Avances de la bioinformática en Costa Rica: vista retrospectiva y perspectivas / Bioinformatics advances in Costa Rica: retrospective view and perspectives

Resumen Introducción: La disciplina científica de la bioinformática tiene el potencial de generar aplicaciones innovadoras para las sociedades humanas. Costa Rica, pequeña en tamaño y población en comparación con otros países de América Latina, ha ido adoptando la disciplina de manera progresiva. El reconocer los avances permite determinar hacia dónde puede dirigirse el país en este campo, así como su contribución a la región latinoamericana. Objetivo: En este manuscrito se reporta evidencia de la evolución de la bioinformática en Costa Rica, para identificar debilidades y fortalezas que permitan definir acciones a futuro. Métodos: Se realizaron búsquedas en bases de datos de publicaciones científicas y repositorios de secuencias, así como información de actividades de capacitación, redes, infraestructura, páginas web y fuentes de financiamiento. Resultados: Se observan avances importantes desde el 2010, incluyendo un aumento en oportunidades de entrenamiento y número de publicaciones, aportes significativos a las bases de datos de secuencias y conexiones por medio de redes. Sin embargo, ciertas áreas, como la masa crítica y la financiación requieren más desarrollo. La comunidad científica y sus patrocinadores deben promover la investigación basada en bioinformática, invertir en la formación de estudiantes de posgrado, aumentar la formación de profesionales, crear oportunidades laborales para carreras en bioinformática y promover colaboraciones internacionales a través de redes. Conclusiones: Se sugiere que para experimentar los beneficios de las aplicaciones de la bioinformática se deben fortalecer tres aspectos clave: la comunidad científica, la infraestructura de investigación y las oportunidades de financiamiento. El impacto de tal inversión sería el desarrollo de proyectos ambiciosos pero factibles y colaboraciones extendidas dentro de la región latinoamericana. Esto permitiría realizar contribuciones significativas para abordar los desafíos globales y la aplicación de nuevos enfoques de investigación, innovación y transferencia de conocimiento para el desarrollo de la economía, dentro de un marco de ética de la investigación.

Abstract Introduction: The scientific discipline of bioinformatics has the potential to generate innovative applications for human societies. Costa Rica, small in size and population compared to other Latin American countries, has been progressively adopting the discipline. Recognizing progress makes it possible to determine where the country can go in this field, as well as its contribution to the Latin American region. Objective: This manuscript reports evidence of the evolution of bioinformatics in Costa Rica, to identify weaknesses and strengths allowing future actions plans. Methods: We searched databases of scientific publications and sequence repositories, as well as information on training activities, networks, infrastructure, web pages and funding sources. Results: Important advances have been observed since 2010, such as increases in training opportunities and the number of publications, significant contributions to the sequence databases and connections through networks. However, areas such as critical mass and financing require further development. The scientific community and its sponsors should promote bioinformatics-based research, invest in graduate student training, increase professional training, create career opportunities in bioinformatics, and promote international collaborations through networks. Conclusions: It is suggested that in order to experience the benefits of bioinformatics applications, three key aspects must be strengthened: the scientific community, the research infrastructure, and funding opportunities. The impact of such investment would be the development of ambitious but feasible projects and extended collaborations within the Latin American region and abroad. This would allow significant contributions to address global challenges and the implementation of new approaches to research, innovation and knowledge transfer for the development of the economy, within an ethics of research framework.

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions.

Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

Recent Trends in Cancer Genomics and Bioinformatics Tools Development.

We overview recent research trends in cancer genomics, bioinformatics tools development and medical genetics, based on results discussed in papers collections "Medical Genetics, Genomics and Bioinformatics" (https://www [...].

Top Trends in Multiomics Research: Evaluation of 52 Published Studies and New Ways of Thinking Terminology and Visual Displays.

