Identification and analysis of methylation call differences between bisulfite microarray and bisulfite sequencing data with statistical learning techniques
| Autor: | Matthias Döring, Jörn Walter, Nico Pfeifer, Karl Nordström, Pavlo Lutsik, Jasmin Gries, Gilles Gasparoni |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
0303 health sciences
Microarray Computer science Applied Mathematics Bisulfite sequencing Methylation Computational biology Bioinformatics Biochemistry Computer Science Applications Bisulfite 03 medical and health sciences 0302 clinical medicine Structural Biology 030220 oncology & carcinogenesis Meeting Abstract DNA methylation Gene silencing Epigenetics DNA microarray Molecular Biology Whole genome bisulfite sequencing 030304 developmental biology Genetic association |
| Zdroj: | BMC Bioinformatics Highlights from the Third International Society for Computational Biology (ISCB) European Student Council Symposium 2014 |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/1471-2105-16-s3-a7 |
| Popis: | Background DNA methylation is an epigenetic modification known to play a prime role in gene silencing and is an important topic in epigenetic research. However, due to technology-dependent errors there are inconsistencies between methylation measurements from different methods [1]. Incorrect methylation calls could result in the discovery of spurious associations between methylation patterns and specific phenotypes in epigenomewide association studies (EWAS). We worked towards assigning a measure of confidence to individual CpGs to down-weigh or exclude positions with inconsistent measurements in such studies. We used methylation measurements from the Infinium HumanMethylation450 microarray (b450K) and whole genome bisulfite sequencing (bWGBS) to evaluate whether locus-specific measurement differences, Δb = b450K − bWGBS, are predictable using statistical learning techniques. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink