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Motivation Understanding the specificity of protein receptor–ligand interactions is pivotal for our comprehension of biological mechanisms and systems. Receptor protein families often have a certain level of sequence diversity that converges into fewer conserved protein structures, allowing the exertion of well-defined functions. T and B cell receptors of the immune system and protein kinases that control the dynamic behaviour and decision processes in eukaryotic cells by catalysing phosphorylation represent prime examples. Driven by the large sequence diversity, the receptors within such protein families are often found to share specificities although divergent at the sequence level. This observation has led to the notion that prediction models of such systems are most effectively handled in a receptor-specific manner. Results We show that this approach in many cases is suboptimal, and describe an alternative improved framework for generating models with pan-receptor-predictive power for receptor protein families. The framework is based on deep artificial neural networks and integrates information from individual receptors into a single pan-receptor model, leveraging information across multiple receptor-specific datasets allowing predictions of the receptor specificity for all members of a given protein family including those described by limited or no ligand data. The approach was applied to the protein kinase superfamily, leading to the method NetPhosPan. The method was extensively validated and benchmarked against state-of-the-art prediction methods and was found to have unprecedented performance in particularly for kinase domains characterized by limited or no experimental data. Availability and implementation The method is freely available to non-commercial users and can be downloaded at http://www.cbs.dtu.dk/services/NetPhospan-1.0. Supplementary information Supplementary data are available at Bioinformatics online.

Motivation: The gene concept has recently changed from the classical one protein notion into a much more diverse picture, where overlapping or fused transcripts, alternative transcription initiation, and genes within genes, add to the complexity generated by alternative splicing. Increased understanding of the mechanisms controlling pre-mRNA splicing is thus important for a wide range of aspects relating to gene expression. Results: We have discovered a convex gene delineating pattern in the strength of 5′ intron splice sites. When comparing the strengths of >18 000 intron containing Human genes, we found that when analysing them separately according to the number of introns they contain, initial splice sites were always stronger on average than subsequent ones, and that a similar reversed trend exist towards the terminal gene part. The convex pattern is strongest for genes with up to 10 introns. Interestingly, when analysing the intron containing gene pool from mouse consisting of >15 000 genes, we found the convex pattern to be conserved despite >75 million years of evolutionary divergence between the two organisms. We also analysed an interesting, novel class of chimeric genes which during spliceosome assembly are fused and in tandem are transcribed and spliced into a single mature mRNA sequence. In their splice site patterns, these genes individually seem to deviate from the convex pattern, offering a possible rationale behind their fusion into a single transcript. Contact: brunak@cbs.dtu.dk Supplementary Information: Supplementary data are available at Bioinformatics online.

Motivation: Single nucleotide polymorphisms (SNPs) analysis is an important means to study genetic variation. A fast and cost-efficient approach to identify large numbers of novel candidates is the SNP mining of large scale sequencing projects. The increasing availability of sequence trace data in public repositories makes it feasible to evaluate SNP predictions on the DNA chromatogram level. MAVIANT, a platform-independent Multipurpose Alignment VIewing and Annotation Tool, provides DNA chromatogram and alignment views and facilitates evaluation of predictions. In addition, it supports direct manual annotation, which is immediately accessible and can be easily shared with external collaborators. Results: Large-scale SNP mining of polymorphisms bases on porcine EST sequences yielded more than 7900 candidate SNPs in coding regions (cSNPs), which were annotated relative to the human genome. Non-synonymous SNPs were analyzed for their potential effect on the protein structure/function using the PolyPhen and SIFT prediction programs. Predicted SNPs and annotations are stored in a web-based database. Using MAVIANT SNPs can visually be verified based on the DNA sequencing traces. A subset of candidate SNPs was selected for experimental validation by resequencing and genotyping. This study provides a web-based DNA chromatogram and contig browser that facilitates the evaluation and selection of candidate SNPs, which can be applied as genetic markers for genome wide genetic studies. Availability: The stand-alone version of MAVIANT program for local use is freely available under GPL license terms at http://snp.agrsci.dk/maviant. Contact: christian.bendixen@agrsci.dk Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Prediction of which peptides will bind a specific major histocompatibility complex (MHC) constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we wish to describe a novel Gibbs motif sampler method ideally suited for recognizing such weak sequence motifs. The method is based on the Gibbs sampling method, and it incorporates novel features optimized for the task of recognizing the binding motif of MHC classes I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight-matrix can be applied to identifying effectively potential MHC binding peptides and to guiding the process of rational vaccine design. Results: We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly, a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation, the performance is quantified using relative operating characteristics curve (ROC) plots and we make a detailed comparison of the performance with that of both the TEPITOPE method and a weight-matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.

Motivation: The primary function of DNA is to carry genetic information through the genetic code. DNA, however, contains a variety of other signals related, for instance, to reading frame, codon bias, pairwise codon bias, splice sites and transcription regulation, nucleosome positioning and DNA structure. Here we study the relationship between the genetic code and DNA structure and address two questions. First, to which degree does the degeneracy of the genetic code and the acceptable amino acid substitution patterns allow for the superimposition of DNA structural signals to protein coding sequences? Second, is the origin or evolution of the genetic code likely to have been constrained by DNA structure? Results: We develop an index for code flexibility with respect to DNA structure. Using five different di- or tri-nucleotide models of sequence-dependent DNA structure, we show that the standard genetic code provides a fair level of flexibility at the level of broad amino acid categories. Thus the code generally allows for the superimposition of any structural signal on any protein-coding sequence, through amino acid substitution. The flexibility observed at the level of single amino acids allows only for the superimposition of punctual and loosely positioned signals to conserved amino acid sequences. The degree of flexibility of the genetic code is low or average with respect to several classes of alternative codes. This result is consistent with the view that DNA structure is not likely to have played a significant role in the origin and evolution of the genetic code. Contact: pfbaldi@ics.uci.edu; baisnee@ics.uci.edu; brunak@cbs.dtu.dk; gorm@cbs.dtu.dk * To whom correspondence should be addressed.

4 Also at the Department of Biological Sciences, University of California, Irvine, USA, to whom all correspondence should be addressed. We provide a unified overview of methods that currently are widely used to assess the accuracy of prediction algorithms, from raw percentages, quadratic error measures and other distances, and correlation coefficients, and to information theoretic measures such as relative entropy and mutual information. We briefly discuss the advantages and disadvantages of each approach. For classification tasks, we derive new learning algorithms for the design of prediction systems by directly optimising the correlation coefficient. We observe and prove several results relating sensitivity and specificity of optimal systems. While the principles are general, we illustrate the applicability on specific problems such as protein secondary structure and signal peptide prediction. Contact: pfbaldi@ics.uci.edu

UNLABELLED MatrixPlot is a program for making high-quality matrix plots, such as mutual information plots of sequence alignments and distance matrices of sequences with known three-dimensional coordinates. The user can add information about the sequences (e.g. a sequence logo profile) along the edges of the plot, as well as zoom in on any region in the plot. AVAILABILITY MatrixPlot can be obtained on request, and can also be accessed online at http://www. cbs.dtu.dk/services/MatrixPlot. CONTACT gorodkin@cbs.dtu.dk