A shallow neural network was used to embed lipid structures in a 2- or 3-dimensional space with the goal that structurally similar species have similar vectors. Tests on complete lipid databanks show that the method automatically produces distributions which follow conventional lipid classifications. The embedding is accompanied by the web-based software, Lipidome Projector. This displays user lipidomes as 2D or 3D scatterplots for quick exploratory analysis, quantitative comparison and interpretation at a structural level. Examples of published data sets were used for a qualitative comparison with literature interpretation.

A lipidome is the set of lipids in a given organism, cell or cell compartment and this set reflects the organism's synthetic pathways and interactions with its environment. Recently, lipidomes of biological model organisms and cell lines were published and the number of functional studies of lipids is increasing. In this study we propose a homology metric that can quantify systematic differences in the composition of a lipidome. Algorithms were developed to 1. consistently convert lipids structure into SMILES, 2. determine structural similarity between molecular species and 3. describe a lipidome in a chemical space model. We tested lipid structure conversion and structure similarity metrics, in detail, using sets of isomeric ceramide molecules and chemically related phosphatidylinositols. Template-based SMILES showed the best properties for representing lipid-specific structural diversity. We also show that sequence analysis algorithms are best suited to calculate distances between such template-based SMILES and we adjudged the Levenshtein distance as best choice for quantifying structural changes. When all lipid molecules of the LIPIDMAPS structure database were mapped in chemical space, they automatically formed clusters corresponding to conventional chemical families. Accordingly, we mapped a pair of lipidomes into the same chemical space and determined the degree of overlap by calculating the Hausdorff distance. We named this metric the 'Lipidome jUXtaposition (LUX) score'. First, we tested this approach for estimating the lipidome similarity on four yeast strains with known genetic alteration in fatty acid synthesis. We show that the LUX score reflects the genetic relationship and growth temperature better than conventional methods although the score is based solely on lipid structures. Next, we applied this metric to high-throughput data of larval tissue lipidomes of Drosophila. This showed that the LUX score is sufficient to cluster tissues and determine the impact of nutritional changes in an unbiased manner, despite the limited information on the underlying structural diversity of each lipidome. This study is the first effort to define a lipidome homology metric based on structures that will enrich functional association of lipids in a similar manner to measures used in genetics. Finally, we discuss the significance of the LUX score to perform comparative lipidome studies across species borders.

SUMMARY: There are many programs that can read the secondary structure of an RNA molecule and draw a diagram, but hardly any that can cope with 10(3) bases. RNAfdl is slow but capable of producing intersection-free diagrams for ribosome-sized structures, has a graphical user interface for adjustments and produces output in common formats. AVAILABILITY AND IMPLEMENTATION: Source code is available under the GNU General Public License v3.0 at http://sourceforge.net/projects/rnafdl for Linux and similar systems or Windows using MinGW. RNAfdl is implemented in C, uses the Cairo 2D graphics library and offers both command line and graphical user interfaces. CONTACT: [email protected]

MOTIVATION: To recognize remote relationships between RNA molecules, one must be able to align structures without regard to sequence similarity. We have implemented a method, which is swift [O(n(2))], sensitive and tolerant of large gaps and insertions. Molecules are broken into overlapping fragments, which are characterized by their memberships in a probabilistic classification based on local geometry and H-bonding descriptors. This leads to a probabilistic similarity measure that is used in a conventional dynamic programming method. RESULTS: Examples are given of database searching, the detection of structural similarities, which would not be found using sequence based methods, and comparisons with a previously published approach. AVAILABILITY AND IMPLEMENTATION: Source code (C and perl) and binaries for linux are freely available at www.zbh.uni-hamburg.de/fries.

From October 5–7, 2005, Hamburg hosted the German Conference on Bioinformatics (GCB 05) and this journal section contains a selection of the material which eventually became full-length papers. The conference (GCB) has been run every year since 1994, moving between host institutions and attracting contributions from around Europe. This gathering had 235 registrants and 126 posters. From 73 extended abstracts and a first round of review and revision, the Program Committee chose 19 oral presentations. The seven papers in this issue were accepted after going through another round of refereeing in the journal's normal review procedure. The selection of papers for publication was based purely on quality and no attempt was made to survey the interests of the meeting, which was more diverse. This selection certainly does not reflect the amount of activity in the analysis of microarray data or the curation of specialized nucleotide and protein datasets. It also does not convey the breadth of topics amongst the plenary speakers. These ranged from molecular dynamics simulations (Case, USA) to evolutionary landscapes (Schuster, Austria). The plenary lectures also included some very topical themes such as the use of sequence analysis in detecting bio-attacks (Slezak, USA) and the story of the SARS virus from the point of view of its genetics and epidemiology (Guo-Ping Zhao, China/USA) and structural biology (Hilgenfeld, Germany). The running of the meeting relied on a bevy of helpers who may be unnamed, but not unappreciated. Scientifically, the process relied on the expertise of the Program Committee and the additional reviewers they drafted. Finally, these papers also reflect the suggestions of a large number of anonymous referees. Thomas Dandekar Roland Eils Robert Giegerich Oliver Kohlbacher Stefan Kurtz Hans-Peter Lenhof Hans-Werner Mewes Matthias Rarey Knut Reinert Dietmar Schomburg Jeremy Smith Peter Stadler Andrew Torda Martin Vingron Ute Willhöft Ralf Zimmer Mohamed I. Abouelhoda Jannis Apostolakis Rolf Backofen Michael Beckstette Fabian Birzele Torsten Blum Nicoleta Bondar Bogdan Costescu Zoe Cournia Pierre Dönnes Qunfeng Dong Katrin Fundel Jan Erik Gewehr Niels Grabe Volker Heun Andreas Hildebrandt Matthias Hoechsmann Annette Höglund Petra Imhof Andreas Kerzmann Robert Küffner Jan Küntzer Vandana Kurkal Alessandro Macri Lars Meinhold Kei Moritsugu Dirk Neumann Frank Noe Heike Pospisil Jörg Rahnenführer Durba Sengupta Florian Sohler Marc Sturm Sebastian Will

MOTIVATION: We propose a general method for deriving amino acid substitution matrices from low resolution force fields. Unlike current popular methods, the approach does not rely on evolutionary arguments or alignment of sequences or structures. Instead, residues are computationally mutated and their contribution to the total energy/score is collected. The average of these values over each position within a set of proteins results in a substitution matrix. RESULTS: Example substitution matrices have been calculated from force fields based on different philosophies and their performance compared with conventional substitution matrices. Although this can produce useful substitution matrices, the methodology highlights the virtues, deficiencies and biases of the source force fields. It also allows a rather direct comparison of sequence alignment methods with the score functions underlying protein sequence to structure threading. AVAILABILITY: Example substitution matrices are available from http://www.rsc.anu.edu.au/~zsuzsa/suppl/matrices.html. SUPPLEMENTARY INFORMATION: The list of proteins used for data collection and the optimized parameters for the alignment are given as supplementary material at http://www.rsc.anu.edu.au/~zsuzsa/suppl/matrices.html.

Abstract Summary: Sausage is a protein sequence threading program, but with remarkable run-time flexibility. Using different scripts, it can calculate protein sequence-structure alignments, search structure libraries, swap force fields, create models from alignments, convert file formats and analyse results. There are several different force fields which might be classed as knowledge-based, although they do not rely on Boltzmann statistics. Different force fields are used for alignment calculations and subsequent ranking of calculated models. Availability: Freely available to academics at ftp://ftp.rsc.anu.edu.au/pub/torda/sausage/README Contact: [email protected]