Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 36 (Database issue), D866-70

Many Microbe Microarrays Database: Uniformly Normalized Affymetrix Compendia With Structured Experimental Metadata

Affiliations

Many Microbe Microarrays Database: Uniformly Normalized Affymetrix Compendia With Structured Experimental Metadata

Jeremiah J Faith et al. Nucleic Acids Res.

Abstract

Many Microbe Microarrays Database (M3D) is designed to facilitate the analysis and visualization of expression data in compendia compiled from multiple laboratories. M3D contains over a thousand Affymetrix microarrays for Escherichia coli, Saccharomyces cerevisiae and Shewanella oneidensis. The expression data is uniformly normalized to make the data generated by different laboratories and researchers more comparable. To facilitate computational analyses, M3D provides raw data (CEL file) and normalized data downloads of each compendium. In addition, web-based construction, visualization and download of custom datasets are provided to facilitate efficient interrogation of the compendium for more focused analyses. The experimental condition metadata in M3D is human curated with each chemical and growth attribute stored as a structured and computable set of experimental features with consistent naming conventions and units. All versions of the normalized compendia constructed for each species are maintained and accessible in perpetuity to facilitate the future interpretation and comparison of results published on M3D data. M3D is accessible at http://m3d.bu.edu/.

Figures

Figure 1.
Figure 1.
All of the available E. coli Affymetrix Antisense2 expression data for the transcription factor lexA and its known target recA were downloaded from NCBI GEO Profiles (A) and from M3D compendium E_coli_v3_Build_1 (B and C). NCBI GEO Profile data is derived from NCBI GEO DataSets that contain only a subset of the data in GEO, therefore many more samples were available for plotting from M3D (445) than from GEO (85). The correlation between lexA and its known target was higher when the raw data was uniformly normalized with RMA (C) rather than normalizing each microarray individually with MAS5 (A and B).
Figure 2.
Figure 2.
Custom datasets constructed on M3D can be visualized with scatterplots (A), histograms of individual genes (B), heatplots (C), histograms of collections of genes (D) and in their genome context using a genome browser (E).

Similar articles

See all similar articles

Cited by 115 PubMed Central articles

See all "Cited by" articles

References

    1. Beer MA, Tavazoie S. Predicting gene expression from sequence. Cell. 2004;117:185–198. - PubMed
    1. Gardner TS, Faith JJ. Reverse-engineering transcription control networks. Phys. Life Rev. 2005;2:65–88. - PubMed
    1. Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, et al. Functional discovery via a compendium of expression profiles. Cell. 2000;102:109–126. - PubMed
    1. Liao JC, Boscolo R, Yang YL, Tran LM, Sabatti C, Roychowdhury VP. Network component analysis: reconstruction of regulatory signals in biological systems. Proc. Natl Acad. Sci. USA. 2003;100:15522–15527. - PMC - PubMed
    1. di Bernardo D, Thompson MJ, Gardner TS, Chobot SE, Eastwood EL, Wojtovich AP, Elliott SJ, Schaus SE, Collins JJ. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 2005;23:377–383. - PubMed

Publication types

MeSH terms

Feedback