Skip to main content
Skip to main menu Skip to spotlight region Skip to secondary region Skip to UGA region Skip to Tertiary region Skip to Quaternary region Skip to unit footer

Slideshow

Ping Ma

Distinguished Research Professor
pingma@uga.edu
<a href="http://www.stat.uiuc.edu/people/faculty/ma.shtml">University of Illinois at Urbana-Champaign</a>

With the rapid development of second-generation sequencing technologies, RNA-Seq has become a popular tool for transcriptome analysis. It offers the chance to detect novel transcripts by obtaining tens of millions of short reads. After mapped to the genome and/or to the reference transcripts,   RNA-Seq data can be summarized by a tremendous number of short-read counts. The huge number of short-read counts enables researchers to make transcript quantification in ultra-high resolution. Recent work found that short-read counts have significant sequence bias, which makes simple transcript quantification methods questionable. Thus, more elaborate statistical models that can effectively remove the sequence bias of the short-read counts are highly desirable to make transcript quantification more accurate.  In this talk, I will present some nonparametric statistical analysis for bias correction in RNA-Seq short-read counts.  Since the sample size is over tens of millions, fitting regular nonparametric model is infeasible. I will present a statistical method.  Real RNA-Seq examples will also be presented to demonstrate the empirical performance of our method.

Support us

We appreciate your financial support. Your gift is important to us and helps support critical opportunities for students and faculty alike, including lectures, travel support, and any number of educational events that augment the classroom experience. Click here to learn more about giving.

Every dollar given has a direct impact upon our students and faculty.