The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets.

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein-protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/

The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.</p

Étude de la régulation anti-sens par l’analyse différentielle de données transcriptomiques dans le domaine végétal

A challenging task in bioinformatics is to decipher cell regulation mechanisms. The objective of this thesis is to study gene networks from apple data with the particularity to integrate anti-sense transcription data. Anti-sense transcripts are mostly non coding RNAs and their different roles in the cell are still not well known. In our study, to explore the role of anti-sense transcripts, we first propose a differential functional analysis that highlights the interest of integrating anti-sense data into a transcriptomic analysis. Then, regarding gene networks, we propose to focus on inference of a core network and we introduce a new differential analysis method that allows to compare a sense network with a sense and anti-sense network. We thus introduce the notion of AS-impacted genes, that allows to identify genes that are highly co-expressed with anti-sense transcripts. We analysed apple data related to ripening of fruits stored in cold storage; biological interpretation of the results of our differential analysisprovides some promising leads to a more targeted experimental study of genes or pathways, which role could be underestimated without integration of anti-sense data.Un des problèmes actuels en bio-informatique est de comprendre les mécanismes de régulation au sein d’une cellule ou d’un organisme. L’objectif de la thèse est d’étudier les réseaux de co-expression de gènes chez le pommier avec la particularité d’y intégrer les transcrits anti-sens. Les transcrits anti-sens sont des ARN généralement non-codants, dont les différents modes d’action sont encore mal connus. Dans notre étude exploratoire du rôle des anti-sens, nous proposons d’une part une analyse fonctionnelle différentielle qui met en évidence l’intérêt de l’intégration des données anti-sens en transcriptomique. D’autre part, concernant les réseaux de gènes, nous proposons de limiter l’inférence à un cœur de réseau et nous introduisons alors une méthode d’analyse différentielle permettant de comparer un réseau obtenu à partir de données sens avec un réseau contenant des données sens et anti-sens. Nous introduisons ainsi la notion de gènes AS-impacté, qui permet d’identifier des gènes dont les interactions au sein d’un réseau de co-expression sont fortement impactées par la prise en compte de transcrits anti-sens. Pour les données pommier que nous avons étudiées et qui concerne la maturation des fruits et leur conservation à basse température, l’interprétation biologique des résultats de notre analyse différentielle fournit des pistes pertinentes pour une étude expérimentale plus ciblée de gènes ou de voies de signalisation dont l’importance pourrait être sous-estimée sans la prise en compte des données anti-sens

Study of the anti-sense regulation by differential analysis of transcriptomic data in plants

Un des problèmes actuels en bio-informatique est de comprendre les mécanismes de régulation au sein d’une cellule ou d’un organisme. L’objectif de la thèse est d’étudier les réseaux de co-expression de gènes chez le pommier avec la particularité d’y intégrer les transcrits anti-sens. Les transcrits anti-sens sont des ARN généralement non-codants, dont les différents modes d’action sont encore mal connus. Dans notre étude exploratoire du rôle des anti-sens, nous proposons d’une part une analyse fonctionnelle différentielle qui met en évidence l’intérêt de l’intégration des données anti-sens en transcriptomique. D’autre part, concernant les réseaux de gènes, nous proposons de limiter l’inférence à un cœur de réseau et nous introduisons alors une méthode d’analyse différentielle permettant de comparer un réseau obtenu à partir de données sens avec un réseau contenant des données sens et anti-sens. Nous introduisons ainsi la notion de gènes AS-impacté, qui permet d’identifier des gènes dont les interactions au sein d’un réseau de co-expression sont fortement impactées par la prise en compte de transcrits anti-sens. Pour les données pommier que nous avons étudiées et qui concerne la maturation des fruits et leur conservation à basse température, l’interprétation biologique des résultats de notre analyse différentielle fournit des pistes pertinentes pour une étude expérimentale plus ciblée de gènes ou de voies de signalisation dont l’importance pourrait être sous-estimée sans la prise en compte des données anti-sens.A challenging task in bioinformatics is to decipher cell regulation mechanisms. The objective of this thesis is to study gene networks from apple data with the particularity to integrate anti-sense transcription data. Anti-sense transcripts are mostly non coding RNAs and their different roles in the cell are still not well known. In our study, to explore the role of anti-sense transcripts, we first propose a differential functional analysis that highlights the interest of integrating anti-sense data into a transcriptomic analysis. Then, regarding gene networks, we propose to focus on inference of a core network and we introduce a new differential analysis method that allows to compare a sense network with a sense and anti-sense network. We thus introduce the notion of AS-impacted genes, that allows to identify genes that are highly co-expressed with anti-sense transcripts. We analysed apple data related to ripening of fruits stored in cold storage; biological interpretation of the results of our differential analysisprovides some promising leads to a more targeted experimental study of genes or pathways, which role could be underestimated without integration of anti-sense data

