MOTIVATION: To understand the evolution of molecular function within protein families, it is important to identify those amino acid residues responsible for functional divergence; i.e. those sites in a protein family that affect cofactor, protein or substrate binding preferences; affinity; catalysis; flexibility; or folding. Type I functional divergence (FD) results from changes in conservation (evolutionary rate) at a site between protein subfamilies, whereas type II FD occurs when there has been a shift in preferences for different amino acid chemical properties. A variety of methods have been developed for identifying both site types in protein subfamilies, both from phylogenetic and information-theoretic angles. However, evaluation of the performance of these methods has typically relied upon a handful of reasonably well-characterized biological datasets or analyses of a single biological example. While experimental validation of many truly functionally divergent sites (true positives) can be relatively straightforward, determining that particular sites do not contribute to functional divergence (i.e. false positives and true negatives) is much more difficult, resulting in noisy 'gold standard' examples. RESULTS: We describe a novel, phylogeny-based functional divergence classifier, FunDi. Unlike previous approaches, FunDi uses a unified mixture model-based approach to detect type I and type II FD. To assess FunDi's overall classification performance relative to other methods, we introduce two methods for simulating functionally divergent datasets. We find that the FunDi method performs better than several other predictors over a wide variety of simulation conditions. AVAILABILITY: http://rogerlab.biochem.dal.ca/Software CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Aligning protein sequences with the best possible accuracy requires sophisticated algorithms. Since the optimal alignment is not guaranteed to be the correct one, it is expected that even the best alignment will contain sites that do not respect the assumption of positional homology. Because formulating rules to identify these sites is difficult, it is common practice to manually remove them. Although considered necessary in some cases, manual editing is time consuming and not reproducible. We present here an automated editing method based on the classification of 'valid' and 'invalid' sites. RESULTS: A support vector machine (SVM) classifier is trained to reproduce the decisions made during manual editing with an accuracy of 95.0%. This implies that manual editing can be made reproducible and applied to large-scale analyses. We further demonstrate that it is possible to retrain/extend the training of the classifier by providing examples of multiple sequence alignment (MSA) annotation. Near optimal training can be achieved with only 1000 annotated sites, or roughly three samples of protein sequence alignments. AVAILABILITY: This method is implemented in the software MANUEL, licensed under the GPL. A web-based application for single and batch job is available at http://fester.cs.dal.ca/manuel. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: An increasing number of bioinformatics methods are considering the phylogenetic relationships between biological sequences. Implementing new methodologies using the maximum likelihood phylogenetic framework can be a time consuming task. RESULTS: The bioinformatics library libcov is a collection of C++ classes that provides a high and low-level interface to maximum likelihood phylogenetics, sequence analysis and a data structure for structural biological methods. libcov can be used to compute likelihoods, search tree topologies, estimate site rates, cluster sequences, manipulate tree structures and compare phylogenies for a broad selection of applications. CONCLUSION: Using this library, it is possible to rapidly prototype applications that use the sophistication of phylogenetic likelihoods without getting involved in a major software engineering project. libcov is thus a potentially valuable building block to develop in-house methodologies in the field of protein phylogenetics.

MOTIVATION: Expressed sequence tag (EST) surveys are an efficient way to characterize large numbers of genes from an organism. The rate of gene discovery in an EST survey depends on the degree of redundancy of the cDNA libraries from which sequences are obtained. However, few statistical methods have been developed to assess and compare redundancies of various libraries from preliminary EST surveys. RESULTS: We consider statistics for the comparison of EST libraries based upon the frequencies with which genes occur in subsamples of reads. These measures are useful in determining which one of several libraries is more likely to yield new genes in future reads and what proportion of additional reads one might want to take from the libraries in order to be likely to obtain new genes. One approach is to compare single sample measures that have been successfully used in species estimation problems, such as coverage of a library, defined as the proportion of the library that is represented in the given sample of reads. Another single library measure is an estimate of the expected number of additional genes that will be found in a new sample of reads. We also propose statistics that jointly use data from all the libraries. Analogous formulas for coverage and the expected numbers of new genes are presented. These measures consider coverage in a single library based upon reads from all libraries and similarly, the expected numbers of new genes that will be discovered by taking reads from all libraries with fixed proportions. Together, the statistics presented provide useful comparative measures for the libraries that can be used to guide sampling from each of the libraries to maximize the rate of gene discovery. Finally, we present tests for whether genes are equally represented or expressed in a set of libraries. Binomial and chi2 tests are presented for gene-by-gene comparisons of expression. Overall tests of the equality of proportional representation are presented and multiple comparisons issues are addressed. These methods can be used to evaluate changes in gene expression reflected in the composition of EST libraries prepared from different tissue types or cells exposed to different environmental conditions. AVAILABILITY: Software will be made available at http://www.mathstat.dal.ca/~tsusko