Multi-criteria algorithms for RNA structure prediction

Les méthodes informatiques de prédiction des structures d'ARN reposent sur deux étapes algorithmiques : proposer des structures (l'échantillonnage), et les trier par pertinence (l'évaluation). Une grande diversité de méthodes d'évaluation existe. Certaines reposent sur des modèles physiques, d'autres sur la similarité à des données déjà observées. Cette thèse propose des méthodes de prédiction de structure combinant deux ou plusieurs critères de tri des solutions, divers d'un point de vue de l'échelle de modélisation (structure secondaire, tertiaire), et du type (theory-based, data-based, compatibilité avec des données expérimentales de sondage chimique). Les méthodes proposées identifient le front de Pareto du problème d'optimisation multiobjectif formé par ces critères. Ceci permet d'identifier des solutions (structures) bien notées selon tous les modèles, et également d'étudier la corrélation entre critères. Les approches présentées exploitent les dernières avancées, comme l'identification de modules ou de réseaux d'interactions récurrents, ainsi que les algorithmes d'apprentissage profond. Deux architectures de réseaux de neurones (un RNN et un CNN) sont adaptées des protéines à l'ARN. Un jeu de données d'ARN est proposé pour entrainer ces réseaux : RNANet. Deux outils logiciels sont proposés : BiORSEO, qui prédit la structure secondaire des ARN sur la base de deux critères (l'un énergétique, l'autre relatif à la présence de modules connus). MOARNA, qui propose des structures 3D gros grains sur la base de 4 critères : l'énergie de la structure secondaire, l'énergie en 3D, la compatibilité avec des données expérimentales de sondage chimique, ou la forme d'une famille connue d'ARN si une famille est identifiée.Computational RNA structure prediction methods rely on two major algorithmic steps : a sampling step, to propose new structure solutions, and a scoring step to sort the solutions by relevance. A wide diversity of scoring methods exists. Some rely on physical models, some on the similarity to already observed data (so-called data based methods, or knowledge based methods). This thesis proposes structure prediction methods combining two or more scoring criterions, diverse regarding the modelling scale (secondary structure, tertiary structure), their type (theory-based, knowledge-based, compatibility with experimental chemical probing results). The methods describe the Pareto front of the multi-objective optimization problem formed by these criteria. This allows to identify solutions (structures) well scored on each criterion, and to study the correlation between criterions. The presented approaches exploit the latest progresses in the field, like the identification of modules or recurrent interaction networks, and the use of deep learning algorithms. Two neural network architectures (a RNN and a CNN) are adapted from proteins to RNA. A dataset is created to train these networks: RNANet. Two software tools are proposed: the first is called BiORSEO, which predicts the secondary structure based on two criterions (one relative to the structure’s energy, the other relative to the presence of known modules). The second is MOARNA, which predicts coarse-grained 3D structures based on four criterions: energy in 2D and 3D, compatibility with experimental probing results, and with the shape of a known RNA family if one has been identified

