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Science at the Edge October 6th 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