Abstract
The accurate annotation of transcription start sites (TSSs) and their usage are critical for the mechanistic understanding of gene regulation in different biological contexts. To fulfill this, specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner, and 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, thus resulting in drastic false positive predictions when applied on the genome scale. Here, we present DeeReCT-TSS, a deep learning-based method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing 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 TSS annotations on 10 cell types, which enables the identification of cell type-specific TSSs. Finally, we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin states. The source code for DeeReCT-TSS is available at https://github.com/JoshuaChou2018/DeeReCT-TSS_release and https://ngdc.cncb.ac.cn/biocode/tools/BT007316.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 959-973 |
| Number of pages | 15 |
| Journal | Genomics, Proteomics and Bioinformatics |
| Volume | 20 |
| Issue number | 5 |
| DOIs | |
| State | Published - Oct 2022 |
Bibliographical note
Funding Information: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. Juexiao Zhou, Bin Zhang, Haoyang Li, Longxi Zhou, Zhongxiao Li, Wenkai Han, and Xin Gao were supported in part by grants from Office of Research Administration (ORA) at King Abdullah University of Science and Technology (KAUST) (Grant Nos. BAS/1/1624-01-01, FCC/1/1976-04-01, URF/1/4098-01-01, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, URF/1/4352-01-01, REI/1/4742-01-01, and URF/1/4663-01-01). Mengran Wang, Huanhuan Cui, Jingjing Li, and Wei Chen were supported in part by the National Natural Science Foundation of China (Grant No. 31970601), the Shenzhen Science and Technology Program (Grant No. KQTD20180411143432337), and the Shenzhen Key Laboratory of Gene Regulation and Systems Biology (Grant No. ZDSYS20200811144002008), China.
Funding Information:
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. Juexiao Zhou, Bin Zhang, Haoyang Li, Longxi Zhou, Zhongxiao Li, Wenkai Han, and Xin Gao were supported in part by grants from Office of Research Administration (ORA) at King Abdullah University of Science and Technology (KAUST) (Grant Nos. BAS/1/1624-01-01, FCC/1/1976-04-01, URF/1/4098-01-01, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, URF/1/4352-01-01, REI/1/4742-01-01, and URF/1/4663-01-01). Mengran Wang, Huanhuan Cui, Jingjing Li, and Wei Chen were supported in part by the National Natural Science Foundation of China (Grant No. 31970601), the Shenzhen Science and Technology Program (Grant No. KQTD20180411143432337), and the Shenzhen Key Laboratory of Gene Regulation and Systems Biology (Grant No. ZDSYS20200811144002008), China.
Publisher Copyright:
© 2022 The Authors
Keywords
- Deep learning
- Machine learning
- Meta-learning
- RNA sequencing
- Transcription start site
ASJC Scopus subject areas
- Biochemistry
- Molecular Biology
- Genetics
- Computational Mathematics