A proposed paradigm for Expressed Sequence Tags data format - An application of hidden markov models

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.