SILE: A Method for the Efficient Management of Smart Genomic Information

[ES] A lo largo de las últimas dos décadas, los datos generados por las tecnologías de secuenciación de nueva generación han revolucionado nuestro entendimiento de la biología humana. Es más, nos han permitido desarrollar y mejorar nuestro conocimiento sobre cómo los cambios (variaciones) en el ADN pueden estar relacionados con el riesgo de sufrir determinadas enfermedades. Actualmente, hay una gran cantidad de datos genómicos disponibles de forma pública, que son consultados con frecuencia por la comunidad científica para extraer conclusiones significativas sobre las asociaciones entre los genes de riesgo y los mecanismos que producen las enfermedades. Sin embargo, el manejo de esta cantidad de datos que crece de forma exponencial se ha convertido en un reto. Los investigadores se ven obligados a sumergirse en un lago de datos muy complejos que están dispersos en más de mil repositorios heterogéneos, representados en múltiples formatos y con diferentes niveles de calidad. Además, cuando se trata de resolver una tarea en concreto sólo una pequeña parte de la gran cantidad de datos disponibles es realmente significativa. Estos son los que nosotros denominamos datos "inteligentes". El principal objetivo de esta tesis es proponer un enfoque sistemático para el manejo eficiente de datos genómicos inteligentes mediante el uso de técnicas de modelado conceptual y evaluación de calidad de los datos. Este enfoque está dirigido a poblar un sistema de información con datos que sean lo suficientemente accesibles, informativos y útiles para la extracción de conocimiento de valor.[CA] Al llarg de les últimes dues dècades, les dades generades per les tecnologies de secuenciació de nova generació han revolucionat el nostre coneixement sobre la biologia humana. És mes, ens han permès desenvolupar i millorar el nostre coneixement sobre com els canvis (variacions) en l'ADN poden estar relacionats amb el risc de patir determinades malalties. Actualment, hi ha una gran quantitat de dades genòmiques disponibles de forma pública i que són consultats amb freqüència per la comunitat científica per a extraure conclusions significatives sobre les associacions entre gens de risc i els mecanismes que produeixen les malalties. No obstant això, el maneig d'aquesta quantitat de dades que creix de forma exponencial s'ha convertit en un repte i els investigadors es veuen obligats a submergir-se en un llac de dades molt complexes que estan dispersos en mes de mil repositoris heterogenis, representats en múltiples formats i amb diferents nivells de qualitat. A m\és, quan es tracta de resoldre una tasca en concret només una petita part de la gran quantitat de dades disponibles és realment significativa. Aquests són els que nosaltres anomenem dades "intel·ligents". El principal objectiu d'aquesta tesi és proposar un enfocament sistemàtic per al maneig eficient de dades genòmiques intel·ligents mitjançant l'ús de tècniques de modelatge conceptual i avaluació de la qualitat de les dades. Aquest enfocament està dirigit a poblar un sistema d'informació amb dades que siguen accessibles, informatius i útils per a l'extracció de coneixement de valor.[EN] In the last two decades, the data generated by the Next Generation Sequencing Technologies have revolutionized our understanding about the human biology. Furthermore, they have allowed us to develop and improve our knowledge about how changes (variants) in the DNA can be related to the risk of developing certain diseases. Currently, a large amount of genomic data is publicly available and frequently used by the research community, in order to extract meaningful and reliable associations among risk genes and the mechanisms of disease. However, the management of this exponential growth of data has become a challenge and the researchers are forced to delve into a lake of complex data spread in over thousand heterogeneous repositories, represented in multiple formats and with different levels of quality. Nevertheless, when these data are used to solve a concrete problem only a small part of them is really significant. This is what we call "smart" data. The main goal of this thesis is to provide a systematic approach to efficiently manage smart genomic data, by using conceptual modeling techniques and the principles of data quality assessment. The aim of this approach is to populate an Information System with data that are accessible, informative and actionable enough to extract valuable knowledge.This thesis was supported by the Research and Development Aid Program (PAID-01-16) under the FPI grant 2137.León Palacio, A. (2019). SILE: A Method for the Efficient Management of Smart Genomic Information [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/131698TESISPremios Extraordinarios de tesis doctorale

Improvement of fire reaction and mould growth resistance of a new bio-based thermal insulation material

In the present paper, the performance of an innovative thermal insulation rigid board is evaluated in terms of fire behaviour and fungal resistance. The board is based on vegetal pith and a natural gum (corn pith and sodium alginate) and it is completely compostable. This new composite was developed in previous work. Here boric acid, aluminium hydroxide and ammonium polyphosphate are used as fire retardants and montan wax, acetic acid and lactic acid are used as water repellent and fungicides respectively. Interactions between these different treatments is investigated. Both flaming and smouldering combustion processes of the different formulations are evaluated by small-scale techniques which include pyrolysis microcalorimetry and thermogravimetric analysis. A medium-scale device is also designed in order to study the impact of the different additives to the smouldering kinetics. Fire behaviour tests show that good improvement is obtained, both in flaming and smouldering combustion when boric acid is added. Although smouldering is not avoided in any case, the addition of 8% of boric acid or aluminium hydroxide slows down the speed of combustion propagation. The effect of the different additives on the moisture content and mould growth at 97% RH and 27 °C is analysed. Under such severe conditions none of the additives is able to prevent mould growth, with the exception of boric acid. None or marginal mould growth was observed on samples containing 8% of boric acid although moisture content was higher than the other cases.Peer ReviewedPreprin

