DGEM — A Microarray Gene Expression Database for Primary Human Disease Tissues

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

MicroRNA and mRNA integrated analysis (MMIA): a web tool for examining biological functions of microRNA expression

MicroRNAs (miRNAs) are small (19–24 nt), nonprotein-coding nucleic acids that regulate specific ‘target’ gene products via hybridization to mRNA transcripts, resulting in translational blockade or transcript degradation. Although miRNAs have been implicated in numerous developmental and adult diseases, their specific impact on biological pathways and cellular phenotypes, in addition to miRNA gene promoter regulation, remain largely unknown. To improve and facilitate research of miRNA functions and regulation, we have developed MMIA (microRNA and mRNA integrated analysis), a versatile and user-friendly web server. By incorporating three commonly used and accurate miRNA prediction algorithms, TargetScan, PITA and PicTar, MMIA integrates miRNA and mRNA expression data with predicted miRNA target information for analyzing miRNA-associated phenotypes and biological functions by gene set analysis, in addition to analysis of miRNA primary transcript gene promoters. To assign biological relevance to the integrated miRNA/mRNA profiles, MMIA uses exhaustive human genome coverage, including classification into various disease-associated genes as well as conventional canonical pathways and Gene Ontology. In summary, this novel web server (cancer.informatics.indiana.edu/mmia) will provide life science researchers with a valuable tool for the study of the biological (and pathological) causes and effects of the expression of this class of interesting protein regulators.

Bioinformatics applications for pathway analysis of microarray data

Changes in transcript levels are assessed by microarray analysis on an individual basis, essentially resulting in long lists of genes that were found to have significantly changed transcript levels. However, in biology these changes do not occur as independent events as such lists suggest, but in a highly coordinated and interdependent manner. Understanding the biological meaning of the observed changes requires elucidating such biological interdependencies. The most common way to achieve this is to project the gene lists onto distinct biological processes often represented in the form of gene-ontology (GO) categories or metabolic and regulatory pathways as derived from literature analysis. This review focuses on different approaches and tools employed for this task, starting form GO-ranking methods, covering pathway mappings, finally converging on biological network analysis. A brief outlook of the application of such approaches to the newest microarray-based technologies (Chromatin-ImmunoPrecipitation, ChIP-on-chip) concludes the review.