Phylogenetic relationships within cation transporter families of Arabidopsis

Uptake and translocation of cationic nutrients play essential roles in physiological processes including plant growth, nutrition, signal transduction, and development. Approximately 5% of the Arabidopsis genome appears to encode membrane transport proteins. These proteins are classified in 46 unique families containing approximately 880 members. In addition, several hundred putative transporters have not yet been assigned to families. In this paper, we have analyzed the phylogenetic relationships of over 150 cation transport proteins. This analysis has focused on cation transporter gene families for which initial characterizations have been achieved for individual members, including potassium transporters and channels, sodium transporters, calcium antiporters, cyclic nucleotide-gated channels, cation diffusion facilitator proteins, natural resistance-associated macrophage proteins (NRAMP), and Zn-regulated transporter Fe-regulated transporter-like proteins. Phylogenetic trees of each family define the evolutionary relationships of the members to each other. These families contain numerous members, indicating diverse functions in vivo. Closely related isoforms and separate subfamilies exist within many of these gene families, indicating possible redundancies and specialized functions. To facilitate their further study, the PlantsT database (http://plantst.sdsc.edu) has been created that includes alignments of the analyzed cation transporters and their chromosomal locations.     

Statistically rigorous automated protein annotation

MOTIVATION: Assignment of putative protein functional annotation by comparative analysis using pre-defined experimental annotations is performed routinely by molecular biologists. The number and statistical significance of these assignments remains a challenge in this era of high-throughput proteomics. A combined statistical method that enables robust, automated protein annotation by reliably expanding existing annotation sets is described. An existing clustering scheme, based on relevant experimental information (e.g. sequence identity, keywords or gene expression data) is required. The method assigns new proteins to these clusters with a measure of reliability. It can also provide human reviewers with a reliability score for both new and previously classified proteins. RESULTS: A dataset of 27 000 annotated Protein Data Bank (PDB) polypeptide chains (of 36 000 chains currently in the PDB) was generated from 23 000 chains classified a priori. AVAILABILITY: PDB annotations and sample software implementation are freely accessible on the Web at http://pmr.sdsc.edu/go     

MITOPRED: a genome-scale method for prediction of nucleus-encoded mitochondrial proteins

MOTIVATION: Currently available methods for the prediction of subcellular location of mitochondrial proteins rely largely on the presence of mitochondrial targeting signals in the protein sequences. However, a large fraction of mitochondrial proteins lack such signals, making those tools ineffective for genome-scale prediction of mitochondria-targeted proteins. Here, we propose a method for genome-scale prediction of nucleus-encoded mitochondrial proteins. The new method, MITOPRED, is based on the Pfam domain occurrence patterns and the amino acid compositional differences between mitochondrial and non-mitochondrial proteins. RESULTS: MITOPRED could predict mitochondrial proteins with 100% specificity at a 44% sensitivity rate and with 67% specificity at 99% sensitivity. Additionally, it was sufficiently robust to predict mitochondrial proteins across different eukaryotic species with similar accuracy. Based on Matthews correlation coefficient measure, the prediction performance of MITOPRED is clearly superior (0.73) to those of the two popular methods TargetP (0.51) and PSORT (0.53). Using this method, we predicted the nucleus-encoded mitochondrial proteins from six complete genomes (three invertebrate, two vertebrate and one plant species) and estimated the total number in each genome. In human, our method estimated the existence of 1362 mitochondrial proteins corresponding to 4.8% of the total proteome. AVAILABILITY: MITOPRED program is freely accessible at http://mitopred.sdsc.edu. Source code is available on request from the authors. SUPPLEMENTARY INFORMATION: Training data sets are also available at http://mitopred.sdsc.edu     

Bioverse: Functional,structural and contextual annotation of proteins and proteomes

Functional annotation is routinely performed for large-scale genomics projects and databases. Researchers working on more specific problems, for instance on an individual pathway or complex, also need to be able to quickly, completely and accurately annotate sequences. The Bioverse sequence annotation server (http://bioverse.compbio.washington.edu) provides a web-based interface to allow users to submit protein sequences to the Bioverse framework. Sequences are functionally and structurally annotated and potential contextual annotations are provided. Researchers can also submit candidate genomes for annotation of all proteins encoded by the genome (proteome).     

Applications of Tree-Maps to hierarchical biological data

SUMMARY: A brief overview of Tree-Maps provides the basis for understanding two new implementations of Tree-Map methods. TreeMapClusterView provides a new way to view microarray gene expression data, and GenePlacer provides a view of gene ontology annotation data. We also discuss the benefits of Tree-Maps to visualize complex hierarchies in functional genomics. AVAILABILITY: Java class files are freely available at http://mendel.mc.duke.edu/bioinformatics/ CONTACT: mccon012@mc.duke.edu SUPPLEMENTARY INFORMATION: For more information on TreeMapClusterView (see http://mendel.mc.duke.edu/bioinformatics/software/boxclusterview/), and http://mendel.mc.duke.edu/bioinformatics/software/geneplacer/).     

