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Author: Brinda Monian
Publisher:
ISBN:
Category :
Languages : en
Pages : 119
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Book Description
Mechanistic understanding of disease has been dramatically enhanced by an explosion of new high-throughput experimental techniques for profiling biological samples, including RNA-Seq, mass spectrometry, and single-cell sequencing However, the ability to gather exponentially more measurements comes with pitfalls of increased Type I error and reduced interpretability In theory, single-cell measurements can be helpful in combating this problem, since each sample of cells represents hundreds to thousands of observations But thinking is still emerging on how best to utilize single-cell data to boost statistics and generate meaningful findings This thesis represents several parallel efforts to develop and apply new bioinformatic techniques to generate robust findings from single-cell data The advances are especially pertinent for small clinical studies in which low sample numbers are limiting In the first part of the thesis, two classes of methods are introduced gene module discovery in single-cell RNA sequencing data using sparse PCA, and probability-based metrics for evaluating the degree of association between paired modalities of single-cell data (in this case, single-cell RNA sequencing and paired TCR sequencing data) The methods are shown on two different human datasets, as proof-of-concept and examples of the biological findings capable of being unearthed In the second part of the thesis, these methods are applied to larger clinical datasets with questions surrounding acquired tolerance and clinical reactivity in food allergy In the first study, T-helper cells from peanut-allergic patients undergoing oral immunotherapy were profiled to identify therapy-induced effects and baseline predictors of outcome Two distinct subsets of expanded TH2 clones were found to be suppressed, but not deleted, by the therapy In the second study, transcriptional correlates of clinical reactivity were evaluated in peanut-activated memory T-helper cells from peanut-allergic adults Cells from more reactive patients had higher expression of TH1 and MHC I gene programs, suggesting activation of auxiliary, non-TH2 cell types In each of these studies, new single-cell analysis techniques were integrated to generate clinical findings with improved robustness and interpretability.
Author: Brinda Monian
Publisher:
ISBN:
Category :
Languages : en
Pages : 119
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Book Description
Mechanistic understanding of disease has been dramatically enhanced by an explosion of new high-throughput experimental techniques for profiling biological samples, including RNA-Seq, mass spectrometry, and single-cell sequencing However, the ability to gather exponentially more measurements comes with pitfalls of increased Type I error and reduced interpretability In theory, single-cell measurements can be helpful in combating this problem, since each sample of cells represents hundreds to thousands of observations But thinking is still emerging on how best to utilize single-cell data to boost statistics and generate meaningful findings This thesis represents several parallel efforts to develop and apply new bioinformatic techniques to generate robust findings from single-cell data The advances are especially pertinent for small clinical studies in which low sample numbers are limiting In the first part of the thesis, two classes of methods are introduced gene module discovery in single-cell RNA sequencing data using sparse PCA, and probability-based metrics for evaluating the degree of association between paired modalities of single-cell data (in this case, single-cell RNA sequencing and paired TCR sequencing data) The methods are shown on two different human datasets, as proof-of-concept and examples of the biological findings capable of being unearthed In the second part of the thesis, these methods are applied to larger clinical datasets with questions surrounding acquired tolerance and clinical reactivity in food allergy In the first study, T-helper cells from peanut-allergic patients undergoing oral immunotherapy were profiled to identify therapy-induced effects and baseline predictors of outcome Two distinct subsets of expanded TH2 clones were found to be suppressed, but not deleted, by the therapy In the second study, transcriptional correlates of clinical reactivity were evaluated in peanut-activated memory T-helper cells from peanut-allergic adults Cells from more reactive patients had higher expression of TH1 and MHC I gene programs, suggesting activation of auxiliary, non-TH2 cell types In each of these studies, new single-cell analysis techniques were integrated to generate clinical findings with improved robustness and interpretability.
