An integrated Arabidopsis annotation database for Affymetrix Genechip data analysis, and tools for regulatory motif searches

Genome-scale sequencing projects have provided the essential information required for the construction of entire genome chips or microarrays for RNA expression studies. The Arabidopsis and rice genomes have been sequenced and whole-genome oligonucleotide arrays are being manufactured. These should soon become available to researchers. Expression studies using genomic-scale expression arrays are providing us with a vast quantity of information at a rapid pace. The rate-limiting step in this type of experiments is not the data generation step but rather the data analysis component of experiments. We report improvements that should facilitate the analysis of Affymetrix Genechip expression data.     

RiceGAAS: an automated annotation system and database for rice genome sequence

An extensive effort of the International Rice Genome Sequencing Project (IRGSP) has resulted in rapid accumulation of genome sequence, and >137 Mb has already been made available to the public domain as of August 2001. This requires a high-throughput annotation scheme to extract biologically useful and timely information from the sequence data on a regular basis. A new automated annotation system and database called Rice Genome Automated Annotation System (RiceGAAS) has been developed to execute a reliable and up-to-date analysis of the genome sequence as well as to store and retrieve the results of annotation. The system has the following functional features: (i) collection of rice genome sequences from GenBank; (ii) execution of gene prediction and homology search programs; (iii) integration of results from various analyses and automatic interpretation of coding regions; (iv) re-execution of analysis, integration and automatic interpretation with the latest entries in reference databases; (v) integrated visualization of the stored data using web-based graphical view. RiceGAAS also has a data submission mechanism that allows public users to perform fully automated annotation of their own sequences. The system can be accessed at http://RiceGAAS.dna.affrc.go.jp/.     

EST2uni: an open,parallel tool for automated EST analysis and database creation,with a data mining web interface and microarray expression data integration

Background  

Expressed sequence tag (EST) collections are composed of a high number of single-pass, redundant, partial sequences, which need to be processed, clustered, and annotated to remove low-quality and vector regions, eliminate redundancy and sequencing errors, and provide biologically relevant information. In order to provide a suitable way of performing the different steps in the analysis of the ESTs, flexible computation pipelines adapted to the local needs of specific EST projects have to be developed. Furthermore, EST collections must be stored in highly structured relational databases available to researchers through user-friendly interfaces which allow efficient and complex data mining, thus offering maximum capabilities for their full exploitation.     

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.     

A web-based platform for rice microarray annotation and data analysis

Rice (Oryza sativa) feeds over half of the global population. A web-based integrated platform for rice microarray annotation and data analysis in various biological contexts is presented, which provides a convenient query for comprehensive annotation compared with similar databases. Coupled with existing rice microarray data, it provides online analysis methods from the perspective of bioinformatics. This comprehensive bioinformatics analysis platform is composed of five modules, including data retrieval, microarray annotation, sequence analysis, results visualization and data analysis. The BioChip module facilitates the retrieval of microarray data information via identifiers of “Probe Set ID”, “Locus ID” and “Analysis Name”. The BioAnno module is used to annotate the gene or probe set based on the gene function, the domain information, the KEGG biochemical and regulatory pathways and the potential microRNA which regulates the genes. The BioSeq module lists all of the related sequence information by a microarray probe set. The BioView module provides various visual results for the microarray data. The BioAnaly module is used to analyze the rice microarray’s data set.     

ASAP: automated sequence annotation pipeline for web-based updating of sequence information with a local dynamic database

The automated sequence annotation pipeline (ASAP) is designed to ease routine investigation of new functional annotations on unknown sequences, such as expressed sequence tags (ESTs), through querying of web-accessible resources and maintenance of a local database. The system allows easy use of the output from one search as the input for a new search, as well as the filtering of results. The database is used to store formats and parameters and information for parsing data from web sites. The database permits easy updating of format information should a site modify the format of a query or of a returned web page.     

Knowledge-based voting algorithm for automated protein functional annotation

Automated annotation of high-throughput genome sequences is one of the earliest steps toward a comprehensive understanding of the dynamic behavior of living organisms. However, the step is often error-prone because of its underlying algorithms, which rely mainly on a simple similarity analysis, and lack of guidance from biological rules. We present herein a knowledge-based protein annotation algorithm. Our objectives are to reduce errors and to improve annotation confidences. This algorithm consists of two major components: a knowledge system, called "RuleMiner," and a voting procedure. The knowledge system, which includes biological rules and functional profiles for each function, provides a platform for seamless integration of multiple sequence analysis tools and guidance for function annotation. The voting procedure, which relies on the knowledge system, is designed to make (possibly) unbiased judgments in functional assignments among complicated, sometimes conflicting, information. We have applied this algorithm to 10 prokaryotic bacterial genomes and observed a significant improvement in annotation confidences. We also discuss the current limitations of the algorithm and the potential for future improvement.     

