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

Abstract

BackgroundDNA 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.Methodology/Principal FindingsThe 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.Conclusions/SignificanceWe 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.

Organization of the autoantibody repertoire in healthy newborns and adults revealed by system level informatics of antigen microarray data

“…We found that reduction to three dimensions extracts most of the relevant information (Ͼ85% of the information as described in section II of SI Text and in Fig. S1), similar to what was found for brain activity data (34) and gene expression data (35). Therefore, we present the immune response information in a 3-dimensional PCA space, whose axes are the three leading principal vectors computed by the PCA algorithm.…”

“…To retrieve this information, as well as information that might be lost in the collective normalization process, we link each pair of subjects by lines, color coded according to the original (nonnormalized) correlations (34,35,42). The results ( Fig.…”

“…The correlation matrices were analyzed using the functional holography (FH) method of Baruchi et al (34), originally devised for analyzing recorded brain activity. This method was recently shown to be useful in the analysis of gene-expression data: it can help identify operons, relations between genes within operons (gene order, gene separation by noncoding segments and start and end regions), and the functional relations between operons (35). The capacity of the FH method to successfully reveal such motifs between genes led us to test whether this method can also identify new functional relations and network motifs in natural antibody repertoires.…”

Functional Holography and Cliques in Brain Activation Patterns

“…Reduction to three dimensions (projection on the three leading principal components) typically extracts most of the relevant information (above 85%), (Baruchi et al, 2006;Madi et al, 2008). To retrieve the information lost as a result of dimension reduction, we link each pair of nodes by lines color coded according to the correlations between them (Baruchi et al, 2006;Madi et al, 2008).…”

Influence of ACPA on the stability of holographic transmission gratings stored in photopolymers

“…También puede ser utilizada en interferometría holográfica para obtener información de propiedades de algunos materiales [6], en la fabricación de lentes [7] y elementos ópticos holográficos [8][9][10], o en la codificación de datos [11,12]. La holografía incluso puede aplicarse a la medida de la humedad relativa [13], a la fabricación de concentradores solares [14] y al campo de la medicina [15,16].…”