Author: Jialiang Yang
Publisher: Frontiers Media SA
ISBN: 2889635287
Category :
Languages : en
Pages : 136
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Book Description
Author: Xinghua Pan
Publisher: Frontiers Media SA
ISBN: 2889459209
Category :
Languages : en
Pages : 129
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Book Description
Single-cell omics is a progressing frontier that stems from the sequencing of the human genome and the development of omics technologies, particularly genomics, transcriptomics, epigenomics and proteomics, but the sensitivity is now improved to single-cell level. The new generation of methodologies, especially the next generation sequencing (NGS) technology, plays a leading role in genomics related fields; however, the conventional techniques of omics require number of cells to be large, usually on the order of millions of cells, which is hardly accessible in some cases. More importantly, harnessing the power of omics technologies and applying those at the single-cell level are crucial since every cell is specific and unique, and almost every cell population in every systems, derived in either vivo or in vitro, is heterogeneous. Deciphering the heterogeneity of the cell population hence becomes critical for recognizing the mechanism and significance of the system. However, without an extensive examination of individual cells, a massive analysis of cell population would only give an average output of the cells, but neglect the differences among cells. Single-cell omics seeks to study a number of individual cells in parallel for their different dimensions of molecular profile on genome-wide scale, providing unprecedented resolution for the interpretation of both the structure and function of an organ, tissue or other system, as well as the interaction (and communication) and dynamics of single cells or subpopulations of cells and their lineages. Importantly single-cell omics enables the identification of a minor subpopulation of cells that may play a critical role in biological process over a dominant subpolulation such as a cancer and a developing organ. It provides an ultra-sensitive tool for us to clarify specific molecular mechanisms and pathways and reveal the nature of cell heterogeneity. Besides, it also empowers the clinical investigation of patients when facing a very low quantity of cell available for analysis, such as noninvasive cancer screening with circulating tumor cells (CTC), noninvasive prenatal diagnostics (NIPD) and preimplantation genetic test (PGT) for in vitro fertilization. Single-cell omics greatly promotes the understanding of life at a more fundamental level, bring vast applications in medicine. Accordingly, single-cell omics is also called as single-cell analysis or single-cell biology. Within only a couple of years, single-cell omics, especially transcriptomic sequencing (scRNA-seq), whole genome and exome sequencing (scWGS, scWES), has become robust and broadly accessible. Besides the existing technologies, recently, multiplexing barcode design and combinatorial indexing technology, in combination with microfluidic platform exampled by Drop-seq, or even being independent of microfluidic platform but using a regular PCR-plate, enable us a greater capacity of single cell analysis, switching from one single cell to thousands of single cells in a single test. The unique molecular identifiers (UMIs) allow the amplification bias among the original molecules to be corrected faithfully, resulting in a reliable quantitative measurement of omics in single cells. Of late, a variety of single-cell epigenomics analyses are becoming sophisticated, particularly single cell chromatin accessibility (scATAC-seq) and CpG methylation profiling (scBS-seq, scRRBS-seq). High resolution single molecular Fluorescence in situ hybridization (smFISH) and its revolutionary versions (ex. seqFISH, MERFISH, and so on), in addition to the spatial transcriptome sequencing, make the native relationship of the individual cells of a tissue to be in 3D or 4D format visually and quantitatively clarified. On the other hand, CRISPR/cas9 editing-based In vivo lineage tracing methods enable dynamic profile of a whole developmental process to be accurately displayed. Multi-omics analysis facilitates the study of multi-dimensional regulation and relationship of different elements of the central dogma in a single cell, as well as permitting a clear dissection of the complicated omics heterogeneity of a system. Last but not the least, the technology, biological noise, sequence dropout, and batch effect bring a huge challenge to the bioinformatics of single cell omics. While significant progress in the data analysis has been made since then, revolutionary theory and algorithm logics for single cell omics are expected. Indeed, single-cell analysis exert considerable impacts on the fields of biological studies, particularly cancers, neuron and neural system, stem cells, embryo development and immune system; other than that, it also tremendously motivates pharmaceutic RD, clinical diagnosis and monitoring, as well as precision medicine. This book hereby summarizes the recent