title: Novel methods to optimize gene and statistic test for evaluation - an application for Escherichia coli.
abstract: BACKGROUND
  Since the recombinant protein was discovered, it has become more popular in many aspects of life science. The value of global pharmaceutical market was $87 billion in 2008 and the sales for industrial enzyme exceeded $4 billion in 2012. This is strong evidence showing the great potential of recombinant protein. However, native genes introduced into a host can cause incompatibility of codon usage bias, GC content, repeat region, Shine-Dalgarno sequence with host's expression system, so the yields can fall down significantly. Hence, we propose novel methods for gene optimization based on neural network, Bayesian theory, and Euclidian distance.
  
  
  RESULTS
  The correlation coefficients of our neural network are 0.86, 0.73, and 0.90 in training, validation, and testing process. In addition, genes optimized by our methods seem to associate with highly expressed genes and give reasonable codon adaptation index values. Furthermore, genes optimized by the proposed methods are highly matched with the previous experimental data.
  
  
  CONCLUSIONS
  The proposed methods have high potential for gene optimization and further researches in gene expression. We built a demonstrative program using Matlab R2014a under Mac OS X. The program was published in both standalone executable program and Matlab function files. The developed program can be accessed from http://www.math.hcmus.edu.vn/~ptbao/paper_soft/GeneOptProg/ .
authors.affiliation: Faculty of Mathematics and Computer Science, VNUHCM-University of Science, 227 Nguyen Van Cu Street, District 5, Ho Chi Minh City, Vietnam.
authors.forename: Tran
authors.initials: T
authors.lastname: Tuan-Anh
authors.affiliation: School of Biotechnology, VNUHCM-International University, Quarter 6, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam.
authors.forename: Le Thi
authors.initials: LT
authors.lastname: Ly
authors.affiliation: Faculty of Information Technology, Ho Chi Minh City University of Pedagogy, 280 An Duong Vuong Street, Ward 4, District 5, Ho Chi Minh City, Vietnam.
authors.forename: Ngo Quoc
authors.initials: NQ
authors.lastname: Viet
authors.affiliation: Faculty of Mathematics and Computer Science, VNUHCM-University of Science, 227 Nguyen Van Cu Street, District 5, Ho Chi Minh City, Vietnam. ptbao@hcmus.edu.vn.
authors.forename: Pham The
authors.initials: PT
authors.lastname: Bao
mesh_terms: D000465:Algorithms; D001482:Base Composition; D001499:Bayes Theorem; D003062:Codon; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism*; D015870:Gene Expression; D016014:Linear Models; D011994:Recombinant Proteins / Q000096:biosynthesis / Q000235:genetics
chemical_list: D003062:Codon; D011994:Recombinant Proteins
keywords: Bayes’ theorem; Codon usage bias; Euclidean distance; Gene optimization; Highly expressed gene; Neural network