Correction: During the proofing stage of our article [1] we made an error by forgetting to put on the list of authors a biology expert Fabrice Beger who cross-checked the reliability and the quality of our database, tested it and gave comments on the manuscript. We apologize for any inconvenience.

BACKGROUND: Microarray experiments have become very popular in life science research. However, if such experiments are only considered independently, the possibilities for analysis and interpretation of many life science phenomena are reduced. The accumulation of publicly available data provides biomedical researchers with a valuable opportunity to either discover new phenomena or improve the interpretation and validation of other phenomena that partially understood or well known. This can only be achieved by intelligently exploiting this rich mine of information. DESCRIPTION: Considering that technologies like microarrays remain prohibitively expensive for researchers with limited means to order their own experimental chips, it would be beneficial to re-use previously published microarray data. For certain researchers interested in finding gene groups (requiring many replicates), there is a great need for tools to help them to select appropriate datasets for analysis. These tools may be effective, if and only if, they are able to re-use previously deposited experiments or to create new experiments not initially envisioned by the depositors. However, the generation of new experiments requires that all published microarray data be completely annotated, which is not currently the case. Thus, we propose the PathEx approach. CONCLUSION: This paper presents PathEx, a human-focused web solution built around a two-component system: one database component, enriched with relevant biological information (expression array, omics data, literature) from different sources, and another component comprising sophisticated web interfaces that allow users to perform complex dataset building queries on the contents integrated into the PathEx database.

BACKGROUND: Microarray data is frequently used to characterize the expression profile of a whole genome and to compare the characteristics of that genome under several conditions. Geneset analysis methods have been described previously to analyze the expression values of several genes related by known biological criteria (metabolic pathway, pathology signature, co-regulation by a common factor, etc.) at the same time and the cost of these methods allows for the use of more values to help discover the underlying biological mechanisms. RESULTS: As several methods assume different null hypotheses, we propose to reformulate the main question that biologists seek to answer. To determine which genesets are associated with expression values that differ between two experiments, we focused on three ad hoc criteria: expression levels, the direction of individual gene expression changes (up or down regulation), and correlations between genes. We introduce the FAERI methodology, tailored from a two-way ANOVA to examine these criteria. The significance of the results was evaluated according to the self-contained null hypothesis, using label sampling or by inferring the null distribution from normally distributed random data. Evaluations performed on simulated data revealed that FAERI outperforms currently available methods for each type of set tested. We then applied the FAERI method to analyze three real-world datasets on hypoxia response. FAERI was able to detect more genesets than other methodologies, and the genesets selected were coherent with current knowledge of cellular response to hypoxia. Moreover, the genesets selected by FAERI were confirmed when the analysis was repeated on two additional related datasets. CONCLUSIONS: The expression values of genesets are associated with several biological effects. The underlying mathematical structure of the genesets allows for analysis of data from several genes at the same time. Focusing on expression levels, the direction of the expression changes, and correlations, we showed that two-step data reduction allowed us to significantly improve the performance of geneset analysis using a modified two-way ANOVA procedure, and to detect genesets that current methods fail to detect.

BACKGROUND: Recent reanalysis of spike-in datasets underscored the need for new and more accurate benchmark datasets for statistical microarray analysis. We present here a fresh method using biologically-relevant data to evaluate the performance of statistical methods. RESULTS: Our novel method ranks the probesets from a dataset composed of publicly-available biological microarray data and extracts subset matrices with precise information/noise ratios. Our method can be used to determine the capability of different methods to better estimate variance for a given number of replicates. The mean-variance and mean-fold change relationships of the matrices revealed a closer approximation of biological reality. CONCLUSIONS: Performance analysis refined the results from benchmarks published previously.We show that the Shrinkage t test (close to Limma) was the best of the methods tested, except when two replicates were examined, where the Regularized t test and the Window t test performed slightly better. AVAILABILITY: The R scripts used for the analysis are available at http://urbm-cluster.urbm.fundp.ac.be/~bdemeulder/.

