Reduced representation bisulfite sequencing design for assessing the methylation of human CpG islands in large samples View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2015-07

AUTHORS

A. S. Tanas, E. B. Kuznetsova, M. E. Borisova, V. V. Rudenko, D. V. Zaletayev, V. V. Strelnikov

ABSTRACT

The reduced representation bisulfite sequencing (RRBS) method has been developed for the high-throughput analysis of DNA methylation based on the sequencing of genomic libraries treated with sodium bisulfite by next-generation approaches. In contrast to whole-genome sequencing, the RRBS approach elaborates specific endonucleases to prepare libraries in order to produce pools of CpG-rich DNA fragments. The original RRBS technology based on the use of the MspI libraries allows one to increase the relative number of CpG islands in the pools of genomic fragments compared to whole-genome bisulfite sequencing. Nevertheless, this technology is rarely used due to the high cost compared with bisulfite methylation analysis with hybridization microarrays and significant residual amount of data represented by the sequences of genomic repeats that complicates the alignment and is not of particular interest for developing DNA methylation markers, which is often the main goal of biomedical research. We have developed an algorithm for estimating the likelihood that recognition sites of restriction endonucleases will be represented in CpG islands and present a method of reducing the effective size of the RRBS library without a significant loss of the CpG islands based on the use of the XmaI endonuclease for library preparation. In silico analysis demonstrates that the optimum range of the XmaI-RRBS fragment lengths is 110–200 base pairs. The sequencing of this library allows one to assess the methylation status of over 125000 CpG dinucleotides, of which over 90000 belong to CpG islands. More... »

PAGES

618-626

Identifiers

URI

http://scigraph.springernature.com/pub.10.1134/s0026893315040184

DOI

http://dx.doi.org/10.1134/s0026893315040184

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1006623722


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