整合BLAST搜索与GO注释的软件GoBlast

随着越来越多基因组测序的完成,人们可以获得大量的序列信息,如何利用这些信息对未知基因的功能进行预测是一个非常重要的问题.Blast是基本的预测新基因功能的工具,但是仅通过Blast的原始搜索结果,尚无法获得相关基因本体论(gene ontology,GO)注释信息.目前,用户为了获得新基因的GO注释信息,首先需要进行Blast搜索,然后用Blast搜索的结果到GO网站去查询相关的GO注释信息.这浪费了大量的时间,尤其是当Blast的结果数据量很大时.为此,基于GO分类系统,整合BLAST 的结果信息,结合bioperl模块,使用perl语言开发了GoBlast软件.通过GoBlast系统,对于新基因,研究人员只须1次分析运算,就可以同时获得Blast搜索结果和GO注释信息,从而有效地提高了基因功能注释的可信度,加速了功能基因组学的研究.GoBlast为B/S（Browser/Server）架构,用户客户端只要有浏览器程序,就可以通过国际互联网在http://bioq.org/goblast上使用GoBlast系统     

结合蛋白质互作与基因表达谱信息大范围预测蛋白质的精细功能

GESTs(gene expression similarity and taxonomy similarity)是结合基因表达相似性和基因功能分类体系Gene Ontology (GO)中的功能概念相似性测度进行功能预测的新方法. 将此预测算法推广应用于蛋白质互相作用数据, 并提出了几种在蛋白质互作网络中为功能待测蛋白质筛选邻居的方法. 与已有的其它蛋白质功能预测方法不同, 新方法在学习过程中自动地从功能分类体系中的各个功能类中选择最合适的尽可能具体细致的功能类, 利用注释于其相近功能类中的互作邻居蛋白质支持对此具体功能类的预测. 使用MIPS提供的酵母蛋白质互作信息与一套基因表达谱数据, 利用特别针对GO体系结构层次特点设计的3种测度, 评价对GO知识体系中的生物过程分支进行蛋白质功能预测的效果. 结果显示, 利用文中的方法, 可以大范围预测蛋白质的精细功能. 此外, 还利用此方法对2004年底Gene Ontology上未知功能的蛋白质进行预测, 其中部分预测结果在2006年4月发布的SGD注释数据中已经得到了证实.     

球毛壳菌(Chaetomium globosum)EST生物信息分析数据库的建立

用基因本体论（Cene Ontology,GO）中的相关的规范术语和BLAST分析结果来对球毛壳菌EST及CONTIG序列信息进行注释,利用GO的语义模型构建不同物种数据库之间的语义联接,在此基础上建立球毛壳菌EST生物信息分析数据库,在概念和联系层面上有效地解决了不同物种生物信息的整合问题,实现了对球毛壳菌生物信息学数据智能化的多重、复合和交叉检索。为球毛壳菌生物信息学的进一步研究奠定了坚实的基础。文中详细论述了基于GO的球毛壳菌EST生物信息学数据库的研究背景、建立过程、查询功能及其维护。     

梅花鹿鹿茸尖端组织ESTs分析

文章通过对东北梅花鹿(Cervus nippon hortulorum)鹿茸尖端组织cDNA文库随机测序获得了906条高质量ESTs,906条ESTs拼接后代表了701个Unigenes,其中包括重叠群86个,单拷贝615个。Blast分析显示具已知和推测功能的基因580个(82.7%),通过Gene Ontology(GO)分类对获得的580个功能基因进行了包括分子功能、生物过程和细胞组分在内的3个层次的功能注释,并根据BLAST的注释结果及进一步的筛选与分析,共得到39条与鹿茸尖端组织生长发育相关的基因。cDNA文库的构建和ESTs分析填补了鹿科动物在NCBI公共数据库上基因组信息的空白,并为科学的开发和利用梅花鹿资源提供了重要的理论依据。     

