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...
| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Online Access: | https://hdl.handle.net/10356/173875 |
| _version_ | 1811686634706436096 |
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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 |
| author2 | School of Biological Sciences |
| author_facet | School of Biological Sciences 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 | NTU |
| 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. |
| first_indexed | 2024-10-01T05:03:32Z |
| format | Journal Article |
| id | ntu-10356/173875 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:03:32Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1738752024-03-04T15:32:21Z 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 School of Biological Sciences Medicine, Health and Life Sciences 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 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. Published version This work was supported by the National Institutes of Health grant R01 AI117017 (J.H.A.) and U19 AI089672 (C.L.). This work was supported by the internal award of the College of Public Health at the University of South Florida. 2024-03-04T07:18:40Z 2024-03-04T07:18:40Z 2023 Journal Article Wang, C., Dong, Y., Li, C., Oberstaller, J., Zhang, M., Gibbons, J., Pires, C. V., Xiao, M., Zhu, L., Jiang, R. H. Y., Kim, K., Miao, J., Otto, T. D., Cui, L., Adams, J. H. & Liu, X. (2023). MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites. Genome Biology, 24(1). https://dx.doi.org/10.1186/s13059-023-03063-z 1474-760X https://hdl.handle.net/10356/173875 10.1186/s13059-023-03063-z 24 2-s2.0-85174420696 1 24 en Genome Biology © The Author(s) 2023. 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publi cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. application/pdf |
| spellingShingle | Medicine, Health and Life Sciences 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 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 | Medicine, Health and Life Sciences Malaria Parasites |
| url | https://hdl.handle.net/10356/173875 |
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