Recognizing short coding sequences of prokaryotic genome using a novel iteratively adaptive sparse partial least squares algorithm.
BACKGROUND
Significant efforts have been made to address the problem of identifying short genes in prokaryotic genomes. However, most known methods are not effective in detecting short genes. Because of the limited information contained in short DNA sequences, it is very difficult to accurately distinguish between protein coding and non-coding sequences in prokaryotic genomes. We have developed a new Iteratively Adaptive Sparse Partial Least Squares (IASPLS) algorithm as the classifier to improve the accuracy of the identification process.


RESULTS
For testing, we chose the short coding and non-coding sequences from seven prokaryotic organisms. We used seven feature sets (including GC content, Z-curve, etc.) of short genes.In comparison with GeneMarkS, Metagene, Orphelia, and Heuristic Approachs methods, our model achieved the best prediction performance in identification of short prokaryotic genes. Even when we focused on the very short length group ([60-100 nt)), our model provided sensitivity as high as 83.44% and specificity as high as 92.8%. These values are two or three times higher than three of the other methods while Metagene fails to recognize genes in this length range.The experiments also proved that the IASPLS can improve the identification accuracy in comparison with other widely used classifiers, i.e. Logistic, Random Forest (RF) and K nearest neighbors (KNN). The accuracy in using IASPLS was improved 5.90% or more in comparison with the other methods. In addition to the improvements in accuracy, IASPLS required ten times less computer time than using KNN or RF.


CONCLUSIONS
It is conclusive that our method is preferable for application as an automated method of short gene classification. Its linearity and easily optimized parameters make it practicable for predicting short genes of newly-sequenced or under-studied species.
School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China. ksong@tju.edu.cn.
Sun
S
Chen
Chun-ying
CY
Zhang
Kai
K
Song
D000465:Algorithms; D001419:Bacteria / Q000235:genetics*; D001426:Bacterial Proteins / Q000235:genetics*; D016018:Least-Squares Analysis; D016366:Open Reading Frames; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*
D001426:Bacterial Proteins