Krishna Choudhary1, Fei Deng1, Sharon Aviran1. 1. Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA 95616, USA.
Abstract
BACKGROUND: Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data. RESULTS: We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy. CONCLUSIONS: To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.
BACKGROUND: Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data. RESULTS: We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy. CONCLUSIONS: To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.
Authors: Pedro J Batista; Benoit Molinie; Jinkai Wang; Kun Qu; Jiajing Zhang; Lingjie Li; Donna M Bouley; Ernesto Lujan; Bahareh Haddad; Kaveh Daneshvar; Ava C Carter; Ryan A Flynn; Chan Zhou; Kok-Seong Lim; Peter Dedon; Marius Wernig; Alan C Mullen; Yi Xing; Cosmas C Giallourakis; Howard Y Chang Journal: Cell Stem Cell Date: 2014-10-16 Impact factor: 24.633
Authors: Jennifer L McGinnis; Qi Liu; Christopher A Lavender; Aishwarya Devaraj; Sean P McClory; Kurt Fredrick; Kevin M Weeks Journal: Proc Natl Acad Sci U S A Date: 2015-02-09 Impact factor: 11.205
Authors: Yin Tang; Emil Bouvier; Chun Kit Kwok; Yiliang Ding; Anton Nekrutenko; Philip C Bevilacqua; Sarah M Assmann Journal: Bioinformatics Date: 2015-04-16 Impact factor: 6.937
Authors: Yue Wan; Kun Qu; Qiangfeng Cliff Zhang; Ryan A Flynn; Ohad Manor; Zhengqing Ouyang; Jiajing Zhang; Robert C Spitale; Michael P Snyder; Eran Segal; Howard Y Chang Journal: Nature Date: 2014-01-30 Impact factor: 49.962
Authors: Kyle E Watters; Krishna Choudhary; Sharon Aviran; Julius B Lucks; Keith L Perry; Jeremy R Thompson Journal: Nucleic Acids Res Date: 2018-03-16 Impact factor: 16.971
Authors: Bernhard C Thiel; Roman Ochsenreiter; Veerendra P Gadekar; Andrea Tanzer; Ivo L Hofacker Journal: Genes (Basel) Date: 2018-08-01 Impact factor: 4.096