Proteomic analysis of log to stationary growth phase Lactobacillus plantarum cells and a 2-DE database

Lactobacillus plantarum is part of the natural microbiota of many food fermentations as well as the human gastro-intestinal tract. The cytosolic fraction of the proteome of L. plantarum WCFS1, whose genome has been sequenced, was studied. 2-DE was used to investigate the proteins from the cytosolic fraction isolated from mid- and late-log, early- and late-stationary phase cells to generate reference maps of different growth conditions offering more knowledge of the metabolic behavior of this bacterium. From this fraction, a total of 200 protein spots were identified by MALDI-MS and a proteome production map was constructed to facilitate further studies such as detection of suitable biomarkers for specific growth conditions. More than half (57%) of the identified proteins were predicted to be involved in metabolic pathways of the bacterium. The protein profile changed during the growth of the bacteria such that 29% of the identified proteins involved in anabolic pathways were at least twofold up-regulated throughout the mid- and late-exponential and early-stationary phases. In the late-stationary phase, six proteins involved in stress or with a potential role for survival during starvation were up-regulated significantly.     

RAPYD--rapid annotation platform for yeast data

Lower eukaryotes of the kingdom Fungi include a variety of biotechnologically important yeast species that are in the focus of genome research for more than a decade. Due to the rapid progress in ultra-fast sequencing technologies, the amount of available yeast genome data increases steadily. Thus, an efficient bioinformatics platform is required that covers genome assembly, eukaryotic gene prediction, genome annotation, comparative yeast genomics, and metabolic pathway reconstruction. Here, we present a bioinformatics platform for yeast genomics named RAPYD addressing the key requirements of extensive yeast sequence data analysis. The first step is a comprehensive regional and functional annotation of a yeast genome. A region prediction pipeline was implemented to obtain reliable and high-quality predictions of coding sequences and further genome features. Functions of coding sequences are automatically determined using a configurable prediction pipeline. Based on the resulting functional annotations, a metabolic pathway reconstruction module can be utilized to rapidly generate an overview of organism-specific features and metabolic blueprints. In a final analysis step shared and divergent features of closely related yeast strains can be explored using the comparative genomics module. An in-depth application example of the yeast Meyerozyma guilliermondii illustrates the functionality of RAPYD. A user-friendly web interface is available at https://rapyd.cebitec.uni-bielefeld.de.     

Optimizing transformations for automated,high throughput analysis of flow cytometry data

Background  

In a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.     

TRAP: automated classification, quantification and annotation of tandemly repeated sequences

TRAP, the Tandem Repeats Analysis Program, is a Perl program that provides a unified set of analyses for the selection, classification, quantification and automated annotation of tandemly repeated sequences. TRAP uses the results of the Tandem Repeats Finder program to perform a global analysis of the satellite content of DNA sequences, permitting researchers to easily assess the tandem repeat content for both individual sequences and whole genomes. The results can be generated in convenient formats such as HTML and comma-separated values. TRAP can also be used to automatically generate annotation data in the format of feature table and GFF files.     

ESTAP--an automated system for the analysis of EST data

The EST Analysis Pipeline (ESTAP) is a set of analytical procedures that automatically verify, cleanse, store and analyze ESTs generated on high-throughput platforms. It uses a relational database to store sequence data and analysis results, which facilitates both the search for specific information and statistical analysis. ESTAP provides for easy viewing of the original and cleansed data, as well as the analysis results via a Web browser. It also allows the data owner to submit selected sequences to dbEST in a semi-automated fashion.     

PhyloGena--a user-friendly system for automated phylogenetic annotation of unknown sequences

MOTIVATION: Phylogenomic approaches towards functional and evolutionary annotation of unknown sequences have been suggested to be superior to those based only on pairwise local alignments. User-friendly software tools making the advantages of phylogenetic annotation available for the ever widening range of bioinformatically uninitiated biologists involved in genome/EST annotation projects are, however, not available. We were particularly confronted with this issue in the annotation of sequences from different groups of complex algae originating from secondary endosymbioses, where the identification of the phylogenetic origin of genes is often more problematic than in taxa well represented in the databases (e.g. animals, plants or fungi). RESULTS: We present a flexible pipeline with a user-friendly, interactive graphical user interface running on desktop computers that automatically performs a basic local alignment search tool (BLAST) search of query sequences, selects a representative subset of them, then creates a multiple alignment from the selected sequences, and finally computes a phylogenetic tree. The pipeline, named PhyloGena, uses public domain software for all standard bioinformatics tasks (similarity search, multiple alignment, and phylogenetic reconstruction). As the major technological innovation, selection of a meaningful subset of BLAST hits was implemented using logic programming, mimicing the selection procedure (BLAST tables, multiple alignments and phylogenetic trees) are displayed graphically, allowing the user to interact with the pipeline and deduce the function and phylogenetic origin of the query. PhyloGena thus makes phylogenomic annotation available also for those biologists without access to large computing facilities and with little informatics background. Although phylogenetic annotation is particularly useful when working with composite genomes (e.g. from complex algae), PhyloGena can be helpful in expressed sequence tag and genome annotation also in other organisms. AVAILABILITY: PhyloGena (executables for LINUX and Windows 2000/XP as well as source code) is available by anonymous ftp from http://www.awi.de/en/phylogena.     