Algorithmes multi-critères pour la prédiction de structures d'ARN

Computational RNA structure prediction methods rely on two major algorithmic steps : a sampling step, to propose new structure solutions, and a scoring step to sort the solutions by relevance. A wide diversity of scoring methods exists. Some rely on physical models, some on the similarity to already observed data (so-called data based methods, or knowledge based methods). This thesis proposes structure prediction methods combining two or more scoring criterions, diverse regarding the modelling scale (secondary structure, tertiary structure), their type (theory-based, knowledge-based, compatibility with experimental chemical probing results). The methods describe the Pareto front of the multi-objective optimization problem formed by these criteria. This allows to identify solutions (structures) well scored on each criterion, and to study the correlation between criterions. The presented approaches exploit the latest progresses in the field, like the identification of modules or recurrent interaction networks, and the use of deep learning algorithms. Two neural network architectures (a RNN and a CNN) are adapted from proteins to RNA. A dataset is created to train these networks: RNANet. Two software tools are proposed: the first is called BiORSEO, which predicts the secondary structure based on two criterions (one relative to the structure’s energy, the other relative to the presence of known modules). The second is MOARNA, which predicts coarse-grained 3D structures based on four criterions: energy in 2D and 3D, compatibility with experimental probing results, and with the shape of a known RNA family if one has been identified.Les méthodes informatiques de prédiction des structures d'ARN reposent sur deux étapes algorithmiques : proposer des structures (l'échantillonnage), et les trier par pertinence (l'évaluation). Une grande diversité de méthodes d'évaluation existe. Certaines reposent sur des modèles physiques, d'autres sur la similarité à des données déjà observées. Cette thèse propose des méthodes de prédiction de structure combinant deux ou plusieurs critères de tri des solutions, divers d'un point de vue de l'échelle de modélisation (structure secondaire, tertiaire), et du type (theory-based, data-based, compatibilité avec des données expérimentales de sondage chimique). Les méthodes proposées identifient le front de Pareto du problème d'optimisation multiobjectif formé par ces critères. Ceci permet d'identifier des solutions (structures) bien notées selon tous les modèles, et également d'étudier la corrélation entre critères. Les approches présentées exploitent les dernières avancées, comme l'identification de modules ou de réseaux d'interactions récurrents, ainsi que les algorithmes d'apprentissage profond. Deux architectures de réseaux de neurones (un RNN et un CNN) sont adaptées des protéines à l'ARN. Un jeu de données d'ARN est proposé pour entrainer ces réseaux : RNANet. Deux outils logiciels sont proposés : BiORSEO, qui prédit la structure secondaire des ARN sur la base de deux critères (l'un énergétique, l'autre relatif à la présence de modules connus). MOARNA, qui propose des structures 3D gros grains sur la base de 4 critères : l'énergie de la structure secondaire, l'énergie en 3D, la compatibilité avec des données expérimentales de sondage chimique, ou la forme d'une famille connue d'ARN si une famille est identifiée

A review of dierent ways to insert known RNA modules into RNAsecondary structures

National audiencecommon approach to RNA folding pipelines is to start by predicting secondarystructures from sequence, and then, tertiary spatial folds from the secondary structure. Inthis review, we are interested in a backward approach: we explore what information fromknown solved RNA 3D structures can be used to improve secondary structure prediction.We propose a Pareto-based method for predicting secondary structures by minimizing abi-objective half energy-based, half knowledge-based potential. The tool outputs the sec-ondary structures from the Pareto set. We use it to compare several approaches to insertRNA modules into the secondary structures and benchmark them against the RNAstrandsecondary structure database. We compare two dierent module data sources, Rna3Dmotifand The RNA Motif Atlas and dierent ways to score the module insertions taking intoaccount module size, module complexity, or module probability according to models likeJAR3D and the recent BayesPairing method

A review of dierent ways to insert known RNA modules into RNAsecondary structures

National audiencecommon approach to RNA folding pipelines is to start by predicting secondarystructures from sequence, and then, tertiary spatial folds from the secondary structure. Inthis review, we are interested in a backward approach: we explore what information fromknown solved RNA 3D structures can be used to improve secondary structure prediction.We propose a Pareto-based method for predicting secondary structures by minimizing abi-objective half energy-based, half knowledge-based potential. The tool outputs the sec-ondary structures from the Pareto set. We use it to compare several approaches to insertRNA modules into the secondary structures and benchmark them against the RNAstrandsecondary structure database. We compare two dierent module data sources, Rna3Dmotifand The RNA Motif Atlas and dierent ways to score the module insertions taking intoaccount module size, module complexity, or module probability according to models likeJAR3D and the recent BayesPairing method

BiORSEO: A bi-objective method to predict RNA secondary structures with pseudoknots using RNA 3D modules

