- Meeting abstract
- Open Access
Epi-letters - how to describe epigenetic signatures
© Dinh; licensee BioMed Central Ltd. 2012
- Published: 14 December 2012
- Chromatin State
- Sequence Logo
- Discrete Category
- Microarray Profile
- Chromatin Mark
The concept of a chromatin-based epigenetic code, proposed more than a decade ago, associates specific combinations of chromatin marks with different gene expression states and their maintenance . High-throughput technologies like microarray profiling or next generation sequencing enable us to examine the validity of the concept, by profiling transcriptomes and multiple chromatin marks for many different samples, conditions and organisms. The large amounts of generated data require efficient and instructive computational methods to identify and interpret biologically relevant correlations and to challenge the hypothesis of an epigenetic code.
Here, I introduce a generally applicable bioinformatic method to group epigenetic information across genome-wide chromatin data sets. It automatically classifies the abundance of chromatin-based signals into discrete categories and transforms the categories into so-called epi-letters. Each genomic region can then be represented as a combined string of epi-letters referring to different chromatin marks. This synoptic compilation can be used for further clustering to determine common epigenetic signatures and can be represented applying the concept of the DNA motif sequence logo .
I propose a new and simple tool for finding and representing epigenetic patterns across genome-wide profiling data of different chromatin marks. I provide a proof-of-concept application with published data, resulting in a classification of epigenetic signatures in Arabidopsis thaliana. The method has also other potentials for de novo discovery and visualization of general genome-wide profiling patterns.
I thank my PhD advisors Arndt von Haeseler and Ortrun Mittelsten Scheid for interesting discussions and support. The traveling fellowship for participating SCS8/ISMB2012 from Swiss Bioinformatics Institute is greatly acknowledged.
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