细菌外毒素序列中特有模体的识别及其基因本体注释分析

【目的】识别细菌外毒素序列中特有模体,进一步理解外毒素的致病机制。【方法】构建非致病性细菌蛋白质数据库,利用InterProScan对数据库中非致病菌蛋白质序列以及收集的经实验确认的89条细菌外毒素蛋白质序列进行模体搜索。【结果】在89条细菌外毒素序列中,分析得到了39个细菌外毒素特有模体。【结论】得到的外毒素特有模体与外毒素功能密切相关,为在致病性细菌基因组内搜索外毒素序列奠定了基础;同时通过对外毒素特有模体的基因本体(Gene ontology,GO)注释分析,进一步阐明了细菌外毒素的致病机制。     

基于质谱的BLAST方法在金鱼胚胎蛋白质鉴定中的应用

未知基因组及蛋白质序列数据库有限的物种的蛋白质组学分析是当前一些非模式生物物种蛋白质组学研究领域的瓶颈之一.基于同源性搜索的BLAST方法（MS BLAST）,是近年新发展起来的一种用于未知基因组的蛋白质鉴定的搜索工具,已成功应用于许多未知基因组物种的蛋白质鉴定.SPITC化学辅助方法是本实验室建立的一种改进的de novo质谱测序方法.采用MS BLAST方法对经Mascot软件数据库搜索未能鉴定到的19个金鱼胚胎蛋白质进行鉴定,其中12个蛋白质是直接测序后进行MS BLAST搜索得到的结果,另外7个蛋白质是联合MS BLAST和SPITC衍生方法得到的鉴定结果.实验结果证明,采用MS BLAST方法进行蛋白质的跨物种鉴定具有可行性和可靠性,给蛋白质的跨物种鉴定提供了一条新的途径.     

羽叶三七的转录组测序与三萜皂苷生物合成的关键酶基因的识别

羽叶三七是五加科人参属的名贵药材,三萜皂苷为羽叶三七最主要的活性成分。为了探索羽叶三七根茎中皂苷物质生物合成的分子基础,采用Illumina Hi Seq 2000高通量测序获得羽叶三七根茎的转录组数据;使用Trinity和TGICL软件实现Uni Gene的de novo拼接;基于BLAST完成Uni Gene的蛋白功能注释、KOG功能注释、GO分类和KEGG代谢通路分析。最终通过de novo拼接注释得到Uni Gene 62 240个。研究发现,羽叶三七根茎部表达的26个Uni Gene与三萜碳环骨架合成相关;三萜合成通路中的关键酶FPS、SS、SE等,分别有11 114个Uni Gene。该研究发现的三萜皂苷合成相关候选基因对于阐明羽叶三七三萜皂苷合成方式研究提供了理论基础。     

日本七鳃鳗(Lampetra japonica)肝脏ESTs 分析与比较转录组研究

以日本七鳃鳗(Lampetra japonica)肝脏为材料构建cDNA文库, 在文库中随机挑选克隆子进行测序共得到10077条有效ESTs(expressed sequence tags)序列. ESTs序列分析显示, 8515条ESTs拼成648条片段重叠群, 共得到2210条转录本, 其中47.06%的转录本预测为全长序列; 利用BLAST程序在GenBank数据库中进行同源性搜索发现2053条转录本有同源序列匹配, 占总转录本的92.9%. 更进一步对这些基因产物进行Gene Ontology注释, 结果发现, 在日本七鳃鳗肝脏中与有颌类免疫、凝血和代谢相关的基因大量表达, 并预测了8个新基因. 通过对日本七鳃鳗与底鳉(Fundulus heteroclitus)、鼠(Mus musculus)、牛(Bos taurus)和人(Homo sapiens)肝脏转录组的比较分析, 发现日本七鳃鳗肝脏中比其他物种优势表达的是甲壳质酶和多糖代谢等相关的基因, 这些基因可能在日本七鳃鳗免疫中发挥重要作用. 此外, 也利用TargetScan软件对日本七鳃鳗肝脏转录组中3′UTR区进行microRNA靶标识别, 结果发现了与人类癌症基因调控同源的microRNA靶标, 这为研究人类癌症提供了有益的线索. 上述结果将为七鳃鳗功能基因和蛋白组学的研究以及脊椎动物的基因组进化提供重要的理论基础.     