International audienceMOTIVATION:RNA loops have been modelled and clustered from solved 3D structures into ordered collections of recurrent non-canonical interactions called" RNA modules", available in databases. This work explores what information from such modules can be used to improve secondary structure prediction. We propose a bi-objective method for predicting RNA secondary structures by minimizing both an energy-based and a knowledge-based potential. The tool, called BiORSEO, outputs secondary structures corresponding to the optimal solutions from the Pareto set.RESULTS:We compare several approaches to predict secondary structures using inserted RNA modules information: two module data sources, Rna3Dmotif and The RNA 3D Motif Atlas, and different ways to score the module insertions: module size, module complexity, or module probability according to models like JAR3D and BayesPairing. We benchmark them against a large set of known secondary structures, including some state-of-the-art tools, and comment on the usefulness of the half physics-based, half data-based approach.AVAILABILITY:The software is available for download on the EvryRNA website, as well as the datasets

iGEM REPORT: Gotta Detect ‘Em All: a multi-STI sensor based on aptamers

/International audienceNowadays, STIs constitute a major public health issue. Indeed, treatments are often started too late because of belated diagnosis resulting in health problems, such as sterility. If prevention is probably the most effective action one can take to prevent the spread of STIs, early detection could help limit their deleterious effects. In this work, a new diagnosis approach based on aptamers is presented. Bound to paper, they allow the detection of HIV and Hepatitis B biomarkers from a blood sample. The associated device is composed of an anchor, the streptavidin protein, allowing the fixation of the aptamer to the paper via biotin (see graphical abstract). With this system, the HIV-1 Reverse Transcriptase (BBa_K1934060 and BBa_K1934061: protein subunits p51 and p66) and HBsAg (surface antigen of Hepatitis B) are specifically targeted. Then, the biomarker/aptamer complex is detected by two methods. The first one is based on fluorescence. As a proof of concept, a paired ATP/aptamer was used and enabled to successfully detect ATP up to 10 µmol.L-1. However, the signal was not detectable with naked eyes or with a cell phone equipped with blue and green filters either. Therefore, a lateral flow assay with nano-sized latex black beads was tested. This second technique showed that a protein biomarker, such as thrombin, could be complexed with latex beads coated with aptamers, in liquid. Finally, the ultimate step, migration of the latex beads inside paper, needs further optimization. Moreover, to easily handle several STI-tests on a single paper strip, an innovative bio-sourced PLA casing was designed and 3D printed to offer an additional intuitive user-interface

iGEM REPORT: Gotta Detect ‘Em All: a multi-STI sensor based on aptamers

/International audienceNowadays, STIs constitute a major public health issue. Indeed, treatments are often started too late because of belated diagnosis resulting in health problems, such as sterility. If prevention is probably the most effective action one can take to prevent the spread of STIs, early detection could help limit their deleterious effects. In this work, a new diagnosis approach based on aptamers is presented. Bound to paper, they allow the detection of HIV and Hepatitis B biomarkers from a blood sample. The associated device is composed of an anchor, the streptavidin protein, allowing the fixation of the aptamer to the paper via biotin (see graphical abstract). With this system, the HIV-1 Reverse Transcriptase (BBa_K1934060 and BBa_K1934061: protein subunits p51 and p66) and HBsAg (surface antigen of Hepatitis B) are specifically targeted. Then, the biomarker/aptamer complex is detected by two methods. The first one is based on fluorescence. As a proof of concept, a paired ATP/aptamer was used and enabled to successfully detect ATP up to 10 µmol.L-1. However, the signal was not detectable with naked eyes or with a cell phone equipped with blue and green filters either. Therefore, a lateral flow assay with nano-sized latex black beads was tested. This second technique showed that a protein biomarker, such as thrombin, could be complexed with latex beads coated with aptamers, in liquid. Finally, the ultimate step, migration of the latex beads inside paper, needs further optimization. Moreover, to easily handle several STI-tests on a single paper strip, an innovative bio-sourced PLA casing was designed and 3D printed to offer an additional intuitive user-interface

iGEM REPORT: Gotta Detect ‘Em All: a multi-STI sensor based on aptamers

/International audienceNowadays, STIs constitute a major public health issue. Indeed, treatments are often started too late because of belated diagnosis resulting in health problems, such as sterility. If prevention is probably the most effective action one can take to prevent the spread of STIs, early detection could help limit their deleterious effects. In this work, a new diagnosis approach based on aptamers is presented. Bound to paper, they allow the detection of HIV and Hepatitis B biomarkers from a blood sample. The associated device is composed of an anchor, the streptavidin protein, allowing the fixation of the aptamer to the paper via biotin (see graphical abstract). With this system, the HIV-1 Reverse Transcriptase (BBa_K1934060 and BBa_K1934061: protein subunits p51 and p66) and HBsAg (surface antigen of Hepatitis B) are specifically targeted. Then, the biomarker/aptamer complex is detected by two methods. The first one is based on fluorescence. As a proof of concept, a paired ATP/aptamer was used and enabled to successfully detect ATP up to 10 µmol.L-1. However, the signal was not detectable with naked eyes or with a cell phone equipped with blue and green filters either. Therefore, a lateral flow assay with nano-sized latex black beads was tested. This second technique showed that a protein biomarker, such as thrombin, could be complexed with latex beads coated with aptamers, in liquid. Finally, the ultimate step, migration of the latex beads inside paper, needs further optimization. Moreover, to easily handle several STI-tests on a single paper strip, an innovative bio-sourced PLA casing was designed and 3D printed to offer an additional intuitive user-interface

iGEM REPORT: Gotta Detect ‘Em All: a multi-STI sensor based on aptamers

/International audienceNowadays, STIs constitute a major public health issue. Indeed, treatments are often started too late because of belated diagnosis resulting in health problems, such as sterility. If prevention is probably the most effective action one can take to prevent the spread of STIs, early detection could help limit their deleterious effects. In this work, a new diagnosis approach based on aptamers is presented. Bound to paper, they allow the detection of HIV and Hepatitis B biomarkers from a blood sample. The associated device is composed of an anchor, the streptavidin protein, allowing the fixation of the aptamer to the paper via biotin (see graphical abstract). With this system, the HIV-1 Reverse Transcriptase (BBa_K1934060 and BBa_K1934061: protein subunits p51 and p66) and HBsAg (surface antigen of Hepatitis B) are specifically targeted. Then, the biomarker/aptamer complex is detected by two methods. The first one is based on fluorescence. As a proof of concept, a paired ATP/aptamer was used and enabled to successfully detect ATP up to 10 µmol.L-1. However, the signal was not detectable with naked eyes or with a cell phone equipped with blue and green filters either. Therefore, a lateral flow assay with nano-sized latex black beads was tested. This second technique showed that a protein biomarker, such as thrombin, could be complexed with latex beads coated with aptamers, in liquid. Finally, the ultimate step, migration of the latex beads inside paper, needs further optimization. Moreover, to easily handle several STI-tests on a single paper strip, an innovative bio-sourced PLA casing was designed and 3D printed to offer an additional intuitive user-interface

iGEM REPORT: Gotta Detect ‘Em All: a multi-STI sensor based on aptamers

/International audienceNowadays, STIs constitute a major public health issue. Indeed, treatments are often started too late because of belated diagnosis resulting in health problems, such as sterility. If prevention is probably the most effective action one can take to prevent the spread of STIs, early detection could help limit their deleterious effects. In this work, a new diagnosis approach based on aptamers is presented. Bound to paper, they allow the detection of HIV and Hepatitis B biomarkers from a blood sample. The associated device is composed of an anchor, the streptavidin protein, allowing the fixation of the aptamer to the paper via biotin (see graphical abstract). With this system, the HIV-1 Reverse Transcriptase (BBa_K1934060 and BBa_K1934061: protein subunits p51 and p66) and HBsAg (surface antigen of Hepatitis B) are specifically targeted. Then, the biomarker/aptamer complex is detected by two methods. The first one is based on fluorescence. As a proof of concept, a paired ATP/aptamer was used and enabled to successfully detect ATP up to 10 µmol.L-1. However, the signal was not detectable with naked eyes or with a cell phone equipped with blue and green filters either. Therefore, a lateral flow assay with nano-sized latex black beads was tested. This second technique showed that a protein biomarker, such as thrombin, could be complexed with latex beads coated with aptamers, in liquid. Finally, the ultimate step, migration of the latex beads inside paper, needs further optimization. Moreover, to easily handle several STI-tests on a single paper strip, an innovative bio-sourced PLA casing was designed and 3D printed to offer an additional intuitive user-interface