Network-Based Classification of Molecular Cytogenetic Data

Author(s): Yuri B. Yurov, Svetlana G. Vorsanova, Ivan Y. Iourov

Journal Name: Current Bioinformatics

Volume 12 , Issue 1 , 2017

Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


With the developments in molecular cytogenetics, it has become evident that correct interpretation of molecular cytogenetic data requires the application of bioinformatics. Furthermore, in silico analysis of chromosome structural and functional variability has been shown to increase the potential of a molecular cytogenetic study. Using systems biology approaches to process data on genome variations or chromosome abnormalities, one can get further insights into molecular and cellular processes in health and disease. A key approach for in silico (bioinformatic) molecular cytogenetics might be the network-based classification of data obtained through uncovering genomic changes at chromosomal (subchromosomal) level. This technology provides interpretation of genomic imbalances by the prioritization of genes and processes involved in the phenotype of a genetic disease. Here, we discuss network-based classification of cytogenetic data in the light of uncovering genetic mechanisms of human diseases in the post-genomic era. Additionally, omics technologies are addressed in the context of chromosome biology. Accordingly, bioinformatic evaluation of genome rearrangements or chromosome imbalances using genome, transcriptome, proteome (intercatome) and metabolome databases is viewed as an important tool for current molecular cytogenetics. Taking into account that bioinformatics has been only recently introduced in molecular cytogenetics, we discuss new opportunities offered by in silico analyses for chromosome biology and medical cytogenetics.

Keywords: Bioinformatics, chromosome abnormalities, genome variations, molecular cytogenetics, omics, prioritization, systems biology.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2017
Published on: 04 January, 2017
Page: [27 - 33]
Pages: 7
DOI: 10.2174/1574893611666160606165119
Price: $65

Article Metrics

PDF: 18