Sequencing Data Interest Group (SDIG)

Sequencing Data Interest Group (SDIG)

The Cancer Center is organizing a biweekly Sequencing Data Interest Group (SDIG) meeting. The goal is to bring all colleagues' effort and expertise together, so we can learn and benefit from each other to DIG out more from the huge data sets since this area is new and moving very fast. Anyone who has an interest in generating sequencing data and data analysis is welcome to attend.

Meetings are held 2:30 pm - 3:30 pm in Wolstein Research Building. Please contact Shuying Sun, PhD if you have any questions.


SDIG Meetings 2012

Date/ Location Speaker Title
Monday, February 27, 2012
WRB 1-402
Olivia Corradin, PhD student
Case Western Reserve University
Utilization of RNA-seq and ChIP-seq Data for the Identification of Enhancers and Their Targets
Monday, March 12, 2012
WRB 1-402
Nicholas Beckloff, PhD
Case Western Reserve University
State of the Genome: Past, Present and Future of the Field of NGS
Monday, March 26, 2012
WRB 1-402
Ricky Chan, PhD
Cleveland Clinic
Genome Sequencing of Plasmodium Vivax Isolates
Monday, April 9, 2012
WRB 1-402
Yaomin Xu, PhD
Cleveland Clinic
Reproducible Analysis on NGS Epigenomic Data
Monday, April 23, 2012
WRB 1-402
Bo Hu, PhD
Cleveland Clinic
Some Statistical Issues in Analyzing Next Generation Sequencing Data
Monday, October 8, 2012
WRB 1-402
Dan Savel, PhD Student
Case Western Reserve University
Pluribus: Correcting Errors in Next Generation Sequencing Data
Monday, October 22, 2012
WRB 3-136
Analisa DiFeo, PhD
CWRU/UH
Using Next-Generation Sequencing to Identify the Functional Target of microRNA's Regulating Ovarian Cancer Pathogenesis
Monday, November 5, 2012
WRB 1-402
Cheryl Thompson, PhD
CWRU/UH
Identifying Biomarkers of Cancer Using Next Generation Sequencing
Monday, November 19, 2012
WRB 1-402
Yaomin Xu, PhD
Cleveland Clinic
Reproducible Analysis on NGS Epigenomic Data
Monday, December 10, 2012
WRB 1-402
Xiaoqing Yu, PhD Student
Case Western Reserve University
HMM-DM: Identifying Differential Methylation Patterns Using A Hidden Markov Model