developments and general considerations of single-cell analysis, with a detailed presentation on selected technologies and applications. Starting with the experimental design on single-cell omics, the book then emphasizes the consideration on heterogeneity of cancer and other systems. It also gives an introduction of the basic methods and key facts for bioinformatics analysis. Secondary, this book provides a summary of two types of popular technologies, the fundamental tools on single-cell isolation, and the developments of single cell multi-omics, followed by descriptions of FISH technologies, though other popular technologies are not covered here due to the fact that they are intensively described here and there recently. Finally, the book illustrates an elastomer-based integrated fluidic circuit that allows a connection between single cell functional studies combining stimulation, response, imaging and measurement, and corresponding single cell sequencing. This is a model system for single cell functional genomics. In addition, it reports a pipeline for single-cell proteomics with an analysis of the early development of Xenopus embryo, a single-cell qRT-PCR application that defined the subpopulations related to cell cycling, and a new method for synergistic assembly of single cell genome with sequencing of amplification product by phi29 DNA polymerase. Due to the tremendous progresses of single-cell omics in recent years, the topics covered here are incomplete, but each individual topic is excellently addressed, significantly interesting and beneficial to scientists working in or affiliated with this field.
Author: Xiangdong Wang
Publisher: Springer
ISBN: 9401797536
Category : Medical
Languages : en
Pages : 184
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Book Description
The volume focuses on the genomics, proteomics, metabolomics, and bioinformatics of a single cell, especially lymphocytes and on understanding the molecular mechanisms of systems immunology. Based on the author’s personal experience, it provides revealing insights into the potential applications, significance, workflow, comparison, future perspectives and challenges of single-cell sequencing for identifying and developing disease-specific biomarkers in order to understand the biological function, activation and dysfunction of single cells and lymphocytes and to explore their functional roles and responses to therapies. It also provides detailed information on individual subgroups of lymphocytes, including cell characters, function, surface markers, receptor function, intracellular signals and pathways, production of inflammatory mediators, nuclear receptors and factors, omics, sequencing, disease-specific biomarkers, bioinformatics, networks and dynamic networks, their role in disease and future prospects. Dr. Xiangdong Wang is a Professor of Medicine, Director of Shanghai Institute of Clinical Bioinformatics, Director of Fudan University Center for Clinical Bioinformatics, Director of the Biomedical Research Center of Zhongshan Hospital, Deputy Director of Shanghai Respiratory Research Institute, Shanghai, China.
Author: Ali Samadikuchaksaraei
Publisher: BoD – Books on Demand
ISBN: 1789237998
Category : Medical
Languages : en
Pages : 102
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Book Description
Genomic variations and phenotypic diversity are closely linked and form the underlying mechanism for development of many human diseases. This book addresses the methods of detection, analysis, and interpretation of genomic variations in clinically relevant scenarios. If your research or clinical practice involves handling of genomic sequencing data, this book is for you. Topics covered include: methods for identifying genetic diversity, the workflow for analyzing whole exome and whole genome sequencing data, local ancestry deconvolution models, the value of molecular patterns and pattern biomarkers in cancer diagnosis and prognosis, and genotyping and profiling resistance-associated variants of hepatitis C. If your research or clinical practice involves handling of genomic sequencing data, this book is for you.
Author: Henry Horng-Shing Lu
Publisher: Springer Nature
ISBN: 3662659026
Category : Science
Languages : en
Pages : 406
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Book Description
Now in its second edition, this handbook collects authoritative contributions on modern methods and tools in statistical bioinformatics with a focus on the interface between computational statistics and cutting-edge developments in computational biology. The three parts of the book cover statistical methods for single-cell analysis, network analysis, and systems biology, with contributions by leading experts addressing key topics in probabilistic and statistical modeling and the analysis of massive data sets generated by modern biotechnology. This handbook will serve as a useful reference source for students, researchers and practitioners in statistics, computer science and biological and biomedical research, who are interested in the latest developments in computational statistics as applied to computational biology.
