title: In silico molecular cytogenetics: a bioinformatic approach to prioritization of candidate genes and copy number variations for basic and clinical genome research.
abstract: BACKGROUND
  The availability of multiple in silico tools for prioritizing genetic variants widens the possibilities for converting genomic data into biological knowledge. However, in molecular cytogenetics, bioinformatic analyses are generally limited to result visualization or database mining for finding similar cytogenetic data. Obviously, the potential of bioinformatics might go beyond these applications. On the other hand, the requirements for performing successful in silico analyses (i.e. deep knowledge of computer science, statistics etc.) can hinder the implementation of bioinformatics in clinical and basic molecular cytogenetic research. Here, we propose a bioinformatic approach to prioritization of genomic variations that is able to solve these problems.
  
  
  RESULTS
  Selecting gene expression as an initial criterion, we have proposed a bioinformatic approach combining filtering and ranking prioritization strategies, which includes analyzing metabolome and interactome data on proteins encoded by candidate genes. To finalize the prioritization of genetic variants, genomic, epigenomic, interactomic and metabolomic data fusion has been made. Structural abnormalities and aneuploidy revealed by array CGH and FISH have been evaluated to test the approach through determining genotype-phenotype correlations, which have been found similar to those of previous studies. Additionally, we have been able to prioritize copy number variations (CNV) (i.e. differentiate between benign CNV and CNV with phenotypic outcome). Finally, the approach has been applied to prioritize genetic variants in cases of somatic mosaicism (including tissue-specific mosaicism).
  
  
  CONCLUSIONS
  In order to provide for an in silico evaluation of molecular cytogenetic data, we have proposed a bioinformatic approach to prioritization of candidate genes and CNV. While having the disadvantage of possible unavailability of gene expression data or lack of expression variability between genes of interest, the approach provides several advantages. These are (i) the versatility due to independence from specific databases/tools or software, (ii) relative algorithm simplicity (possibility to avoid sophisticated computational/statistical methodology) and (iii) applicability to molecular cytogenetic data because of the chromosome-centric nature. In conclusion, the approach is able to become useful for increasing the yield of molecular cytogenetic techniques.
authors.affiliation: Mental Health Research Center, Russian Academy of Medical Sciences, 117152 Moscow, Russia ; Russian National Research Medical University named after N.I. Pirogov, Separated Structural Unit "Clinical Research Institute of Pediatrics", Ministry of Health of Russian Federation, 125412 Moscow, Russia ; Department of Medical Genetics, Russian Medical Academy of Postgraduate Education, Moscow, 123995 Russia.
authors.forename: Ivan Y
authors.initials: IY
authors.lastname: Iourov
authors.affiliation: Mental Health Research Center, Russian Academy of Medical Sciences, 117152 Moscow, Russia ; Russian National Research Medical University named after N.I. Pirogov, Separated Structural Unit "Clinical Research Institute of Pediatrics", Ministry of Health of Russian Federation, 125412 Moscow, Russia.
authors.forename: Svetlana G
authors.initials: SG
authors.lastname: Vorsanova
authors.affiliation: Mental Health Research Center, Russian Academy of Medical Sciences, 117152 Moscow, Russia ; Russian National Research Medical University named after N.I. Pirogov, Separated Structural Unit "Clinical Research Institute of Pediatrics", Ministry of Health of Russian Federation, 125412 Moscow, Russia.
authors.forename: Yuri B
authors.initials: YB
authors.lastname: Yurov
keywords: Bioinformatics; Candidate genes; Chromosome imbalances; Copy number variation; Gene expression; Molecular cytogenetics; Somatic mosacism