AnsNGS

An annotation system to sequence variations of next generation sequencing data for disease-related rhenotypes

Young Ji Na, Yonglae Cho, Ju Han Kim

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

Objectives: Next-generation sequencing (NGS) data in the identification of disease-causing genes provides a promising opportunity in the diagnosis of disease. Beyond the previous efforts for NGS data alignment, variant detection, and visualization, developing a comprehensive annotation system supported by multiple layers of disease phenotype-related databases is essential for deciphering the human genome. To satisfy the impending need to decipher the human genome, it is essential to develop a comprehensive annotation system supported by multiple layers of disease phenotype-related databases. Methods: AnsNGS (Annotation system of sequence variations for next-generation sequencing data) is a tool for contextualizing variants related to diseases and examining their functional consequences. The AnsNGS integrates a variety of annotation databases to attain multiple levels of annotation. Results: The AnsNGS assigns biological functions to variants, and provides gene (or disease)-centric queries for finding disease-causing variants. The AnsNGS also connects those genes harbouring variants and the corresponding expression probes for downstream analysis using expression microarrays. Here, we demonstrate its ability to identify disease-related variants in the human genome. Conclusions: The AnsNGS can give a key insight into which of these variants is already known to be involved in a disease-related phenotype or located in or near a known regulatory site. The AnsNGS is available free of charge to academic users and can be obtained from http://snubi.org/software/AnsNGS/.

Original languageEnglish
Pages (from-to)50-55
Number of pages6
JournalHealthcare Informatics Research
Volume19
Issue number1
DOIs
StatePublished - 12 Jun 2013

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Genes
Human Genome
Databases
Phenotype
Microarrays
Software
Visualization

Keywords

  • Disease
  • Dna sequence analysis
  • Genome structural variation
  • High-throughput nucleotide sequencing
  • Molecular sequence annotation

Cite this

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abstract = "Objectives: Next-generation sequencing (NGS) data in the identification of disease-causing genes provides a promising opportunity in the diagnosis of disease. Beyond the previous efforts for NGS data alignment, variant detection, and visualization, developing a comprehensive annotation system supported by multiple layers of disease phenotype-related databases is essential for deciphering the human genome. To satisfy the impending need to decipher the human genome, it is essential to develop a comprehensive annotation system supported by multiple layers of disease phenotype-related databases. Methods: AnsNGS (Annotation system of sequence variations for next-generation sequencing data) is a tool for contextualizing variants related to diseases and examining their functional consequences. The AnsNGS integrates a variety of annotation databases to attain multiple levels of annotation. Results: The AnsNGS assigns biological functions to variants, and provides gene (or disease)-centric queries for finding disease-causing variants. The AnsNGS also connects those genes harbouring variants and the corresponding expression probes for downstream analysis using expression microarrays. Here, we demonstrate its ability to identify disease-related variants in the human genome. Conclusions: The AnsNGS can give a key insight into which of these variants is already known to be involved in a disease-related phenotype or located in or near a known regulatory site. The AnsNGS is available free of charge to academic users and can be obtained from http://snubi.org/software/AnsNGS/.",
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AnsNGS : An annotation system to sequence variations of next generation sequencing data for disease-related rhenotypes. / Na, Young Ji; Cho, Yonglae; Kim, Ju Han.

In: Healthcare Informatics Research, Vol. 19, No. 1, 12.06.2013, p. 50-55.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Cho, Yonglae

AU - Kim, Ju Han

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