Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

2.10

CiteScore

Yen-I Chiang This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, and Guan-I Wu1

1Department of Information Management, Chang Gung University, Taoyuan, Taiwan, R.O.C.
2Bioinformatics Center, Chang Gung University, Taoyuan, Taiwan, R.O.C.


 

Received: February 25, 2008
Accepted: April 25, 2009
Publication Date: September 1, 2009

Download Citation: ||https://doi.org/10.6180/jase.2009.12.3.10  


ABSTRACT


In the era of post-Human Genome Project, researches have shifted the emphasis from the mapping of human genomic to the discovery of correlation between genetic markers and clinical phenotypes, where finding effective treatment against disease are becoming crucial and applicable goals. The Expressed Sequence Tags (ESTs) data plays an important role in the completion of the Human Genome Sequencing and is widely used for gene discovery, polymorphism analysis, expression studies, and gene prediction. However, due to the chemical properties and manufacturing processes, ESTs data might contain errors, which might mislead Bioinformatics researchers that attempt to use EST-libraries to identify Single Nucleotide Polymorphisms (SNPs). Therefore this study proposes a paradigm for EST data, where users might better address this issue and use them to correctly identify SNPs.


Keywords: Expressed Sequence Tags, Hidden Markov Models, Base-Calling, Electropherogram


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