SARS-CoV(BJ01)基因预测及功能推测

通过对有关SARS—Cov文献的调研,指出了有关基因预测和功能研究的不足。为制备有效的药物和疫苗,对SARS—CoV(BJ01)重新进行了基因预测和功能推测。比较12种基因预测方法对冠状病毒属中已知基因的预测优劣,选用Heuristic models、Gene Identification、ZCURVE—CoV和ORF FINDER4种较好的方法来预测基因,然后运用AT—Gpr分析第一起始密码子的可能性及是否符合Kozak规则,同时搜索转录调控序列,以提高基因预测的准确性。共预测出34个ORF,排除NCBI及有关文献中完全相同或有微弱差别的13个,得到21个大于50个氨基酸的可能新基因。对于预测出的蛋白质,运用ProtParam分析它们的物理化学特征,用SignaIP分析蛋白是否有信号肽,用BLAST、FASTA分析是否有相似序列,用TMPred、TMHMM、PFAM和HMMTOP分析结构域或模体,以提高基因功能推测的可靠性。根据4种基因预测方法使用情况、与其他冠状病毒属已知基因匹配分值、匹配预期值、已知基因与预测基因长度差别,将21个可能的新基因按出现可能性分为4类。同时对结果进行了讨论。     

微生物基因功能的研究策略与方法

微生物基因功能的研究对于揭示微生物生命活动的规律及其在食品发酵、医药卫生、工农业生产等领域的应用机制具有重要意义。经过数十年的发展,微生物基因功能的研究方法已经从传统的同源重组技术发展到基于核酸内切酶的高效打靶技术,将微生物基因功能的研究推向了新的高度。文章就微生物基因功能的研究策略及常用方法做一综述,主要包括生物信息学方法预测、基因表达谱分析、基因敲除技术、基因敲入技术、基因沉默技术和基因编辑技术等。     

Compositional features of eukaryotic genomes for checking predicted genes

Gene prediction relies on the identification of characteristic features of coding sequences that distinguish them from non-coding DNA. The recent large-scale sequencing of entire genomes from higher eukaryotes, in conjunction with currently used gene prediction algorithms, has provided an abundance of putative genes that can now be analysed for their compositional properties. Strong, systematic differences still exist, in several species, between the compositional properties of sets of ex novo predicted genes and genes that have been experimentally detected and/or verified. This is particularly evident in the estimated gene set (>45,000 genes) of the recently sequenced rice genome, where roughly half the predicted genes are compositionally unusual and have no known orthologues in the dicot Arabidopsis. In a few cases such differences might suggest a bias in experimental gene-finding protocols, but the quasi-random nature of the compositionally aberrant predicted genes is a strong indication that many, if not most, of them are false positives. It therefore appears that some important features of coding regions have not yet been taken into account in existing gene prediction programs. Statistical base compositional properties of curated gene data sets from vertebrates, which we briefly review here, should therefore provide a useful benchmark for fine-tuning probabilistic gene models and model parameters that are currently in use.     

人CDC73基因编码蛋白的理化性质及功能研究

采用生物信息学方法对人CDC73 (cell division cycle 73)基因编码蛋白的理化性质、亲疏水性、跨膜区域、信号肽区域、二级结构、三级结构、蛋白质之间的相互作用、亚细胞定位进行预测分析。使用多种分析软件对人CDC73基因编码蛋白进行预测分析。研究可知,人CDC73基因属于抑癌基因,该基因编码一个由531个氨基酸组成的肿瘤抑制因子Parafibromin,其等电点为9.63,半衰期为30 h且在哺乳动物中高度保守;二级结构预测发现13个α螺旋和10个β折叠片层,三级结构预测结果的可靠性达69.01%,亚细胞定位主要分布于细胞质及细胞核。由本研究可知,人CDC73基因编码蛋白是一个存在核定位序列的不稳定亲水蛋白,在细胞内广泛分布并参与多种生命活动过程,且能够抑制肿瘤的产生。对人CDC73基因编码蛋白结构和功能的预测分析,可为其进一步的研究提供一定的理论依据,也为相关疾病的诊治提供新的思路。     

A Method for Identification of Selenoprotein Genes in Archaeal Genomes

The genetic codon UGA has a dual function: serving as a terminator and encoding selenocysteine. However, most popular gene annotation programs only take it as a stop signal, resulting in misannotation or completely missing selenoprotein genes. We developed a computational method named Asec-Prediction that is specific for the prediction of archaeal selenoprotein genes. To evaluate its effectiveness, we first applied it to 14 archaeal genomes with previously known selenoprotein genes, and Asec-Prediction identified all reported selenoprotein genes without redundant results. When we applied it to 12 archaeal genomes that had not been researched for selenoprotein genes, Asec-Prediction detected a novel selenoprotein gene in Methanosarcina acetivorans. Further evidence was also collected to support that the predicted gene should be a real selenoprotein gene. The result shows that Asec-Prediction is effective for the prediction of archaeal selenoprotein genes.     

A brief review of computational gene prediction methods

With the development of genome sequencing for many organisms, more and more raw sequences need to be annotated. Gene prediction by computational methods for finding the location of protein coding regions is one of the essential issues in bioinformatics. Two classes of methods are generally adopted: similarity based searches and ab initio prediction. Here, we review the development of gene prediction methods, summarize the measures for evaluating predictor quality, highlight open problems in this area, and discuss future research directions.     