La fonction en biologie. Une critique spinoziste

International audienc

Large scale study of anti-sense regulation by differential network analysis

Abstract Background Systems biology aims to analyse regulation mechanisms into the cell. By mapping interactions observed in different situations, differential network analysis has shown its power to reveal specific cellular responses or specific dysfunctional regulations. In this work, we propose to explore on a large scale the role of natural anti-sense transcription on gene regulation mechanisms, and we focus our study on apple (Malus domestica) in the context of fruit ripening in cold storage. Results We present a differential functional analysis of the sense and anti-sense transcriptomic data that reveals functional terms linked to the ripening process. To develop our differential network analysis, we introduce our inference method of an Extended Core Network; this method is inspired by C3NET, but extends the notion of significant interactions. By comparing two extended core networks, one inferred with sense data and the other one inferred with sense and anti-sense data, our differential analysis is first performed on a local view and reveals AS-impacted genes, genes that have important interactions impacted by anti-sense transcription. The motifs surrounding AS-impacted genes gather transcripts with functions mostly consistent with the biological context of the data used and the method allows us to identify new actors involved in ripening and cold acclimation pathways and to decipher their interactions. Then from a more global view, we compute minimal sub-networks that connect the AS-impacted genes using Steiner trees. Those Steiner trees allow us to study the rewiring of the AS-impacted genes in the network with anti-sense actors. Conclusion Anti-sense transcription is usually ignored in transcriptomic studies. The large-scale differential analysis of apple data that we propose reveals that anti-sense regulation may have an important impact in several cellular stress response mechanisms. Our data mining process enables to highlight specific interactions that deserve further experimental investigations

Construction et Analyse de Réseaux de Gènes Contextuels dans le Domaine Végétal

National audienceUn des problèmes actuels en bioinformatique est de comprendre les mécanismes de régulation au sein d'une cellule. Notre travail concerne l'étude des réseaux de gènes, avec la particularité d'y intégrer les acteurs encore mal connus que sont les ARN anti-sens. Plusieurs études pointent l'importance de la régulation par les anti-sens dans les phénomènes de réponses aux stress. Pour étudier l'impact des anti-sens dans les réseaux de gènes, nous étudions leur comportement dans deux contextes expérimentaux d'un processus biologique de stress

A Constraint Language For University Timetabling Problems

International audienceWe present a domain-specific modeling language for a class of university timetabling problems (UTP) that involve course scheduling, resource allocation and student sectioning. The UTP language combines a formal domain model and a rules formalism to state constraints. The model is based on a multi-scale schedule horizon (i.e., weeks, weekdays and daily slots), a hierarchical course structure (i.e., course parts, part classes and class sessions), and an extended set of resources (i.e., rooms, lecturers, students and student groups). Student groups must be formed to populate classes and class sessions are to be scheduled individually and allocated single or multiple rooms and lecturers. The model encodes sectioning constraints on classes, core scheduling constraints on sessions as well as compatibility, capacity and cardinality constraints on resource allocation. Rules allow to state conjunctions of constraints on selected sets of entities and sessions using a catalog of timetabling predicates and a syntax to group, filter and bind entities and sessions. As for implementation, the UTP language is based on XML and comes with a tool chain that flattens rules into constraints and converts instances to solver-compatible formats. We present here the abstract syntax of the UTP language and alternative constraint programming models developed in MiniZinc and CHR together with preliminary experiments on a real case study

Visualize omics data on networks with Omics Visualizer, a Cytoscape App.

Cytoscape is an open-source software used to analyze and visualize biological networks. In addition to being able to import networks from a variety of sources, Cytoscape allows users to import tabular node data and visualize it onto networks. Unfortunately, such data tables can only contain one row of data per node, whereas omics data often have multiple rows for the same gene or protein, representing different post-translational modification sites, peptides, splice isoforms, or conditions. Here, we present a new app, Omics Visualizer, that allows users to import data tables with several rows referring to the same node, connect them to one or more networks, and visualize the connected data onto networks. Omics Visualizer uses the Cytoscape enhancedGraphics app to show the data either in the nodes (pie visualization) or around the nodes (donut visualization), where the colors of the slices represent the imported values. If the user does not provide a network, the app can retrieve one from the STRING database using the Cytoscape stringApp. The Omics Visualizer app is freely available at https://apps.cytoscape.org/apps/omicsvisualizer