ChipInfo: Software for extracting gene annotation and gene ontology information for microarray analysis

To date, assembling comprehensive annotation information for all probe sets of any Affymetrix microarrays remains a time-consuming, error-prone and challenging task. ChipInfo is designed for retrieving annotation information from online databases such as NetAffx and Gene Ontology and organizing such information into easily interpretable tabular format outputs. As companion software to dChip and GoSurfer, ChipInfo enables users to independently update the information resource files of these software packages. It also has functions for computing related summary statistics of probe sets and Gene Ontology terms. ChipInfo is available at http://biosun1.harvard.edu/complab/chipinfo/.     

BioEditor-simplifying macromolecular structure annotation

SUMMARY: BioEditor is an application to enable scientists and educators to prepare and present structure annotations containing formatted text, graphics, sequence data, and interactive molecular views. It is intended to bridge the gap between printed journal articles and Internet presentation formats. BioEditor is relevant in the era of structural genomics, where annotation and publication could become the rate determining step in structure determination. AVAILABILITY: BioEditor is available at http://bioeditor.sdsc.edu. The Web site includes the latest version of the software for Microsoft Windows, including documentation, the opportunity to submit bug reports and suggestions, example documentaries prepared with BioEditor and a repository where users can submit documentaries for posting to the site.     

Con-Struct Map: a comparative contact map analysis tool

Con-Struct Map is a graphical tool for the comparative study of protein structures. The tool detects potential conserved residue contacts shared by multiple protein structures by superimposing their contact maps according to a multiple structure alignment. In general, Con-Struct Map allows the study of structural changes resulting from, e.g. sequence substitutions, or alternatively, the study of conserved components of a structure framework across structurally aligned proteins. Specific applications include the study of sequence-structure relationship in distantly related proteins and the comparisons of wild type and mutant proteins. AVAILABILITY: http://pdbrs3.sdsc.edu/ConStructMap/viewer_argument_generator/singleArguments. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

pDAWG: An Integrated Database for Plant Cell Wall Genes

We have recently developed a database, pDAWG, focused on information related to plant cell walls. Currently, pDAWG contains seven complete plant genomes, 12 complete algal genomes, along with computed information for individual proteins encoded in these genomes of the following types: (a) carbohydrate active enzyme (CAZy) family information when applicable; (b) phylogenetic trees of cell wall-related CAZy family proteins; (c) protein structure models if available; (d) physical and predicted interactions among proteins; (e) subcellular localization; (f) Pfam domain information; and (g) homology-based functional prediction. A querying system with a graphical interface allows a user to quickly compose information of different sorts about individual genes/proteins and to display the composite information in an intuitive manner, facilitating comparative analyses and knowledge discovery about cell wall genes. pDAWG can be accessed at http://csbl1.bmb.uga.edu/pDAWG/.     

Building an automated classification of DNA-binding protein domains

Intensive growth in 3D structure data on DNA-protein complexes as reflected in the Protein Data Bank (PDB) demands new approaches to the annotation and characterization of these data and will lead to a new understanding of critical biological processes involving these data. These data and those from other protein structure classifications will become increasingly important for the modeling of complete proteomes. We propose a fully automated classification of DNA-binding protein domains based on existing 3D-structures from the PDB. The classification, by domain, relies on the Protein Domain Parser (PDP) and the Combinatorial Extension (CE) algorithm for structural alignment. The approach involves the analysis of 3D-interaction patterns in DNA-protein interfaces, assignment of structural domains interacting with DNA, clustering of domains based on structural similarity and DNA-interacting patterns. Comparison with existing resources on describing structural and functional classifications of DNA-binding proteins was used to validate and improve the approach proposed here. In the course of our study we defined a set of criteria and heuristics allowing us to automatically build a biologically meaningful classification and define classes of functionally related protein domains. It was shown that taking into consideration interactions between protein domains and DNA considerably improves the classification accuracy. Our approach provides a high-throughput and up-to-date annotation of DNA-binding protein families which can be found at http://spdc.sdsc.edu.     

The protein information resource (PIR)

The Protein Information Resource (PIR) produces the largest, most comprehensive, annotated protein sequence database in the public domain, the PIR-International Protein Sequence Database, in collaboration with the Munich Information Center for Protein Sequences (MIPS) and the Japan International Protein Sequence Database (JIPID). The expanded PIR WWW site allows sequence similarity and text searching of the Protein Sequence Database and auxiliary databases. Several new web-based search engines combine searches of sequence similarity and database annotation to facilitate the analysis and functional identification of proteins. New capabilities for searching the PIR sequence databases include annotation-sorted search, domain search, combined global and domain search, and interactive text searches. The PIR-International databases and search tools are accessible on the PIR WWW site at http://pir.georgetown.edu and at the MIPS WWW site at http://www. mips.biochem.mpg.de. The PIR-International Protein Sequence Database and other files are also available by FTP.     

iProClass: an integrated, comprehensive and annotated protein classification database

The iProClass database is an integrated resource that provides comprehensive family relationships and structural and functional features of proteins, with rich links to various databases. It is extended from ProClass, a protein family database that integrates PIR superfamilies and PROSITE motifs. The iProClass currently consists of more than 200,000 non-redundant PIR and SWISS-PROT proteins organized with more than 28,000 superfamilies, 2600 domains, 1300 motifs, 280 post-translational modification sites and links to more than 30 databases of protein families, structures, functions, genes, genomes, literature and taxonomy. Protein and family summary reports provide rich annotations, including membership information with length, taxonomy and keyword statistics, full family relationships, comprehensive enzyme and PDB cross-references and graphical feature display. The database facilitates classification-driven annotation for protein sequence databases and complete genomes, and supports structural and functional genomic research. The iProClass is implemented in Oracle 8i object-relational system and available for sequence search and report retrieval at http://pir.georgetown.edu/iproclass/.     