日本七鳃鳗(Lampetra japonica)肝脏ESTs 分析与比较转录组研究

以日本七鳃鳗(Lampetra japonica)肝脏为材料构建cDNA文库, 在文库中随机挑选克隆子进行测序共得到10077条有效ESTs(expressed sequence tags)序列. ESTs序列分析显示, 8515条ESTs拼成648条片段重叠群, 共得到2210条转录本, 其中47.06%的转录本预测为全长序列; 利用BLAST程序在GenBank数据库中进行同源性搜索发现2053条转录本有同源序列匹配, 占总转录本的92.9%. 更进一步对这些基因产物进行Gene Ontology注释, 结果发现, 在日本七鳃鳗肝脏中与有颌类免疫、凝血和代谢相关的基因大量表达, 并预测了8个新基因. 通过对日本七鳃鳗与底鳉(Fundulus heteroclitus)、鼠(Mus musculus)、牛(Bos taurus)和人(Homo sapiens)肝脏转录组的比较分析, 发现日本七鳃鳗肝脏中比其他物种优势表达的是甲壳质酶和多糖代谢等相关的基因, 这些基因可能在日本七鳃鳗免疫中发挥重要作用. 此外, 也利用TargetScan软件对日本七鳃鳗肝脏转录组中3′UTR区进行microRNA靶标识别, 结果发现了与人类癌症基因调控同源的microRNA靶标, 这为研究人类癌症提供了有益的线索. 上述结果将为七鳃鳗功能基因和蛋白组学的研究以及脊椎动物的基因组进化提供重要的理论基础.     

一株出芽短梗霉全基因组序列测定及其分析

出芽短梗霉因其发酵产物种类的多样性而具有广阔的工业应用前景。本研究利用下一代测序技术,对一株高产普鲁兰多糖的出芽短梗霉菌株(Aureobasidium pullulans CCTCC M 2012259)全基因组进行测序、组装和生物信息学分析。研究表明,该菌株的基因组全长约为26.37 Mb,共包含36条scaffolds和76 contigs,Gen Bank登录号:PRJNA350822。利用Gene Mark-ES软件对该基因组进行基因预测,共得到10 069个编码蛋白的基因。使用Blastp将其与Uniprot KB数据库中所有已知真菌蛋白进行比对,发现有6 218个预测蛋白与Uniprot KB数据库中的4 925个已知蛋白高度相似。利用DAVID工具对这些蛋白进行GO基因功能注释、KEGG通路注释和蛋白酶分析,分别注释得到4 444条GO功能条目、1 566条KEGG通路条目和1 740条蛋白酶信息。测定与分析为今后针对出芽短梗霉的功能基因挖掘以及分子遗传改造等工作的开展奠定了坚实的理论基础。     

GoFigure: automated Gene Ontology annotation

SUMMARY: We have developed a web tool to predict Gene Ontology (GO) terms. The tool accepts an input DNA or protein sequence, and uses BLAST to identify homologous sequences in GO annotated databases. A graph is returned to the user via email. AVAILABILITY: The tool is freely available at: http://udgenome.ags.udel.edu/frm_go.html/     

SimHap GUI: An intuitive graphical user interface for genetic association analysis

Background

The expressed sequence tag (EST) methodology is an attractive option for the generation of sequence data for species for which no completely sequenced genome is available. The annotation and comparative analysis of such datasets poses a formidable challenge for research groups that do not have the bioinformatics infrastructure of major genome sequencing centres. Therefore, there is a need for user-friendly tools to facilitate the annotation of non-model species EST datasets with well-defined ontologies that enable meaningful cross-species comparisons. To address this, we have developed annot8r, a platform for the rapid annotation of EST datasets with GO-terms, EC-numbers and KEGG-pathways.

Results

annot8r automatically downloads all files relevant for the annotation process and generates a reference database that stores UniProt entries, their associated Gene Ontology (GO), Enzyme Commission (EC) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) annotation and additional relevant data. For each of GO, EC and KEGG, annot8r extracts a specific sequence subset from the UniProt dataset based on the information stored in the reference database. These three subsets are then formatted for BLAST searches. The user provides the protein or nucleotide sequences to be annotated and annot8r runs BLAST searches against these three subsets. The BLAST results are parsed and the corresponding annotations retrieved from the reference database. The annotations are saved both as flat files and also in a relational postgreSQL results database to facilitate more advanced searches within the results. annot8r is integrated with the PartiGene suite of EST analysis tools.