MOTIVATION: Homology or comparative modeling is currently the most accurate method to predict the three-dimensional structure of proteins. It generally consists in four steps: (1) databanks searching to identify the structural homolog, (2) target-template alignment, (3) model building and optimization, and (4) model evaluation. The target-template alignment step is generally accepted as the most critical step in homology modeling. RESULTS: We present here ESyPred3D, a new automated homology modeling program. The method gets benefit of the increased alignment performances of a new alignment strategy. Alignments are obtained by combining, weighting and screening the results of several multiple alignment programs. The final three-dimensional structure is build using the modeling package MODELLER. ESyPred3D was tested on 13 targets in the CASP4 experiment (Critical Assessment of Techniques for Proteins Structural Prediction). Our alignment strategy obtains better results compared to PSI-BLAST alignments and ESyPred3D alignments are among the most accurate compared to those of participants having used the same template. AVAILABILITY: ESyPred3D is available through its web site at http://www.fundp.ac.be/urbm/bioinfo/esypred/ CONTACT: [email protected]; http://www.fundp.ac.be/~lambertc

MOTIVATION: The prediction reliability of seven multiple alignment servers currently available on the Internet (ClustalW, MAP, PIMA, Block Maker, MSA, MEME and Match-Box) has been evaluated in terms of power (sensitivity) and confidence (selectivity). Therefore, the alignments obtained have been respectively compared to refined structural alignments for 20 families of related proteins with low levels of identity. RESULTS: Results clearly show that any powerful method remains reliable when the rate of identity falls. For some methods, power and confidence decrease linearly with the rate of identity, while other methods emphasize reliability at the cost of a lower power. Increasing the number of related sequences included in the alignment may either improve or decrease the quality of the predictions substantially. For some methods, the gain in power or in confidence is quite systematic; for others, the effect of the addition of homologous sequences is highly unpredictable. Extracting the consensus between two different methods may increase the overall confidence of the predictions tremendously. Our conclusions induce users of sequence alignment methods on the Internet to select the most suitable technique according to their requirements in terms of selectivity and sensitivity. AVAILABILITY: The aligned sequences of the 20 alignments of structure can be obtained automatically by sending the message 'send: cabios_tests.txt' by e-mail to '[email protected]'. CONTACT: [email protected]

MOTIVATION: The Match-Box software comprises protein sequence alignment tools based on strict statistical thresholds of similarity between protein segments. The method circumvents the gap penalty requirement: gaps being the result of the alignment and not a governing parameter of the procedure. The reliable conserved regions outlined by Match-Box are particularly relevant for homology modelling of protein structures, prediction of essential residues for site-directed mutagenesis and oligonucleotide design for cloning homologous genes by polymerase chain reaction (PCR). RESULTS: The method produces reliable results, as assessed by tests performed on protein families of known structures and of low sequence similarity. A reliability score is computed in relation to a threshold of similarity progressively raised to extend the aligned regions to their maximal length, up to the significance limit of matching segments. The score obtained at each position is printed below the sequences and allows a discriminant reading of each aligned region. AVAILABILITY: Sequences may be submitted to a Web server at http://www.fundp.ac.be/sciences/biologie/bms/+ ++matchbox_submit.html or sent by e-mail to matchbox/biq.fundp.ac.be (help available by just mailing help).

Original algorithms for simultaneous alignment of protein sequences are presented, including sequence clustering and within- or between-groups multiple alignment. The way of matching similar regions is fundamentally new. Complete matches are formed by segments more similar than expected by random, according to a given probability limit. Any classic or user-defined score matrix can be used to express the similarity between the residues. The algorithm seeks for complete matches common to all the sequences without performing pairwise alignment and regardless of gap weighting. An automatic screening delineates all the similar regions (boxes) that may be defined for a given maximal shift between the sequences. The shift can be large enough to allow the matching of any region of a sequence with any region of another one. It can also be short and used to refine the alignment around anchor points. The algorithm provides the most likely optimal alignment and a comprehensive list of the alignment dilemma. Duality between automatism and interactivity is provided. Depending on the problem complexity, a final alignment is obtained fully automatically or requires some interactive handling to discriminate alternative pathways.