Using the IRAP to explore natural language statements

This study explored a modification to the typical presentation of label and target stimuli on Implicit Relational Assessment Procedure (IRAP) effects. We asked whether combining the labels and targets into a single phrase would influence performances. The key purpose of the study was to determine the feasibility of altering the way in which stimuli are presented within the IRAP, so as to potentially employ more complex natural language-like statements in future research. In the Typical IRAP employed here, labels and targets were presented as separate words, while in the Natural Language IRAP they were combined to form a single statement. The results demonstrated no substantive differences in the effects recorded on both types of IRAP, thus supporting the future use of a Natural Language version

Dirichlet multinomial mixtures: generative models for microbial metagenomics

We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct ‘metacommunities’, and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the ‘evidence framework’ (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the ‘Anna Karenina principle (AKP)’ applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community

Data mining in bioinformatics using Weka

The Weka machine learning workbench provides a general purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it

Estimating empirical codon hidden Markov models

Empirical codon models (ECMs) estimated from a large number of globular protein families outperformed mechanistic codon models in their description of the general process of protein evolution. Among other factors, ECMs implicitly model the influence of amino acid properties and multiple nucleotide substitutions (MNS). However, the estimation of ECMs requires large quantities of data, and until recently, only few suitable data sets were available. Here, we take advantage of several new Drosophila species genomes to estimate codon models from genome-wide data. The availability of large numbers of genomes over varying phylogenetic depths in the Drosophila genus allows us to explore various divergence levels. In consequence, we can use these data to determine the appropriate level of divergence for the estimation of ECMs, avoiding overestimation of MNS rates caused by saturation. To account for variation in evolutionary rates along the genome, we develop new empirical codon hidden Markov models (ecHMMs). These models significantly outperform previous ones with respect to maximum likelihood values, suggesting that they provide a better fit to the evolutionary process. Using ECMs and ecHMMs derived from genome-wide data sets, we devise new likelihood ratio tests (LRTs) of positive selection. We found classical LRTs very sensitive to the presence of MNSs, showing high false-positive rates, especially with small phylogenies. The new LRTs are more conservative than the classical ones, having acceptable false-positive rates and reduced power.Publisher PDFPeer reviewe