AnsNGS: An Annotation System to Sequence Variations of Next Generation Sequencing Data for Disease-Related Phenotypes / 대한의료정보학회지
Healthcare Informatics Research
; : 50-55, 2013.
Article
in En
| WPRIM
| ID: wpr-197309
Responsible library:
WPRO
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/.
Key words
Full text:
1
Index:
WPRIM
Main subject:
Phenotype
/
Genome, Human
/
Sequence Analysis, DNA
/
Fees and Charges
/
Genomic Structural Variation
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High-Throughput Nucleotide Sequencing
/
Molecular Sequence Annotation
Type of study:
Health_economic_evaluation
Limits:
Humans
Language:
En
Journal:
Healthcare Informatics Research
Year:
2013
Type:
Article