Refining microRNA target predictions: Sorting the wheat from the chaff

microRNAs are short RNAs that reduce gene expression by binding to their targets. The accurate prediction of microRNA targets is essential to understanding the function of microRNAs. Computational predictions indicate that all human genes may be regulated by microRNAs, with each microRNA possibly targeting thousands of genes. Here we discuss computational methods for identifying mammalian microRNA targets and refining them for further experimental validation. We describe microRNA target prediction resources and procedures and how they integrate with various types of experimental techniques that aim to validate them or further explore their function. We also provide a list of target prediction databases and explain how these are curated.     

根据基因表达谱筛选间接互作蛋白质进行蛋白质功能预测

利用基因表达谱数据,通过计算互作蛋白质的表达相关系数,来筛选、优化蛋白质互作网络。结果显示,利用经过筛选的互作数据,根据邻居计数法和卡方法进行功能预测的预测效果明显提高,距离待测蛋白质较远的邻居也包含着与待测蛋白质功能一致的信息。     

Random forest for gene selection and microarray data classification

A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods.     

When maladaptive gene flow does not increase selection

Populations receiving high maladaptive gene flow are expected to experience strong directional selection—because gene flow pulls mean phenotypes away from local fitness peaks. We tested this prediction by means of a large and replicated mark‐recapture study of threespine stickleback (Gasterosteus aculeatus) in two stream populations. One of the populations (outlet) experiences high gene flow from the lake population and its morphology is correspondingly poorly adapted. The other population (inlet) experiences very low gene flow from the lake population and its morphology is correspondingly well adapted. Contrary to the above prediction, selection was not stronger in the outlet than in the inlet, a result that forced us to consider potential reasons for why maladaptive gene flow might not increase selection. Of particular interest, we show by means of a simple population genetic model that maladaptive gene flow can—under reasonable conditions—decrease the strength of directional selection. This outcome occurs when immigrants decrease mean fitness in the resident population, which decreases the strength of selection against maladapted phenotypes. We argue that this previously unrecognized effect of gene flow deserves further attention in theoretical and empirical studies.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interaction data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automatically selects the most appropriate functional classes as specific as possible during the learning process, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

Widely predicting specific protein functions based on protein-protein interaction data and gene expression profile

GESTs (gene expression similarity and taxonomy similarity), a gene functional prediction approach previously proposed by us, is based on gene expression similarity and concept similarity of functional classes defined in Gene Ontology (GO). In this paper, we extend this method to protein-protein interac-tion data by introducing several methods to filter the neighbors in protein interaction networks for a protein of unknown function(s). Unlike other conventional methods, the proposed approach automati-cally selects the most appropriate functional classes as specific as possible during the learning proc-ess, and calls on genes annotated to nearby classes to support the predictions to some small-sized specific classes in GO. Based on the yeast protein-protein interaction information from MIPS and a dataset of gene expression profiles, we assess the performances of our approach for predicting protein functions to “biology process” by three measures particularly designed for functional classes organ-ized in GO. Results show that our method is powerful for widely predicting gene functions with very specific functional terms. Based on the GO database published in December 2004, we predict some proteins whose functions were unknown at that time, and some of the predictions have been confirmed by the new SGD annotation data published in April, 2006.     

东乡野生稻内生放线菌Streptomyces sp. PRh5的全基因组测序及序列分析

【目的】Streptomyces sp. PRh5是从东乡野生稻(Oryza rufipogon Griff.)中分离获得的一株对细菌和真菌都具有较强抗菌活性的内生放线菌。为深入研究PRh5菌株抗菌机制及挖掘次级代谢产物基因资源,有必要解析PRh5菌株的基因组序列信息。【方法】采用高通量测序技术对PRh5菌株进行全基因组测序,然后使用相关软件对测序数据进行基因组组装、基因预测与功能注释、直系同源簇(COG)聚类分析、共线性分析及次级代谢产物合成基因簇预测等。【结果】基因组组装获得290 contigs,整个基因组大小约11.1 Mb,GC含量为71.1%,序列已提交至GenBank数据库,登录号为JABQ00000000。同时,预测得到50个次级代谢产物合成基因簇。【结论】将为Streptomyces sp. PRh5的功能基因组学研究及相关次级代谢产物的生物合成途径与异源表达研究提供基础。     

Exploiting single-molecule transcript sequencing for eukaryotic gene prediction

We develop a method to predict and validate gene models using PacBio single-molecule, real-time (SMRT) cDNA reads. Ninety-eight percent of full-insert SMRT reads span complete open reading frames. Gene model validation using SMRT reads is developed as automated process. Optimized training and prediction settings and mRNA-seq noise reduction of assisting Illumina reads results in increased gene prediction sensitivity and precision. Additionally, we present an improved gene set for sugar beet (Beta vulgaris) and the first genome-wide gene set for spinach (Spinacia oleracea). The workflow and guidelines are a valuable resource to obtain comprehensive gene sets for newly sequenced genomes of non-model eukaryotes.

Electronic supplementary material

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