variant analysis

Variations and similarities in our individual genomes are part of our history, our heritage, and our identity. Some human genomic variants are associated with common traits such as hair and eye color, while others are associated with susceptibility to disease or response to drug treatment.

Identifying the human variations producing clinically relevant phenotypic changes is critical for providing accurate and personalized diagnosis, prognosis, and treatment for diseases.

Furthermore, a better understanding of the molecular underpinning of disease can lead to development of new drug targets for precision medicine. Several resources have been designed for collecting and storing human genomic variations in highly structured, easily accessible databases.

Unfortunately, a vast amount of information about these genetic variants and their functional and phenotypic associations is currently buried in the literature, only accessible by manual curation or sophisticated text text-mining technology to extract the relevant information.

In addition, the low cost of sequencing technologies coupled with increasing computational power has enabled the development of numerous computational methodologies to predict the pathogenicity of human variants.

This review provides a detailed comparison of current human variant resources, including HGMD, OMIM, ClinVar, and UniProt/Swiss-Prot, followed by an overview of the computational methods and techniques used to leverage the available data to predict novel deleterious variants.

Videos

 Variant analysis by Ingenuity

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See also

 variant-calling softwares
 variant call

Open references

 FAVR (Filtering and Annotation of Variants that are Rare): methods to facilitate the analysis of rare germline genetic variants from massively parallel sequencing datasets. Pope BJ, Nguyen-Dumont T, Odefrey F, Hammet F, Bell R, Tao K, Tavtigian SV, Goldgar DE, Lonie A, Southey MC, Park DJ. BMC Bioinformatics. 2013 Feb 25;14:65. doi : 10.1186/1471-2105-14-65 PMID: 23441864 [Free]

 Computational and bioinformatics frameworks for next-generation whole exome and genome sequencing. Dolled-Filhart MP, Lee M Jr, Ou-Yang CW, Haraksingh RR, Lin JC. ScientificWorldJournal. 2013;2013:730210. doi : 10.1155/2013/730210 PMID: 23365548 [Free]

 A likelihood-based framework for variant calling and de novo mutation detection in families. Li B, Chen W, Zhan X, Busonero F, Sanna S, Sidore C, Cucca F, Kang HM, Abecasis GR. PLoS Genet. 2012;8(10):e1002944. doi : 10.1371/journal.pgen.1002944 PMID: 23055937 [Free]

References

 Towards precision medicine: advances in computational approaches for the analysis of human variants. Peterson TA, Doughty E, Kann MG. J Mol Biol. 2013 Nov 1;425(21):4047-63. doi : 10.1016/j.jmb.2013.08.008 PMID: 23962656