EpiGRAPH: A user-friendly software for advanced (epi-) genome analysis and prediction
EpiGRAPH is a software for genome and epigenome analysis. This page provides a brief overview, but we encourage you to consult the introduction page and the background page (linked below) for more detailed information. To start using EpiGRAPH, please click the "Register" button or log in with you user account.
Important tips for first-time users (please read!)
- Please take your time to read the explanations on these pages! EpiGRAPH addresses complex problems, hence some aspects of the analysis are not entirely trivial nor obvious from a trial-and-error approach. And please let us know if you're stuck somewhere, we'll be happy to help!
- We recommend that you create your own EpiGRAPH account using the "Register" button on the right. Alternatively, you can click "Be a Guest", upon which EpiGRAPH will generate a test account for you (the username will be displayed on the right and the password for future logins is "guest").
- It is not recommended to open more than one EpiGRAPH browser window at the same time (while it is possible to open several EpiGRAPH results pages in parallel browser windows, only the last one can be edited).
Links to background information:
EpiGRAPH Introduction Page (provides a general overview of EpiGRAPH)
EpiGRAPH Background Page (includes tutorial, technical information and the source code of EpiGRAPH)
Main Paper on EpiGRAPH (Genome Biology 10: R14)
Tutorial Paper on EpiGRAPH (Methods in Molecular Biology, in press)
What's new?
3 December 2008: Official release of EpiGRAPH 1.0
After several months of extensive testing and optimization, we are now confident that EpiGRAPH is sufficiently fast, robust and error-free to be officially released for use by the research community. We have also uploaded preprint versions of two papers describing EpiGRAPH, one presenting the method and software and the other providing a tutorial-style introduction into genome analysis with EpiGRAPH and Galaxy. Furthermore, we can report that EpiGRAPH has already been used by well over a hundred researchers ahead of publication, and we hope that the software continues to be useful for the community. Please don't hesitate to contact us with practical questions or error reports!
13 July 2009: Technical problems resolved
We had a couple of unexpected downtimes recently, caused by excessive control set calculations with millions of regions. We have now introduced mild restrictions on the number of regions that can be analyzed, but this should not cause any practical problems. In general, our experience shows that the accuracy improvements beyond 1000 genomic regions are neglectible and not worth the longer runtime.
29 July 2010: Archival of old EpiGRAPH analyses
As EpiGRAPH becomes increasingly popular, we had to revise our data storage policy. From now on, analyses that are older than a few months will be archived and won't be accessible through the front-end anymore. If you need access to any archived analyses, please write us an e-mail and we will be happy to restore them for you. Alternatively, you can make backups of important analyses yourself, by clicking the "Download XML Documentation" button. The resulting XML files can always be uploaded back into EpiGRAPH to view and change the analysis results.
Key features of EpiGRAPH
EpiGRAPH analyses are performed with established statistical methods and powerful machine learning algorithms. Two scenarios are treated separately, the class analysis (which compares different classes of genomic regions) and the use of EpiGRAPH as an advanced genome calculator and data retrieval tool.
Class analysis
The class analysis is tailored to the analysis of sets of regions that belong to different classes, such as methylated vs. unmethylated promoters, as experimentally determined for a particular cell line. EpiGRAPH implements statistical tests that identify attributes exhibiting significant differences between the classes, as well as more sophisticated machine learning methods that make it possible to assess the global relationship between the classes and entire groups of logically related attributes. In addition, classification algorithms such as support vector machines, logistic regression and ensemble learning methods can be used to prediction the class value for regions that have not been analyzed experimentally.
Genome attribute calculator
EpiGRAPH utilizes a highly customizable genome attribute calculator in order to access a large database of genome and epigenome attributes. Users who prefer to perform statistical analysis outside EpiGRAPH can use this component directly. In contrast to BioMart, EpiGRAPH’s genome calculator does not only enable data acquisition but also supports complex attributes as well as frequency and overlap calculations.
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