Improved recognition of splice sites in A. thaliana by incorporating secondary structure information into sequence-derived features: a computational study.

ABSTRACT: Identification of splice sites is an important aspect with regard to the prediction of gene structure. In most of the existing splice site prediction studies, machine learning algorithms coupled with sequence-derived features have been successfully employed for splice site recognition. However, the splice site identification by incorporating the secondary structure information is lacking, particularly in plant species. Thus, we made an attempt in this study to evaluate the performance of structural features on the splice site prediction accuracy in Arabidopsis thaliana. Prediction accuracies were evaluated with the sequence-derived features alone as well as by incorporating the structural features into the sequence-derived features, where support vector machine (SVM) was employed as

SUBMITTER: Meher PK 

PROVIDER: S-EPMC8558126 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature