Onyx: describing emotions on the web of data

There are several different standardised and widespread formats to represent emotions. However, there is no standard semantic model yet. This paper presents a new ontology, called Onyx, that aims to become such a standard while adding concepts from the latest Semantic Web models. In particular, the ontology focuses on the representation of Emotion Analysis results. But the model is abstract and inherits from previous standards and formats. It can thus be used as a reference representation of emotions in any future application or ontology. To prove this, we have translated resources from EmotionML representation to Onyx. We also present several ways in which developers could benefit from using this ontology instead of an ad-hoc presentation. Our ultimate goal is to foster the use of semantic technologies for emotion Analysis while following the Linked Data ideals

Enhancer-gene specificity in development and disease

Enhancers control the establishment of spatiotemporal gene expression patterns throughout development. Over the past decade, the development of new technologies has improved our capacity to link enhancers with their target genes based on their colocalization within the same topological domains. However, the mechanisms that regulate how enhancers specifically activate some genes but not others within a given domain remain unclear. In this Review, we discuss recent insights into the factors controlling enhancer specificity, including the genetic composition of enhancers and promoters, the linear and 3D distance between enhancers and their target genes, and cell-type specific chromatin landscapes. We also discuss how elucidating the molecular principles of enhancer specificity might help us to better understand and predict the pathological consequences of human genetic, epigenetic and structural variants.Work in the Rada-Iglesias laboratory is funded by the Ministerio de Ciencia e Innovación and the Agencia Españ ola de Investigación; by the European Regional Development Fund (PGC2018-095301-B-I00 and RED2018-102553-T); by the European Research Council (862022); and by the European Commission (H2020-MSCA-ITN-2019-860002

Identification of candidate regulatory SNPs by combination of transcription-factor-binding site prediction, SNP genotyping and haploChIP

Disease-associated SNPs detected in large-scale association studies are frequently located in non-coding genomic regions, suggesting that they may be involved in transcriptional regulation. Here we describe a new strategy for detecting regulatory SNPs (rSNPs), by combining computational and experimental approaches. Whole genome ChIP-chip data for USF1 was analyzed using a novel motif finding algorithm called BCRANK. 1754 binding sites were identified and 140 candidate rSNPs were found in the predicted sites. For validating their regulatory function, seven SNPs found to be heterozygous in at least one of four human cell samples were investigated by ChIP and sequence analysis (haploChIP). In four of five cases where the SNP was predicted to affect binding, USF1 was preferentially bound to the allele containing the consensus motif. Allelic differences in binding for other proteins and histone marks further reinforced the SNPs regulatory potential. Moreover, for one of these SNPs, H3K36me3 and POLR2A levels at neighboring heterozygous SNPs indicated effects on transcription. Our strategy, which is entirely based on in vivo data for both the prediction and validation steps, can identify individual binding sites at base pair resolution and predict rSNPs. Overall, this approach can help to pinpoint the causative SNPs in complex disorders where the associated haplotypes are located in regulatory regions. Availability: BCRANK is available from Bioconductor (http://www.bioconductor.org/)

Forces driving the three-dimensional folding of eukaryotic genomes

The last decade has radically renewed our understanding of higher order chromatin folding in the eukaryotic nucleus. As a result, most current models are in support of a mostly hierarchical and relatively stable folding of chromosomes dividing chromosomal territories into A- (active) and B- (inactive) compartments, which are then further partitioned into topologically associating domains (TADs), each of which is made up from multiple loops stabilized mainly by the CTCF and cohesin chromatin-binding complexes. Nonetheless, the structure-to-function relationship of eukaryotic genomes is still not well understood. Here, we focus on recent work highlighting the biophysical and regulatory forces that contribute to the spatial organization of genomes, and we propose that the various conformations that chromatin assumes are not so much the result of a linear hierarchy, but rather of both converging and conflicting dynamic forces that act on it

MAIA: an event-based modular architecture for intelligent agents

Online services are no longer isolated. The release of public APIs and technologies such as web hooks are allowing users and developers to access their information easily. Intelligent agents could use this information to provide a better user experience across services, connecting services with smart automatic. behaviours or actions. However, agent platforms are not prepared to easily add external sources such as web services, which hinders the usage of agents in the so-called Evented or Live Web. As a solution, this paper introduces an event-based architecture for agent systems, in accordance with the new tendencies in web programming. In particular, it is focused on personal agents that interact with several web services. With this architecture, called MAIA, connecting to new web services does not involve any modification in the platform

EUROSENTIMENT: Linked Data Sentiment Analysis

Sentiment and Emotion Analysis strongly depend on quality language resources, especially sentiment dictionaries. These resources are usually scattered, heterogeneous and limited to specific domains of appli- cation by simple algorithms. The EUROSENTIMENT project addresses these issues by 1) developing a common language resource representation model for sentiment analysis, and APIs for sentiment analysis services based on established Linked Data formats (lemon, Marl, NIF and ONYX) 2) by creating a Language Resource Pool (a.k.a. LRP) that makes avail- able to the community existing scattered language resources and services for sentiment analysis in an interoperable way. In this paper we describe the available language resources and services in the LRP and some sam- ple applications that can be developed on top of the EUROSENTIMENT LRP

Linguistic linked data for sentiment analysis

In this paper we describe the specification of amodel for the semantically interoperable representation of language resources for sentiment analysis. The model integrates "lemon", an RDF-based model for the specification of ontology-lexica (Buitelaar et al. 2009), which is used increasinglyfor the representation of language resources asLinked Data, with Marl, an RDF-based model for the representation of sentiment annotations (West-erski et al., 2011; Sánchez-Rada et al., 2013

A linked data approach to sentiment and emotion analysis of twitter in the financial domain

Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked Data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities or providers, and complements existing initiatives such as FIBO. The ap- proach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information

Epigenetic mechanisms of Strip2 in differentiation of pluripotent stem cells

Significant evidence points to Strip2 being a key regulator of the differentiation processes of pluripotent embryonic stem cells. However, Strip2 mediated epigenetic regulation of embryonic differentiation and development is quite unknown. Here, we identified several interaction partners of Strip2, importantly the co-repressor molecular protein complex nucleosome remodeling deacetylase/Tripartite motif-containing 28/Histone deacetylases/Histone-lysine N-methyltransferase SETDB1 (NuRD/TRIM28/HDACs/SETDB1) histone methyltransferase, which is primarily involved in regulation of the pluripotency of embryonic stem cells and its differentiation. The complex is normally activated by binding of Krueppel-associated box zinc-finger proteins (KRAB-ZFPs) to specific DNA motifs, causing methylation of H3 to Lysin-9 residues (H3K9). Our data showed that Strip2 binds to a DNA motif (20 base pairs), like the KRAB-ZFPs. We establish that Strip2 is an epigenetic regulator of pluripotency and differentiation by modulating DNA KRAB-ZFPs as well as the NuRD/TRIM28/HDACs/SETDB1 histone methyltransferase complex

Generating Linked-Data based Domain-Specific Sentiment Lexicons from Legacy Language and Semantic Resources

We present a methodology for legacy language resource adaptation that generates domain-specific sentiment lexicons organized around domain entities described with lexical information and sentiment words described in the context of these entities. We explain the steps of the methodology and we give a working example of our initial results. The resulting lexicons are modelled as Linked Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in the Linguistic Linked Open Data cloud