Alcohol drinking pattern analysis - an in silico tool to model and predict addictive behaviors

We present methods for the systematic modelling and clustering of time series. Our data is associated with behavioral studies of alcoholism in animals. We analyze multivariate time series obtained from an automated drinkometer system. Here, rats have free access to water and three alcoholic solutions (this being the baseline treatment level), which is then interrupted by repeated deprivation phases. We develop a methodology to simultaneously classify into- and characterize dynamic patterns of the observed drinking behavior. This is achieved by extending known results on generalized linear models (GLM) for univariate time series to the multivariate case. We simplify the computational fitting procedure, by assuming a shared seasonal pattern throughout individuals and implementing an estimation maximization (EM) algorithm to fit mixtures of the mentioned multivariate GLM. A partition of the data, as well as a characterization of each group is obtained. The observed patterns of drinking behavior differ in their preference profile for the three alcoholic solutions, and also in the net alcohol intake. We observe an evolution of the drinking behavior over the repeated cycles of alcohol admission and deprivation, with a clear initial preference profile and a development to one of the advanced profiles. Furthermore, to measure the alcohol deprivation effect in this 4-bottle setting, a new criterion is developed, which enables us to classify each rat into presenting ADE or not. This classification shows that the rats develop a tolerance to taste adulteration after few deprivation phases. The proposed framework can be employed to find differences in behavior between different conditions and/or groups of animals and in the prediction of alcoholism from early phases of alcohol intake. The developed methods can also be used in different fields, where the analysis of time series plays an important role (e.g. microarray analysis and neuroscience)

De novo annotation of the transcriptome of the Northern Wheatear (Oenanthe oenanthe)

We have sequenced a partial transcriptome of the Northern Wheatear (Oenanthe oenanthe), a species with one of the longest migrations on Earth. The transcriptome was constructed de novo using RNA-Seq sequence data from the pooled mRNA of six different tissues: brain, muscle, intestine, liver, adipose tissue and skin. The samples came from nine captive-bred wheatears collected at three different stages of the endogenous autumn migratory period: (1) lean birds prior the onset of migration, (2) during the fattening stage and (3) individuals at their migratory body mass plateau, when they have almost doubled their lean body mass. The sample structure used to build up the transcriptome of the Northern Wheatears concerning tissue composition and time guarantees the future survey of the regulatory genes involved in the development of the migratory phenotype. Through the pre-migratory period, birds accomplish outstanding physical and behavioural changes that involve all organ systems. Nevertheless, the molecular mechanisms through which birds synchronize and control hyperphagia, fattening, restlessness increase, immunity boosting and tuning the muscles for such endurance flight are still largely unknown. The use of RNA-Seq has emerged as a powerful tool to analyse complex traits on a broad scale, and we believe it can help to characterize the migratory phenotype of wheatears at an unprecedented level. The primary challenge to conduct quantitative transcriptomic studies in non-model species is the availability of a reference transcriptome, which we have constructed and described in this paper. The cDNA was sequenced by pyrosequencing using the Genome Sequencer Roche GS FLX System; with single paired-end reads of about 400 bp. We estimate the total number of genes at 15,640, of which  67% could be annotated using Turkey and Zebra Finch genomes, or protein sequence information from SwissProt and NCBI databases. With our study, we have made a first step towards understanding the migratory phenotype regarding gene expression of a species that has become a model to study birds long-distance migrations

A Revised and Improved Version of the Northern Wheatear (Oenanthe oenanthe) Transcriptome

This work presents an updated and more complete version of the transcriptome of a long-distance migrant, the Northern Wheatear (Oenanthe oenanthe). The improved transcriptome was produced from the independent mRNA sequencing of adipose tissue, brain, intestines, liver, skin, and muscle tissues sampled during the autumnal migratory season. This new transcriptome has better sequencing coverage and is more representative of the species’ migratory phenotype. We assembled 20,248 transcripts grouped into 16,430 genes, from which 78% were successfully annotated. All the standard assembly quality parameters were improved in the second transcriptome version