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BY-NC-ND 4.0 license Open Access Published by De Gruyter April 20, 2017

Jllumina - A comprehensive Java-based API for statistical Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data processing

  • Diogo Almeida , Ida Skov , Jesper Lund , Afsaneh Mohammadnejad , Artur Silva , Fabio Vandin , Qihua Tan , Jan Baumbach and Richard Röttger EMAIL logo

Summary

Measuring differential methylation of the DNA is the nowadays most common approach to linking epigenetic modifications to diseases (called epigenome-wide association studies, EWAS). For its low cost, its efficiency and easy handling, the Illumina HumanMethylation450 BeadChip and its successor, the Infinium MethylationEPIC BeadChip, is the by far most popular techniques for conduction EWAS in large patient cohorts. Despite the popularity of this chip technology, raw data processing and statistical analysis of the array data remains far from trivial and still lacks dedicated software libraries enabling high quality and statistically sound downstream analyses. As of yet, only R-based solutions are freely available for low-level processing of the Illumina chip data. However, the lack of alternative libraries poses a hurdle for the development of new bioinformatic tools, in particular when it comes to web services or applications where run time and memory consumption matter, or EWAS data analysis is an integrative part of a bigger framework or data analysis pipeline. We have therefore developed and implemented Jllumina, an open-source Java library for raw data manipulation of Illumina Infinium HumanMethylation450 and Infinium MethylationEPIC BeadChip data, supporting the developer with Java functions covering reading and preprocessing the raw data, down to statistical assessment, permutation tests, and identification of differentially methylated loci. Jllumina is fully parallelizable and publicly available at http://dimmer.compbio.sdu.dk/download.html

Published Online: 2017-4-20
Published in Print: 2016-10-1

© 2016 The Author(s). Published by Journal of Integrative Bioinformatics.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Downloaded on 22.9.2023 from https://www.degruyter.com/document/doi/10.1515/jib-2016-294/html
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