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PIC-Me: paralogs and isoforms classifier based on machine-learning approaches
BACKGROUND: Paralogs formed through gene duplication and isoforms formed through alternative splicing have been important processes for increasing protein diversity and maintaining cellular homeostasis. Despite their recognized importance and the advent of large-scale genomic and transcriptomic anal...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529730/ https://www.ncbi.nlm.nih.gov/pubmed/34674638 http://dx.doi.org/10.1186/s12859-021-04229-x |
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author | Oh, Jooseong Lee, Sung-Gwon Park, Chungoo |
author_facet | Oh, Jooseong Lee, Sung-Gwon Park, Chungoo |
author_sort | Oh, Jooseong |
collection | PubMed |
description | BACKGROUND: Paralogs formed through gene duplication and isoforms formed through alternative splicing have been important processes for increasing protein diversity and maintaining cellular homeostasis. Despite their recognized importance and the advent of large-scale genomic and transcriptomic analyses, paradoxically, accurate annotations of all gene loci to allow the identification of paralogs and isoforms remain surprisingly incomplete. In particular, the global analysis of the transcriptome of a non-model organism for which there is no reference genome is especially challenging. RESULTS: To reliably discriminate between the paralogs and isoforms in RNA-seq data, we redefined the pre-existing sequence features (sequence similarity, inverse count of consecutive identical or non-identical blocks, and match-mismatch fraction) previously derived from full-length cDNAs and EST sequences and described newly discovered genomic and transcriptomic features (twilight zone of protein sequence alignment and expression level difference). In addition, the effectiveness and relevance of the proposed features were verified with two widely used support vector machine (SVM) and random forest (RF) models. From nine RNA-seq datasets, all AUC (area under the curve) scores of ROC (receiver operating characteristic) curves were over 0.9 in the RF model and significantly higher than those in the SVM model. CONCLUSIONS: In this study, using an RF model with five proposed RNA-seq features, we implemented our method called Paralogs and Isoforms Classifier based on Machine-learning approaches (PIC-Me) and showed that it outperformed an existing method. Finally, we envision that our tool will be a valuable computational resource for the genomics community to help with gene annotation and will aid in comparative transcriptomics and evolutionary genomics studies, especially those on non-model organisms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04229-x. |
format | Online Article Text |
id | pubmed-8529730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85297302021-10-25 PIC-Me: paralogs and isoforms classifier based on machine-learning approaches Oh, Jooseong Lee, Sung-Gwon Park, Chungoo BMC Bioinformatics Research BACKGROUND: Paralogs formed through gene duplication and isoforms formed through alternative splicing have been important processes for increasing protein diversity and maintaining cellular homeostasis. Despite their recognized importance and the advent of large-scale genomic and transcriptomic analyses, paradoxically, accurate annotations of all gene loci to allow the identification of paralogs and isoforms remain surprisingly incomplete. In particular, the global analysis of the transcriptome of a non-model organism for which there is no reference genome is especially challenging. RESULTS: To reliably discriminate between the paralogs and isoforms in RNA-seq data, we redefined the pre-existing sequence features (sequence similarity, inverse count of consecutive identical or non-identical blocks, and match-mismatch fraction) previously derived from full-length cDNAs and EST sequences and described newly discovered genomic and transcriptomic features (twilight zone of protein sequence alignment and expression level difference). In addition, the effectiveness and relevance of the proposed features were verified with two widely used support vector machine (SVM) and random forest (RF) models. From nine RNA-seq datasets, all AUC (area under the curve) scores of ROC (receiver operating characteristic) curves were over 0.9 in the RF model and significantly higher than those in the SVM model. CONCLUSIONS: In this study, using an RF model with five proposed RNA-seq features, we implemented our method called Paralogs and Isoforms Classifier based on Machine-learning approaches (PIC-Me) and showed that it outperformed an existing method. Finally, we envision that our tool will be a valuable computational resource for the genomics community to help with gene annotation and will aid in comparative transcriptomics and evolutionary genomics studies, especially those on non-model organisms. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04229-x. BioMed Central 2021-10-21 /pmc/articles/PMC8529730/ /pubmed/34674638 http://dx.doi.org/10.1186/s12859-021-04229-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Oh, Jooseong Lee, Sung-Gwon Park, Chungoo PIC-Me: paralogs and isoforms classifier based on machine-learning approaches |
title | PIC-Me: paralogs and isoforms classifier based on machine-learning approaches |
title_full | PIC-Me: paralogs and isoforms classifier based on machine-learning approaches |
title_fullStr | PIC-Me: paralogs and isoforms classifier based on machine-learning approaches |
title_full_unstemmed | PIC-Me: paralogs and isoforms classifier based on machine-learning approaches |
title_short | PIC-Me: paralogs and isoforms classifier based on machine-learning approaches |
title_sort | pic-me: paralogs and isoforms classifier based on machine-learning approaches |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529730/ https://www.ncbi.nlm.nih.gov/pubmed/34674638 http://dx.doi.org/10.1186/s12859-021-04229-x |
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