Estimating and evaluating the statistics of gapped local-alignment scores

We present a novel maximum-likelihood-based algorithm for estimating the distribution of alignment scores from the scores of unrelated sequences in a database search. Using a new method for measuring the accuracy of p-values, we show that our maximum-likelihood-based algorithm is more accurate than existing regression-based and lookup table methods. We explore a more sophisticated way of modeling and estimating the score distributions (using a two-component mixture model and expectation maximization), but conclude that this does not improve significantly over simply ignoring scores with small E-values during estimation. Finally, we measure the classification accuracy of p-values estimated in different ways and observe that inaccurate p-values can, somewhat paradoxically, lead to higher classification accuracy. We explain this paradox and argue that statistical accuracy, not classification accuracy, should be the primary criterion in comparisons of similarity search methods that return p-values that adjust for target sequence length.

Assessing computational tools for the discovery of transcription factor binding sites

The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

Inhibition of COXs and 5-LOX and activation of PPARs by Australian Clematis species (Ranunculaceae)

The species of Clematis (Ranunculaceae) have been traditionally used for inflammatory conditions by indigenous Australians. We have previously reported that the ethanol extract of Clematis pickeringii inhibited COX-1. In this study, we examined the ethanol extracts and fractions of three Clematis species, Clematis pickeringii, Clematis glycinoides and Clematis microphylla, on cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2) and 5-lipoxygenase (5-LOX). We further examined the activating effects on the protein expression of peroxisome proliferator-activated receptor alpha (PPAR alpha) and gamma (PPAR-gamma) in HepG2 cells. The ethanol extracts of three Clematis species inhibited the activities of COX-1, COX-2 and 5-LOX in the different extents. The stem extract of Clematis pickeringii showed the highest inhibitory activities among the three species on COX-1, COX-2 and 5-LOX with the IC50 values of 73.5, 101.2 and 29.3 mu g/mL. One of its fractions also significantly elevated PPAR gamma expression by 173, 280 and 435% and PPAR gamma expression by 140, 228 and 296% at 4, 8 and 16 mu g/mL, respectively. (c) 2005 Elsevier Ireland Ltd. All rights reserved.

Segmenting eukaryotic genomes with the generalized Gibbs sampler

Eukaryotic genomes display segmental patterns of variation in various properties, including GC content and degree of evolutionary conservation. DNA segmentation algorithms are aimed at identifying statistically significant boundaries between such segments. Such algorithms may provide a means of discovering new classes of functional elements in eukaryotic genomes. This paper presents a model and an algorithm for Bayesian DNA segmentation and considers the feasibility of using it to segment whole eukaryotic genomes. The algorithm is tested on a range of simulated and real DNA sequences, and the following conclusions are drawn. Firstly, the algorithm correctly identifies non-segmented sequence, and can thus be used to reject the null hypothesis of uniformity in the property of interest. Secondly, estimates of the number and locations of change-points produced by the algorithm are robust to variations in algorithm parameters and initial starting conditions and correspond to real features in the data. Thirdly, the algorithm is successfully used to segment human chromosome 1 according to GC content, thus demonstrating the feasibility of Bayesian segmentation of eukaryotic genomes. The software described in this paper is available from the author's website (www.uq.edu.au/similar to uqjkeith/) or upon request to the author.