Science at the Edge

October 6^th 2017

1400 Biomedical Physical Sciences

11:30 am

Statistical data integration for  genomic and epigenomic data

Abstract: My group works on high dimensional statistical genomics and epigenomics with a specific emphasis on data integration problems. In this seminar, I will present our statistical approaches for NGS read-level data integration for studying repetitive regions of the genome and data integration for genomewide-association studies.

X. Zeng, B. Li, R. Welch, C. Rojo-Alfaro, Y. Zheng, C. Dewey, and S. Keles. Perm-seq: Mapping protein-DNA interactions in segmental duplication and highly repetitive regions of genomes with prior-enhanced read mapping. PLoS Computational Biology, 11(10):e1004491, 2015.

C. Zuo, S. Shin, and S. Keles. atSNP: transcription factor binding affinity testing for regulatory SNP detection. Bioinformatics, 31(20):3353-5, 2015.

P. Liu, R. Sanalkumar, E. H. Bresnick, S. Keles*, and C. N. Dewey*. Integrative analysis with ChIP-seq advances the limits of transcript quantification from RNA-seq, Genome Research, 26(8): 1124–1133, 2016.

S. Shin and S. Keles. Annotation regression for genome-wide association studies with an application to psychiatric genomic consortium data. Statistics in Biosciences, 9(1):50-72, 2017.

Lerena R. Heintzelman

Department of Physics & Astronomy

Michigan State University

567 Wilson Rd. Room 3261

East Lansing, MI 48824

517-884-5513