Integrative Analysis Of Single Cell And Or Bulk Multi Omics Sequencing Data
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Integrative Analysis Of Single Cell And Or Bulk Multi Omics Sequencing Data
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Author : Geng Chen
language : en
Publisher: Frontiers Media SA
Release Date : 2023-03-13
Integrative Analysis Of Single Cell And Or Bulk Multi Omics Sequencing Data written by Geng Chen and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-13 with Science categories.
Multimodal And Integrative Analysis Of Single Cell Or Bulk Sequencing Data
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Author : Geng Chen
language : en
Publisher: Frontiers Media SA
Release Date : 2021-04-07
Multimodal And Integrative Analysis Of Single Cell Or Bulk Sequencing Data written by Geng Chen and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-07 with Science categories.
Computational Methods For Single Cell Data Analysis
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Author : Guo-Cheng Yuan
language : en
Publisher: Humana Press
Release Date : 2019-02-14
Computational Methods For Single Cell Data Analysis written by Guo-Cheng Yuan and has been published by Humana Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-02-14 with Science categories.
This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.
Immunological Precision Therapeutics Integrating Multi Omics Technologies And Comprehensive Approaches For Personalized Immune Intervention
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Author : Wenyi Jin
language : en
Publisher: Frontiers Media SA
Release Date : 2025-03-20
Immunological Precision Therapeutics Integrating Multi Omics Technologies And Comprehensive Approaches For Personalized Immune Intervention written by Wenyi Jin and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-20 with Medical categories.
In the realm of modern immunology, the pursuit of precision therapeutics has emerged as a paramount endeavor. This research field harnesses the power of advanced multi-omics technologies and comprehensive methodologies to revolutionize personalized immune interventions. Immunotherapy, a cornerstone of precision medicine, targets the intricate dynamics of the immune system to combat diseases ranging from cancer to autoimmune disorders. Integrating multi-omics analyses, including genomics, transcriptomics, proteomics, and metabolomics, enables a holistic understanding of immune responses at various molecular levels. Immune signatures derived from these analyses unveil individualized patterns, offering crucial insights into disease susceptibility and treatment efficacy. Leveraging this wealth of data through sophisticated computational models and machine learning algorithms enhances our ability to predict immune responses and identify optimal therapeutic strategies. By amalgamating diverse approaches, from single-cell profiling to spatial transcriptomics, we delve deeper into the complexities of immune regulation and cellular interactions within the microenvironment. Through collaborative efforts, the pursuit of immunological precision therapeutics aims to tailor interventions precisely to each patient's immune landscape, ushering in a new era of personalized immune modulation.
Integrative Multi Omics For Diagnosis Treatments And Drug Discovery Of Aging Related Neuronal Diseases
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Author : Min Tang
language : en
Publisher: Frontiers Media SA
Release Date : 2022-11-23
Integrative Multi Omics For Diagnosis Treatments And Drug Discovery Of Aging Related Neuronal Diseases written by Min Tang and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-23 with Science categories.
As the cost of high-throughput sequencing goes down, huge volumes of biological and medical data have been produced from various sequencing platforms at multiple molecular levels including genome, transcriptome, proteome, epigenome, metabolome, and so on. For a long time, data analysis on single molecular levels has paved the way to answer many important research questions. However, many Aging-Related Neuronal Diseases (ARNDs) and Central Nervous System (CNS) aging involve interactions of molecules from multiple molecular levels, in which conclusions based on single molecular levels are usually incomplete and sometimes misleading. In these scenarios, multi-omics data analysis has unprecedentedly helped capture much more useful information for the diagnosis, treatment, prognosis, and drug discovery of ARNDs. The first step towards a multi-omics analysis is to establish reliable and robust multi-omics datasets. In the past years, a few important ARNDs-associated multi-omics databases like Allen Brain have been constructed, which raised immediate needs like data curation, normalization, interpretation, and visualization for integrative multi-omics explorations. Though there have been several well-established multi-omics databases for ARNDs like Alzheimer’s disease, similar databases for other ARNDs are still in urgent need. After the databases establish, many computational tools and experiential strategies should be developed specifically for them. First, the multi-omics data are usually extremely noisy, complex, heterogeneous and in high dimension, which presents the need for appropriate denoising and dimension reduction methods. Second, since the multi-omics and non-omics data like pathological and clinical data are usually in different data spaces, a useful algorithm to mapping them into the same data space and integrate them is nontrivial. In the multi-omics era, there are numerous data-centric tools for the integration of multi-omics datasets, which could be generally divided into three categories: unsupervised, supervised, and semi-supervised methods. Commonly used algorithms include but not limited to Bayesian-based methods, Network-based methods, multi-step analysis methods, and multiple kernel learning methods. Third, methods are needed in studying and verifying the association between two or more levels of multi-omics data and non-omics data. For example, expression quantitative trait loci (eQTL) analysis is widely used to infer the association between a single nucleotide polymorphism (SNP) and the expression of a gene. Recently, the association between omics data and more complex data like pathological and clinical imaging data has been a hot research topic. The outcomes may reveal the underlying molecular mechanism and promote de novo drug design as well as drug repurposing for ARNDs. Here, we welcome investigators to share their Original Research, Review, Mini Review, Hypothesis and Theory, Perspective, Conceptual Analysis, Data Report, Brief Research Report, Code related to multi-omics studies of ARNDs, which can be applied for better diagnosis, treatment, prognosis and drug discovery of human diseases in the future era of precision medicine. Potential contents include but are not limited to the following: ▪ Methods for integrating, interpreting, or visualizing two or more omics data. ▪ Methods for identifying interactions between different data modalities. ▪ Methods for disease subtyping, biomarker prediction. ▪ Machine learning or deep learning methods on dimensional reduction and feature selection for big noisy data. ▪ Methods for studying the association among different omics data or between omics and non-omics data like clinical, pathological, and imaging data. ▪ Review of multi-omics resource about ARNDs and/or CNS aging. ▪ Experimental validation of biomarkers identified from multi-omics data analysis. ▪ Disease diagnosis and prognosis prediction from imaging and non-imaging data analysis, or both. ▪ Clinical applications or validations of findings from multi-omics data analysis.
Introduction To Single Cell Omics
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Author : Xinghua Pan
language : en
Publisher: Frontiers Media SA
Release Date : 2019-09-19
Introduction To Single Cell Omics written by Xinghua Pan and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-19 with categories.
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.
Handbook Of Statistical Bioinformatics
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Author : Henry Horng-Shing Lu
language : en
Publisher: Springer Nature
Release Date : 2022-12-08
Handbook Of Statistical Bioinformatics written by Henry Horng-Shing Lu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-08 with Science categories.
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.
Proteomics Multi Omics And Systems Biology In Optic Nerve Regeneration
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Author : Sanjoy K. Bhattacharya
language : en
Publisher: Elsevier
Release Date : 2025-01-28
Proteomics Multi Omics And Systems Biology In Optic Nerve Regeneration written by Sanjoy K. Bhattacharya and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-28 with Science categories.
Proteomics, Multi-Omics and Systems Biology in Optic Nerve Regeneration is a comprehensive reference that covers all vistas of standardization of axon regeneration, as well as all multi-omics and system level data and integration tools. By adopting a translational approach, the book bridges current research in the field to clinical applications, and readers can expect to learn standardization approaches for axon regeneration, multi-omics datasets, different databases, search engines, multiple dataset integrative tools, pathway convergence approaches and tools, outcome and outcome measures that unify bench research with clinical outcome.The axon regeneration from existing neurons in central nervous system (CNS) have become a potential possibility in the last decade. The potential possibility of long-distance axon growth has opened the possibility of re-connectivity of axons of retinal ganglion cell neurons within the lateral geniculate nucleus in the brain. The long-distance axon regeneration and re-connectivity is a promise to restore lost vision in the optic nerve. Further, long-distance regeneration and re-innervation is equally helpful for other fields such as spinal cord injuries. - Includes updates on the use of multi-omics datasets for selecting molecules for axon regeneration - Bridges the preclinical and clinical world, from selection of the molecules to outcome leading to IND filing and their use - Includes system level knowledge needed for central nervous system axon and dendrite regeneration, and standardizes the system level biology for axon regeneration - Explores the current state of multi-omics in axon and dendrite regeneration in the optic nerve and its comparison to other CNS regeneration
Multi Omic Data Integration In Oncology
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Author : Chiara Romualdi
language : en
Publisher: Frontiers Media SA
Release Date : 2020-12-03
Multi Omic Data Integration In Oncology written by Chiara Romualdi and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-03 with Medical categories.
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
Gene Expression Analysis
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Author : Nalini Raghavachari
language : en
Publisher: Springer Nature
Release Date : 2025-02-03
Gene Expression Analysis written by Nalini Raghavachari and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-03 with Science categories.
This second edition volume expands on the previous edition with updates on the latest methodologies in the transcriptomics field. The chapters in this book cover topics such as spatial omics, long-read sequencing technology, tissue microarrays, analysis of saliva and extracellular vesicles, machine learning and artificial intelligence-based approaches for analysis of singe cells transcriptome, and large sets of data on multi-omics including transcriptomics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Cutting-edge and practical, Gene Expression Analysis: Methods and Protocols, Second Edition is a valuable resource for advanced undergraduate and graduate students studying gene expression analysis, and scientists interested in learning more about this rapidly advancing field.