Evolutionarily conserved substrate substructures for automated annotation of enzyme superfamilies

The evolution of enzymes affects how well a species can adapt to new environmental conditions. During enzyme evolution, certain aspects of molecular function are conserved while other aspects can vary. Aspects of function that are more difficult to change or that need to be reused in multiple contexts are often conserved, while those that vary may indicate functions that are more easily changed or that are no longer required. In analogy to the study of conservation patterns in enzyme sequences and structures, we have examined the patterns of conservation and variation in enzyme function by analyzing graph isomorphisms among enzyme substrates of a large number of enzyme superfamilies. This systematic analysis of substrate substructures establishes the conservation patterns that typify individual superfamilies. Specifically, we determined the chemical substructures that are conserved among all known substrates of a superfamily and the substructures that are reacting in these substrates and then examined the relationship between the two. Across the 42 superfamilies that were analyzed, substantial variation was found in how much of the conserved substructure is reacting, suggesting that superfamilies may not be easily grouped into discrete and separable categories. Instead, our results suggest that many superfamilies may need to be treated individually for analyses of evolution, function prediction, and guiding enzyme engineering strategies. Annotating superfamilies with these conserved and reacting substructure patterns provides information that is orthogonal to information provided by studies of conservation in superfamily sequences and structures, thereby improving the precision with which we can predict the functions of enzymes of unknown function and direct studies in enzyme engineering. Because the method is automated, it is suitable for large-scale characterization and comparison of fundamental functional capabilities of both characterized and uncharacterized enzyme superfamilies.     

CHIKVPRO - a protein sequence annotation database for chikungunya virus

In the recent past, there has been a resurgence of interest in Chikungunya virus (CHIKV) attributed to massive outbreaks of Chikungunya fever in the South-East Asia Region. This has reflected in substantial increase in submission of CHIKV genome sequences to NCBI (National Center for Biotechnology Information) database. Hereby we submit a database "CHIKVPRO" containing structural and functional annotation of Chikungunya virus proteins (25 strains) submitted in the NCBI repository. The CHIKV genome encodes for 9 proteins:4 non-structural and 5 structural. The CHIKVPRO database aims to provide the virology community with a single accession authoritative resource for CHIKV proteome- with reference to physiochemical and molecular properties, proteolytic cleavage sites, hydrophobicity, transmembrane prediction, and classification into functional families using SVMProt and other Expasy tools. AVAILABILITY: The database is freely available at http://www.chikvpro.info/     

The comparative analysis of statistics,based on the likelihood ratio criterion,in the automated annotation problem

Background  

This paper discusses the problem of automated annotation. It is a continuation of the previous work on the A⁴-algorithm (Adaptive algorithm of automated annotation) developed by Leontovich and others.     

An automated system designed for large scale NMR data deposition and annotation: application to over 600 assigned chemical shift data entries to the BioMagResBank from the Riken Structural Genomics/Proteomics Initiative internal database

Biomolecular NMR chemical shift data are key information for the functional analysis of biomolecules and the development of new techniques for NMR studies utilizing chemical shift statistical information. Structural genomics projects are major contributors to the accumulation of protein chemical shift information. The management of the large quantities of NMR data generated by each project in a local database and the transfer of the data to the public databases are still formidable tasks because of the complicated nature of NMR data. Here we report an automated and efficient system developed for the deposition and annotation of a large number of data sets including (1)H, (13)C and (15)N resonance assignments used for the structure determination of proteins. We have demonstrated the feasibility of our system by applying it to over 600 entries from the internal database generated by the RIKEN Structural Genomics/Proteomics Initiative (RSGI) to the public database, BioMagResBank (BMRB). We have assessed the quality of the deposited chemical shifts by comparing them with those predicted from the PDB coordinate entry for the corresponding protein. The same comparison for other matched BMRB/PDB entries deposited from 2001-2011 has been carried out and the results suggest that the RSGI entries greatly improved the quality of the BMRB database. Since the entries include chemical shifts acquired under strikingly similar experimental conditions, these NMR data can be expected to be a promising resource to improve current technologies as well as to develop new NMR methods for protein studies.