ArticleBMC bioinformatics2017
SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
17 citing papers in PubMed, 27 citations in OpenAlex.
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- Nucleoporins in cardiovascular disease.Journal of molecular and cellular cardiology · 2020Review
- Multiomics of azacitidine-treated AML cells reveals variable and convergent targets that remodel the cell-surface proteome.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- Integration of genome-wide mRNA and miRNA expression, and DNA methylation data of three cell lines exposed to ten carbon nanomaterials.Data in brief · 2018Article
- Multi-omics analysis of ten carbon nanomaterials effects highlights cell type specific patterns of molecular regulation and adaptation.NanoImpact · 2018Article
- Statistical and integrative system-level analysis of DNA methylation data.Nature reviews. Genetics · 2018Review
- ATACseqQC: a Bioconductor package for post-alignment quality assessment of ATAC-seq data.BMC genomics · 2018Article
- Network assessment of demethylation treatment in melanoma: Differential transcriptome-methylome and antigen profile signatures.PloS one · 2018Article
- Epigenetic regulation of placental gene expression in transcriptional subtypes of preeclampsia.Clinical epigenetics · 2018Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundThe 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.
resultsSMITE 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.
conclusionsSMITE 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.