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2.
OMICS ; 25(11): 681-692, 2021 11.
Article in English | MEDLINE | ID: covidwho-1541502

ABSTRACT

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.


Subject(s)
Computational Biology/trends , Genomics/trends , Metabolomics/trends , Proteomics/trends , Animals , COVID-19 , Computer Graphics , Epigenomics/trends , Gene Expression Profiling/trends , Humans , Pharmacogenetics/trends , Publications , SARS-CoV-2 , Terminology as Topic
3.
J Proteome Res ; 20(12): 5241-5263, 2021 12 03.
Article in English | MEDLINE | ID: covidwho-1483082

ABSTRACT

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.


Subject(s)
Proteome , Proteomics/trends , Aging/genetics , COVID-19/genetics , Databases, Protein , Hemostasis/genetics , Humans , Mass Spectrometry , Proteome/genetics
4.
Brief Bioinform ; 22(6)2021 11 05.
Article in English | MEDLINE | ID: covidwho-1369063

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.


Subject(s)
COVID-19/drug therapy , Drug Discovery , Drug Repositioning , SARS-CoV-2/genetics , Algorithms , Artificial Intelligence , COVID-19/genetics , COVID-19/virology , Genomics/trends , Humans , Pandemics , Proteomics/trends , SARS-CoV-2/drug effects , SARS-CoV-2/pathogenicity , Transcriptome/genetics
6.
Hum Genomics ; 14(1): 35, 2020 10 02.
Article in English | MEDLINE | ID: covidwho-810348

ABSTRACT

Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.


Subject(s)
Coronavirus Infections/genetics , Diabetes Mellitus/genetics , Neoplasms/genetics , Pneumonia, Viral/genetics , Precision Medicine/trends , COVID-19 , Cardiomyopathies , Coronavirus Infections/epidemiology , Data Analysis , Diabetes Mellitus/epidemiology , Genomics/trends , Humans , Metabolomics/trends , Neoplasms/epidemiology , Pandemics , Pneumonia, Viral/epidemiology , Proteomics/trends
7.
Immunol Cell Biol ; 99(2): 168-176, 2021 02.
Article in English | MEDLINE | ID: covidwho-751690

ABSTRACT

Big data has become a central part of medical research, as well as modern life generally. "Omics" technologies include genomics, proteomics, microbiomics and increasingly other omics. These have been driven by rapid advances in laboratory techniques and equipment. Crucially, improved information handling capabilities have allowed concepts such as artificial intelligence and machine learning to enter the research world. The COVID-19 pandemic has shown how quickly information can be generated and analyzed using such approaches, but also showed its limitations. This review will look at how "omics" has begun to be translated into clinical practice. While there appears almost limitless potential in using big data for "precision" or "personalized" medicine, the reality is that this remains largely aspirational. Oncology is the only field of medicine that is widely adopting such technologies, and even in this field uptake is irregular. There are practical and ethical reasons for this lack of translation of increasingly affordable techniques into the clinic. Undoubtedly, there will be increasing use of large data sets from traditional (e.g. tumor samples, patient genomics) and nontraditional (e.g. smartphone) sources. It is perhaps the greatest challenge of the health-care sector over the coming decade to integrate these resources in an effective, practical and ethical way.


Subject(s)
Genomics/trends , Metabolomics/trends , Precision Medicine/trends , /trends , Artificial Intelligence/trends , COVID-19/epidemiology , Genomics/methods , Humans , Medical Oncology/methods , Medical Oncology/trends , Metabolomics/methods , Pandemics , Precision Medicine/methods , Proteomics/methods , Proteomics/trends , Time Factors , /methods
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