UNLABELLED: The Galaxy platform has developed into a fully featured collaborative workbench, with goals of inherently capturing provenance to enable reproducible data analysis, and of making it straightforward to run one's own server. However, many Galaxy platform tools rely on the presence of reference data, such as alignment indexes, to function efficiently. Until now, the building of this cache of data for Galaxy has been an error-prone manual process lacking reproducibility and provenance. The Galaxy Data Manager framework is an enhancement that changes the management of Galaxy's built-in data cache from a manual procedure to an automated graphical user interface (GUI) driven process, which contains the same openness, reproducibility and provenance that is afforded to Galaxy's analysis tools. Data Manager tools allow the Galaxy administrator to download, create and install additional datasets for any type of reference data in real time. AVAILABILITY AND IMPLEMENTATION: The Galaxy Data Manager framework is implemented in Python and has been integrated as part of the core Galaxy platform. Individual Data Manager tools can be defined locally or installed from a ToolShed, allowing the Galaxy community to define additional Data Manager tools as needed, with full versioning and dependency support.

MOTIVATION: Cancer researchers seeking immunotherapy targets in cancer cells need tools to locate highly expressed proteins unique to cancer cells. Missense mutation and frameshift location reporter (MMuFLR), a Galaxy-based workflow, analyzes next-generation sequencing paired read RNA-seq output to reliably identify small frameshift mutations and missense mutations in highly expressed protein-coding genes. MMuFLR ignores known SNPs, low quality reads and poly-A/T sequences. For each frameshift and missense mutation identified, MMuFLR provides the location and sequence of the amino acid substitutions in the novel protein candidates for direct input into epitope evaluation tools. AVAILABILITY: http://toolshed.g2.bx.psu.edu/ CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Aura satellite (launch July, 2004) contains four instruments (http://aura.usfc. nasa.gov/) for studying the chemistry and dynamics of the atmosphere. The instruments include the High-Resolution Dynamics Limb Sounder (HIRDLS), Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Tropospheric Emission Spectrometer (TES). This presentation discusses the Aura standard data products. Tools for accessing, reading, visualization and analysis of these data are also presented

Abstract Summary: TableView is a generalized scientific visualization program for exploration of various biological data, including EST, SAGE, microarray and annotation data. Written in Java, TableView is portable, is easily used together with other software including DBMSs and is versatile enough to be applied to any tabular data Availability: TableView is freely available at: http://ccgb.umn.edu/software/java/apps/TableView/ Contact: [email protected] * To whom correspondence should be addressed. † Present address: Macalester College, Mathematics and Computer Science, St Paul, MN 55105, USA.

Abstract Motivation: Protein sequence and family data is accumulating at such a rapid rate that state-of-the-art databases and interface tools are required to aid curators with their classifications. We have designed such a system, MetaFam, to facilitate the comparison and integration of public protein sequence and family data. This paper presents the global schema, integration issues, and query capabilities of MetaFam. Results: MetaFam is an integrated data warehouse of information about protein families and their sequences. This data has been collected into a consistent global schema, and stored in an Oracle relational database. The warehouse implementation allows for quick removal of outdated data sets. In addition to the relational implementation of the primary schema, we have developed several derived tables that enable efficient access from data visualization and exploration tools. Through a series of straightforward SQL queries, we demonstrate the usefulness of this data warehouse for comparing protein family classifications and for functional assignment of new sequences. Availability: Access to the MetaFam database is provided through a Java applet called MetaFamView, which can be run from the MetaFam web site at http://www.metafam.ahc.umn.edu/. Access to the relational data via named Oracle accounts can be arranged with the authors. Arrangements can also be made to obtain the data in Oracle ‘export dump’ format. Contact: [email protected] Supplementary information: The complete relational schema, integration scripts, and analysis queries are available from the authors. * To whom correspondence should be addressed.

Abstract Motivation: Protein sequence classification is becoming an increasingly important means of organizing the voluminous data produced by large-scale genome sequencing projects. At present, there are several independent classification methods. To aid the general classification effort, we have created a unified protein family resource, MetaFam. MetaFam is a protein family classification built upon 10 publicly-accessible protein family databases (Blocks\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \(+\) \end{document}, DOMO, Pfam, PIR-ALN, PRINTS, PROSITE, ProDom, PROTOMAP, SBASE, and SYSTERS). MetaFam’s family ‘supersets’, as we call them, are created automatically using set-theory to compare families among the databases. Families of one database are matched to those in another when the intersection of their members exceeds all other possible family pairings between the two databases. Pairwise family matches are drawn together transitively to create a new list of protein family supersets. Results: MetaFam family supersets have several useful features: (1) each superset contains more members than the families from which it is composed, because each of the component family databases only works with a subset of our full non-redundant set of proteins; (2) conflicting assignments can be pinpointed quickly, since our analysis identifies individual members that are in conflict with the majority consensus; (3) family descriptions that are absent from automated databases can frequently be assigned; (4) statistics have been computed comparing domain boundaries, family size distributions, and overall quality of MetaFam supersets; (5) the supersets have been loaded into a relational database to allow for complex queries and visualization of the connections among families in a superset and the consensus of individual domain members; and (6) the quality of individual supersets has been assessed using numerous quantitative measures such as family consistency, connectedness, and size. We anticipate this new resource will be particularly useful to genomic database curators. Availability: Free access to the MetaFam web server is provided to all users at http://metafam.ahc.umn.edu/. Contact: [email protected] Supplementary information: Detailed distribution plots on MetaFam 2.0 supersets and its constituent family databases (e.g. superset/family sizes, domain boundary comparisons) are shown at http://metafam.ahc.umn.edu/mf2.0/stats.html. Statistics on the current release of MetaFam can be found at http://metafam.ahc.umn.edu/current_release/stats.html. * To whom correspondence should be addressed.

Abstract Summary: We present PANAL, an integrated resource for protein sequence analysis. The tool allows the user to simultaneously search a protein sequence for motifs from several databases, and to view the result as an intuitive graphical summary. Availability: PANAL can be freely accessed on our World Wide Web server: http://mgd.ahc.umn.edu/panal/. Source code is available upon request to non-profit organizations. Contact: [email protected] * Authors to whom correspondence should be addressed.