SUMMARY: Computational Structural Biology Toolbox (CSB) is a cross-platform Python class library for reading, storing and analyzing biomolecular structures with rich support for statistical analyses. CSB is designed for reusability and extensibility and comes with a clean, well-documented API following good object-oriented engineering practice. AVAILABILITY: Stable release packages are available for download from the Python Package Index (PyPI) as well as from the project's website http://csb.codeplex.com. CONTACTS: [email protected] or [email protected]

Abstract Motivation: Over the last decade, both static and dynamic fragment libraries for protein structure prediction have been introduced. The former are built from clusters in either sequence or structure space and aim to extract a universal structural alphabet. The latter are tailored for a particular query protein sequence and aim to provide local structural templates that need to be assembled in order to build the full-length structure. Results: Here, we introduce HHfrag, a dynamic HMM-based fragment search method built on the profile–profile comparison tool HHpred. We show that HHfrag provides advantages over existing fragment assignment methods in that it: (i) improves the precision of the fragments at the expense of a minor loss in sequence coverage; (ii) detects fragments of variable length (6–21 amino acid residues); (iii) allows for gapped fragments and (iv) does not assign fragments to regions where there is no clear sequence conservation. We illustrate the usefulness of fragments detected by HHfrag on targets from most recent CASP. Availability: A web server for running HHfrag is available at http://toolkit.tuebingen.mpg.de/hhfrag. The source code is available at http://www.eb.tuebingen.mpg.de/departments/1-protein-evolution/michael-habeck/HHfrag.tar.gz Contact: [email protected] Supplementary Information: Supplementary data are available at Bioinformatics online.

SUMMARY: Recently, several methods for analyzing phenotype data have been published, but only few are able to cope with data sets generated in different studies, with different methods, or for different species. We developed an online system in which more than 300 000 phenotypes from a wide variety of sources and screening methods can be analyzed together. Clusters of similar phenotypes are visualized as networks of highly similar phenotypes, inducing gene groups useful for functional analysis. This system is part of PhenomicDB, providing the world's largest cross-species phenotype data collection with a tool to mine its wealth of information. AVAILABILITY: Freely available at http://www.phenomicdb.de