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Bioinformatics challenges for personalized medicine.

Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB - Bioinformatics (2011)

Bottom Line: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine.New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice. russ.altman@stanford.edu

View Article: PubMed Central - PubMed

Affiliation: Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA.

ABSTRACT

Motivation: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.

Results: This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.

Contact: russ.altman@stanford.edu

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Number of validated human SNPs in dbSNP overtime.
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Figure 2: Number of validated human SNPs in dbSNP overtime.

Mentions: In the strictest definition, a SNP is a single nucleotide variant where the allele frequency in the human population is higher then 1%. In this review, we use the term SNP in a broader sense to also include rare variants that occur in a smaller fraction of the population. Important issues for predicting the impact of SNPs are data management, retrieval and quality control. During the last few years, the number of known SNPs has increased at an exponential rate (Fig. 2). The dbSNP database (Sherry et al., 2001) is the most comprehensive repository of SNPs data from different organisms. At the time of writing this review, the database contains about 20 million validated human SNPs (Build 132, September 2010). The Human Gene Mutation Database (HGMD) is a comprehensive collection of germline mutations in genes that are associated with human inherited diseases. The free version for academic and non-profit users contains more than 76 000 mutations from ~2900 genes. The SwissVar is a database of manually annotated missense SNPs (mSNPs) and contains 56 000 mSNPs from >11 000 genes.Fig. 2.


Bioinformatics challenges for personalized medicine.

Fernald GH, Capriotti E, Daneshjou R, Karczewski KJ, Altman RB - Bioinformatics (2011)

Number of validated human SNPs in dbSNP overtime.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117361&req=5

Figure 2: Number of validated human SNPs in dbSNP overtime.
Mentions: In the strictest definition, a SNP is a single nucleotide variant where the allele frequency in the human population is higher then 1%. In this review, we use the term SNP in a broader sense to also include rare variants that occur in a smaller fraction of the population. Important issues for predicting the impact of SNPs are data management, retrieval and quality control. During the last few years, the number of known SNPs has increased at an exponential rate (Fig. 2). The dbSNP database (Sherry et al., 2001) is the most comprehensive repository of SNPs data from different organisms. At the time of writing this review, the database contains about 20 million validated human SNPs (Build 132, September 2010). The Human Gene Mutation Database (HGMD) is a comprehensive collection of germline mutations in genes that are associated with human inherited diseases. The free version for academic and non-profit users contains more than 76 000 mutations from ~2900 genes. The SwissVar is a database of manually annotated missense SNPs (mSNPs) and contains 56 000 mSNPs from >11 000 genes.Fig. 2.

Bottom Line: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine.New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice. russ.altman@stanford.edu

View Article: PubMed Central - PubMed

Affiliation: Biomedical Informatics Training Program, Stanford University School of Medicine, Department of Bioengineering, Stanford University, Stanford, CA, USA.

ABSTRACT

Motivation: Widespread availability of low-cost, full genome sequencing will introduce new challenges for bioinformatics.

Results: This review outlines recent developments in sequencing technologies and genome analysis methods for application in personalized medicine. New methods are needed in four areas to realize the potential of personalized medicine: (i) processing large-scale robust genomic data; (ii) interpreting the functional effect and the impact of genomic variation; (iii) integrating systems data to relate complex genetic interactions with phenotypes; and (iv) translating these discoveries into medical practice.

Contact: russ.altman@stanford.edu

Show MeSH