A biophysical approach to large-scale protein-DNA binding data

About this book * Cutting-edge genome analysis methods from leading bioinformaticians An accurate description of current scientific developments in the field of bioinformatics and computational implementation is presented by research of the BioSapiens Network of Excellence. Bioinformatics is essential for annotating the structure and function of genes, proteins and the analysis of complete genomes and to molecular biology and biochemistry. Included is an overview of bioinformatics, the full spectrum of genome annotation approaches including; genome analysis and gene prediction, gene regulation analysis and expression, genome variation and QTL analysis, large scale protein annotation of function and structure, annotation and prediction of protein interactions, and the organization and annotation of molecular networks and biochemical pathways. Also covered is a technical framework to organize and represent genome data using the DAS technology and work in the annotation of two large genomic sets: HIV/HCV viral genomes and splicing alternatives potentially encoded in 1% of the human genome

Applying negative rule mining to improve genome annotation

<p>Abstract</p> <p>Background</p> <p>Unsupervised annotation of proteins by software pipelines suffers from very high error rates. Spurious functional assignments are usually caused by unwarranted homology-based transfer of information from existing database entries to the new target sequences. We have previously demonstrated that data mining in large sequence annotation databanks can help identify annotation items that are strongly associated with each other, and that exceptions from strong positive association rules often point to potential annotation errors. Here we investigate the applicability of negative association rule mining to revealing erroneously assigned annotation items.</p> <p>Results</p> <p>Almost all exceptions from strong negative association rules are connected to at least one wrong attribute in the feature combination making up the rule. The fraction of annotation features flagged by this approach as suspicious is strongly enriched in errors and constitutes about 0.6% of the whole body of the similarity-transferred annotation in the PEDANT genome database. Positive rule mining does not identify two thirds of these errors. The approach based on exceptions from negative rules is much more specific than positive rule mining, but its coverage is significantly lower.</p> <p>Conclusion</p> <p>Mining of both negative and positive association rules is a potent tool for finding significant trends in protein annotation and flagging doubtful features for further inspection.</p

PEDANT genome database: 10 years online

The PEDANT genome database provides exhaustive annotation of 468 genomes by a broad set of bioinformatics algorithms. We describe recent developments of the PEDANT Web server. The all-new Graphical User Interface (GUI) implemented in Java™ allows for more efficient navigation of the genome data, extended search capabilities, user customization and export facilities. The DNA and Protein viewers have been made highly dynamic and customizable. We also provide Web Services to access the entire body of PEDANT data programmatically. Finally, we report on the application of association rule mining for automatic detection of potential annotation errors. PEDANT is freely accessible to academic users at

MIPS: analysis and annotation of genome information in 2007

The Munich Information Center for Protein Sequences (MIPS-GSF, Neuherberg, Germany) combines automatic processing of large amounts of sequences with manual annotation of selected model genomes. Due to the massive growth of the available data, the depth of annotation varies widely between independent databases. Also, the criteria for the transfer of information from known to orthologous sequences are diverse. To cope with the task of global in-depth genome annotation has become unfeasible. Therefore, our efforts are dedicated to three levels of annotation: (i) the curation of selected genomes, in particular from fungal and plant taxa (e.g. CYGD, MNCDB, MatDB), (ii) the comprehensive, consistent, automatic annotation employing exhaustive methods for the computation of sequence similarities and sequence-related attributes as well as the classification of individual sequences (SIMAP, PEDANT and FunCat) and (iii) the compilation of manually curated databases for protein interactions based on scrutinized information from the literature to serve as an accepted set of reliable annotated interaction data (MPACT, MPPI, CORUM). All databases and tools described as well as the detailed descriptions of our projects can be accessed through the MIPS web server (http://mips.gsf.de)

FGDB: revisiting the genome annotation of the plant pathogen Fusarium graminearum

The MIPS Fusarium graminearum Genome Database (FGDB) was established as a comprehensive genome database on one of the most devastating fungal plant pathogens of wheat, barley and maize. The current version of FGDB v3.1 provides information on the full manually revised gene set based on the Broad Institute assembly FG3 genome sequence. The results of gene prediction tools were integrated with the help of comparative data on related species to result in a set of 13.718 annotated protein coding genes. This rigorous approach involved adding or modifying gene models and represents a coding sequence gold standard for the genus Fusarium. The gene loci improvements results in 2461 genes which either are new or have different structures compared to the Broad Institute assembly 3 gene set. Moreover the database serves as a convenient entry point to explore expression data results and to obtain information on the Affymetrix GeneChip probe sets. The resource is accessible on http://mips.gsf.de/genre/proj/FGDB/

