BACKGROUND: Recent development of novel technologies paved the way for quantitative proteomics. One of the most important among them is iTRAQ, employing isobaric tags for relative or absolute quantitation. Despite large progress in technology development, still many challenges remain for derivation and interpretation of quantitative results. One of these challenges is the consistent assignment of peptides to proteins. RESULTS: We have developed Peptide Profiling Guided Identification of Proteins (PPINGUIN), a statistical analysis workflow for iTRAQ data addressing the problem of ambiguous peptide quantitations. Motivated by the assumption that peptides uniquely derived from the same protein are correlated, our method employs clustering as a very early step in data processing prior to protein inference. Our method increases experimental reproducibility and decreases variability of quantitations of peptides assigned to the same protein. Giving further support to our method, application to a type 2 diabetes dataset identifies a list of protein candidates that is in very good agreement with previously performed transcriptomics meta analysis. Making use of quantitative properties of signal patterns identified, PPINGUIN can reveal new isoform candidates. CONCLUSIONS: Regarding the increasing importance of quantitative proteomics we think that this method will be useful in practical applications like model fitting or functional enrichment analysis. We recommend to use this method if quantitation is a major objective of research.

BACKGROUND: Diabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics. RESULTS: We present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods. CONCLUSIONS: The combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.

MOTIVATION: The increasing use of customized oligo-DNA-microarrays has created a demand for convenient design of oligo nucleotides. The oligo-nucleotides have to serve as specific templates in the hybridization reaction. RESULTS: We have devised a web-based system for interactive design of suitable oligos for transcription profiling experiments with user-defined parameters. Oligodb is based on predicted human gene transcripts from the ENSEMBL project. The web interface allows the user to retrieve oligos for human transcripts in a flexible manner. Criteria for selecting the oligos are low similarity to all other known human genes, the absence of stable secondary structures, the length of the oligo, and the absence of low complexity regions. Furthermore, the user may select oligos corresponding to the desired position in the 5'-3' direction of the transcript, and based on the melting temperature. All data and results are tightly web-linked to the corresponding data entries of ENSEMBL project.

MOTIVATION: Even though a number of genome projects have been finished on the sequence level, still only a small proportion of DNA regulatory elements have been identified. Growing amounts of gene expression data provide the possibility of finding coregulated genes by clustering methods. By analysis of the promoter regions of those genes, rather weak signals of transcription factor binding sites may be detected. RESULTS: We introduce the new algorithm ITB, an Integrated Tool for Box finding, which combines frequency and positional information to predict transcription factor binding sites in upstream regions of coregulated genes. Motifs are extracted by exhaustive analysis of regular expression-like patterns and by estimating probabilities of positional clusters of motifs. ITB detects consensus sequences of experimentally verified transcription factor binding sites of the yeast Saccharomyces cerevisiae. Moreover, a number of new binding site candidates with significant scores are predicted. Besides applying ITB on yeast upstream regions, the program is run on human promoter sequences. AVAILABILITY: ITB is available upon request.

MOTIVATION: Data representation and encoding are essential for classification of protein sequences with artificial neural networks (ANN). Biophysical properties are appropriate for low dimensional encoding of protein sequence data. However, in general there is no a priori knowledge of the relevant properties for extraction of representative features. RESULTS: An adaptive encoding artificial neural network (ACN) for recognition of sequence patterns is described. In this approach parameters for sequence encoding are optimized within the same process as the weight vectors by an evolutionary algorithm. The method is applied to the prediction of signal peptide cleavage sites in human secretory proteins and compared with an established predictor for signal peptides. CONCLUSION: Knowledge of physico-chemical properties is not necessary for training an ACN. The advantage is a low dimensional data representation leading to computational efficiency, easy evaluation of the detected features, and high prediction accuracy. AVAILABILITY: A cleavage site prediction server is located at the Humboldt University http://itb.biologie.hu-berlin.de/ approximately jo/sig-cleave/ACNpredictor.cgi CONTACT: [email protected]; [email protected]

The potential of artificial neural filter systems for feature extraction from amino acid sequences is discussed. Analysis of signal peptidase I cleavage-sites in protein precursor sequences serves as an example application. Trained neural networks can be used as the fitness function in an evolutionary protein design cycle termed 'simulated molecular evolution' which is an entirely computer-based method for the rational design of locally encoded amino acid sequence features. The design procedure itself is regarded as an optimization process which can follow several schemes. Gradient search, diffusive search, and evolution strategy have been compared with regard to their usefulness for optimization. It turns out that gradient search is well suited for optimization in smooth fitness landscapes without local minima, whereas evolution strategy seems to be a method of choice for optimization in a high-dimensional multimodal search space. This is concluded from optimization experiments using a multimodal example function.