W1 – Computational Epigenomics

Short Description

Recent advances in single cell sequencing lead to computational challenges in working with this type of uncertain and sparse data. The simultaneous high-throughput measurement of multiple genomewide features in individual cells such as gene expression and epigenetic modifications is in reach, techniques such as NOMe-Seq allow to determine DNA methylation and chromatin accessibility/ nucleosome positioning in single cells. Such new data sets offer the exciting opportunity to directly link mechanisms of epigenetic control to gene expression, without the confounding factor of (unknown) population structures which can affect bulk data sets. While significant advancements were made in processing and evaluating single cell RNA-seq cohorts, much less attention is brought to protocols related to epigenomic data.

While epigenetic effects are widely associated with functional/ phenotypic outcomes, the approaches to integrating epigenetic with other genomic and/or phenotypic data are often ad-hoc and based on heuristics. A systematic characterisation of the underlying mechanisms of epigenetic control of gene expression is still missing. Several studies have highlighted associations between DNA methylation at CpG islands and gene silencing and, more recently, the interaction between histone modifications and gene expression has also attracted considerable attention. Nevertheless, many of these associations are purely correlative, and several other associations can be found, for example between transcription factor binding and histone modifications. Such observations call for more effective computational methods to jointly model multiple epigenomic and other high-throughput data sets, however this poses formidable computational challenges due to the dimensionality, size and complexity of each individual data set. Moreover, mathematical models that describe hypotheses about the mechanisms underlying the formation of DNA methylation patterns and histone modifications will contribute to the understanding of the functioning and interplay between methylation enzymes and chromatin structures. The workshop will review recent progress, and stimulate further work on this topic of central importance in high-throughput bioinformatics.

Preliminary Program

Saturday, September 8

Session 1 – Single cell epigenomics

Chair: Jörn Walter

09:00 09:30 N.N.

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09:30 10:00 N.N.

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10:00 10:30 N.N.

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10:30 11:00 Coffee/Tea break
11:00 11:30 N.N.

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11:30 12:30 Oliver Stegle (keynote)

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12:30 14:00 Lunch break

Session 2 – Epigenome-wide association studies

Chair: Yassen Assenov

14:30 15:00 N.N.

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15:00 15:30 Coffee/Tea break
15:30 16:00 N.N.

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16:00 16:30 N.N.

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16:30 17:00 N.N.

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17:00 18:30 Poster session
Sunday, September 9

Session 3 – Integrative regulatory genomics

Char: Guido Sanguinetti

09:00 09:30 N.N.

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09:30 10:00 N.N.

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10:00 10:30 N.N.

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10:30 11:00 Coffee/Tea break
11:00 11:30 N.N.

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11:30 12:30 Anshul Kundaje (keynote)

Interpretable deep learning approaches for integrative regulatory genomics

12:30 14:00 Lunch break

Session 4 – Modeling of epigenetic dynamics

Chair: Verena Wolf

14:30 15:00 N.N.

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14:30 15:00 N.N.

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15:00 15:30 Coffee/Tea break
15:30 16:30 Uwe Ohler (keynote)

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16:30 17:00 Joint discussion:

Current Challenges in Computational Epigenomics

Organizers

Yassen Assenov

Group Leader, Computational Epigenomics, German Cancer Research Center

Guido Sanguinetti

Professor, Computational Bioinformatics, University of Edinburgh

Jordana Bell

Senior Lecturer, Epigenomics Research Group, King’s College London

Jörn Walter

Professor, Genetics/Epigenetics, Saarland University

Christoph Bock

Principal Investigator, Medical Epigenomics Laboratory, 

CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences

Verena Wolf

Professor, Modeling and Simulation, Saarland University