Multiomics study designs have significantly increased understanding of complex biological systems. The multiomics literature is rapidly expanding and so is their heterogeneity. However, the intricacy and fragmentation of omics data are impeding further research. To examine current trends in multiomics field, we reviewed 52 articles from PubMed and Web of Science, which used an integrated omics approach, published between March 2006 and January 2021. From studies, data regarding investigated loci, species, omics type, and phenotype were extracted, curated, and streamlined according to standardized terminology, and summarized in a previously developed graphical summary. Evaluated studies included 21 omics types or applications of omics technology such as genomics, transcriptomics, metabolomics, epigenomics, environmental omics, and pharmacogenomics, species of various phyla including human, mouse, Arabidopsis thaliana, Saccharomyces cerevisiae, and various phenotypes, including cancer and COVID-19. In the analyzed studies, diverse methods, protocols, results, and terminology were used and accordingly, assessment of the studies was challenging. Adoption of standardized multiomics data presentation in the future will further buttress standardization of terminology and reporting of results in systems science. This shall catalyze, we suggest, innovation in both science communication and laboratory medicine by making available scientific knowledge that is easier to grasp, share, and harness toward medical breakthroughs.

Influential Usage of Big Data and Artificial Intelligence in Healthcare.

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

The evolving landscape of untargeted metabolomics.

AIMS: Untargeted Metabolomics is a "hypothesis-generating discovery strategy" that compares groups of samples (e.g., cases vs controls); identifies the metabolome and establishes (early signs of) perturbations. Targeted Metabolomics helped gather key information in life sciences and disclosed novel strategies for the treatment of major clinical entities (e.g., malignancy, cardiovascular diabetes mellitus, drug toxicity). Because of its relevance in biomarker discovery, attention is now devoted to improving the translational potential of untargeted Metabolomics. DATA SYNTHESIS: Expertise in laboratory medicine and in bioinformatics helps solve challenges/pitfalls that may bias metabolite profiling in untargeted Metabolomics. Clinical validation (availability/reliability of analytical instruments) and profitability (how many people will use the test) are mandatory steps for potential biomarkers. Biomarkers to predict individual patient response, patient populations that will best respond to specific strategies and/or approaches for an optimal response to treatment are now being developed. Additional help is expected from professional, and regulatory Agencies as to guidelines for study design and data acquisition and analysis, to be applied from the very beginning of a project. Evidence from food, plant, human, environmental, and animal research argues for the need of miniaturized approaches that employ low-cost, easy to use, mobile devices. ELISA kits with such characteristics that employ targeted metabolites are already available. CONCLUSIONS: Improving knowledge of the mechanisms behind the disease status (pathophysiology) will help untargeted Metabolomics gather a direct positive impact on welfare and industrial advancements, and fade uncertainties perceived by regulators/payers and patients concerning variables related to miniaturised instruments and user-friendly software and databases.

Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm.

Accurately identifying protein-ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein-ATP binding residues; however, as new machine-learning techniques are being developed, the prediction performance could be further improved. In this paper, an ensemble predictor that combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three subclassifiers have been developed, including a multi-incepResNet-based predictor, a multi-Xception-based predictor, and a LightGBM predictor. The final prediction result is the combination of outputs from three subclassifiers with optimized weight distribution. We examined the performance of our proposed predictor using two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor achieved area under the curve (AUC) values of 0.925 and 0.902 and Matthews Correlation Coefficient (MCC) values of 0.639 and 0.642, respectively, which are both better than other state-of-art prediction methods.

Single-Cell RNA Sequencing to Disentangle the Blood System.

The blood system is often represented as a tree-like structure with stem cells that give rise to mature blood cell types through a series of demarcated steps. Although this representation has served as a model of hierarchical tissue organization for decades, single-cell technologies are shedding new light on the abundance of cell type intermediates and the molecular mechanisms that ensure balanced replenishment of differentiated cells. In this Brief Review, we exemplify new insights into blood cell differentiation generated by single-cell RNA sequencing, summarize considerations for the application of this technology, and highlight innovations that are leading the way to understand hematopoiesis at the resolution of single cells. Graphic Abstract: A graphic abstract is available for this article.

