The PRINTS database: a resource for identification of protein families

The PRINTS database houses a collection of protein fingerprints, which may be used to assign family and functional attributes to uncharacterised sequences, such as those currently emanating from the various genome-sequencing projects. The April 2002 release includes 1,700 family fingerprints, encoding approximately 10,500 motifs, covering a range of globular and membrane proteins, modular polypeptides and so on. Fingerprints are groups of conserved motifs that, taken together, provide diagnostic protein family signatures. They derive much of their potency from the biological context afforded by matching motif neighbours; this makes them at once more flexible and powerful than single-motif approaches. The technique further departs from other pattern-matching methods by readily allowing the creation of fingerprints at superfamily-, family- and subfamily-specific levels, thereby allowing more fine-grained diagnoses. Here, we provide an overview of the method of protein fingerprinting and how the results of fingerprint analyses are used to build PRINTS and its relational cousin, PRINTS-S.     

Protein family alignment annotation

For bioscientists studying protein structure and function, the Protein Family Alignment Annotation Tool (Pfaat) is a useful and simple program for annotating collections of proteins. This open-source software includes methods for viewing and aligning protein families, and for annotating sequence structure and residues with known functions. It offers new options to aid the study of proteins, and an extensible annotation tool for bioinformatics developers.     

Viewing and annotating sequence data with Artemis

Artemis is a widely used software tool for annotating and viewing sequence data. No database is required to use Artemis. Instead, individual sequence data files can be analysed with little or no formatting, making it particularly suited to the study of small genomes and chromosomes, and straightforward for a novice user to get started. Since its release in 1999, Artemis has been used to annotate a diverse collection of prokaryotic and eukaryotic genomes, ranging from Streptomyces coelicolor to, more recently, a large proportion of the Plasmodium falciparum genome. Artemis allows annotated genomes to be easily browsed and makes it simple to add useful biological information to raw sequence data. This paper gives an overview of some of the features of Artemis and includes how it facilitates manual gene prediction and can provide an overview of entire chromosomes or small compact genomes--useful for uncovering unusual features such as pathogenicity islands.     

The role of pattern databases in sequence analysis

In the wake of the numerous now-fruitful genome projects, we are entering an era rich in biological data. The field of bioinformatics is poised to exploit this information in increasingly powerful ways, but the abundance and growing complexity both of the data and of the tools and resources required to analyse them are threatening to overwhelm us. Databases and their search tools are now an essential part of the research environment. However, the rate of sequence generation and the haphazard proliferation of databases have made it difficult to keep pace with developments. In an age of information overload, researchers want rapid, easy-to-use, reliable tools for functional characterisation of newly determined sequences. But what are those tools? How do we access them? Which should we use? This review focuses on a particular type of database that is increasingly used in the task of routine sequence analysis--the so-called pattern database. The paper aims to provide an overview of the current status of pattern databases in common use, outlining the methods behind them and giving pointers on their diagnostic strengths and weaknesses.     

枫香叶片变色期全长转录组测序及分析

枫香因其树形优美,入秋后叶色红艳或橙黄,极具观赏价值,是优良的景观生态树种。为了解枫香叶片变色及其次级代谢过程的遗传基础,该文以枫香5个叶片变色期叶片混合样品为材料,利用单分子实时测序技术(PacBio平台)对其进行全长转录组测序。结果表明:(1)全长转录组测序共获得41.04 Gb的高质量数据,从中鉴定出全长非嵌合序列563 180条,通过聚类和去冗余,获得27 269条高质量全长转录本。在27 269条全长转录本中预测到2 035条长链非编码RNA(lncRNA),并检测出14 892个简单重复序列(SSR)位点和1 856个转录因子。(2)基因注释结果表明,NR、GO、COG、KEGG 等8个数据库共注释了24 857条转录本,KEGG数据库共获得了124个条代谢途径,主要有核糖体、碳代谢、氨基酸生物合成等,在类黄酮和叶绿素代谢途径中分别有49和71个转录本参与。上述结果初步揭示了枫香叶片变色期转录组信息以及功能特性,为后续研究枫香叶片变色分子机制、色素代谢合成途径和调控、相关功能基因克隆以及叶色改良提供基础数据。     

Predicting stable functional peptides from the intergenic space of E. coli

Expression of synthetic proteins from intergenic regions of E. coli and their functional association was recently demonstrated (Dhar et al. in J Biol Eng 3:2, 2009. doi:10.1186/1754-1611-3-2). This gave birth to the question: if one can make ‘user-defined’ genes from non-coding genome—how big is the artificially translatable genome? (Dinger et al. in PLoS Comput Biol 4, 2008; Frith et al. in RNA Biol 3(1):40–48, 2006a; Frith et al. in PLoS Genet 2(4):e52, 2006b). To answer this question, we performed a bioinformatics study of all reported E. coli intergenic sequences, in search of novel peptides and proteins, unexpressed by nature. Overall, 2500 E. coli intergenic sequences were computationally translated into ‘protein sequence equivalents’ and matched against all known proteins. Sequences that did not show any resemblance were used for building a comprehensive profile in terms of their structure, function, localization, interactions, stability so on. A total of 362 protein sequences showed evidence of stable tertiary conformations encoded by the intergenic sequences of E. coli genome. Experimental studies are underway to confirm some of the key predictions. This study points to a vast untapped repository of functional molecules lying undiscovered in the non-expressed genome of various organisms.     

籼稻'93-11'一段基因组序列的完善及分析

超级杂交稻父本‘93-11＇的基因组序列测定的完成,为进行作物遗传改良和不同作物之间的比较基因组学研究提供了又一重要序列资源.但是,该基因组序列中还存在很多“缺口”,为使‘93-11＇的基因组序列更加精确,同时提供一些“缺口”填补策略和方法,本研究采用PCR扩增、回收克隆测序的方法对该基因组中一段长约160 kb、含有6个“缺口”的基因组序列进行了完善,并运用相关分子生物学和生物信息学软件进行了详细分析,结果表明：该6个“缺口”中,存在1个“缺口”估计错误,2个序列拼接错误;“缺口”主要位于非编码区,位于编码区的只有1个,其改变了对本处基因的注释,使此基因由原来的9个外显子增加为11个;填补“缺口”后,基因密度增加.     

An automated protein annotation filter for integrating web-based annotation tools

A wide range of web based prediction and annotation tools are frequently used for determining protein function from sequence. However, parallel processing of sequences for annotation through web tools is not possible due to several constraints in functional programming for multiple queries. Here, we propose the development of APAF as an automated protein annotation filter to overcome some of these difficulties through an integrated approach.