title: SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information.
abstract: BACKGROUND
  The molecular assays that test gene expression, transcriptional, and epigenetic regulation are increasingly diverse and numerous. The information generated by each type of assay individually gives an insight into the state of the cells tested. What should be possible is to add the information derived from separate, complementary assays to gain higher-confidence insights into cellular states. At present, the analysis of multi-dimensional, massive genome-wide data requires an initial pruning step to create manageable subsets of observations that are then used for integration, which decreases the sizes of the intersecting data sets and the potential for biological insights. Our Significance-based Modules Integrating the Transcriptome and Epigenome (SMITE) approach was developed to integrate transcriptional and epigenetic regulatory data without a loss of resolution.
  
  
  RESULTS
  SMITE combines p-values by accounting for the correlation between non-independent values within data sets, allowing genes and gene modules in an interaction network to be assigned significance values. The contribution of each type of genomic data can be weighted, permitting integration of individually under-powered data sets, increasing the overall ability to detect effects within modules of genes. We apply SMITE to a complex genomic data set including the epigenomic and transcriptomic effects of Toxoplasma gondii infection on human host cells and demonstrate that SMITE is able to identify novel subnetworks of dysregulated genes. Additionally, we show that SMITE outperforms Functional Epigenetic Modules (FEM), the current paradigm of using the spin-glass algorithm to integrate gene expression and epigenetic data.
  
  
  CONCLUSIONS
  SMITE represents a flexible, scalable tool that allows integration of transcriptional and epigenetic regulatory data from genome-wide assays to boost confidence in finding gene modules reflecting altered cellular states.
authors.affiliation: Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.
authors.forename: N Ari
authors.initials: NA
authors.lastname: Wijetunga
authors.affiliation: Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.
authors.forename: Andrew D
authors.initials: AD
authors.lastname: Johnston
authors.affiliation: Division of Obstetrics and Gynecology, Yamaguchi University, 677-1 Yoshida, Yamaguchi Prefecture, 753-8511, Japan.
authors.forename: Ryo
authors.initials: R
authors.lastname: Maekawa
authors.affiliation: Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.|Department of Obstetrics, Gynecology and Women's Health, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.
authors.forename: Fabien
authors.initials: F
authors.lastname: Delahaye
authors.affiliation: Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.|Department of Microbiology and Immunology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.
authors.forename: Netha
authors.initials: N
authors.lastname: Ulahannan
authors.affiliation: Department of Microbiology and Immunology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.|Department of Pathology, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.|Department of Medicine, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA.
authors.forename: Kami
authors.initials: K
authors.lastname: Kim
authors.affiliation: Department of Genetics, Albert Einstein College of Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA. john.greally@einstein.yu.edu.
authors.forename: John M
authors.initials: JM
authors.lastname: Greally
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D044127:Epigenesis, Genetic; D057890:Epigenomics; D005347:Fibroblasts / Q000166:cytology / Q000378:metabolism; D052816:Foreskin / Q000166:cytology / Q000378:metabolism; D053263:Gene Regulatory Networks; D006801:Humans; D008297:Male; D008962:Models, Theoretical; D012984:Software; D014122:Toxoplasma / Q000235:genetics / Q000302:isolation & purification; D059467:Transcriptome
keywords: Bioinformatics; Epigenetic; Gene expression; Genomic; Interaction network; Modules