In higher eukaryotes such as humans, genes are transcribed to pre-mRNAs, in which exons (RNA segments that code for proteins) are separated from each other by intervening introns (RNA segments that do not code for proteins). A gene may generate different mature mRNA transcripts by selectively including different combinations of exons. This biological process is referred to as alternative splicing and is the main strategy for genes to generate proteomic diversity. Extenstive evidences indicate that over 95% of human genes undergo alternative splicing. Aberrant splicing of pre-mRNAs can cause various human diseases, such as cancers, aging related diseases, heart diseases and neuro-development diseases. Recent studies have shown that small non-coding RNAs play an essential regulatory role in alternative splicing. For example, small interfering RNAs (siRNAs) can regulate alternative splicing by modulating chromatin structure. The overarching goal of this project is to develop a set of novel statistical tools to advance our knowledge of the regulatory role of small RNAs on alternative splicing. More specifically, the invetigators of this project will (1) develop effective significance testing theory and methods via generalized smoothing spline ANOVA models to identify genome-wide small RNA targets; (2) develop a new statistical framework for isoform assembly and quantification via joint modeling multisample RNA-seq data; (3) bridge the research gap in the study of small RNAs by elucidating the regulatory role of small RNAs on isoform expression. Although the proposed methods are developed to address the current analytical challenges in isoform and small RNA analysis, a burgeoning area in biology studies, the statistical theory and methods can be broadly applied to many research fields.

Public Health Relevance

In this project, we will develop suite of statistical methods to enhance our understanding of the regulatoty role of small RNAs on alternative splicing. The results from this project may protype of gene chips for human intervention of aberrant alternative spicing related diseases.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM122080-02
Application #
9331728
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Brazhnik, Paul
Project Start
2016-08-15
Project End
2020-07-31
Budget Start
2017-08-01
Budget End
2018-07-31
Support Year
2
Fiscal Year
2017
Total Cost
Indirect Cost
Name
University of Georgia
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
004315578
City
Athens
State
GA
Country
United States
Zip Code
30602
Wang, HaiYing; Zhu, Rong; Ma, Ping (2018) Optimal Subsampling for Large Sample Logistic Regression. J Am Stat Assoc 113:829-844
Zhang, Liyun; Zhang, Xinlian; Zhang, Gaonan et al. (2017) Expression profiling of the retina of pde6c, a zebrafish model of retinal degeneration. Sci Data 4:170182
Xing, Xin; Liu, Jun S; Zhong, Wenxuan (2017) MetaGen: reference-free learning with multiple metagenomic samples. Genome Biol 18:187
Liu, Yiwen; Ma, Ping; Cassidy, Paige A et al. (2017) Statistical Analysis of Zebrafish Locomotor Behaviour by Generalized Linear Mixed Models. Sci Rep 7:2937
Akay, Alper; Di Domenico, Tomas; Suen, Kin M et al. (2017) The Helicase Aquarius/EMB-4 Is Required to Overcome Intronic Barriers to Allow Nuclear RNAi Pathways to Heritably Silence Transcription. Dev Cell 42:241-255.e6
Sun, Xiaoxiao; Dalpiaz, David; Wu, Di et al. (2016) Statistical inference for time course RNA-Seq data using a negative binomial mixed-effect model. BMC Bioinformatics 17:324
Helwig, Nathaniel E; Shorter, K Alex; Ma, Ping et al. (2016) Smoothing spline analysis of variance models: A new tool for the analysis of cyclic biomechanical data. J Biomech 49:3216-3222
Zhu, Gaohua; Liu, Jun; Zheng, Qiye et al. (2016) Tuning thermal conductivity in molybdenum disulfide by electrochemical intercalation. Nat Commun 7:13211
Zhang, Liyun; Xiang, Lue; Liu, Yiwen et al. (2016) A Naturally-Derived Compound Schisandrin B Enhanced Light Sensation in the pde6c Zebrafish Model of Retinal Degeneration. PLoS One 11:e0149663