A suite of computational tools that incorporate state-of-the-art statistical techniques for the analysis of DNAm data. minfi provides methods for preprocessing, quality assessment and detection of differentially methylated regions from the kilobase to the megabase scale. Several preprocessing algorithms are available and the infrastructure provides a convenient way for developers to easily implement their techniques as Bioconductor tools. By making SNP annotation available, users can choose to be cautious about probes that may behave unexpectedly due to the inclusion of a SNP in the probe sequence. minfi is unique in that it provides both bump hunting and block finding capabilities, and the assessment of statistical significance for the identified regions. Finally, because the package is implemented in Bioconductor, it gives users access to the countless analysis and visualization tools available in R.

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Artistic License version 2.0

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Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA; Lieber Institute of Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA, Department of Computer Science, University of Maryland, College Park, MD, USA, Department of Epidemiology, USA, Center for Epigenetics and Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA

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