Yeast genomic databases and the challenge of the post-genomic era

Since the completion of the yeast genome sequence in 1996, three genomic databases, the Saccharomyces Genome Database, the Yeast Proteome Database, and MIPS (produced by the Munich Information Center for Protein Sequences), have organized published knowledge of yeast genes and proteins onto the framework of the genome. Now, post-genomic technologies are producing large-scale datasets of many types, and these pose new challenges for knowledge integration. This review first examines the structure and content of the three genomic databases, and then draws from them and other resources to examine the ways knowledge from the literature, genome, and post-genomic experiments is stored, integrated, and disseminated. To better understand the impact of post-genomic technologies, 20 collections of post-genomic data were analyzed relative to a set of 243 previously uncharacterized genes. The results indicate that post-genomic technologies are providing rich new information for nearly all yeast genes, but data from these experiments is scattered across many Web sites and the results from these experiments are poorly integrated with other forms of yeast knowledge. Goals for the next generation of databases are set forth which could lead to better access to yeast knowledge for yeast researchers and the entire scientific community. Electronic Publication     

Transgenic Plant Monitor (TPM): a relational database for the management and analysis of information in plant tissue culture and transformation laboratories

Increased public concern and strict statutory regulations relating tothe generation and exploitation of genetically modified organisms, make itimperative to track accurately individual plants through DNA transformationprogrammes. The ability to rapidly retrieve information associated withspecifictransgenic events and to provide accurate reports on demand is an increasinglyimportant feature for public research laboratories. Transgenic Plant Monitor(TPM) has been developed as a database structured to allow efficient recording,monitoring and analysis of the extensive and complex data generated in planttissue culture and transformation experiments. TPM is built upon the widelyavailable Microsoft Access database engine and can be readily adoptedand/or adapted by other users. The key features and the utility of TPM as aresearch tool are discussed in this article.     

Pharmacophore-based molecular docking to account for ligand flexibility

Rapid computational mining of large 3D molecular databases is central to generating new drug leads. Accurate virtual screening of large 3D molecular databases requires consideration of the conformational flexibility of the ligand molecules. Ligand flexibility can be included without prohibitively increasing the search time by docking ensembles of precomputed conformers from a conformationally expanded database. A pharmacophore-based docking method whereby conformers of the same or different molecules are overlaid by their largest 3D pharmacophore and simultaneously docked by partial matches to that pharmacophore is presented. The method is implemented in DOCK 4.0.     

YPED:An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database(YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a singlelaboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry(LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring(MRM)/selective reaction monitoring(SRM) assay development. We have linked YPED's database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.     

tsORFdb: Theoretical Small Open Reading Frames (ORFs) database and massProphet: Peptide Mass Fingerprinting (PMF) tool for unknown small functional ORFs

Peptide mass fingerprinting (PMF) has become one of the most widely used methods for rapid identification of proteins in proteomics research. Many peaks, however, remain unassigned after PMF analysis, partly because of post-translational modification and the limited scope of protein sequences. Almost all PMF tools employ only known or predicted protein sequences and do not include open reading frames (ORFs) in the genome, which eliminates the chance of finding novel functional peptides. Unlike most tools that search protein sequences from known coding sequences, the tool we developed uses a database for theoretical small ORFs (tsORFs) and a PMF application using a tsORFs database (tsORFdb). The tsORFdb is a database for ORFeome that encompasses all potential tsORFs derived from whole genome sequences as well as the predicted ones. The massProphet system tries to extend the search scope to include the ORFeome using the tsORFdb. The tsORFdb and massProphet should be useful for proteomics research to give information about unknown small ORFs as well as predicted and registered proteins.     

Using reaction mechanism to measure enzyme similarity

The concept of reaction similarity has been well studied in terms of the overall transformation associated with a reaction, but not in terms of mechanism. We present the first method to give a quantitative measure of the similarity of reactions based upon their explicit mechanisms. Two approaches are presented to measure the similarity between individual steps of mechanisms: a fingerprint-based approach that incorporates relevant information on each mechanistic step; and an approach based only on bond formation, cleavage and changes in order. The overall similarity for two reaction mechanisms is then calculated using the Needleman-Wunsch alignment algorithm. An analysis of MACiE, a database of enzyme mechanisms, using our measure of similarity identifies some examples of convergent evolution of chemical mechanisms. In many cases, mechanism similarity is not reflected by similarity according to the EC system of enzyme classification. In particular, little mechanistic information is conveyed by the class level of the EC system.     

东亚小花蝽成虫对西花蓟马若虫的捕食功能反应与搜寻效应

为探明东亚小花蝽对西花蓟马的控制效能,开展了东亚小花蝽成虫对西花蓟马若虫的捕食功能反应与搜寻效应研究。结果表明,在供试温度下,该捕食功能反应符合HollingⅡ型方程。在相同温度下,东亚小花蝽成虫的捕食量随着猎物密度的增加而增大,搜寻效应随着猎物密度的增加而降低。在18℃～26℃内,随着温度的升高,东亚小花蝽成虫对西花蓟马若虫的捕食量增加,而在26℃～34℃则有相反的趋势。在相同猎物密度条件下,随着东亚小花蝽成虫密度的增大,其平均捕食量逐渐减少,捕食作用率也相应地降低,东亚小花蝽成虫之间存在分摊竞争。     

Long‐term datasets: From descriptive to predictive data using ecoinformatics

This Special Feature includes contributions on data‐processing of large ecological datasets under the heading ecoinformatics. Herewith the latter term is now al so established in the Journal of Vegetation Science. Ecoinfomatics is introduced as a rapid growing field within community ecology which is generating exciting new developments in ecology and in particular vegetation ecology. In our field, ecoinformatics deals with the understanding of patterns of species distributions at local and regional scales, and on the assemblages of species in relation to their properties, the local environment and their distribution in the region. Community ecology using ecoinformatics is related to bioinformatics, community ecology, biogeography and macroecology. We make clear how ecoinformatics in vegetation science and particularly the IAVS Working Group on Ecoinformatics has developed from the work of the old Working Group for Data Processing which was active during the 1970s and 1980s. Recent developments, including the creation of TURBOVEG and Syn Bio Sys in Europa and VEGBANK in the USA, form a direct link with these pioneer activities, both scientifically and personally. The contributions collected in this Special Feature present examples of seco‐infeveral types of the use of databases and the application of programmes and models. The main types are the study of long‐term vegetation dynamics in different cases of primary and secondary succession and the understanding of successional developments in terms of species traits. Among the future developments of great significance we mention the use of a variety of different large datasets for the study of the distribution and ecology and conservation of rare and threatened species.     

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence,full structure,and active site microenvironment similarity

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods.