Quantitative analysis of high‐throughput biological data

Abstract

The study of multiple “omes,” such as the genome, transcriptome, proteome, and metabolome has become widespread in biomedical research. High‐throughput techniques enable the rapid generation of high‐dimensional multiomics data. This multiomics approach provides a more complete perspective to study biological systems compared with traditional methods. However, the quantitative analysis and integration of distinct types of high‐dimensional omics data remain a challenge. Here, we provide an up‐to‐date and comprehensive review of the methods used for omics data quantification and integration. We first review the quantitative analysis of not only bulk but also single‐cell transcriptomics data, as well as proteomics data. Current methods for reducing batch effects and integrating heterogeneous high‐dimensional data are then introduced. Network analysis on large‐scale biomedical data can capture the global properties of drugs, targets, and disease relationships, thus enabling a better understanding of biological systems. Current trends in the applications and methods used to extend quantitative omics data analysis to biological networks are also discussed. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning

Postmortem Minimally Invasive Autopsy in Critically Ill COVID-19 Patients at the Bedside: A Proof-of-Concept Study at the ICU

“…This contrasts with the need for any novel disease to obtain information on organ involvement and pathogenesis to develop adequate therapies. To comply with these requirements, high-throughput multiorgan analyses of proteomics, genomics, and microbiomics are the most appropriate tools [21]. This need was apparent during the COVID-19 pandemic for depicting SARS-CoV-2 interactions between host organs and tissues.…”

“…Traditional, century-old laborious autopsy practices represent a major disincentive; lack of standard operating procedures (SOPs), workforce cuts (pathologists and technical personnel), economic restrictions, strengthened safety regulations for infectious diseases, and concerns of patients/relatives regarding autopsies may explain the decline of conventional autopsies. Additionally, long postmortem intervals (PMI-the time between death and autopsy) are often more than 24-48 h, resulting in advanced autolysis and reduced usability for techniques such as electron microscopy, molecular assays (e.g., genomics, proteomics, and microbiomics), and cell culture models (e.g., spheroids and organoids) [21].…”

“…No. 322350, Advanced Cell Diagnostics, ACD, Bio-Techne, Newark, CA, USA) and SARS-CoV-2 RNAscope ® ISH Probe (21,303 Cat. No.…”

Combination of multiple omics techniques for a personalized therapy or treatment selection

“…Combination of data from multiple “omics” profile from a single patient will provide powerful tool to generate a holistic view of molecular, metabolic and immunological effects, which can be used to predict response to therapy. Different strategies based on machine learning have been developed during the last years to perform data integration and have recently been reviewed in various articles ( 165 – 168 ). However, it is noteworthy and has to be taken into account that “omics” data are fundamentally different.…”