Accurate prediction of human essential genes using only nucleotide composition and association information.
BACKGROUND
Previously constructed classifiers in predicting eukaryotic essential genes integrated a variety of features including experimental ones. If we can obtain satisfactory prediction using only nucleotide (sequence) information, it would be more promising. Three groups recently identified essential genes in human cancer cell lines using wet experiments and it provided wonderful opportunity to accomplish our idea. Here we improved the Z curve method into the λ-interval form to denote nucleotide composition and association information and used it to construct the SVM classifying model.


RESULTS
Our model accurately predicted human gene essentiality with an AUC higher than 0.88 both for 5-fold cross-validation and jackknife tests. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. We re-predicted the negative dataset by our Pheg server and 118 genes were additionally predicted as essential. Among them, 20 were found to be homologues in mouse essential genes, indicating that some of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the λ-interval Z curve method could be effectively extended to classification issues of other DNA elements.


METHODS
http://cefg.uestc.edu.cn/Pheg.


BACKGROUND
fbguo@uestc.edu.cn.


BACKGROUND
Supplementary data are available at Bioinformatics online.
School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
Feng-Biao
FB
Guo
School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
Chuan
C
Dong
School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
Hong-Li
HL
Hua
School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
Shuo
S
Liu
Department of Physics, Tianjin University, Tianjin, China.
Hao
H
Luo
School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
Hong-Wan
HW
Zhang
School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
Yan-Ting
YT
Jin
School of Life Science and Technology, Center for Informational Biology and Key Laboratory for Neuro-information of the Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China.
Kai-Yue
KY
Zhang
D000818:Animals; D001482:Base Composition; D056890:Eukaryota / Q000235:genetics; D020043:Genes, Essential; D006801:Humans; D051379:Mice; D008957:Models, Genetic; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software