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An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs
MicroRNA are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR is a web tool realized to predict the targets of a set of microRNAs, starting fro...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493982/ https://www.ncbi.nlm.nih.gov/pubmed/32938407 http://dx.doi.org/10.1186/s12859-020-3519-5 |
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author | Bertolazzi, Giorgio Benos, Panayiotis V. Tumminello, Michele Coronnello, Claudia |
author_facet | Bertolazzi, Giorgio Benos, Panayiotis V. Tumminello, Michele Coronnello, Claudia |
author_sort | Bertolazzi, Giorgio |
collection | PubMed |
description | MicroRNA are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR is a web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR was trained with the information regarding binding sites in the 3’utr region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein--a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3’utr and coding regions, should be considered in comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’utr based one. |
format | Online Article Text |
id | pubmed-7493982 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-74939822020-09-23 An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs Bertolazzi, Giorgio Benos, Panayiotis V. Tumminello, Michele Coronnello, Claudia BMC Bioinformatics Research MicroRNA are small non-coding RNAs that post-transcriptionally regulate the expression levels of messenger RNAs. MicroRNA regulation activity depends on the recognition of binding sites located on mRNA molecules. ComiR is a web tool realized to predict the targets of a set of microRNAs, starting from their expression profile. ComiR was trained with the information regarding binding sites in the 3’utr region, by using a reliable dataset containing the targets of endogenously expressed microRNA in D. melanogaster S2 cells. This dataset was obtained by comparing the results from two different experimental approaches, i.e., inhibition, and immunoprecipitation of the AGO1 protein--a component of the microRNA induced silencing complex. In this work, we tested whether including coding region binding sites in ComiR algorithm improves the performance of the tool in predicting microRNA targets. We focused the analysis on the D. melanogaster species and updated the ComiR underlying database with the currently available releases of mRNA and microRNA sequences. As a result, we find that ComiR algorithm trained with the information related to the coding regions is more efficient in predicting the microRNA targets, with respect to the algorithm trained with 3’utr information. On the other hand, we show that 3’utr based predictions can be seen as complementary to the coding region based predictions, which suggests that both predictions, from 3’utr and coding regions, should be considered in comprehensive analysis. Furthermore, we observed that the lists of targets obtained by analyzing data from one experimental approach only, that is, inhibition or immunoprecipitation of AGO1, are not reliable enough to test the performance of our microRNA target prediction algorithm. Further analysis will be conducted to investigate the effectiveness of the tool with data from other species, provided that validated datasets, as obtained from the comparison of RISC proteins inhibition and immunoprecipitation experiments, will be available for the same samples. Finally, we propose to upgrade the existing ComiR web-tool by including the coding region based trained model, available together with the 3’utr based one. BioMed Central 2020-09-16 /pmc/articles/PMC7493982/ /pubmed/32938407 http://dx.doi.org/10.1186/s12859-020-3519-5 Text en © The Author(s). 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Bertolazzi, Giorgio Benos, Panayiotis V. Tumminello, Michele Coronnello, Claudia An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs |
title | An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs |
title_full | An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs |
title_fullStr | An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs |
title_full_unstemmed | An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs |
title_short | An improvement of ComiR algorithm for microRNA target prediction by exploiting coding region sequences of mRNAs |
title_sort | improvement of comir algorithm for microrna target prediction by exploiting coding region sequences of mrnas |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7493982/ https://www.ncbi.nlm.nih.gov/pubmed/32938407 http://dx.doi.org/10.1186/s12859-020-3519-5 |
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