基于迭代自学习的操纵子结构预测

原核生物操纵子结构的准确注释对基因功能和基因调控网络的研究具有重要意义,通过生物信息学方法计算预测是当前基因组操纵子结构注释的最主要来源.当前的预测算法大都需要实验确认的操纵子作为训练集,但实验确认的操纵子数据的缺乏一直成为发展算法的瓶颈.基于对操纵子结构的认识,从基因间距离、转录翻译相关的调控信号以及COG功能注释等特征出发,建立了描述操纵子复杂结构的概率模型,并提出了不依赖于特定物种操纵子数据作为训练集的迭代自学习算法.通过对实验验证的操纵子数据集的测试比较,结果表明算法对于预测操纵子结构非常有效.在不依赖于任何已知操纵子信息的情况下,算法在总体预测水平上超过了目前最好的操纵子预测方法,而且这种自学习的预测算法要优于依赖特定物种进行训练的算法.这些特点使得该算法能够适用于新测序的物种,有别于当前常用的操纵子预测方法.对细菌和古细菌的基因组进行大规模比较分析,进一步提高了对基因组操纵子结构的普遍特征和物种特异性的认识.     

Operon prediction by comparative genomics: an application to the Synechococcus sp. WH8102 genome

We present a computational method for operon prediction based on a comparative genomics approach. A group of consecutive genes is considered as a candidate operon if both their gene sequences and functions are conserved across several phylogenetically related genomes. In addition, various supporting data for operons are also collected through the application of public domain computer programs, and used in our prediction method. These include the prediction of conserved gene functions, promoter motifs and terminators. An apparent advantage of our approach over other operon prediction methods is that it does not require many experimental data (such as gene expression data and pathway data) as input. This feature makes it applicable to many newly sequenced genomes that do not have extensive experimental information. In order to validate our prediction, we have tested the method on Escherichia coli K12, in which operon structures have been extensively studied, through a comparative analysis against Haemophilus influenzae Rd and Salmonella typhimurium LT2. Our method successfully predicted most of the 237 known operons. After this initial validation, we then applied the method to a newly sequenced and annotated microbial genome, Synechococcus sp. WH8102, through a comparative genome analysis with two other cyanobacterial genomes, Prochlorococcus marinus sp. MED4 and P.marinus sp. MIT9313. Our results are consistent with previously reported results and statistics on operons in the literature.     

Operon prediction using both genome-specific and general genomic information

We have carried out a systematic analysis of the contribution of a set of selected features that include three new features to the accuracy of operon prediction. Our analyses have led to a number of new insights about operon prediction, including that (i) different features have different levels of discerning power when used on adjacent gene pairs with different ranges of intergenic distance, (ii) certain features are universally useful for operon prediction while others are more genome-specific and (iii) the prediction reliability of operons is dependent on intergenic distances. Based on these new insights, our newly developed operon-prediction program achieves more accurate operon prediction than the previous ones, and it uses features that are most readily available from genomic sequences. Our prediction results indicate that our (non-linear) decision tree-based classifier can predict operons in a prokaryotic genome very accurately when a substantial number of operons in the genome are already known. For example, the prediction accuracy of our program can reach 90.2 and 93.7% on Bacillus subtilis and Escherichia coli genomes, respectively. When no such information is available, our (linear) logistic function-based classifier can reach the prediction accuracy at 84.6 and 83.3% for E.coli and B.subtilis, respectively.     

Optimal gene partition into operons correlates with gene functional order

Gene arrangement into operons varies between bacterial species. Genes in a given system can be on one operon in some organisms and on several operons in other organisms. Existing theories explain why genes that work together should be on the same operon, since this allows for advantageous lateral gene transfer and accurate stoichiometry. But what causes the frequent separation into multiple operons of co-regulated genes that act together in a pathway? Here we suggest that separation is due to benefits made possible by differential regulation of each operon. We present a simple mathematical model for the optimal distribution of genes into operons based on a balance of the cost of operons and the benefit of regulation that provides 'just-when-needed' temporal order. The analysis predicts that genes are arranged such that genes on the same operon do not skip functional steps in the pathway. This prediction is supported by genomic data from 137 bacterial genomes. Our work suggests that gene arrangement is not only the result of random historical drift, genome re-arrangement and gene transfer, but has elements that are solutions of an evolutionary optimization problem. Thus gene functional order may be inferred by analyzing the operon structure across different genomes.     

Identifying conserved gene clusters in the presence of homology families.

The study of conserved gene clusters is important for understanding the forces behind genome organization and evolution, as well as the function of individual genes or gene groups. In this paper, we present a new model and algorithm for identifying conserved gene clusters from pairwise genome comparison. This generalizes a recent model called "gene teams." A gene team is a set of genes that appear homologously in two or more species, possibly in a different order yet with the distance of adjacent genes in the team for each chromosome always no more than a certain threshold. We remove the constraint in the original model that each gene must have a unique occurrence in each chromosome and thus allow the analysis on complex prokaryotic or eukaryotic genomes with extensive paralogs. Our algorithm analyzes a pair of chromosomes in O(mn) time and uses O(m+n) space, where m and n are the number of genes in the respective chromosomes. We demonstrate the utility of our methods by studying two bacterial genomes, E. coli K-12 and B. subtilis. Many of the teams identified by our algorithm correlate with documented E. coli operons, while several others match predicted operons, previously suggested by computational techniques. Our implementation and data are publicly available at euler.slu.edu/ approximately goldwasser/homologyteams/.     

PPO: predictor for prokaryotic operons

SUMMARY: We present an operon predictor for prokaryotic operons (PPO), which can predict operons in the entire prokaryotic genome. The prediction algorithm used in PPO allows the user to select binary particle swarm optimization (BPSO), a genetic algorithm (GA) or some other methods introduced in the literature to predict operons. The operon predictor on our web server and the provided database are easy to access and use. The main features offered are: (i) selection of the prediction algorithm; (ii) adjustable parameter settings of the prediction algorithm; (iii) graphic visualization of results; (iv) integrated database queries; (v) listing of experimentally verified operons; and (vi) related tools. Availability and implementation: PPO is freely available at http://bio.kuas.edu.tw/PPO/.     

A fuzzy guided genetic algorithm for operon prediction

Motivation: The operon structure of the prokaryotic genome isa critical input for the reconstruction of regulatory networksat the whole genome level. As experimental methods for the detectionof operons are difficult and time-consuming, efforts are beingput into developing computational methods that can use availablebiological information to predict operons. Method: A genetic algorithm is developed to evolve a startingpopulation of putative operon maps of the genome into progressivelybetter predictions. Fuzzy scoring functions based on multiplecriteria are used for assessing the ‘fitness’ ofthe newly evolved operon maps and guiding their evolution. Results: The algorithm organizes the whole genome into operons.The fuzzy guided genetic algorithm-based approach makes it possibleto use diverse biological information like genome sequence data,functional annotations and conservation across multiple genomes,to guide the organization process. This approach does not requireany prior training with experimental operons. The predictionsfrom this algorithm for Escherchia coli K12 and Bacillus subtilisare evaluated against experimentally discovered operons forthese organisms. The accuracy of the method is evaluated usingan ROC (receiver operating characteristic) analysis. The areaunder the ROC curve is around 0.9, which indicates excellentaccuracy. Contact: roschen_csir{at}rediffmail.com