Deterministic methods to choose between gene expression analysis and splicing analysis to analyze differential RNA-seq data (computational biology)
Steven Brenner, Professor
Plant and Microbial Biology
Closed. This professor is continuing with Spring 2024 apprentices on this project; no new apprentices needed for Fall 2024.
RNA-seq has been widely used in biological and medical research because of its capability to quantify transcriptome changes. Researchers usually use their impressions and experience to choose whether to analyze transcriptome changes in gene expression or alternative splicing levels. A more systematic way to determine whether to focus on gene expression or splicing analysis should be developed and deployed to better guide future research direction. Hence, in this project, we aim to develop statistical strategies to answer whether gene expression or splicing has a more significant change in the transcriptome. More broadly, we aim to create a framework that fairly compares the changes in gene expression and the changes in splicing to provide insights and directions for the downstream analyses.
Our initial analyses have demonstrated that the statistical strategies we developed can successfully distinguish transcription factors and splicing factors based on the changes in gene expression and splicing levels quantified by our methods. Next, we aim to: (1) Explore and design more statistical strategies to provide more accurate methods for comparing gene expression and splicing changes. (2) Collect public RNA-seq data to test our methods and select the best one. (3) Summarize our gene expression and splicing analyses of public RNA-seq data in a report. (4) Develop a web tool to make our tool available to the public to offer researchers a standard method in choosing between gene expression or splicing for downstream analyses.
Qualifications: (1) Willing to learn about RNA biology and having software development capability.
(2) Knowledge of RNA-seq data analysis or sophisticated scientific development experience is a plus.
(3) Candidates must:
• Attend a 3-hour lab meeting every week.
• Attend a research subgroup meeting every week.
• Adhere to all lab policies (including weekly notebooks to track research and semester reports).
• Must register for credits, regardless of program-specific requirements.
(4) The student is required to continue the project during the Spring 2023 semester and full time in Summer 2023, if the student is invited to do so by the lab.
(5) Applicants with GPA under 3.6 will be considered only in exceptional circumstances.
Day-to-day supervisor for this project: Yu-Jen (Jennifer) Lin, Graduate Student
Hours: 12 or more hours
Related website: http://compbio.berkeley.edu
Biological & Health Sciences Engineering, Design & Technologies