Abstract
The motivation for this work is the study and prediction of wind ramp events occurring in a large-scale wind farm located in the US Midwest. In this paper we introduce the SHRED framework, a stream-based model that continuously learns a discrete HMM model from wind power and wind speed measurements. We use a supervised learning algorithm to learn HMM parameters from discretized data, where ramp events are HMM states and discretized wind speed data are HMM observations. The discretization of the historical data is obtained by running the SAX algorithm over the first order variations in the original signal. SHRED updates the HMM using the most recent historical data and includes a forgetting mechanism to model natural time dependence in wind patterns. To forecast ramp events we use recent wind speed forecasts and the Viterbi algorithm, that incrementally finds the most probable ramp event to occur. We compare SHRED framework against Persistence baseline in predicting ramp events occurring in short-time horizons, ranging from 30 minutes to 90 minutes. SHRED consistently exhibits more accurate and cost-effective results than the baseline. © 2012 Springer-Verlag Berlin Heidelberg.

Abstract
Motivation: A ubiquitous and fundamental step in high-throughput sequencing analysis is the alignment (mapping) of the generated reads to a reference sequence. To accomplish this task numerous software tools have been proposed. Determining the mappers that are most suitable for a specific application is not trivial. Results: This survey focuses on classifying mappers through a wide number of characteristics. The goal is to allow practitioners to compare the mappers more easily and find those that are most suitable for their specific problem.

Abstract
SummaryMaximum-likelihood methods based on models of codon substitution have been widely used to infer positively selected amino acid sites that are responsible for adaptive changes. Nevertheless, in order to use such an approach, software applications are required to align protein and DNA sequences, infer a phylogenetic tree and run the maximum-likelihood models. Therefore, a significant effort is made in order to prepare input files for the different software applications and in the analysis of the output of every analysis. In this paper we present the ADOPS (Automatic Detection Of Positively Selected Sites) software. It was developed with the goal of providing an automatic and flexible tool for detecting positively selected sites given a set of unaligned nucleotide sequence data. An example of the usefulness of such a pipeline is given by showing, under different conditions, positively selected amino acid sites in a set of 54 Coffea putative S-RNase sequences. ADOPS software is freely available and can be downloaded from http://sing.ei.uvigo.es/ADOPS.

Abstract
Maximum-likelihood methods based on models of codon substitution have been widely used to infer positively selected amino acid sites that are responsible for adaptive changes. Nevertheless, in order to use such an approach, software applications are required to align protein and DNA sequences, infer a phylogenetic tree and run the maximum-likelihood models. Therefore, a significant effort is made in order to prepare input files for the different software applications and in the analysis of the output of every analysis. In this paper we present the ADOPS (Automatic Detection Of Positively Selected Sites) software. It was developed with the goal of providing an automatic and flexible tool for detecting positively selected sites given a set of unaligned nucleotide sequence data. An example of the usefulness of such a pipeline is given by showing, under different conditions, positively selected amino acid sites in a set of 54 Coffea putative S-RNase sequences. ADOPS software is freely available and can be downloaded from http://sing.ei.uvigo.es/ADOPS.

Abstract
In this paper we present the ADOPS (Automatic Detection Of Positively Selected Sites) software that is ideal for research projects involving the analysis of tens of genes. ADOPS is a novel software pipeline that is being implemented with the goal of providing an automatic and flexible tool for detecting positively selected sites given a set of unaligned nucleotide sequence data.

Abstract
Chromosomal inversions can originate from breakage and repair by non-homologous end-joining. Nevertheless, they can also originate from ectopic recombination between transposable elements located on the same chromosome inserted in opposite orientations. Here, we show that a MITE element (DAIBAM), previously involved in the origin of one Drosophila americana polymorphic inversion, is also involved in the origin of one fixed inversion between D. virilis and D. americana and another D. americana polymorphic inversion. Therefore, DAIBAM is responsible for at least 20% of the chromosomal rearrangements that are observed within and between species of the virilis phylad (D. virilis, D. lummei, D. novamexicana and D. americana), having thus played a significant role in the chromosomal evolution of this group of closely related species.