| Title | Comprehensive understanding of Tn5 insertion preference improves transcription regulatory element identification |
| Authors | Zhang, Houyu Lu, Ting Liu, Shan Yang, Jianyu Sun, Guohuan Cheng, Tao Xu, Jin Chen, Fangyao Yen, Kuangyu |
| Affiliation | Chinese Acad Med Sci & Peking Union Med Coll, Natl Clin Res Ctr Blood Dis, State Key Lab Expt Hematol, Inst Hematol & Blood Dis Hosp, Tianjin 300020, Peoples R China South China Univ Technol, Sch Biol & Biol Engn, Guangzhou 510006, Peoples R China Southern Med Univ, Sch Basic Med Sci, Dept Dev Biol, Guangzhou 510515, Peoples R China South China Univ Technol, Sch Med, Div Cell Dev & Integrat Biol, Guangzhou 510006, Peoples R China Xi An Jiao Tong Univ, Hlth Sci Ctr, Sch Publ Hlth, Dept Epidemiol & Biostat, Xian 710061, Shaanxi, Peoples R China Peking Univ, Acad Adv Interdisciplinary Studies, Chinese Inst Brain Res, Beijing 100871, Peoples R China Penn State Univ, Bioinformat & Genom Intercoll Grad Program, University Pk, PA 16802 USA |
| Keywords | DNA SHAPE READ ALIGNMENT BINDING SITES CHROMATIN SEQ ACCESSIBILITY SPECIFICITY TRANSPOSITION TAGMENTATION PREDICTION |
| Issue Date | Dec-2021 |
| Publisher | NAR GENOMICS AND BIOINFORMATICS |
| Abstract | Tn5 transposase, which can efficiently tagment the genome, has been widely adopted as a molecular tool in next-generation sequencing, from short-read sequencing to more complex methods such as assay for transposase-accessible chromatin using sequencing (ATAC-seq). Here, we systematically map Tn5 insertion characteristics across several model organisms, finding critical parameters that affect its insertion. On naked genomic DNA, we found that Tn5 insertion is not uniformly distributed or random. To uncover drivers of these biases, we used a machine learning framework, which revealed that DNA shape cooperatively works with DNA motif to affect Tn5 insertion preference. These intrinsic insertion preferences can be modeled using nucleotide dependence information from DNA sequences, and we developed a computational pipeline to correct for these biases in ATAC-seq data. Using our pipeline, we show that bias correction improves the overall performance of ATAC-seq peak detection, recovering many potential false-negative peaks. Furthermore, we found that these peaks are bound by transcription factors, underscoring the biological relevance of capturing this additional information. These findings highlight the benefits of an improved understanding and precise correction of Tn5 insertion preference. |
| URI | http://hdl.handle.net/20.500.11897/637107 |
| DOI | 10.1093/nargab/lqab094 |
| Indexed | ESCI |
| Appears in Collections: | 前沿交叉学科研究院 |
