Genome Holography: Deciphering Function-Form Motifs from Gene Expression Data

• Published in: PloS one Vol. 3; no. 7; p. e2708
• Main Authors: Madi, Asaf, Friedman, Yonatan, Roth, Dalit, Regev, Tamar, Bransburg-Zabary, Sharron, Jacob, Eshel Ben
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.07.2008; Public Library of Science (PLoS)
• Subjects: Algorithms; Amino Acid Motifs; Analysis; Anti-Bacterial Agents - pharmacology; Antibiotics; Applied mathematics; Astronomy; Bacillus; Bacillus subtilis; Bacillus subtilis - genetics; Bacteria; Biosynthesis; Cancer; Chip formation; Clustering; Computational Biology - methods; Computational Biology/Genomics; Computational Biology/Systems Biology; Computational Biology/Transcriptional Regulation; Correlation; Correlation analysis; Data Interpretation, Statistical; Deoxyribonucleic acid; DNA; DNA chips; DNA microarrays; Embedded structures; Enzymes; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Bacterial; Gene loci; Gene Regulatory Networks; Genes; Genetic Markers; Genetics; Genetics and Genomics/Bioinformatics; Genetics and Genomics/Functional Genomics; Genetics and Genomics/Gene Expression; Genomes; Genomics; Holography; Information processing; Lethal levels; Measurement methods; Methods; Models, Genetic; Morphogenesis; Neural networks; Oligonucleotide Array Sequence Analysis; Operon; Operons; Physics; Principal components analysis; Proteins; Sporulation; Time Factors; Values; Israel; California; Tel Aviv Israel; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0002708

DNA chips allow simultaneous measurements of genome-wide response of thousands of genes, i.e. system level monitoring of the gene-network activity. Advanced analysis methods have been developed to extract meaningful information from the vast amount of raw gene-expression data obtained from the microarray measurements. These methods usually aimed to distinguish between groups of subjects (e.g., cancer patients vs. healthy subjects) or identifying marker genes that help to distinguish between those groups. We assumed that motifs related to the internal structure of operons and gene-networks regulation are also embedded in microarray and can be deciphered by using proper analysis. The analysis presented here is based on investigating the gene-gene correlations. We analyze a database of gene expression of Bacillus subtilis exposed to sub-lethal levels of 37 different antibiotics. Using unsupervised analysis (dendrogram) of the matrix of normalized gene-gene correlations, we identified the operons as they form distinct clusters of genes in the sorted correlation matrix. Applying dimension-reduction algorithm (Principal Component Analysis, PCA) to the matrices of normalized correlations reveals functional motifs. The genes are placed in a reduced 3-dimensional space of the three leading PCA eigen-vectors according to their corresponding eigen-values. We found that the organization of the genes in the reduced PCA space recovers motifs of the operon internal structure, such as the order of the genes along the genome, gene separation by non-coding segments, and translational start and end regions. In addition to the intra-operon structure, it is also possible to predict inter-operon relationships, operons sharing functional regulation factors, and more. In particular, we demonstrate the above in the context of the competence and sporulation pathways. We demonstrated that by analyzing gene-gene correlation from gene-expression data it is possible to identify operons and to predict unknown internal structure of operons and gene-networks regulation.