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dc.contributor.authorZhou, Juexiao
dc.contributor.authorzhang, bin
dc.contributor.authorLi, Haoyang
dc.contributor.authorZhou, Longxi
dc.contributor.authorLi, Zhongxiao
dc.contributor.authorLong, Yongkang
dc.contributor.authorHan, Wenkai
dc.contributor.authorWang, Mengran
dc.contributor.authorCui, Huanhuan
dc.contributor.authorChen, Wei
dc.contributor.authorGao, Xin
dc.date.accessioned2021-06-22T06:08:58Z
dc.date.available2021-06-22T06:08:58Z
dc.date.issued2021-06-21
dc.identifier.citationZhou, J., zhang, bin, Li, H., Zhou, L., Li, Z., Long, Y., … Gao, X. (2021). DeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data. doi:10.21203/rs.3.rs-640669/v1
dc.identifier.doi10.21203/rs.3.rs-640669/v1
dc.identifier.urihttp://hdl.handle.net/10754/669746
dc.description.abstractAbstract The accurate annotation of transcription start sites (TSSs) and their usage is critical for the mechanistic understanding of gene regulation under different biological contexts. To fulfil this, on one hand, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner. On the other hand, various computational tools have also been developed for in silico prediction of TSSs solely based on genomic sequences. Most of these computational tools cast the problem as a binary classification task on a balanced dataset and thus result in drastic false positive predictions when applied on the genome-scale. To address these issues, we present DeeReCT-TSS, a deep-learning-based method that is capable of TSSs identification across the whole genome based on both DNA sequences and conventional RNA-seq data. We show that by effectively incorporating these two sources of information, DeeReCT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell types. Furthermore, we develop a meta-learning-based extension for simultaneous transcription start site (TSS) annotation on 10 cell types, which enables the identification of cell-type-specific TSS. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets from the ENCODE project by correlating our predicted TSSs with experimentally defined TSS chromatin states. Our application, pre-trained models and data are available at https://github.com/JoshuaChou2018/DeeReCT-TSS_release.
dc.description.sponsorshipWe apologize to all colleagues whose work could not be cited due to space constraints. We thank all past and present members in Structural and Functional Bioinformatics (SFB) Group for their constructive feedback on this project. We also thank Mohammed Saif for providing generous support on computational resources.
dc.publisherResearch Square Platform LLC
dc.relation.urlhttps://www.researchsquare.com/article/rs-640669/v1
dc.rightsArchived with thanks to Research Square Platform LLC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDeeReCT-TSS: A novel meta-learning-based method annotates TSS in multiple cell types based on DNA sequences and RNA-seq data
dc.typePreprint
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Science and Engineering (CEMSE) Division
dc.contributor.departmentStructural and Functional Bioinformatics Group
dc.eprint.versionPre-print
dc.contributor.institutionSouthern University of Science and Technology
dc.contributor.institutionDepartment of Biology, Southern University of Science and Technology, Shenzhen, Guangdong 518005
kaust.personZhou, Juexiao
kaust.personLi, Haoyang
kaust.personZhou, Longxi
kaust.personLi, Zhongxiao
kaust.personLong, Yongkang
kaust.personHan, Wenkai
kaust.personGao, Xin
refterms.dateFOA2021-06-22T06:10:56Z


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