Affiliations: Institute for Infocomm Research, 21 Heng Mui Keng
Terrace, Singapore, 119613. E-mail: {huiqing, hanhao, jinyan,
limsoon}@i2r.a-star.edu.sg
Abstract: The translation initiation site (TIS) prediction problem is about
how to correctly identify TIS in mRNA, cDNA, or other types of genomic
sequences. High prediction accuracy can be helpful in a better understanding of
protein coding from nucleotide sequences. This is an important step in genomic
analysis to determine protein coding from nucleotide sequences. In this paper,
we present an in silico method to predict translation initiation sites in
vertebrate cDNA or mRNA sequences. This method consists of three sequential
steps as follows. In the first step, candidate features are generated using
k-gram amino acid patterns. In the second step, a small
number of top-ranked features are selected by an entropy-based algorithm. In
the third step, a classification model is built to recognize true TISs by
applying support vector machines or ensembles of decision trees to the selected
features. We have tested our method on several independent data sets, including
two public ones and our own extracted sequences. The experimental results
achieved are better than those reported previously using the same data sets.
Our high accuracy not only demonstrates the feasibility of our method, but also
indicates that there might be "amino acid" patterns around TIS in cDNA and
mRNA sequences.