Conclusion

annot8r is a tool that assigns GO, EC and KEGG annotations for data sets resulting from EST sequencing projects both rapidly and efficiently. The benefits of an underlying relational database, flexibility and the ease of use of the program make it ideally suited for non-model species EST-sequencing projects.     

MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data

MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP . The results are displayed in a searchable browser, allowing the user to rapidly identify GO terms with over-represented numbers of gene-expression changes. Clicking on GO terms generates GenMAPP graphical files where gene relationships can be explored, annotated, and files can be freely exchanged.     

Analysis of expressed sequence tags from cDNA library of Fusarium culmorum infected barley (Hordeum vulgare L.) roots

Fusarium culmorum is one of the most common and globally important causal agent of root and crown rot diseases of cereals. These diseases cause grain yield loss and reduced grain quality in barley. In this study, we have analyzed an expressed sequence tag (EST) database derived from F. culmorum infected barley root tissues available at the National Center for Biotechnology Information (NCBI). The 2294 sequences were assembled into 1619 non-redundant sequences consisting of 359 contigs and 1260 singletons using the program CAP3. BLASTX analysis for these sequences was conducted in order to find similar sequences in all databases. Gene Ontology search, enzyme search, KEGG mapping and InterProScan search were done using Blast2GO 3.0.7 tool. By BLASTX analysis, 41.7%, 7.7%, 3.2% and 47.4% of ESTs were categorized as annotated, unannotated, not mapping and without blast hits, respectively. BLASTX analysis revealed that the majority of top hits were barley proteins (43.5%). Based on Gene Ontology classification, 38.3%, 31.3%, and 16% of ESTs were assigned to molecular function, biological process, and cellular component GO terms, respectively. Most abundant GO terms were as follows: 157 sequences were related to response to stress (biological process), 207 sequences were related to ion binding (molecular function), and 160 sequences were related to plastid (cellular component). Furthermore, based on KEGG mapping, 369 sequences could be assigned to 264 enzymes and 83 different KEGG pathways. According to Enzyme Commission (EC) distribution; 94 sequences were transferases (EC2) while 70 sequences were hydrolases (EC3).     

MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data

MAPPFinder is a tool that creates a global gene-expression profile across all areas of biology by integrating the annotations of the Gene Ontology (GO) Project with the free software package GenMAPP http://www.GenMAPP.org. The results are displayed in a searchable browser, allowing the user to rapidly identify GO terms with over-represented numbers of gene-expression changes. Clicking on GO terms generates GenMAPP graphical files where gene relationships can be explored, annotated, and files can be freely exchanged.     

Fuzzy measures on the Gene Ontology for gene product similarity

One of the most important objects in bioinformatics is a gene product (protein or RNA). For many gene products, functional information is summarized in a set of Gene Ontology (GO) annotations. For these genes, it is reasonable to include similarity measures based on the terms found in the GO or other taxonomy. In this paper, we introduce several novel measures for computing the similarity of two gene products annotated with GO terms. The fuzzy measure similarity (FMS) has the advantage that it takes into consideration the context of both complete sets of annotation terms when computing the similarity between two gene products. When the two gene products are not annotated by common taxonomy terms, we propose a method that avoids a zero similarity result. To account for the variations in the annotation reliability, we propose a similarity measure based on the Choquet integral. These similarity measures provide extra tools for the biologist in search of functional information for gene products. The initial testing on a group of 194 sequences representing three proteins families shows a higher correlation of the FMS and Choquet similarities to the BLAST sequence similarities than the traditional similarity measures such as pairwise average or pairwise maximum.     

Applications of InterPro in protein annotation and genome analysis

The applications of InterPro span a range of biologically important areas that includes automatic annotation of protein sequences and genome analysis. In automatic annotation of protein sequences InterPro has been utilised to provide reliable characterisation of sequences, identifying them as candidates for functional annotation. Rules based on the InterPro characterisation are stored and operated through a database called RuleBase. RuleBase is used as the main tool in the sequence database group at the EBI to apply automatic annotation to unknown sequences. The annotated sequences are stored and distributed in the TrEMBL protein sequence database. InterPro also provides a means to carry out statistical and comparative analyses of whole genomes. In the Proteome Analysis Database, InterPro analyses have been combined with other analyses based on CluSTr, the Gene Ontology (GO) and structural information on the proteins.