This review provides a historical overview of the inception and development of bioinformatics research in the Netherlands. Rooted in theoretical biology by foundational figures such as Paulien Hogeweg (at Utrecht University since the 1970s), the developments leading to organizational structures supporting a relatively large Dutch bioinformatics community will be reviewed. We will show that the most valuable resource that we have built over these years is the close-knit national expert community that is well engaged in basic and translational life science research programmes. The Dutch bioinformatics community is accustomed to facing the ever-changing landscape of data challenges and working towards solutions together. In addition, this community is the stable factor on the road towards sustainability, especially in times where existing funding models are challenged and change rapidly.

Briefings in Bioinformatics, 2017. https://doi.org/10.1093/bib/bbx087 The authors have corrected the acknowledgements section to include Antoine van Kampen. The section has been corrected online and now reads as follows: Huge thanks are due to Gert Vriend, Jacob de Vlieg and Bob Hertzberger for securing funding for NBIC, initiating its activities, and for leading NBIC during its initial years. We are indebted in the same vein to Antoine van Kampen, who chaired and directed NBIC from 2006 to 2010. The authors would also like to acknowledge the Dutch bioinformatics community at large for its commitment and energy to drive the field further.

UNLABELLED: Warp2D is a novel time alignment approach, which uses the overlapping peak volume of the reference and sample peak lists to correct misleading peak shifts. Here, we present an easy-to-use web interface for high-throughput Warp2D batch processing time alignment service using the Dutch Life Science Grid, reducing processing time from days to hours. This service provides the warping function, the sample chromatogram peak list with adjusted retention times and normalized quality scores based on the sum of overlapping peak volume of all peaks. Heat maps before and after time alignment are created from the arithmetic mean of the sum of overlapping peak area rearranged with hierarchical clustering, allowing the quality control of the time alignment procedure. Taverna workflow and command line tool are provided for remote processing of local user data. AVAILABILITY: online data processing service is available at http://www.nbpp.nl/warp2d.html. Taverna workflow is available at myExperiment with title '2D Time Alignment-Webservice and Workflow' at http://www.myexperiment.org/workflows/1283.html. Command line tool is available at http://www.nbpp.nl/Warp2D_commandline.zip. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Statistical methods that compare observed and expected distributions of experimental observables provide powerful tools for the quality control of protein structures. The distribution of backbone dihedral angles (‘Ramachandran plot’) has often been used for such quality control, but without a firm statistical foundation. A new and simple method is presented for judging the quality of a protein structure based on the distribution of backbone dihedral angles. Inputs to the method are 60 torsion angle distributions extracted from protein structures solved at high resolution; one for each combination of residue type and tri-state secondary structure. Output for a protein is a Ramachandran Z-score, expressing the quality of the Ramachandran plot relative to current state-of-the-art structures. The Ramachandran test is available as part of the free WHAT_CHECK program. Information about this program can be obtained on the WWW from http://swift.embl-heidelberg.de/whatcheck/ E-mail: [email protected]

MOTIVATION: The Protein Data Bank currently contains more than 4700 protein coordinate sets. It is often desirable to make a selection from these files based on a criterion like R-factor, experimental method, length of the amino acid sequence, or the number of homologous sequences in SWISSPROT. Doing this using the distributed form of the Protein Data Bank can be a tedious task, because (1) this requires reading one file for every single entry, and (2) not all of the information is present in a consistent computer readable way in all of the entries. RESULTS: The PDBFINDER database provides an easy to interpret file containing summary information about all Protein Data Bank files. Summary information from the DSSP (Definition of Secondary Structure of Proteins) and HSSP (Homology derived Secondary Structure of Proteins) databases is also included. Furthermore, where essential data were missing from the Protein Data Bank file, this information has been retrieved from the original literature. AVAILABILITY: The latest version of the PDBFINDER database can be downloaded by anonymous ftp from swift.embl-heidelberg.de, directory:/pdbfinder. CONTACT: E-mail address [email protected].