Controlling chaotic transport in two-dimensional periodic potentials

We uncover and characterize different chaotic transport scenarios in perfect two-dimensional periodic potentials by controlling the chaotic dynamics of particles subjected to periodic external forces in the absence of a ratchet effect i.e., with no directed transport by symmetry breaking of zero-mean forces . After identifying relevant symmetries of the equations of motion, analytical estimates in parameter space for the occurrence of different transport scenarios are provided and confirmed by numerical simulations. These scenarios are highly sensitive to variations of the system’s asymmetry parameters, including the eccentricity of the two-dimensional periodic potential and the direction of dc and ac forces, which could be useful for particle sorting purposes in those cases where chaos is unavoidablePostprint (published version

Conceptual Model of Proteins

The following conceptual model represents the knowledge associated to the protein domain, including protein-protein interactions, pathways, functionality, post translational modifications, and association with disease.León Palacio, A.; Pastor López, O. (2020). Conceptual Model of Proteins. http://hdl.handle.net/10251/14788

Towards a Shared, Conceptual Model-Based Understanding of Proteins and Their Interactions

[EN] Understanding the human genome is a big research challenge. The huge complexity and amount of genome data require extremely effective and efficient data management policies. A first crucial point is to obtain a shared understanding of the domain, which becomes a very hard task considering the number of different genome data sources. To make things more complicated, those data sources deal with different parts of genome-based information: we not only need to understand them well, but also to integrate and intercommunicate all the relevant information. The protein perspective is a good example: rich, well-known repositories such as UniProt provide a lot of valuable information that it is not easy to interpret and manage when we want to generate useful results. Proteomes and basic information, protein-protein interaction, protein structure, protein processing events, protein function, etc. provide a lot of information is that needs to be conceptually characterized and delimited. To facilitate the essential common understanding of the domain, this paper uses the case of proteins to analyze the data provided by Uniprot in order to make a sound conceptualization work for identifying the relevant domain concepts. A conceptual model of proteins is the result of this conceptualization process, explained in detail in this work. This holistic conceptual model of proteins presented in this paper is the result of achieving a precise ontological commitment. It establishes concepts and their relationships that are significant in order to have a solid basis to efficiently manage relevant genome data related to proteins.This work was supported in part by the Spanish State Research Agency under Grant TIN2016-80811-P, and in part by the Generalidad Valenciana under Grant PROMETEO/2018/176, co-financed with ERDF.León-Palacio, A.; Pastor López, O. (2021). Towards a Shared, Conceptual Model-Based Understanding of Proteins and Their Interactions. IEEE Access. 9:73608-73623. https://doi.org/10.1109/ACCESS.2021.3080040S7360873623

Conceptual Modeling of Proteins Based on UniProt

Clinical disease states reflect the interaction of a myriad of genetic and environ-mental contributions. In this context, a major challenge is to develop information systems and algorithms that can describe this complexity to facilitate an under-standing of the disease mechanisms as well as to guide the development and ap-plication of therapies. This work focuses on describing how a shared understand-ing of the domain can be achieved by analyzing the conceptual precision of the main concepts that should constitute the ontological commitment that is strictly required when studying an important area of research: the role that proteins play in the different functions carried out within the cell of any living systems. The contribution of this paper is to show the conceptual complexity of the UniProtKB database, and to let users face and manage that complexity by providing a sound and well-grounded conceptual background to achieve the shared understanding of the domain, a crucial aspect to allow the design of any fruitful data analytics-based strategy. A conceptual model for proteins is carefully developed taking the UniProtKB database as data source, explaining in detail the problems that have been faced together with their corresponding solutions.León Palacio, A.; Pastor López, O. (2020). Conceptual Modeling of Proteins Based on UniProt. http://hdl.handle.net/10251/14561

Enhancing Precision Medicine: A Big Data-Driven Approach for the Management of Genomic Data

[EN] The management of the exponential growth of data that Next Generation Sequencing techniques produce has become a challenge for researchers that are forced to delve into an ocean of complex data in order to extract new insights to unravel the secrets of human diseases. Initially, this can be faced as a Big Data-related problem, but the genomic data have particular and relevant challenges that make them different from other Big Data working domains. Genomic data are much more heterogeneous; they are spread in hundreds of repositories, represented in multiple formats, and have different levels of quality. In addition, getting meaningful conclusions from genomic data requires considering all of the relevant surrounding knowledge that is under continuous evolution. In this scenario, the precise identification of what makes Genome Data Management so different is essential in order to provide effective Big Data-based solutions. Genomic projects require dealing with the technological problems associated with data management, nomenclature standards, and quality issues that only robust Information Systems that use Big Data techniques can provide. The main contribution of this paper is to present a Big Data-driven approach for managing genomic data, that is adapted to the particularities of the domain and to show its applicability to improve genetic diagnoses, which is the core of the development of accurate Precision Medicine.This work was supported by the Spanish State Research Agency (grant number TIN2016-80811-P) and the Generalitat Valenciana (grant number PROMETEO/2018/176), and co-financed with ERDF.León-Palacio, A.; Pastor López, O. (2021). Enhancing Precision Medicine: A Big Data-Driven Approach for the Management of Genomic Data. Big Data Research. 26:1-11. https://doi.org/10.1016/j.bdr.2021.100253S1112

Aspectos fitosanitarios del cultivo del azafrán del Jiloca: enfermedades de etiología bacteriana

Proyecto INIA PET 2007-14-C05-0