Power of proteomics and progress in precision medicine - 13th central and eastern European proteomic conference (CEEPC), Ustron, Poland.

INTRODUCTION: Central and Eastern European Proteomic Conference (CEEPC) provides a platform for researchers to discuss multi-disciplinary integrated approaches to address a range of challenges from present day viral pandemic to on-going progress in Precision Medicine. CEEPC brings together various multi-omics entwined with novel enabling technologies, thus facilitating conceptual advances from cell to society for the benefit of mankind. AREAS COVERED: Proteomic methodologies, databases and software has revolutionized our ability to assess protein interactions and cellular changes, allowing the establishment of biological connections and identification of important cellular regulatory proteins and pathways previously unknown or not fully understood. Additionally, Mass spectrometry (MS) remains a major driving force in the field of 'multi-omics' and a powerful technology for the structural characterization of biomolecules and for analysis of proteins and small molecules such as lipids, sugars and metabolites. Combination of measurements from proteomics, genomics, epigenomics, transcriptomics and metabolomics, present a powerful decision-making format allowing deeper interpretation of a disease scenario in Precision medicine. EXPERT COMMENTARY: Precision Medicine offers novel and promising ways to identify and treat a wide range of diseases. The future success of these therapies will be underpinned by novel proteo-genomic approaches linked to sophisticated databases to evaluate and predict drug-patient interactions.

Multi-omics Visualization Platform: An extensible Galaxy plug-in for multi-omics data visualization and exploration.

BACKGROUND: Proteogenomics integrates genomics, transcriptomics, and mass spectrometry (MS)-based proteomics data to identify novel protein sequences arising from gene and transcript sequence variants. Proteogenomic data analysis requires integration of disparate 'omic software tools, as well as customized tools to view and interpret results. The flexible Galaxy platform has proven valuable for proteogenomic data analysis. Here, we describe a novel Multi-omics Visualization Platform (MVP) for organizing, visualizing, and exploring proteogenomic results, adding a critically needed tool for data exploration and interpretation. FINDINGS: MVP is built as an HTML Galaxy plug-in, primarily based on JavaScript. Via the Galaxy API, MVP uses SQLite databases as input-a custom data type (mzSQLite) containing MS-based peptide identification information, a variant annotation table, and a coding sequence table. Users can interactively filter identified peptides based on sequence and data quality metrics, view annotated peptide MS data, and visualize protein-level information, along with genomic coordinates. Peptides that pass the user-defined thresholds can be sent back to Galaxy via the API for further analysis; processed data and visualizations can also be saved and shared. MVP leverages the Integrated Genomics Viewer JavaScript framework, enabling interactive visualization of peptides and corresponding transcript and genomic coding information within the MVP interface. CONCLUSIONS: MVP provides a powerful, extensible platform for automated, interactive visualization of proteogenomic results within the Galaxy environment, adding a unique and critically needed tool for empowering exploration and interpretation of results. The platform is extensible, providing a basis for further development of new functionalities for proteogenomic data visualization.

Tumor Copy Number Deconvolution Integrating Bulk and Single-Cell Sequencing Data.

Characterizing intratumor heterogeneity (ITH) is crucial to understanding cancer development, but it is hampered by limits of available data sources. Bulk DNA sequencing is the most common technology to assess ITH, but involves the analysis of a mixture of many genetically distinct cells in each sample, which must then be computationally deconvolved. Single-cell sequencing is a promising alternative, but its limitations-for example, high noise, difficulty scaling to large populations, technical artifacts, and large data sets-have so far made it impractical for studying cohorts of sufficient size to identify statistically robust features of tumor evolution. We have developed strategies for deconvolution and tumor phylogenetics combining limited amounts of bulk and single-cell data to gain some advantages of single-cell resolution with much lower cost, with specific focus on deconvolving genomic copy number data. We developed a mixed membership model for clonal deconvolution via non-negative matrix factorization balancing deconvolution quality with similarity to single-cell samples via an associated efficient coordinate descent algorithm. We then improve on that algorithm by integrating deconvolution with clonal phylogeny inference, using a mixed integer linear programming model to incorporate a minimum evolution phylogenetic tree cost in the problem objective. We demonstrate the effectiveness of these methods on semisimulated data of known ground truth, showing improved deconvolution accuracy relative to bulk data alone.

