, 300, and 400 interactions for each miRNA are selected for the validation in the PRAD dataset from TCGA.

Mentions: It is very useful to utilise the wealth amount of data in TCGA for exploring miRNA functions in cancer. In this scenario, we assume that we would like to apply a causal inference method (IDA) for predicting miRNA targets in the Prostate adenocarcinoma (PRAD) dataset. We will firstly use the built-in getData function to get the matched samples of miRNA and mRNA expression profiles from TCGA directly. We can then conduct the DE analysis for the downloaded PRAD dataset using the function DiffExpAnalysis. After pre-processing the dataset, we use IDA to infer miRNA-mRNA interactions and validate them by using two types of ground truth (experimentally confirmed data and miRNA perturbation data). The R code for the whole process is shown in the following. If we respectively validate the top 100, 200, 300, 400 targets of each miRNA and summarise the results, we will have the total number of confirmed interactions as shown in Fig 4.


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 number of confirmed miRNA-mRNA interactions by experimentally confirmed data and perturbation data.The top 100, 200, 300, and 400 interactions for each miRNA are selected for the validation in the PRAD dataset from TCGA.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0145386.g004: The number of confirmed miRNA-mRNA interactions by experimentally confirmed data and perturbation data.The top 100, 200, 300, and 400 interactions for each miRNA are selected for the validation in the PRAD dataset from TCGA.
Mentions: It is very useful to utilise the wealth amount of data in TCGA for exploring miRNA functions in cancer. In this scenario, we assume that we would like to apply a causal inference method (IDA) for predicting miRNA targets in the Prostate adenocarcinoma (PRAD) dataset. We will firstly use the built-in getData function to get the matched samples of miRNA and mRNA expression profiles from TCGA directly. We can then conduct the DE analysis for the downloaded PRAD dataset using the function DiffExpAnalysis. After pre-processing the dataset, we use IDA to infer miRNA-mRNA interactions and validate them by using two types of ground truth (experimentally confirmed data and miRNA perturbation data). The R code for the whole process is shown in the following. If we respectively validate the top 100, 200, 300, 400 targets of each miRNA and summarise the results, we will have the total number of confirmed interactions as shown in Fig 4.

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