miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditiona...

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Autores principales:	Upendra Kumar Pradhan, Nitesh Kumar Sharma, Prakash Kumar, Ashwani Kumar, Sagar Gupta, Ravi Shankar
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/004e9ee57b874e3cad7fba90c0662956
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spelling	oai:doaj.org-article:004e9ee57b874e3cad7fba90c06629562021-12-02T20:19:18ZmiRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.1932-620310.1371/journal.pone.0258550https://doaj.org/article/004e9ee57b874e3cad7fba90c06629562021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258550https://doaj.org/toc/1932-6203Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.Upendra Kumar PradhanNitesh Kumar SharmaPrakash KumarAshwani KumarSagar GuptaRavi ShankarPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258550 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Upendra Kumar Pradhan Nitesh Kumar Sharma Prakash Kumar Ashwani Kumar Sagar Gupta Ravi Shankar miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.
description	Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.
format	article
author	Upendra Kumar Pradhan Nitesh Kumar Sharma Prakash Kumar Ashwani Kumar Sagar Gupta Ravi Shankar
author_facet	Upendra Kumar Pradhan Nitesh Kumar Sharma Prakash Kumar Ashwani Kumar Sagar Gupta Ravi Shankar
author_sort	Upendra Kumar Pradhan
title	miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.
title_short	miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.
title_full	miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.
title_fullStr	miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.
title_full_unstemmed	miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.
title_sort	mirbiom: machine-learning on bayesian causal nets of rbp-mirna interactions successfully predicts mirna profiles.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/004e9ee57b874e3cad7fba90c0662956
work_keys_str_mv	AT upendrakumarpradhan mirbiommachinelearningonbayesiancausalnetsofrbpmirnainteractionssuccessfullypredictsmirnaprofiles AT niteshkumarsharma mirbiommachinelearningonbayesiancausalnetsofrbpmirnainteractionssuccessfullypredictsmirnaprofiles AT prakashkumar mirbiommachinelearningonbayesiancausalnetsofrbpmirnainteractionssuccessfullypredictsmirnaprofiles AT ashwanikumar mirbiommachinelearningonbayesiancausalnetsofrbpmirnainteractionssuccessfullypredictsmirnaprofiles AT sagargupta mirbiommachinelearningonbayesiancausalnetsofrbpmirnainteractionssuccessfullypredictsmirnaprofiles AT ravishankar mirbiommachinelearningonbayesiancausalnetsofrbpmirnainteractionssuccessfullypredictsmirnaprofiles
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miRbiom: Machine-learning on Bayesian causal nets of RBP-miRNA interactions successfully predicts miRNA profiles.

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