Author: Bairong Shen
Publisher: Springer Science & Business Media
ISBN: 9400779755
Category : Science
Languages : en
Pages : 219
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Book Description
The book introduces the bioinformatics tools, databases and strategies for the translational research, focuses on the biomarker discovery based on integrative data analysis and systems biological network reconstruction. With the coming of personal genomics era, the biomedical data will be accumulated fast and then it will become reality for the personalized and accurate diagnosis, prognosis and treatment of complex diseases. The book covers both state of the art of bioinformatics methodologies and the examples for the identification of simple or network biomarkers. In addition, bioinformatics software tools and scripts are provided to the practical application in the study of complex diseases. The present state, the future challenges and perspectives were discussed. The book is written for biologists, biomedical informatics scientists and clinicians, etc. Dr. Bairong Shen is Professor and Director of Center for Systems Biology, Soochow University; he is also Director of Taicang Center for Translational Bioinformatics.
Author:
Publisher: Academic Press
ISBN: 0128186763
Category : Science
Languages : en
Pages : 588
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Book Description
Tumor Immunology and Immunotherapy – Cellular Methods Part B, Volume 632, the latest release in the Methods in Enzymology series, continues the legacy of this premier serial with quality chapters authored by leaders in the field. Topics covered include Quantitation of calreticulin exposure associated with immunogenic cell death, Side-by-side comparisons of flow cytometry and immunohistochemistry for detection of calreticulin exposure in the course of immunogenic cell death, Quantitative determination of phagocytosis by bone marrow-derived dendritic cells via imaging flow cytometry, Cytofluorometric assessment of dendritic cell-mediated uptake of cancer cell apoptotic bodies, Methods to assess DC-dependent priming of T cell responses by dying cells, and more. Contains content written by authorities in the field Provides a comprehensive view on the topics covered Includes a high level of detail
Author: Rishabha Malviya
Publisher: CRC Press
ISBN: 1000638901
Category : Computers
Languages : en
Pages : 357
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Book Description
Nowadays, raw biological data can be easily stored as databases in computers but extracting the required information is the real challenge for researchers. For this reason, bioinformatics tools perform a vital role in extracting and analyzing information from databases. Bioinformatics Tools and Big Data Analytics for Patient describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery for patient care. The book gives details about the recent developments in the fields of artificial intelligence, cloud computing, and data analytics. It highlights the advances in computational techniques used to perform intelligent medical tasks. Features: Presents recent developments in the fields of artificial intelligence, cloud computing, and data analytics for improved patient care. Describes the applications of bioinformatics, data management, and computational techniques in clinical studies and drug discovery. Summarizes several strategies, analyses, and optimization methods for patient healthcare. Focuses on drug discovery and development by cloud computing and data-driven research The targeted audience comprises academics, research scholars, healthcare professionals, hospital managers, pharmaceutical chemists, the biomedical industry, software engineers, and IT professionals.
Author: Sudheer Menon
Publisher: Pencil
ISBN: 9358839325
Category : Comics & Graphic Novels
Languages : en
Pages : 125
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Book Description
"Contemporary Research in Bioinformatics" is a comprehensive exploration of the dynamic field that lies at the intersection of biology, data science, and computation. This book serves as a roadmap for readers to navigate the evolving landscapes of genomics, transcriptomics, proteomics, structural biology, machine learning, and more. In an age where the deluge of biological data presents both opportunities and challenges, bioinformatics emerges as the guiding light that empowers us to decipher the complexities of life. This book is designed to cater to a diverse audience, including researchers, students, educators, and professionals seeking to gain a deeper understanding of bioinformatics and its pivotal role in shaping modern biology and healthcare.
