Inge Jonassen

ELIXIR, the European life science infrastructure for biological information, is a unique initiative to consolidate Europe's national centres, services, and core bioinformatics resources into a single, coordinated infrastructure. ELIXIR brings together Europe's major life-science data archives and connects these with national bioinformatics infrastructures  - the ELIXIR Nodes. This editorial introduces the ELIXIR channel in F1000Research; the aim of the channel is to collect and present ELIXIR's scientific and operational output, engage with the broad life science community and encourage discussion on proposed infrastructure solutions. Submissions will be assessed by the ELIXIR channel Editorial Board to ensure they are relevant to ELIXIR community, and subjected to F1000Research open peer review process.

... 206 Stanislav Angelov, Sanjeev Khanna, Li Li, and Fernando Pereira Local Search Heuristic for Rigid Protein Docking..... ... 350 Tanya Y. Berger-Wolf Relation of Residues in the Variable Region of 16S rDNA Sequences and Their Relevance to Genus-Specificity ...

this paper is as follows. This introduction is followed in Section 2 by a briefintroduction to some problems in machine learning, and especially to some approaches to learningfrom strings. In Section 3 we discuss the problem of discovering bio-patterns, on the backgroundprovided in Section 2. The major part of the thesis is a portfolio of research papers of the author,with

... 2.13. Immunoelectrophoretic techniques Crossed immunoelectrophoresis and tandem crossed immunoelectrophoresis was carried out as described by AXELSEN et al. (2). 105 09C T ~ O75 o ~ 0.60 z < 045 0 < 0 3 0 015 CM I CMIII car ...

© The Author (s) 2012. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons. org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

An important problem in sequence analysis is to discover patterns matching subsets ofa given set of bio-sequences. When a pattern common to a subset is found, the quality ofthe match should be evaluated. This paper proposes that an evaluation scheme for measuringthe quality of a match between a sequence set and a common pattern should takeinto account both the strength

The amount of publicly shared proteomics data has grown exponentially over the last decade as the solutions for sharing and storing the data have improved. However, the use of the data is often limited by the manner of which it is made available. There are two main approaches: download and inspect the proteomics data locally, or interact with the data via one or more web pages. The first is limited by having to download the data and thus requires local computational skills and resources, while the latter most often is limited in terms of interactivity and the analysis options available. A solution is to develop web-based systems supporting distributed and fully interactive visual analysis of proteomics data. The use of a distributed architecture makes it possible to perform the computational analysis at the server, while the results of the analysis can be displayed via a web browser without the need to download the whole dataset. Here the challenges related to developing such systems for omics data will be discussed. Especially how this allows for multiple connected interactive visual displays of omics dataset in a web-based setting, and the benefits this provide for computational analysis of proteomics data. The approach detailed for better computational analysis of shared proteomics data via a web-based distributed architecture can greatly improve the ease of which shared proteomics data is utilized. Especially the support for multiple connected interactive visual displays of the same omics dataset has the potential of transforming what is now mainly static information into interactive resources, greatly simplifying the re-analysis of shared proteomics data and the extraction of biological knowledge.

Microarrays have emerged as the preferred platform for high throughput gene expression analysis. Cross-hybridization among genes with high sequence similarities can be a source of error reducing the reliability of DNA microarray results. We have developed a tool called XHM (cross hybridization on microarrays) for assessment of the reliability of hybridization signals by detecting potential cross-hybridizations on DNA microarrays. This is done by comparing the sequences of the probes against an extensive database representing the transcriptome of the organism in question. XHM is available online at http://www.bioinfo.no/tools/xhm/. Using XHM with its user-adjustable parameters will enable scientists to check their lists of differentially expressed genes from microarray experiments for potential cross-hybridizations. This provides information that may be useful in the validation of the microarray results.

Methods for extracting useful information from the datasets produced by microarray experiments are at present of much interest. Here we present new methods for finding gene sets that are well suited for distinguishing experiment classes, such as healthy versus diseased tissues. Our methods are based on evaluating genes in pairs and evaluating how well a pair in combination distinguishes two experiment classes. We tested the ability of our pair-based methods to select gene sets that generalize the differences between experiment classes and compared the performance relative to two standard methods. To assess the ability to generalize class differences, we studied how well the gene sets we select are suited for learning a classifier. We show that the gene sets selected by our methods outperform the standard methods, in some cases by a large margin, in terms of cross-validation prediction accuracy of the learned classifier. We show that on two public datasets, accurate diagnoses can be ...

Motivation: Gene expression is dependent on two main types of signals; one involving transcription factors which initiates gene transcription, and another which regulates the translation of a nascent mRNA. These post- transcriptional events play an important yet incompletely understood role in regulating gene expression and cellular behavior. Many of the identified cis acting elements for translational regulation occur within the

A method is described for the refinement of rough protein models based on finding a selection of structural fragments that match the model. Unlike most fragment-based methods, these are not necessarily contiguous in the sequence and form a tiling (tessellation) that covers most of the structure. The residue positions of the fragments are then used as a target for the model atoms to generate a revised model which is used as the basis of a subsequent pattern definition and search. The method was shown to improve the recognition of the native fold in a series of decoys largely as a result of improved secondary structure representation.

We used a protein structure prediction method to generate a variety of folds as alpha-carbon models with realistic secondary structures and good hydrophobic packing. The prediction method used only idealized constructs that are not based on known protein structures or fragments of them, producing an unbiased distribution. Model and native fold comparison used a topology-based method as superposition can only be relied on in similar structures. When all the models were compared to a nonredundant set of all known structures, only one-in-ten were found to have a match. This large excess of novel folds was associated with each protein probe and if true in general, implies that the space of possible folds is larger than the space of realized folds, in much the same way that sequence-space is larger than fold-space. The large excess of novel folds exhibited no unusual properties and has been likened to cosmological dark matter.