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Motivation: Large public repositories of gene expression measurements offer the opportunity to position a new experiment into the context of earlier studies. While previous methods rely on experimental annotation or global similarity of expression profiles across genes or gene sets, we compare experiments by measuring similarity based on an unsupervised, data-driven regulatory model around pre-specified genes of interest. Our experiment retrieval approach is novel in two conceptual respects: (i) targetable focus and interpretability: the analysis is targeted at regulatory relationships of genes that are relevant to the analyst or come from prior knowledge; (ii) regulatory model-based similarity measure: related experiments are retrieved based on the strength of inferred regulatory links between genes.Results: We learn a model for the regulation of specific genes from a data repository and exploit it to construct a similarity metric for an information retrieval task. We use the Fisher kernel, a rigorous similarity measure that typically has been applied to use generative models in discriminative classifiers. Results on human and plant microarray collections indicate that our method is able to substantially improve the retrieval of related experiments against standard methods. Furthermore, it allows the user to interpret biological conditions in terms of changes in link activity patterns. Our study of the osmotic stress network for Arabidopsis thaliana shows that the method successfully identifies relevant relationships around given key genes.Availability: The code (R) is available at http://research.ics.tkk.fi/mi/software.shtml.Contact: elisabeth.georgii@aalto.fi; jarkko.salojarvi@helsinki.fi; samuel.kaski@hiit.fiSupplementary Information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Motivation: Rapid advances in biomedical sciences and genetics have increased the pressure on drug development companies to promptly translate new knowledge into treatments for disease. Impelled by the demand and facilitated by technological progress, the number of compounds evaluated during the initial high-throughput screening (HTS) step of drug discovery process has steadily increased. As a highly automated large-scale process, HTS is prone to systematic error caused by various technological and environmental factors. A number of error correction methods have been designed to reduce the effect of systematic error in experimental HTS (Brideau et al., 2003; Carralot et al., 2012; Kevorkov and Makarenkov, 2005; Makarenkov et al., 2007; Malo et al., 2010). Despite their power to correct systematic error when it is present, the applicability of those methods in practice is limited by the fact that they can potentially introduce a bias when applied to unbiased data. We describe two new methods for eliminating systematic error from HTS data based on a prior knowledge of the error location. This information can be obtained using a specific version of the t-test or of the χ2 goodness-of-fit test as discussed in Dragiev et al. (2011). We will show that both new methods constitute an important improvement over the standard practice of not correcting for systematic error at all as well as over the B-score correction procedure (Brideau et al., 2003) which is widely used in the modern HTS. We will also suggest a more general data preprocessing framework where the new methods can be applied in combination with the Well Correction procedure (Makarenkov et al., 2007). Such a framework will allow for removing systematic biases affecting all plates of a given screen as well as those relative to some of its individual plates.Contact: makarenkov.vladimir@uqam.caSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM PUBLISHER]

Abstract: A number of recent studies have shown that loop-design is more efficient than reference control design. Data analysis for loop-design microarray experiments is commonly undertaken using linear models and statistical tests. These techniques require specialized knowledge in statistical programming. However, limited loop-design web-based tools are available. We have developed the THEME (Tsing Hua Engine of Microarray Experiment) that exploits all necessary data analysis tools for loop-design microarray studies. THEME allows users to construct linear models and to apply multiple user-defined statistical tests of hypotheses for detection of DEG (differentially expressed genes). Users can modify entries of design matrix for experimental design as well as that of contrast matrix for statistical tests of hypotheses. The output of multiple user-defined statistical tests of hypotheses, DEG lists, can be cross-validated. The web platform provides data assessment and visualization tools that significantly assist users when evaluating the performance of microarray experimental procedures. THEME is also a MIAME (Minimal Information About a Microarray Experiment) compliant system, which enables users to export formatted files for GEO (Gene Expression Omnibus) submission. THEME offers comprehensive web services to biologists for data analysis of loop-design microarray experiments. This web-based resource is especially useful for core facility service as well as collaboration projects when researchers are not at the same site. Data analysis procedures, starting from uploading raw data files to retrieving DEG lists, can be flexibly operated with natural workflows. These features make THEME a reliable and powerful on-line system for data analysis of loop-design microarrays. The THEME server is available at http://metadb.bmes.nthu.edu.tw/theme/. [Copyright &y& Elsevier]

Motivation: The genomic era in molecular biology has brought on a rapidly widening gap between the amount of sequence data and first-hand experimental characterization of proteins. Fortunately, the theory of evolution provides a simple solution: functional and structural information can be transferred between homologous proteins. Sequence similarity searching followed by k-nearest neighbor classification is the most widely used tool to predict the function or structure of anonymous gene products that come out of genome sequencing projects.Results: We present a novel word filter, suffix array neighborhood search (SANS), to identify protein sequence similarities in the range of 50–100% identity with sensitivity comparable to BLAST and 10 times the speed of USEARCH. In contrast to these previous approaches, the complexity of the search is proportional only to the length of the query sequence and independent of database size, enabling fast searching and functional annotation into the future despite rapidly expanding databases.Availability and implementation: The software is freely available to non-commercial users from our website http://ekhidna.biocenter.helsinki.fi/downloads/sans.Contact: liisa.holm@helsinki.fi. [ABSTRACT FROM AUTHOR]