SIMAP: the similarity matrix of proteins

Similarity Matrix of Proteins (SIMAP) () provides a database based on a pre-computed similarity matrix covering the similarity space formed by >4 million amino acid sequences from public databases and completely sequenced genomes. The database is capable of handling very large datasets and is updated incrementally. For sequence similarity searches and pairwise alignments, we implemented a grid-enabled software system, which is based on FASTA heuristics and the Smith–Waterman algorithm. Our ProtInfo system allows querying by protein sequences covered by the SIMAP dataset as well as by fragments of these sequences, highly similar sequences and title words. Each sequence in the database is supplemented with pre-calculated features generated by detailed sequence analyses. By providing WWW interfaces as well as web-services, we offer the SIMAP resource as an efficient and comprehensive tool for sequence similarity searches

A European Pathogenic Microorganism Proteome Database: Construction and Maintenance

A relational database structure based on MS-Access and MySQL to store and manage proteomics data was established. This system may be used to publish two-dimensional electrophoretic proteomics data, and also may be accessed by external users who want to compare their own data with those in the databases. The maintenance of the database is managed centrally. The producers of proteomics data do not need to construct a database themselves. Users can introduce mass spectra into the database, which allows the searching of peptide mass fingerprints against their own protein sequence databases. The first release published in January 2002 contains data from Mycobacterium tuberculosis, Helicobacter pylori, Borrelia garinii, Francisella tularensis, Chlamydia pneumoniae, Mycoplasma pneumoniae, Jurkat T-cells and mouse mammary gland projects (http://www.mpiib-berlin. mpg.de/2D-PAGE/)

PROMPT: a protein mapping and comparison tool

BACKGROUND: Comparison of large protein datasets has become a standard task in bioinformatics. Typically researchers wish to know whether one group of proteins is significantly enriched in certain annotation attributes or sequence properties compared to another group, and whether this enrichment is statistically significant. In order to conduct such comparisons it is often required to integrate molecular sequence data and experimental information from disparate incompatible sources. While many specialized programs exist for comparisons of this kind in individual problem domains, such as expression data analysis, no generic software solution capable of addressing a wide spectrum of routine tasks in comparative proteomics is currently available. RESULTS: PROMPT is a comprehensive bioinformatics software environment which enables the user to compare arbitrary protein sequence sets, revealing statistically significant differences in their annotation features. It allows automatic retrieval and integration of data from a multitude of molecular biological databases as well as from a custom XML format. Similarity-based mapping of sequence IDs makes it possible to link experimental information obtained from different sources despite discrepancies in gene identifiers and minor sequence variation. PROMPT provides a full set of statistical procedures to address the following four use cases: i) comparison of the frequencies of categorical annotations between two sets, ii) enrichment of nominal features in one set with respect to another one, iii) comparison of numeric distributions, and iv) correlation of numeric variables. Analysis results can be visualized in the form of plots and spreadsheets and exported in various formats, including Microsoft Excel. CONCLUSION: PROMPT is a versatile, platform-independent, easily expandable, stand-alone application designed to be a practical workhorse in analysing and mining protein sequences and associated annotation. The availability of the Java Application Programming Interface and scripting capabilities on one hand, and the intuitive Graphical User Interface with context-sensitive help system on the other, make it equally accessible to professional bioinformaticians and biologically-oriented users. PROMPT is freely available for academic users from

sgTarget: a target selection resource for structural genomics

sgTarget () is a web-based resource to aid the selection and prioritization of candidate proteins for structure determination. The system annotates user submitted gene or protein sequences, identifying sequence families with no homologues of known structure, and characterizing each protein according to a range of physicochemical properties that may affect its expression, solubility and likelihood to crystallize. Summaries of these analyses are available for individual sequences, as well as whole datasets. This type of analysis enables structural biologists to iteratively select targets from their genomic sequences of interest and according to their research needs. All sequence datasets submitted to sgTarget are available for users to select and rank using their choice of criteria. sgTarget was developed to support individual laboratories collaborating in structural and functional genomics projects and should be valuable to structural biologists wishing to employ the wealth of available genome sequences in their structural quests

FGDB: a comprehensive fungal genome resource on the plant pathogen Fusarium graminearum

The MIPS Fusarium graminearum Genome Database (FGDB) is a comprehensive genome database on one of the most devastating fungal plant pathogens of wheat and barley. FGDB provides information on two gene sets independently derived by automated annotation of the F.graminearum genome sequence. A complete manually revised gene set will be completed within the near future. The initial results of systematic manual correction of gene calls are already part of the current gene set. The database can be accessed to retrieve information from bioinformatics analyses and functional classifications of the proteins. The data are also organized in the well established MIPS catalogs and novel query techniques are available to search the data. The comprehensive set of gene calls was also used for the design of an Affymetrix GeneChip. The resource is accessible on