Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS  

作　　者：Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao 

机构地区：[1]Computer Science Program,Computer,Electrical and Mathematical Sciences and Engineering Division,King Abdullah University of Science and Technology,Thuwal 23955-6900,Saudi Arabia [2]Computational Bioscience Research Center,King Abdullah University of Science and Technology,Thuwal 23955-6900,Saudi Arabia [3]Department of Biology,School of Life Sciences,Southern University of Science and Technology,Shenzhen 518055,China [4]Shenzhen Key Laboratory of Gene Regulation and Systems Biology,School of Life Sciences,Southern University of Science and Technology,Shenzhen 518055,China [5]Academy for Advanced Interdisciplinary Studies,Southern University of Science and Technology,Shenzhen 518055,China

出　　处：《Genomics, Proteomics & Bioinformatics》2022年第5期959-973,共15页基因组蛋白质组与生物信息学报（英文版）

基　　金：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/1/197604-01,URF/1/4098-01-01,REI/1/0018-01-01,REI/1/4216-0101,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);supported in part by the National Natural Science Foundation of China(Grant No.31970601);the Shenzhen Science and Technology Program(Grant No.KQTD20180411143432337);the Shenzhen Key Laboratory of Gene Regulation and Systems Biology(Grant No.ZDSYS20200811144002008),China。

摘　　要：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 Dee Re CT-TSS,a deep learningbased 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,Dee Re CT-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 Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://ngdc.cncb.ac.cn/biocode/tools/BT007316.

关 键 词：Transcription start site Machine learning Deep learning META-LEARNING RNA sequencing 

分 类 号：Q811.4[生物学—生物工程]