Contribution of microarray data to the advancement of knowledge on the Mycobacterium tuberculosis interactome: Use of the random partial least squares approach

Abstract

Following the central dogma of molecular biology, where data flows from gene to protein through transcript, information on gene expression provides information on the functional state of an organism. Microarray technology arose to measure the expression level of thousands of genes simultaneously. These vast amounts of data generated at all levels of biological organization help to identify co-expressed genes, which may reveal proteins interacting in a complex or acting in the same pathway without direct physical contact. Discovering associations of regulatory patterns of characterized proteins with those of hypothetical proteins may identify functional relationships between them and facilitate the characterization of proteins of unknown function. Here we make use of the random partial least squares regression technique (r-PLS) to trace connections between co-expressed genes in Mycobacterium tuberculosis using data downloaded from public microarray databases. We generated the overall topology of a microbial co-expression network with the exact complexity of the model. This approach provides a general method for generating a co-expression network of an organism for the purpose of systems-level analyses.

A Systems Level Comparison of Mycobacterium tuberculosis, Mycobacterium leprae and Mycobacterium smegmatis Based on Functional Interaction Network Analysis

“…We also used an informationtheory based technique proposed by Mazandu and Mulder [21] to derive PPI's from protein sequence similarity and signatures as well as shared domains. In line with Mazandu et al [22], the microarray data for MTB were downloaded from the Standford Microarray Database (SMD), at http:smd.stanford.edu/ (accessed on 28 October, 2011) and NCBI Gene Expression Omnibus (GEO) (see, http://www.ncbi.nlm. nih.gov/geo/ (accessed on 12 September, 2012).…”

“…Out of the seven experiments used in analyzing the microarray data for the MTB network, two with file ids 15569 and 15575 were downloaded from SMD and the remaining five; GSM219305, GSM219324, GSM219694, GSM219695, GSM219696 from GEO [22]. For MSM, we retrieved 23 experiments from the Array Express database: four with GEO accessions: GSM743320, GSM743321, GSM743322, and GSM743323.…”

“…The remaining 19 ids are GSM748761, GSM748762, GSM748763, to GSM748779. We then employed a random partial least squares regression approach described by Mazandu et al [22] to generate functional association scores between pairs of interacting proteins. However, for MLP, we downloaded only four experiments contained in the GSE17191 series matrix from GEO (http://www.ncbi.…”

Designing Data-Driven Learning Algorithms: A Necessity to Ensure Effective Post-Genomic Medicine and Biomedical Research

“…This integrative approach increases the likelihood of effectively identifying suitable candidate genes [53] and biological pathways that may be critical in the etiology and pathogenesis of the disease, and in the drug response. The next goal is to integrate large-scale datasets from heterogeneous sources [2,54] to move beyond a single genomic approach and foster a whole genome-based integrative approach to achieve global view [55]. A biological network, which is a network modeling a biological system as an entity composed of sub-units connected as a whole, has become a useful tool enabling the integration of heterogeneous datasets into a single framework [26].…”

A Comparative Study of the Physical and Mechanical Properties of Hydrogen Using Data Mining Research Techniques

“…9 It is widely used in almost all scientific disciplines and is considered to be the oldest multivariate technique. 10 The second approach applied to datasets is PLS regression. It has been proven that PLS is an efficient statistical regression technique and is quite similar to PCA.…”