Location
This workshop was held at SAMSI in Research Triangle Park, NC.
Description
Methylation is the cornerstone of epigenetics. With the next generation sequencing (NGS) technology, whole genome nucleotide resolution methylation data (NRMD) are increasingly available. However, small sample size, correlation between CpG sites, and large variability in the signals, all present difficulties in detecting differentially methylated regions. Although there are a few statistical methods proposed for analyzing such data to date, there are still many unanswered questions. To complicate matter further, a mapping is required to infer NRMD for a cost-effective methylation detection technology, but statistical models for accomplishing this task are lacking. Another area of epigenetics that has gained a great deal of attention in recent years is the spatial organization of genome and regulation. Recent NGS-aided technologies, such as Hi-C and ChIA-PET, have led to the generation of genome-scale chromatin interaction data, which made it possible to recapitulate 3D chromatin structure and detect spatial interactions between important proteins and their target genes. These big data pose a number of challenging statistical issues, including “random collisions” (false positives), highly correlated data due to many-to-many mappings, over-dispersion, and very large dimensionality of the parameter space for reconstructing 3D structures. A number of statistical and computational tools have been proposed, but a great deal is yet to be done for the statistical community to make an impact in these emerging areas of epigenetics. Most importantly, statistical methods proposed need to be completely in sync with pressing biological issues being addressed in the scientific community.
This workshop aimed to:
(1) introduce participants at all levels (from graduate students, to postdocs, to junior and senior faculty in academic institutions) and backgrounds (biological, computational, and statistical) to current state-of-the-art genomic technology and research in statistical methods for these two areas of epigenetics,
(2) create a forum for generation and discussion of ideas for tackling the challenges in analyzing methylation and chromatin interaction data, and
(3) build a bridge for new, and strengthen existing, collaboration among participants.
Participants could optionally submit an abstract for a short presentation at the Poster session on Monday (March 9th, 2015) evening. A small subset of the submitted abstracts were selected for short presentations at the workshop. Abstracts for presentation at the workshop: Oral or a poster presentation. If submitted, but not selected, for an oral presentation, the author may still present it as a poster.
Questions: email [email protected]
Schedule and Supporting Media
Participant List
Speaker Titles and Abstracts
Posters
Monday, March 9, 2015
at SAMSI
| Time | Description | Speaker | Slides | Videos |
|---|---|---|---|---|
| 8:00 | Shuttle to SAMSI | |||
| 8:30-8:50 | Registration | |||
| 8:50-9:00 | Introduction and Welcome | Sujit K. Ghosh, SAMSI Shili Lin, Ohio State University |
||
| 9:00-10:30 | Session 1: Epigenetics and Statistical Methods for Analyzing Epigenetic Data | Chair: Shili Lin, Ohio State University | ||
| Challenges and Biases Associated with Whole-Genome Bisulfite Sequencing Data | Bob Schmitz, University of Georgia | |||
| Computational Advances in ChIP-seq and ChIA-PET Data Analysis | Michael Zhang, University of Texas, Dallas | |||
| 10:30-11:00 | Break | |||
| 11:00-12:30 | Session 2: DNA Methylation I | Chair: Yong Seok Park, University of Pittsburgh | ||
| Dynamic Cytosine Modification in Human Diseases | Peng Jin, Emory University | |||
| Analysis Tool for Combined DNA Methylation and 5-Hydroxymethylcytosine Data | Maureen Sartor, University of Michigan | |||
| Differential Methylation Analysis from Whole Genome Bisulfite Sequencing: a Matter of Spatial Correlation, Coverage Dept, and Biological Variance | Hao Wu, Emory University | |||
| 12:30-2:00 | Lunch | |||
| 2:00-3:00 | Session 3: Modeling of Long-range Chromatin Interaction I | Chair: Ming Hu, New York University | ||
| Deciphering Dynamic Chromatin 3D Organization: from Structure to Gene Regulation | Inkyung Jung, University of California, San Diego | |||
| Statistical Modeling and Analysis of ChIA-PET Data | Liang Niu, University of Cincinnati | |||
| 3:00-3:30 | Break | |||
| 3:30-5:00 | Session 4: Contributed talks (4 20-minute talks) | Chair: Victor Jin, University of Texas, San Antonio Health Science Center | ||
| Bayesian Screening for Group Differences in Methylation Array Data | Erick Lock, University of Minnesota | |||
| Faster and Efficient Tests for Detection of Differentially Methylated Region from MethylCap-seq Data | Deepak Ayyala, Ohio State University | |||
| The Impact of Mitochondrial Dysfunction on the Epigenome | Oswaldo Lozoya, NIH/NIEHS | |||
| Chenchen Zou, Jackson Laboratory for Genomic Medicine | Multi-track Structure Inference Model for Genome-wide Chromatin Conformation-capturing Data | |||
| 5:00-7:00 | Poster Session and Reception (SAMSI, 2nd floor Commons) | |||
| 7:00 | Shuttle to Hotel |
Tuesday, March 10, 2015
at SAMSI
| Time | Description | Speaker | Slides | Videos |
|---|---|---|---|---|
| 8:00 | Shuttle to SAMSI | |||
| 8:45-9:00 | Registration and Announcements | Shili Lin, Ohio State University | ||
| 9:00-10:30 | Session 5: DNA Methylation II | Chair: Steve Qin, Emory University | ||
| DNA Methylation Profiling in Cancer: Perspectives from a Genomics/Computation Group | Pearlly Yan, Ohio State University | |||
| Statistical Analysis of DNA Methylation using Within-Fragment Information | Yong Seok Park, University of Pittsburgh | |||
| DNA Methylation, Gene Expression, and Aging: what Can We Learn from Cross-Sectional Microarray Data? | Karen Conneely, Emory University | |||
| 10:30-11:00 | Break | |||
| 11:00-12:30 | Session 6: Modeling of Long-Range Chromatin Interaction II | Chair: Hao Wu, Emory University | ||
| How the 3D Genome Folds – Now in the Loop | Miriam Huntley, Harvard University | |||
| A Hidden Markov Random Field Based Bayesian Method for the Detection of Long-Range Chromosomal Interactions in Hi-C Data | Ming Hu, New York University | |||
| Genomic Analysis of Three-Dimensional Data Identifies Functional Enhancer-Mediated Looping | Victor Jin, Univ. of Texas, San Antonio Health Science Center | |||
| 12:30-2:00 | Lunch and Adjourn |