Editorial : Curriculum Applications in Microbiology: Bioinformatics in the Classroom

We would like to thank all of the authors who submitted to this special topic, committed to the furthering of academic creativity, excellence, and rigor in the challenging and virtual instructional world of SARS-CoV-2 (COVID-19). To you and all of our educators globally, you are indispensable.Non peer reviewedPublisher PD

Contemporary Challenges and Solutions

CA18131 CP16/00163 NIS-3317 NIS-3318 decision 295741 C18/BM/12585940The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 “ML4Microbiome” that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.publishersversionpublishe

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies

Precision Medicine Informatics: Principles, Prospects, and Challenges

Precision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace of the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological perspective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses how other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide-scale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.Comment: 22 pages, 8 figures, 5 tables, journal pape

Sistema de información de Gliomas y análisis de expresión diferencial de líneas celulares de Glioblastoma Multiforme

Trabajo fin de máster en Bioinformática y Biología ComputacionalThe Glioblastoma Multiforme (GBM) is the most aggressive brain tumor in the adult, with an average survival of 15 months. It is currently a research object by the HM hospitals research foundation (FiHM), in order to discover biomarkers aiming at becoming therapeutical targets for patient treatment and which in turn can cut down experimental test cost. The Foundation counts on a large volume of data regarding tumor line experiments, otherwise untreatable on a manual basis. Bioinformatics tools have been employed in order to design an information system and a data model with facilitates usage, storage and data retrieval. On the other hand, a differential expression analysis has been carried out among U373, U87 and LN229 glioblastoma tumor lines, by means of Nextpresso pipeline. Different expression outcome discloses mismatches among GBM tumor lines, amounting 4357 differentially expressed genes (DE) between lines U373 and U87, 6007 genes between lines LN229 and U373 as well as 4860 between lines LN229 and U87. Distinct active routes related to angiogenesis have been located too, chiefly in those lines with a lesser proliferation of blood vessels, concurring with the output of previous FiHM experimental trials such as ELISA and inmunofluorescence. Accordingly, we suggest the implementation of further surveys regarding relevant genes for their likely utilization as biomarkersEl Glioblastoma Multiforme (GBM) se trata del tumor cerebral más agresivo en el adulto, con una supervivencia media de 15 meses. Es objeto de estudio por la fundación de investigación HM hospitales (FiHM) para encontrar biomarcadores que sirvan de dianas terapéuticas para el tratamiento de pacientes y puedan reducir los costes de ensayos experimentales. La fundación dispone de gran cantidad de datos de experimentos de líneas tumorales difícilmente tratables de manera manual. Se han empleado herramientas bioinformáticas para diseñar un sistema de información y un modelo de datos que facilita el acceso, almacenamiento y recuperación de los datos. Además, se ha realizado un análisis de expresión diferencial entre las líneas tumorales de Glioblastoma U373, U87 y LN229, mediante el pipeline de Nextpresso. Los resultados de expresión diferencial revelan diferencias entre las líneas tumorales de GBM con un total de 4357 genes diferencialmente expresados (DE) entre las líneas U373 y U87, 6007 genes entre las líneas LN229 y U373 y 4860 entre las líneas LN229 y U87. También se han encontrado diferentes rutas activas relacionadas con la angiogénesis en mayor medida en las líneas con menor proliferación de vasos sanguíneos, coincidiendo con resultados de previos experimentos de la FiHM como ELISA e inmunofluorescencia. Por lo que sugerimos nuevos estudios sobre genes relevantes de este estudio para un posible uso como biomarcadore

Precision medicine needs pioneering clinical bioinformaticians.

Success in precision medicine depends on accessing high-quality genetic and molecular data from large, well-annotated patient cohorts that couple biological samples to comprehensive clinical data, which in conjunction can lead to effective therapies. From such a scenario emerges the need for a new professional profile, an expert bioinformatician with training in clinical areas who can make sense of multi-omics data to improve therapeutic interventions in patients, and the design of optimized basket trials. In this review, we first describe the main policies and international initiatives that focus on precision medicine. Secondly, we review the currently ongoing clinical trials in precision medicine, introducing the concept of 'precision bioinformatics', and we describe current pioneering bioinformatics efforts aimed at implementing tools and computational infrastructures for precision medicine in health institutions around the world. Thirdly, we discuss the challenges related to the clinical training of bioinformaticians, and the urgent need for computational specialists capable of assimilating medical terminologies and protocols to address real clinical questions. We also propose some skills required to carry out common tasks in clinical bioinformatics and some tips for emergent groups. Finally, we explore the future perspectives and the challenges faced by precision medicine bioinformatics

Information Technology Resources for Precision Medicine

Healthcare delivery organizations have an opportunity to use insights from the emerging field of precision medicine to improve the quality of patient care; however, information technology resources to fully enable precision medicine are lacking. The specific problem was that people have limited information to use when making decisions regarding information technology resources for precision medicine in healthcare delivery organizations given the emerging state of precision medicine. The purpose of this Delphi study was to determine how a panel of precision medicine information technology experts view information technology resource importance and feasibility for precision medicine in healthcare delivery organizations. The research question asked how does a panel of precision medicine information technology experts view information technology resource importance and feasibility for precision medicine in healthcare delivery organizations. The resource-based view of the firm served as the conceptual framework. Data were collected in three consecutive rounds of questionnaires. Thematic analysis was performed to develop a list of information technology resources that were rated by participants in terms of importance and feasibility, which were analyzed to assess if there was consensus among the participants. Of the 159 information technology resources that were rated, 77 information technology resources were considered important and feasible. The study results could lead to positive social change at individual, organizational, and societal levels. At a societal level, the study results could give rise to positive social change by creating a shared vision of what is needed to fulfill information technology resource requirements for precision medicine in healthcare delivery organizations and enable progress toward improved healthcare quality