Keyan Zhao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Keyan Zhao has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Juw Won Park
Department of Microbiology, Immunology & Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
1 shared publication - Qing Zhou
University of California, Los Angeles
1 shared publication - Yi Xing
Center for Computational and Genomic Medicine, Children’s Hospital of Philadelphia, Philadelphia, USA
1 shared publication - Zhi-Xiang Lu
Revvity, Inc., 77 4th Ave, Waltham, MA 02451, USA
1 shared publication
Latest Publications
- Open Access
- 22 July 2013
- Published by Springer Nature in Genome Biology
- Vol. 14 (7) , R74
GLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data
To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms.
- 22 July 2013
- Published by Springer Nature in Genome Biology
- Vol. 14 (7) , R74
Open AccessGLiMMPS: robust statistical model for regulatory variation of alternative splicing using RNA-seq data
To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms.