Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism

The Adaptive Biasing Force Method: Everything You Always Wanted To Know but Were Afraid To Ask

International audienceIn the host of numerical schemes devised to calculate free energy differences by way of geometric transformations, the adaptive biasing force algorithm has emerged as a promising route to map complex free-energy landscapes. It relies upon the simple concept that as a simulation progresses, a continuously updated biasing force is added to the equations of motion, such that in the long-time limit it yields a Hamiltonian devoid of an average force acting along the transition coordinate of interest. This means that sampling proceeds uniformly on a flat free-energy surface, thus providing reliable free-energy estimates. Much of the appeal of the algorithm to the practitioner is in its physically intuitive underlying ideas and the absence of any requirements for prior knowledge about free-energy landscapes. Since its inception in 2001, the adaptive biasing force scheme has been the subject of considerable attention, from in-depth mathematical analysis of convergence properties to novel developments and extensions. The method has also been successfully applied to many challenging problems in chemistry and biology. In this contribution, the method is presented in a comprehensive, self-contained fashion, discussing with a critical eye its properties, applicability, and inherent limitations, as well as introducing novel extensions. Through free-energy calculations of prototypical molecular systems, many methodological aspects are examined, from stratification strategies to overcoming the so-called hidden barriers in orthogonal space, relevant not only to the adaptive biasing force algorithm but also to other importance-sampling schemes. On the basis of the discussions in this paper, a number of good practices for improving the efficiency and reliability of the computed free-energy differences are proposed

The variability of multisensory processes of natural stimuli in human and non-human primates in a detection task

BACKGROUND:Behavioral studies in both human and animals generally converge to the dogma that multisensory integration improves reaction times (RTs) in comparison to unimodal stimulation. These multisensory effects depend on diverse conditions among which the most studied were the spatial and temporal congruences. Further, most of the studies are using relatively simple stimuli while in everyday life, we are confronted to a large variety of complex stimulations constantly changing our attentional focus over time, a modality switch that can impact on stimuli detection. In the present study, we examined the potential sources of the variability in reaction times and multisensory gains with respect to the intrinsic features of a large set of natural stimuli. METHODOLOGY/PRINCIPLE FINDINGS:Rhesus macaque monkeys and human subjects performed a simple audio-visual stimulus detection task in which a large collection of unimodal and bimodal natural stimuli with semantic specificities was presented at different saliencies. Although we were able to reproduce the well-established redundant signal effect, we failed to reveal a systematic violation of the race model which is considered to demonstrate multisensory integration. In both monkeys and human species, our study revealed a large range of multisensory gains, with negative and positive values. While modality switch has clear effects on reaction times, one of the main causes of the variability of multisensory gains appeared to be linked to the intrinsic physical parameters of the stimuli. CONCLUSION/SIGNIFICANCE:Based on the variability of multisensory benefits, our results suggest that the neuronal mechanisms responsible of the redundant effect (interactions vs. integration) are highly dependent on the stimulus complexity suggesting different implications of uni- and multisensory brain regions. Further, in a simple detection task, the semantic values of individual stimuli tend to have no significant impact on task performances, an effect which is probably present in more cognitive tasks

Odor/taste integration and the perception of flavor

Perceptions of the flavors of foods or beverages reflect information derived from multiple sensory afferents, including gustatory, olfactory, and somatosensory fibers. Although flavor perception therefore arises from the central integration of multiple sensory inputs, it is possible to distinguish the different modalities contributing to flavor, especially when attention is drawn to particular sensory characteristics. Nevertheless, our experiences of the flavor of a food or beverage are also simultaneously of an overall unitary perception. Research aimed at understanding the mechanisms behind this integrated flavor perception is, for the most part, relatively recent. However, psychophysical, neuroimaging and neurophysiological studies on cross-modal sensory interactions involved in flavor perception have started to provide an understanding of the integrated activity of sensory systems that generate such unitary perceptions, and hence the mechanisms by which these signals are "functionally united when anatomically separated". Here we review this recent research on odor/taste integration, and propose a model of flavor processing that depends on prior experience with the particular combination of sensory inputs, temporal and spatial concurrence, and attentional allocation. We propose that flavor perception depends upon neural processes occurring in chemosensory regions of the brain, including the anterior insula, frontal operculum, orbitofrontal cortex and anterior cingulate cortex, as well as upon the interaction of this chemosensory "flavor network" with other heteromodal regions including the posterior parietal cortex and possibly the ventral lateral prefrontal cortex