Chromatin loop anchors can predict transcript and exon usage
Epigenomics and transcriptomics data from high-throughput sequencing techniques such as RNA-seq and ChIP-seq have been successfully applied in predicting gene transcript expression. However, ChIA-PET has never been used. Here, we developed machine learning models to investigate if ChIA-PET could co...
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sg-ntu-dr.10356-1696182023-07-31T15:32:29Z Chromatin loop anchors can predict transcript and exon usage Zhang, Yu Cai, Yichao Francesc Xavier, Roca Castella Chee, Keong Kwoh Fullwood, Melissa Jane School of Biological Sciences School of Computer Science and Engineering National University of Singapore Institute of Molecular and Cell Biology, A*STAR Science::Biological sciences Gene Expression Exon Usage Epigenomics and transcriptomics data from high-throughput sequencing techniques such as RNA-seq and ChIP-seq have been successfully applied in predicting gene transcript expression. However, ChIA-PET has never been used. Here, we developed machine learning models to investigate if ChIA-PET could contribute to the transcript and exon usage prediction. In doing so, we used a large set of transcription factors as well as ChIA-PET data, which indicates locations of chromatin loops in the genome. We developed different Gradient Boosting Trees (GB) models according to the different tasks on the integrated dataset from three cell lines, including GM12878, HeLaS3 and K562. We validated the models via 10-fold cross validation, chromosome-split validation and cross-cell validation. Our results show that both transcript and splicing-derived exon usage can be effectively predicted with at least 0.7512 and 0.7459 of accuracy, respectively, on all cell lines from all kinds of validations. Examining the predictive features, we found that RNA Polymerase II ChIA-PET was one of the most important features in both transcript and exon usage prediction, suggesting that chromatin loop anchors are predictive of both transcript and exon usage. Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) Published version National Research Foundation (NRF) Singapore through an NRF Fellowship (NRF-NRFF2012-054 to M.J.F); Nanyang Technological University start-up funds to M.J.F.; RNA Biology Center at the Cancer Science Institute of Singapore, NUS, as part of funding under the Singapore Ministry of Education Academic Research Fund Tier 3 to D.T. as lead PI with M.J.F. as co-investigator (MOE2014-T3-1-006); National Research Foundation Competitive Research Programme grant to V.T. as lead PI and M.J.F. as co-PI (NRF-CRP17- 2017-02); National Research Foundation Singapore and the Singapore Ministry of Education under its Research Centres of Excellence initiative; Ministry of Education Tier II grant to M.J.F. (T2EP30120-0020); Advancing Creativity and Excellence (ACE) grant awarded by Nanyang Technological University to M.J.F. as PI, C.K.K. as co-PI and X.R. as co-PI (NTU-ACE2019-03). 2023-07-26T06:22:38Z 2023-07-26T06:22:38Z 2021 Journal Article Zhang, Y., Cai, Y., Francesc Xavier, R. C., Chee, K. K. & Fullwood, M. J. (2021). Chromatin loop anchors can predict transcript and exon usage. Briefings in Bioinformatics, 22(6), bbab254-. https://dx.doi.org/10.1093/bib/bbab254 1467-5463 https://hdl.handle.net/10356/169618 10.1093/bib/bbab254 6 22 bbab254 en NRF-NRFF2012-054 MOE2014-T3-1-006 NRF-CRP17-2017-02 MOE-T2EP30120-0020 NTU-ACE2019-03 Briefings in Bioinformatics © 2021 The Author(s). Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com application/pdf |
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Science::Biological sciences Gene Expression Exon Usage Zhang, Yu Cai, Yichao Francesc Xavier, Roca Castella Chee, Keong Kwoh Fullwood, Melissa Jane Chromatin loop anchors can predict transcript and exon usage |
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Epigenomics and transcriptomics data from high-throughput sequencing techniques such as RNA-seq and ChIP-seq have been successfully applied in predicting gene transcript expression. However, ChIA-PET has never been used. Here, we developed machine learning models to investigate if ChIA-PET could contribute to the transcript and exon usage prediction. In doing so, we used a large set of transcription factors as well as ChIA-PET data, which indicates locations of chromatin loops in the genome. We developed different Gradient Boosting Trees (GB) models according to the different tasks on the integrated dataset from three cell lines, including GM12878, HeLaS3 and K562. We validated the models via 10-fold cross validation, chromosome-split validation and cross-cell validation. Our results show that both transcript and splicing-derived exon usage can be effectively predicted with at least 0.7512 and 0.7459 of accuracy, respectively, on all cell lines from all kinds of validations. Examining the predictive features, we found that RNA Polymerase II ChIA-PET was one of the most important features in both transcript and exon usage prediction, suggesting that chromatin loop anchors are predictive of both transcript and exon usage. |
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School of Biological Sciences |
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School of Biological Sciences Zhang, Yu Cai, Yichao Francesc Xavier, Roca Castella Chee, Keong Kwoh Fullwood, Melissa Jane |
format |
Article |
author |
Zhang, Yu Cai, Yichao Francesc Xavier, Roca Castella Chee, Keong Kwoh Fullwood, Melissa Jane |
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Zhang, Yu |
title |
Chromatin loop anchors can predict transcript and exon usage |
title_short |
Chromatin loop anchors can predict transcript and exon usage |
title_full |
Chromatin loop anchors can predict transcript and exon usage |
title_fullStr |
Chromatin loop anchors can predict transcript and exon usage |
title_full_unstemmed |
Chromatin loop anchors can predict transcript and exon usage |
title_sort |
chromatin loop anchors can predict transcript and exon usage |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/169618 |
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1773551369950593024 |