An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools' predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To enable comprehensive evaluation of variants, the predictions are complemented with annotations from eight databases. The web server is freely available to the community at http://loschmidt.chemi.muni.cz/predictsnp2.
- MeSH
- Databases, Nucleic Acid MeSH
- Databases, Protein MeSH
- Genetic Variation MeSH
- Genome, Human MeSH
- Genomics statistics & numerical data MeSH
- Polymorphism, Single Nucleotide * MeSH
- Humans MeSH
- Software * MeSH
- Computational Biology MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
- MeSH
- Molecular Diagnostic Techniques methods utilization MeSH
- Genetics standards statistics & numerical data trends MeSH
- Genomics standards statistics & numerical data trends MeSH
- Humans MeSH
- Molecular Biology methods statistics & numerical data trends MeSH
- Quality Control MeSH
- Check Tag
- Humans MeSH
- Publication type
- Practice Guideline MeSH
- MeSH
- Molecular Diagnostic Techniques methods utilization MeSH
- Genetics standards statistics & numerical data trends MeSH
- Genomics methods standards statistics & numerical data MeSH
- Humans MeSH
- Molecular Biology methods statistics & numerical data trends MeSH
- Quality Control MeSH
- Check Tag
- Humans MeSH
- Publication type
- Practice Guideline MeSH
CGH arrays se v dnešní době staly významnou technikou v analýze genomu. Využívají se pro detekci změn v počtu kopií genů nebo chromozomů, popřípadě jiných chromosomových přestaveb. Základní metodou je porovnávání DNA vzorku a zdravé kontroly pomocí komparativní genomové hybridizace (CGH). Analýza genetické nestability u nádoru a s ní spojené hledání nádorových markerů se dá využít jak v diagnostice nádorů (klasifikace nádorů do již existujících skupin, hledání nových podskupin), tak i v predikci jejich odpovědi na léčbu. Výhodou technologie CGH arrays je možnost analýzy celého genomu v jediném experimentu. To ovšem znamená, že metoda produkuje velké množství dat, které musí být správně matematicky vyhodnoceny a základním cílem článku je tak představení těchto matematických metod a jejich softwarové implementace.
Nowadays, array CGH experiments became a powerful technique for analysing changes in DNA by comparing control DNA and DNA of interest. This is widely used for example in genome cancer studies. Analysis of the tumour genome instabilities and the search for tumour markers can be used for the tumour diagnostics (tumour classification, detection of the new clinical groups of tumours) or for the prediction of the response to therapy. The method produce huge amount of data and special statistic techniques for detecting of genomic changes are necessary. The purpose of this paper is to provide brief summary of existing statistical methods used in CGH array analysis and their software implementation.
- MeSH
- DNA, Neoplasm diagnostic use genetics isolation & purification MeSH
- Financing, Organized MeSH
- Genomics methods statistics & numerical data trends MeSH
- Hybridization, Genetic MeSH
- Internet trends utilization MeSH
- Medical Oncology methods trends MeSH
- Humans MeSH
- Software classification statistics & numerical data trends MeSH
- Comparative Genomic Hybridization MeSH
- Models, Statistical MeSH
- Statistics as Topic MeSH
- Database Management Systems utilization MeSH
- Models, Theoretical MeSH
- DNA Breaks MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review MeSH