Drug resistance in Giardia: Mechanisms and alternative treatments for Giardiasis.

The use of chemotherapeutic drugs is the main resource against clinical giardiasis due to the lack of approved vaccines. Resistance of G. duodenalis to the most used drugs to treat giardiasis, metronidazole and albendazole, is a clinical issue of growing concern and yet unknown impact, respectively. In the search of new drugs, the completion of the Giardia genome project and the use of biochemical, molecular and bioinformatics tools allowed the identification of ligands/inhibitors for about one tenth of ≈150 potential drug targets in this parasite. Further, the synthesis of second generation nitroimidazoles and benzimidazoles along with high-throughput technologies have allowed not only to define overall mechanisms of resistance to metronidazole but to screen libraries of repurposed drugs and new pharmacophores, thereby increasing the known arsenal of anti-giardial compounds to some hundreds, with most demonstrating activity against metronidazole or albendazole-resistant Giardia. In particular, cysteine-modifying agents which include omeprazole, disulfiram, allicin and auranofin outstand due to their pleiotropic activity based on the extensive repertoire of thiol-containing proteins and the microaerophilic metabolism of this parasite. Other promising agents derived from higher organisms including phytochemicals, lactoferrin and propolis as well as probiotic bacteria/fungi have also demonstrated significant potential for therapeutic and prophylactic purposes in giardiasis. In this context the present chapter offers a comprehensive review of the current knowledge, including commonly prescribed drugs, causes of therapeutic failures, drug resistance mechanisms, strategies for the discovery of new agents and alternative drug therapies.

Messages from the third International Conference on Clinical Metagenomics (ICCMg3).

Clinical metagenomics (CMg), referring to as the application of metagenomic sequencing of clinical samples in order to recover clinically-relevant information, has been rapidly evolving these last years. Following this trend, we held the third International Conference on Clinical Metagenomics (ICCMg3) in Geneva in October 2018. During the two days of the conference, several aspects of CMg were addressed, which we propose to summarize in the present manuscript. During this ICCMg3, we kept on following the progresses achieved worldwide on clinical metagenomics, but also this year in clinical genomics. Besides, the use of metagenomics in cancer diagnostic and management was addressed. Some new challenges have also been raised such as the way to report clinical (meta)genomics output to clinicians and the pivotal place of ethics in this expanding field.

Computational Strategies for Niche-Dependent Cell Conversion to Assist Stem Cell Therapy.

The field of regenerative medicine has blossomed in recent decades. However, the ultimate goal of tissue regeneration - replacing damaged or aged cells with healthy functioning cells - still faces a number of challenges. In particular, better understanding of the role of the cellular niche in shaping stem cell phenotype and conversion would aid in improving current protocols for stem cell therapies. In this regard, the implementation of novel computational approaches that consider the niche effect on stem cells would be valuable. Here we discuss current problems in stem cell transplantation and rejuvenation, and we propose computational strategies to control niche-dependent cell conversion to overcome them.

Data Lakes, Clouds, and Commons: A Review of Platforms for Analyzing and Sharing Genomic Data.

Data commons collate data with cloud computing infrastructure and commonly used software services, tools, and applications to create biomedical resources for the large-scale management, analysis, harmonization, and sharing of biomedical data. Over the past few years, data commons have been used to analyze, harmonize, and share large-scale genomics datasets. Data ecosystems can be built by interoperating multiple data commons. It can be quite labor intensive to curate, import, and analyze the data in a data commons. Data lakes provide an alternative to data commons and simply provide access to data, with the data curation and analysis deferred until later and delegated to those that access the data. We review software platforms for managing, analyzing, and sharing genomic data, with an emphasis on data commons, but also cover data ecosystems and data lakes.