水稻蛋白激酶的规模化克隆、表达及活性研究(简报)

蛋白激酶几乎与所有重要的发育代谢过程有关,已知在植物的发育、自交不亲和、雄性不育、抗逆和抗病等生命活动过程中起重要的调控作用[1]。蛋白激酶在各种生物中广泛存在,根据网络公布的水稻全基因组序列图谱,通过同源序列比对,TIGR Rice Genome Annotation-Release4共找到了1532个具有激酶结构域的蛋白质(PF00069)[2],拟南芥中也存在1053个激酶[1],这些激酶与它们的上下游蛋白质组成了一个复杂而有序的网络,调节着植物的正常生长发育和对外界环境的反应。     

高通量蛋白质组学研究中一种基于 GO 的蛋白质功能分析策略

挖掘高通量实验数据蕴含的生物学意义是蛋白质组学研究面临的一大挑战 . 基于等级化结构化的词汇表 GO (Gene Ontology) 和相关数据库中的蛋白质功能注释,发展了一种对蛋白质组学研究中得到的表达谱 (Expression profile) 进行功能分析的策略 . 在对蛋白质表达谱进行功能注释的基础上给出蛋白质表达谱中蛋白质功能的分布,同时给出感兴趣功能类别的统计信息 . 这有助于对表达谱蛋白质功能的整体理解和深入的生物信息学分析 . 该策略已经成功应用胎肝蛋白表达谱研究中,用户可以通过访问网址 http://www.hupo.org.cn/GOfact/ 使用或者下载我们的程序 .     

OrchidBase: a collection of sequences of the transcriptome derived from orchids

Orchids are one of the most ecological and evolutionarily significant plants, and the Orchidaceae is one of the most abundant families of the angiosperms. Genetic databases will be useful not only for gene discovery but also for future genomic annotation. For this purpose, OrchidBase was established from 37,979,342 sequence reads collected from 11 in-house Phalaenopsis orchid cDNA libraries. Among them, 41,310 expressed sequence tags (ESTs) were obtained by using Sanger sequencing, whereas 37,908,032 reads were obtained by using next-generation sequencing (NGS) including both Roche 454 and Solexa Illumina sequencers. These reads were assembled into 8,501 contigs and 76,116 singletons, resulting in 84,617 non-redundant transcribed sequences with an average length of 459 bp. The analysis pipeline of the database is an automated system written in Perl and C#, and consists of the following components: automatic pre-processing of EST reads, assembly of raw sequences, annotation of the assembled sequences and storage of the analyzed information in SQL databases. A web application was implemented with HTML and a Microsoft .NET Framework C# program for browsing and querying the database, creating dynamic web pages on the client side, analyzing gene ontology (GO) and mapping annotated enzymes to KEGG pathways. The online resources for putative annotation can be searched either by text or by using BLAST, and the results can be explored on the website and downloaded. Consequently, the establishment of OrchidBase will provide researchers with a high-quality genetic resource for data mining and facilitate efficient experimental studies on orchid biology and biotechnology. The OrchidBase database is freely available at http://lab.fhes.tn.edu.tw/est.     

Validating annotations for uncharacterized proteins in Shewanella oneidensis

Proteins of unknown function are a barrier to our understanding of molecular biology. Assigning function to these "uncharacterized" proteins is imperative, but challenging. The usual approach is similarity searches using annotation databases, which are useful for predicting function. However, since the performance of these databases on uncharacterized proteins is basically unknown, the accuracy of their predictions is suspect, making annotation difficult. To address this challenge, we developed a benchmark annotation dataset of 30 proteins in Shewanella oneidensis. The proteins in the dataset were originally uncharacterized after the initial annotation of the S. oneidensis proteome in 2002. In the intervening 5 years, the accumulation of new experimental evidence has enabled specific functions to be predicted. We utilized this benchmark dataset to evaluate several commonly utilized annotation databases. According to our criteria, six annotation databases accurately predicted functions for at least 60% of proteins in our dataset. Two of these six even had a "conditional accuracy" of 90%. Conditional accuracy is another evaluation metric we developed which excludes results from databases where no function was predicted. Also, 27 of the 30 proteins' functions were correctly predicted by at least one database. These represent one of the first performance evaluations of annotation databases on uncharacterized proteins. Our evaluation indicates that these databases readily incorporate new information and are accurate in predicting functions for uncharacterized proteins, provided that experimental function evidence exists.