Overview

NGSmethDB is a dedicated database for the storage, browsing and data mining of whole-genome, single-base-pair resolution methylomes.

Just now, the database is being updated. Major novelties include:

  1. Higher-quality maps. In addition to the stringent quality controls included in the previous versions of the NGSmethDB, we now include indels detection and automatic M-bias trimming. This will potentially reduce biases not considered before for the estimation of methylation levels.
  2. Differential methylation. Given the increasing biological relevance of differential methylation, a section of the database is now dedicated to precompiled differentially methylated cytosines (DMCs).
  3. Improved data access. A RESTful API now serve numeric methylome data, allowing the selection by species, assembly, chromosome and genome region. Data for a pair of tissues can be simultaneously retrieved in table format, thus enabling the comparison of differential methylation among different tissues or different physiological/pathological conditions.
  4. Data sharing and visualization. The browser implemented in previous versions is now replaced by a local instance of the UCSC Genome Browser, thus allowing access to all the Genome Browser toolset to view and compare methylation data.
  5. Data confidentiality. The RESTful API will be made available soon for download. In this way, the user will no longer need to upload private data to the server to carry out comparative analyses.

How to cite

[1] Stefanie Geisen, Guillermo Barturen, Ángel M. Alganza, Michael Hackenberg and José L. Oliver. 2014. NGSmethDB: an updated genome resource for high quality, single-cytosine resolution methylomes. Nucl. Acids Res. (1 January 2014) 42 (D1): D53-D59 first published online November 22, 2013 doi:10.1093/nar/gkt1202
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[2] Michael Hackenberg, Guillermo Barturen and José L. Oliver. 2010. NGSmethDB: A database for next-generation sequencing single-cytosine-resolution DNA methylation data
Nucleic Acids Research, 2010, 1–5 [PDF]
http://dx.doi.org/10.1093/nar/gkq942

Computational Genomics and Bioinformatics, UGR, Spain