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FEM specifications

Information


Unique identifier OMICS_16936
Name FEM
Alternative name Functional Epigenetic Modules
Software type Package/Module
Interface Command line interface
Restrictions to use None
Biological technology Illumina
Operating system Unix/Linux
Computer skills Advanced
Version 2.6.0
Stability Stable
Maintained Yes

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Documentation


Maintainer


  • person_outline Andrew Teschendorff

Publication for Functional Epigenetic Modules

FEM citations

 (6)
library_books

Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network

2017
BMC Bioinformatics
PMCID: 5335754
PMID: 28253853
DOI: 10.1186/s12859-017-1567-2

[…] gation scheme []. In BMRF, the parameter T, which controls the sharpness of the distribution of network score function, was set to 1 and the other parameter d distance was tested ranging from 1 to 3. Functional epigenetic modules (FEM) algorithm is a functional supervised algorithm. It encapsulates the strength of associations of the genes with the phenotype in terms of the edge weights, in order […]

library_books

Integrative epigenome wide analysis demonstrates that DNA methylation may mediate genetic risk in inflammatory bowel disease

2016
Nat Commun
PMCID: 5133631
PMID: 27886173
DOI: 10.1038/ncomms13507

[…] in T-cells (CD4+ and CD8+) but lower in monocytes. Using a method that explores methylation within TSS and/or known regulatory regions and gene expression within gene networks has highlighted several functional epigenetic modules of biological relevance that were significantly associated with IBD (). […]

library_books

DNA methylation outliers in normal breast tissue identify field defects that are enriched in cancer

2016
Nat Commun
PMCID: 4740178
PMID: 26823093
DOI: 10.1038/ncomms10478

[…] To assess if the DNA methylation changes between normal-adjacent and normal tissue, and within the inferred functional epigenetic modules (from the FEM/EpiMod analysis), are occurring in a coordinated or mutually exclusive fashion within a sample, we first constructed a binary deviation matrix over all gene […]

library_books

The integrative epigenomic transcriptomic landscape of ER positive breast cancer

2015
Clin Epigenetics
PMCID: 4673726
PMID: 26664652
DOI: 10.1186/s13148-015-0159-0

[…] of deregulation within individual tumours, similar to what is observed at the copy number level []? In order to address these questions, we decided to apply a functional supervised algorithm, called Functional Epigenetic Modules (FEMs) [, ], which performs an integrative analysis of DNA methylation and gene expression data at a system level. The system-level integration is done using a comprehens […]

library_books

Genome scale hypomethylation in the cord blood DNAs associated with early onset preeclampsia

2015
Clin Epigenetics
PMCID: 4371797
PMID: 25806090
DOI: 10.1186/s13148-015-0052-x

[…] as originally developed to analyze Illumina 27 K data. We modified the code to analyze 450 K data by replacing the methylation values of each gene with the sum of TSS200, similar to the newer package Functional Epigenetic Modules (FEM) from the same developers of EpiMods []. […]

call_split

Role of DNA Methylation and Epigenetic Silencing of HAND2 in Endometrial Cancer Development

2013
PLoS Med
PMCID: 3825654
PMID: 24265601
DOI: 10.1371/journal.pmed.1001551
call_split See protocol

[…] hberg procedure . GSEA was performed separately for top-ranked hypermethylated and hypomethylated CpGs, and at the gene level in order to avoid overcounting multiple CpGs mapping to the same gene.The Functional Epigenetic Modules (FEM) algorithm is a novel direct extension of the EpiMod algorithm developed by us previously . Full details can be found in . Briefly, it is an integrative epigenome-tr […]

Citations

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FEM institution(s)
Computational Systems Genomics, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai, China; Department of Women’s Cancer, UCL Elizabeth Garrett Anderson Institute for Women’s Health, London, UK; Statistical Genomics Group, Paul O’Gorman Building, UCL Cancer Institute, University College London, London, UK
FEM funding source(s)
This work was supported by the Chinese Academy of Sciences, the Shanghai Institute for Biological Sciences and the Max-Planck Gesellschaft.

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