Analysis of cis-Regulatory Elements in Gene Co-expression Networks in Cancer.

Analysis of gene co-expression networks is a powerful "data-driven" tool, invaluable for understanding cancer biology and mechanisms of tumor development. Yet, despite of completion of thousands of studies on cancer gene expression, there were few attempts to normalize and integrate co-expression data from scattered sources in a concise "meta-analysis" framework. Here we describe an integrated approach to cancer expression meta-analysis, which combines generation of "data-driven" co-expression networks with detailed statistical detection of promoter sequence motifs within the co-expression clusters.

Drug Repositioning through Systematic Mining of Gene Coexpression Networks in Cancer.

Gene coexpression network analysis is a powerful "data-driven" approach essential for understanding cancer biology and mechanisms of tumor development. Yet, despite the completion of thousands of studies on cancer gene expression, there have been few attempts to normalize and integrate co-expression data from scattered sources in a concise "meta-analysis" framework. We generated such a resource by exploring gene coexpression networks in 82 microarray datasets from 9 major human cancer types.

Systems-based analyses of brain regions functionally impacted in Parkinson's disease reveals underlying causal mechanisms.

Detailed analysis of disease-affected tissue provides insight into molecular mechanisms contributing to pathogenesis. Substantia nigra, striatum, and cortex are functionally connected with increasing degrees of alpha-synuclein pathology in Parkinson's disease. We undertook functional and causal pathway analysis of gene expression and proteomic alterations in these three regions, and the data revealed pathways that correlated with disease progression.

Drug target prediction and repositioning using an integrated network-based approach.

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs.

Exploring the transcriptome of ciliated cells using in silico dissection of human tissues.

Cilia are cell organelles that play important roles in cell motility, sensory and developmental functions and are involved in a range of human diseases, known as ciliopathies. Here, we search for novel human genes related to cilia using a strategy that exploits the previously reported tendency of cell type-specific genes to be coexpressed in the transcriptome of complex tissues. Gene coexpression networks were constructed using the noise-resistant WGCNA algorithm in 12 publicly available microarray datasets from human tissues rich in motile cilia: airways, fallopian tubes and brain.

Coexpression network analysis identifies transcriptional modules related to proastrocytic differentiation and sprouty signaling in glioma.

Gliomas are primary brain tumors with high mortality and heterogeneous biology that is insufficiently understood. In this study, we performed a systematic analysis of the intrinsic organization of complex glioma transcriptome to gain deeper knowledge of the tumor biology. Gene coexpression relationships were explored in 790 glioma samples from 5 published patient cohorts treated at different institutions.

OrthoFocus: program for identification of orthologs in multiple genomes in family-focused studies.

The identification of orthologs to a set of known genes is often the starting point for evolutionary studies focused on gene families of interest. To date, the existing orthology detection tools (COG, InParanoid, OrthoMCL, etc.) are aimed at genome-wide ortholog identification and lack flexibility for the purposes of case studies.

Microarray retriever: a web-based tool for searching and large scale retrieval of public microarray data.

The major public microarray repositories Gene Expression Omnibus and ArrayExpress are growing rapidly. This enables meta-analysis studies, in which expression data from multiple individual studies are combined. To facilitate these types of studies, we developed Microarray Retriever for searching and retrieval of data from GEO and ArrayExpress.