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MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites
Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSED, for predicting chromatin profiles in malaria pa...
| Autores principales: | , , , , , , , , , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
BioMed Central
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577899/ https://www.ncbi.nlm.nih.gov/pubmed/37845769 http://dx.doi.org/10.1186/s13059-023-03063-z |
| _version_ | 1785121405154623488 |
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| author | Wang, Chengqi Dong, Yibo Li, Chang Oberstaller, Jenna Zhang, Min Gibbons, Justin Pires, Camilla Valente Xiao, Mianli Zhu, Lei Jiang, Rays H. Y. Kim, Kami Miao, Jun Otto, Thomas D. Cui, Liwang Adams, John H. Liu, Xiaoming |
| author_facet | Wang, Chengqi Dong, Yibo Li, Chang Oberstaller, Jenna Zhang, Min Gibbons, Justin Pires, Camilla Valente Xiao, Mianli Zhu, Lei Jiang, Rays H. Y. Kim, Kami Miao, Jun Otto, Thomas D. Cui, Liwang Adams, John H. Liu, Xiaoming |
| author_sort | Wang, Chengqi |
| collection | PubMed |
| description | Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSED, for predicting chromatin profiles in malaria parasites. The MalariaSED performance was validated by published ChIP-qPCR and TF motifs results. Applying MalariaSED to ~ 1.3 million variants shows that geographically differentiated noncoding variants are associated with parasite invasion and drug resistance. Further analysis reveals chromatin accessibility changes at Plasmodium falciparum rings are partly associated with artemisinin resistance. MalariaSED illuminates the potential functional roles of noncoding variants in malaria parasites. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03063-z. |
| format | Online Article Text |
| id | pubmed-10577899 |
| institution | National Center for Biotechnology Information |
| language | English |
| publishDate | 2023 |
| publisher | BioMed Central |
| record_format | MEDLINE/PubMed |
| spelling | pubmed-105778992023-10-17 MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites Wang, Chengqi Dong, Yibo Li, Chang Oberstaller, Jenna Zhang, Min Gibbons, Justin Pires, Camilla Valente Xiao, Mianli Zhu, Lei Jiang, Rays H. Y. Kim, Kami Miao, Jun Otto, Thomas D. Cui, Liwang Adams, John H. Liu, Xiaoming Genome Biol Method Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria parasites remain elusive. We developed a sequence-based, ab initio deep learning framework, MalariaSED, for predicting chromatin profiles in malaria parasites. The MalariaSED performance was validated by published ChIP-qPCR and TF motifs results. Applying MalariaSED to ~ 1.3 million variants shows that geographically differentiated noncoding variants are associated with parasite invasion and drug resistance. Further analysis reveals chromatin accessibility changes at Plasmodium falciparum rings are partly associated with artemisinin resistance. MalariaSED illuminates the potential functional roles of noncoding variants in malaria parasites. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-03063-z. BioMed Central 2023-10-16 /pmc/articles/PMC10577899/ /pubmed/37845769 http://dx.doi.org/10.1186/s13059-023-03063-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 | Method Wang, Chengqi Dong, Yibo Li, Chang Oberstaller, Jenna Zhang, Min Gibbons, Justin Pires, Camilla Valente Xiao, Mianli Zhu, Lei Jiang, Rays H. Y. Kim, Kami Miao, Jun Otto, Thomas D. Cui, Liwang Adams, John H. Liu, Xiaoming MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites |
| title | MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites |
| title_full | MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites |
| title_fullStr | MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites |
| title_full_unstemmed | MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites |
| title_short | MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites |
| title_sort | malariased: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites |
| topic | Method |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10577899/ https://www.ncbi.nlm.nih.gov/pubmed/37845769 http://dx.doi.org/10.1186/s13059-023-03063-z |
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