BackgroundReverse-engineering gene networks from expression profiles is a difficult problem for which a multitude of techniques have been developed over the last decade. The yearly organized DREAM challenges allow for a fair evaluation and unbiased comparison of these methods.ResultsWe propose an inference algorithm that combines confidence matrices, computed as the standard scores from single-gene knockout data, with the down-ranking of feed-forward edges. Substantial improvements on the predictions can be obtained after the execution of this second step.ConclusionsOur algorithm was awarded the best overall performance at the DREAM4 In Silico 100-gene network sub-challenge, proving to be effective in inferring medium-size gene regulatory networks. This success demonstrates once again the decisive importance of gene expression data obtained after systematic gene perturbations and highlights the usefulness of graph analysis to increase the reliability of inference.

Summary: End-to-end next-generation sequencing microbiology data analysis requires a diversity of tools covering bacterial resequencing, de novo assembly, scaffolding, bacterial RNA-Seq, gene annotation and metagenomics. However, the construction of computational pipelines that use different software packages is difficult owing to a lack of interoperability, reproducibility and transparency. To overcome these limitations we present Orione, a Galaxy-based framework consisting of publicly available research software and specifically designed pipelines to build complex, reproducible workflows for next-generation sequencing microbiology data analysis. Enabling microbiology researchers to conduct their own custom analysis and data manipulation without software installation or programming, Orione provides new opportunities for data-intensive computational analyses in microbiology and metagenomics.Availability and implementation: Orione is available online at http://orione.crs4.it.Contact: gianmauro.cuccuru@crs4.itSupplementary information: Supplementary data are available at Bioinformatics online.

Affective gaming is a hot field of research that exploits human emotion for the enhancement of player's experience during gameplay. Physiological signal is an effective modality that can provide a better understanding of the emotional states and is very promising to be applied to affective gaming. Most physiological-based affective gaming applications evaluate player's emotion on an overall game fragment. These approaches fail to capture the emotion change in the dynamic game context. In order to achieve a better understanding of psychophysiological response with a better time sensitivity, we present a study that evaluates the psychophysiological responses related to the game events. More specifically, we present a multi-modal database DAG that contains peripheral physiological signals (ECG, EDA, respiration, EMG, temperature), accelerometer signals, facial and screening recordings as well as player's self-reported eventrelated emotion assessment through game playing. We then investigate physiological-based emotion detection and recognition by using machine learning techniques. Common challenges for physiological-based affective model such as signal segmentation, feature normalization, relevant features are addressed. We also discuss factors that influence the performance of the affective models.

alf@crs4.it.

With the rapid extension of clinical data and knowledge, decision making becomes a complex task for manual sleep staging. In this process, there is a need for integrating and analyzing information from heterogeneous data sources with high accuracy. This paper proposes a novel decision support algorithm-Symbolic Fusion for sleep staging application. The proposed algorithm provides high accuracy by combining data from heterogeneous sources, like EEG, EOG and EMG. This algorithm is developed for implementation in portable embedded systems for automatic sleep staging at low complexity and cost. The proposed algorithm proved to be an efficient design support method and achieved up to 76% overall agreement rate on our database of 12 patients.