Identification of differentially expressed genes and enriched pathways in lung cancer using bioinformatics analysis.

Lung cancer is the leading cause of cancer­associated mortality worldwide. The aim of the present study was to identify the differentially expressed genes (DEGs) and enriched pathways in lung cancer by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid microarray data of 31 pairs of lung cancer tissues and matched normal samples (GSE19804) were obtained from the Gene Expression Omnibus database. Significance analysis of the gene expression profile was used to identify DEGs between cancer tissues and normal tissues, and a total of 1,970 DEGs, which were significantly enriched in biological processes, were screened. Through the Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, 77 KEGG pathways associated with lung cancer were identified, among which the Toll­like receptor pathway was observed to be important. Protein­protein interaction network analysis extracted 1,770 nodes and 10,667 edges, and identified 10 genes with key roles in lung cancer with highest degrees, hub centrality and betweenness. Additionally, the module analysis of protein­protein interactions revealed that 'chemokine signaling pathway', 'cell cycle' and 'pathways in cancer' had a close association with lung cancer. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of lung cancer, and certain genes (including advanced glycosylation end­product specific receptor and epidermal growth factor receptor) may be used as candidate target molecules to diagnose, monitor and treat lung cancer.

Bioinformatics analysis of differentially expressed genes in rotator cuff tear patients using microarray data.

BACKGROUND: Rotator cuff tear (RCT) is a common shoulder disorder in the elderly. Muscle atrophy, denervation and fatty infiltration exert secondary injuries on torn rotator cuff muscles. It has been reported that satellite cells (SCs) play roles in pathogenic process and regenerative capacity of human RCT via regulating of target genes. This study aims to complement the differentially expressed genes (DEGs) of SCs that regulated between the torn supraspinatus (SSP) samples and intact subscapularis (SSC) samples, identify their functions and molecular pathways. METHODS: The gene expression profile GSE93661 was downloaded and bioinformatics analysis was made. RESULTS: Five hundred fifty one DEGs totally were identified. Among them, 272 DEGs were overexpressed, and the remaining 279 DEGs were underexpressed. Gene ontology (GO) and pathway enrichment analysis of target genes were performed. We furthermore identified some relevant core genes using gene-gene interaction network analysis such as GNG13, GCG, NOTCH1, BCL2, NMUR2, PMCH, FFAR1, AVPR2, GNA14, and KALRN, that may contribute to the understanding of the molecular mechanisms of secondary injuries in RCT. We also discovered that GNG13/calcium signaling pathway is highly correlated with the denervation atrophy pathological process of RCT. CONCLUSION: These genes and pathways provide a new perspective for revealing the underlying pathological mechanisms and therapy strategy of RCT.

Mutalisk: a web-based somatic MUTation AnaLyIS toolKit for genomic, transcriptional and epigenomic signatures.

Somatic genome mutations occur due to combinations of various intrinsic/extrinsic mutational processes and DNA repair mechanisms. Different molecular processes frequently generate different signatures of somatic mutations in their own favored contexts. As a result, the regional somatic mutation rate is dependent on the local DNA sequence, the DNA replication/RNA transcription dynamics and epigenomic chromatin organization landscape in the genome. Here, we propose an online computational framework, termed Mutalisk, which correlates somatic mutations with various genomic, transcriptional and epigenomic features in order to understand mutational processes that contribute to the generation of the mutations. This user-friendly tool explores the presence of localized hypermutations (kataegis), dissects the spectrum of mutations into the maximum likelihood combination of known mutational signatures and associates the mutation density with numerous regulatory elements in the genome. As a result, global patterns of somatic mutations in any query sample can be efficiently screened, thus enabling a deeper understanding of various mutagenic factors. This tool will facilitate more effective downstream analyses of cancer genome sequences to elucidate the diversity of mutational processes underlying the development and clonal evolution of cancer cells. Mutalisk is freely available at http://mutalisk.org.