Click headers to hide sections

Resources

Publications

The following publications describe the data provided on this site.

Galagan, J.E., Minch, K., Peterson, M., Lyubetskaya, A., Azizi, E., Sweet, L., Gomes, A., Rustad, T., Dolganov, G., Glotova, I., Abeel, T., Mahwinney, C., Kennedy, A.D., Allard, R., Brabant, W., Krueger, A., Jaini, S., Honda, B., Yu, W.H., Hickey, M.J., Zucker, J., Garay, C., Weiner, B., Sisk, P., Stolte, C., Winkler, J.K., Van de Peer, Y., Iazzetti, P., Camacho, D., Dreyfuss, J., Liu, Y., Dorhoi, A., Mollenkopf, H.J., Drogaris, P., Lamontagne, J., Zhou, Y., Piquenot, J., Park, S.T., Raman, S., Kaufmann, S.H., Mohney, R.P., Chelsky, D., Moody, D.B., Sherman, D.R., Schoolnik, G.K. (2013) The Mycobacterium tuberculosis regulatory network and hypoxia. Nature.499(7457):178-83
Galagan, J., Lyubetskaya, A., Gomes, A. (2013) ChIP-Seq and the Complexity of Bacterial Transcriptional Regulation. Current topics in microbiology and immunology.363:43-68
Jaini, S., Lyubetskaya, A., Gomes, A., Peterson, M., Park, S.T., Raman, S., Schoolnik, G., Galagan, J.E. Transcription Factor Binding Site Mapping Using ChIP-Seq. In: Hatfull G, Jacobs WR, Jr., editors. Molecular Genetics of Mycobacteria, 2nd Edition: ASM Press; In Press.

Acknowledgements

Use of these data should include the following acknowledgement:

Data used was generated in whole or in part with Federal funds fromthe National Institute of Allergy and Infectious DiseasesNational Institute of Health, Department of Health and Human Services, under contract no. HHSN272200800059C

ChIP-Seq

Coverage data for individual TFs are provided as both wig files and tdf files.

A file with all predicted binding sites is available (right click to save). See this help page for information about binding sites

A file with all predicted interactions is available (right click to save). See this help page for information about interactions

TF Induction Expression Data

Expression data following TF induction has been deposited in GEO under accession GSE43466

Software

We have developed a number of software packages for data analysis.

CSDeconv CSDeconv is a computational method that determines the locations of transcription factor binding from ChIP-seq data. CSDeconv differs from prior methods in that it uses a blind deconvolution approach that allows multiple closely-spaced binding sites within a single promoter region to be called accurately. The method is described in Lun et al. (2009) . The source code is available here .

GenomeView GenomeView is a stand-alone genome editor and viewer that provides fast and dynamic visualization of aligned short read data, genome sequences and annotations, and genome alignments. GenomeView allows users to load .bam files directly or through a URL. GenomeView was developed by a visiting graduate student in the Galagan lab, Thomas Abeel from Ghent University . It can be accessed and downloaded from its sourceforge page

E-Flux E-Flux is novel method for modeling metabolic states using whole cell measurements of gene expression. The method extends the technique of Flux Balance Analysis (FBA) by modeling maximum flux constraints as a function of measured gene expression. The method is described in Colijn et al. (2009).

Other Resources

We use variety of third party software applications, publically available resources, and experimental methods.

Third Party Software

CLR The Context Likelihood of Relatedness (CLR) algorithm is an extension of the relevance networks approach for identifying transcriptional regulatory interactions using gene expression data. The algorithm, developed by the lab of Tim Gardner, compares the mutual information between a transcription factor/gene pair to the background distribution of mutual information scores for all possible transcription factor/gene pairs that include either the transcription factor or its target. The algorithm is described in Faith et al. (2007) .

BioTapestry BioTapestry is an interactive tool for building, visualizing, and simulating genetic regulatory networks. BioTapestry was developed as a joint effort between the Bolouri Lab at the Institute for Systems Biology and the Davidson Lab at Caltech The tool provides a circuit diagram layout of genetic regulatory networks using a list of genetic interactions that can be manually our automatically generated. The software and example data sets can be downloaded here.

Cytoscape Cytoscape is a publically available open source software application for visualizing molecular interaction networks and integrating state information on the network. Cytoscape provides a general and flexible view of networks based on any type of underlying set of components and interactions, and is thus a standard tool for the initial visual interrogation of complex interaction maps. The software is available here.