Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2006;7(5):R37.
doi: 10.1186/gb-2006-7-5-r37. Epub 2006 May 5.

Inferring transcriptional modules from ChIP-chip, motif and microarray data

Karen Lemmens  1 , Thomas Dhollander, Tijl De Bie, Pieter Monsieurs, Kristof Engelen, Bart Smets, Joris Winderickx, Bart De Moor, Kathleen Marchal
Affiliations

Inferring transcriptional modules from ChIP-chip, motif and microarray data

Karen Lemmens et al. Genome Biol. 2006.

Abstract

'ReMoDiscovery' is an intuitive algorithm to correlate regulatory programs with regulators and corresponding motifs to a set of co-expressed genes. It exploits in a concurrent way three independent data sources: ChIP-chip data, motif information and gene expression profiles. When compared to published module discovery algorithms, ReMoDiscovery is fast and easily tunable. We evaluated our method on yeast data, where it was shown to generate biologically meaningful findings and allowed the prediction of potential novel roles of transcriptional regulators.

PubMed Disclaimer

Figures

Figure 1
Figure 1
ReMoDiscovery analysis flow. ReMoDiscovery consists of a seed discovery step followed by a seed extension step. ChIP-chip data, motif data, and expression data are used as input for the algorithm. These three datasets can be represented as matrices in which the rows represent the genes. For the ChIP-chip data (R) the columns represent the regulators, for the motif data (M) they represent the motifs and for the expression data (A) the different experiments. (a) The seed discovery step identifies sets of genes that are co-expressed, bind the same regulators, and have the same motifs in their intergenic region. (b) The gene content of the seed modules can be extended during the seed extension step using less stringent criteria. The logarithms of the module enrichment p values (y-axis) are plotted for all regulators (motifs) as a function of the correlation threshold (x-axis). Each line in the sample plot shows the module enrichment p values for the enrichment of its corresponding regulator (motif) as a function of the gene expression correlation threshold used.
Figure 2
Figure 2
Overview of the seed modules identified in the Spellman dataset [12]. For visualization purposes, seed modules with similar function are combined (indicated in green). A regulator or motif that is part of a regulatory program of an extended module is indicated in the figure by a bold edge from the regulator or motif to its module.
Figure 3
Figure 3
Representative examples from the module content similarity analysis. The significance of the similarity in module content between ReMoDiscovery seed modules and GRAM [9] and SAMBA [3] output is shown at different parameter settings. The color bar on the right indicates the normalized Jaccard similarity score, that is, the number of standard deviations from the mean of the distribution of Jaccard similarity scores on randomized module partitioning. (a) Regulator content similarity between ReMoDiscovery and GRAM, with varying GRAM module p value cutoff and ReMoDiscovery Chip-chip threshold. (b) Gene content similarity between ReMoDiscovery and GRAM, with varying GRAM core profile p value cutoff and ReMoDiscovery correlation threshold. (c) Gene content similarity between ReMoDiscovery and SAMBA, with varying SAMBA overlap prior factor and ReMoDiscovery correlation threshold.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Greenbaum D, Luscombe NM, Jansen R, Qian J, Gerstein M. Interrelating different types of genomic data, from proteome to secretome: 'oming in on function. Genome Res. 2001;11:1463–1468. doi: 10.1101/gr.207401. - DOI - PubMed
    1. Cavalieri D, De Filippo C. Bioinformatic methods for integrating whole-genome expression results into cellular networks. Drug Discov Today. 2005;10:727–734. doi: 10.1016/S1359-6446(05)03433-1. - DOI - PubMed
    1. Tanay A, Sharan R, Kupiec M, Shamir R. Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc Natl Acad Sci USA. 2004;101:2981–2986. doi: 10.1073/pnas.0308661100. - DOI - PMC - PubMed
    1. Segal E, Shapira M, Regev A, Pe'er D, Botstein D, Koller D, Friedman N. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet. 2003;34:166–176. - PubMed
    1. Van den Bulcke T, Lemmens K, Van de Peer Y, Marchal K. Inferring transcriptional networks by mining 'omics' data. Current Bioinformatics.

Publication types