[ Ссылка ] - global #HPC conference ONLINE, March 23–25, 2020
Supercomputer Frontiers Europe 2020 | Day-2 #OMICS
VLADIMIR BRUSIC | School of Computer Science, University of Nottingham Ningbo China
Title: Single cell transcriptomics – new challenges for Big Data analytics
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Supercomputer Frontiers Europe 2020 | Day-2 #OMICS
VLADIMIR BRUSIC | School of Computer Science, University of Nottingham Ningbo China
Title: Single cell transcriptomics – new challenges for Big Data analytics
Abstract: #Transcriptomics is the study of complete set of gene expression (expressed RNA) produced by transcription of DNA in a given biological sample. Biological samples typically contain mixtures of cells. The analysis of transcriptome aims to identify genes that show differences in expression between samples that represent different biological conditions, such as different cell types, different activation status, developmental stages, or disease states. This knowledge provides insight into biological processes, understanding the role of genes in biological processes, and changes related to various biological conditions. Transcriptomics has many practical applications in all fields of life sciences. Prominent areas of application include developmental biology, immunology, virology, agriculture and food science, and medicine. Clinical applications of transcriptomics include discovery of biomarkers, disease diagnosis and prognosis, selection and optimization of therapies, and disease monitoring. A great promise of clinical transcriptomics is the possibility of screening for multiple diseases, understanding the disease causes, estimating the disease progression, and assessing likely responses to specific treatment, all in a single transcriptomics experiment.
Bulk transcriptomics measures expression of tens of thousands of gene from the overall mixture of cells. On the other hand, single-cell transcriptomics (SCT) determines levels of expression of tens of thousands of genes in individual cells from a given biological sample. The main advantages of bulk sequencing is the ability to perform “deep“ sequencing, allowing detection of transcripts that are present in minute concentrations. However, bulk transcriptomics will miss differential expression of transcripts between cell types and subtypes present in the sample. SCT offers significant advantages as compared to bulk sequencing, including ability to profile different cell types and subtypes and identification of novel or rare cell types. The tradeof of SCT sequencing is that it is more “shallow“ in comparison to bulk sequencing, resulting in lower transcript coverage, noisier data, and larger variability than bulk sequencing. Cell populations that have very similar bulk transcriptomes often show remarkably variable transcriptome profiles of single cells because of inherent biological variability, a mix of cell-cycle stages, random variability, and shallow sequencing.
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Dr. Brusic is a Li Dak Sum Chair Professor in Computer Science at the University of Nottingham Ningbo China and Adjunct Professor at Boston University (USA). He studied at University of Belgrade (Serbia), La Trobe University (Australia), Royal Melbourne Institute of Technology (Australia), and Rutgers University (USA) where he earned BEng, MEng, MAppSci, PhD, and MBA degrees. Professor Brusic had previously held senior research or academic positions internationally, including Dana-Farber Cancer Institute (USA), University of Queensland (Australia), Institute for Infocomm Research (Singapore), Walter and Eliza Hall Institute of Medical Research (Australia), and University of Belgrade (Serbia). He has published more than 200 scientific and technology articles that have attracted more than 14,000 citations and has published two patents related to medical diagnostics and vaccine design. Prof. Brusic has worked in Knowledge Management for nearly 30 years. He developed new artificial intelligence solutions for vaccine research, immunology, infectious disease, autoimmunity and cancer research. His current projects include applications of #ArtificialIntelligence, machine learning, statistics, mathematical modeling, and computer models in monitoring health, medical diagnostics, vaccine development, and food authentication.
