Congting Ye1, Yuqi Long2, Guoli Ji2, Qingshun Quinn Li1,3, Xiaohui Wu2. 1. Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China. 2. Department of Automation, Xiamen University, Xiamen, Fujian 361005, China. 3. Graduate College of Biomedical Sciences, Western University of Health Sciences, Pomona, CA 91766, USA.
Abstract
Motivation: Alternative polyadenylation (APA) has been increasingly recognized as a crucial mechanism that contributes to transcriptome diversity and gene expression regulation. As RNA-seq has become a routine protocol for transcriptome analysis, it is of great interest to leverage such unprecedented collection of RNA-seq data by new computational methods to extract and quantify APA dynamics in these transcriptomes. However, research progress in this area has been relatively limited. Conventional methods rely on either transcript assembly to determine transcript 3' ends or annotated poly(A) sites. Moreover, they can neither identify more than two poly(A) sites in a gene nor detect dynamic APA site usage considering more than two poly(A) sites. Results: We developed an approach called APAtrap based on the mean squared error model to identify and quantify APA sites from RNA-seq data. APAtrap is capable of identifying novel 3' UTRs and 3' UTR extensions, which contributes to locating potential poly(A) sites in previously overlooked regions and improving genome annotations. APAtrap also aims to tally all potential poly(A) sites and detect genes with differential APA site usages between conditions. Extensive comparisons of APAtrap with two other latest methods, ChangePoint and DaPars, using various RNA-seq datasets from simulation studies, human and Arabidopsis demonstrate the efficacy and flexibility of APAtrap for any organisms with an annotated genome. Availability and implementation: Freely available for download at https://apatrap.sourceforge.io. Contact: liqq@xmu.edu.cn or xhuister@xmu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.
Motivation: Alternative polyadenylation (APA) has been increasingly recognized as a crucial mechanism that contributes to transcriptome diversity and gene expression regulation. As RNA-seq has become a routine protocol for transcriptome analysis, it is of great interest to leverage such unprecedented collection of RNA-seq data by new computational methods to extract and quantify APA dynamics in these transcriptomes. However, research progress in this area has been relatively limited. Conventional methods rely on either transcript assembly to determine transcript 3' ends or annotated poly(A) sites. Moreover, they can neither identify more than two poly(A) sites in a gene nor detect dynamic APA site usage considering more than two poly(A) sites. Results: We developed an approach called APAtrap based on the mean squared error model to identify and quantify APA sites from RNA-seq data. APAtrap is capable of identifying novel 3' UTRs and 3' UTR extensions, which contributes to locating potential poly(A) sites in previously overlooked regions and improving genome annotations. APAtrap also aims to tally all potential poly(A) sites and detect genes with differential APA site usages between conditions. Extensive comparisons of APAtrap with two other latest methods, ChangePoint and DaPars, using various RNA-seq datasets from simulation studies, human and Arabidopsis demonstrate the efficacy and flexibility of APAtrap for any organisms with an annotated genome. Availability and implementation: Freely available for download at https://apatrap.sourceforge.io. Contact: liqq@xmu.edu.cn or xhuister@xmu.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.
Authors: Mina Ryten; Harpreet Saini; Juan A Botia; Siddharth Sethi; David Zhang; Sebastian Guelfi; Zhongbo Chen; Sonia Garcia-Ruiz; Emmanuel O Olagbaju Journal: Nat Commun Date: 2022-04-27 Impact factor: 17.694
Authors: Nitika Kandhari; Calvin A Kraupner-Taylor; Paul F Harrison; David R Powell; Traude H Beilharz Journal: Int J Mol Sci Date: 2021-05-18 Impact factor: 5.923