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| tcga rna-seq data: RSEM: Accurate Transcript Quantification from RNA-Seq Data with Or Without a Reference Genome Applied Research Applied Research Press, 2015-09-16 RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive. |
| tcga rna-seq data: RNA-Seq Analysis: Methods, Applications and Challenges Filippo Geraci, Indrajit Saha, Monica Bianchini, 2020-06-08 |
| tcga rna-seq data: Systems Biology and the Challenge of Deciphering the Metabolic Mechanisms Underlying Cancer Osbaldo Resendis-Antonio, Christian Diener, 2017-11-23 Since the discovery of the Warburg effect in the 1920s cancer has been tightly associated with the genetic and metabolic state of the cell. One of the hallmarks of cancer is the alteration of the cellular metabolism in order to promote proliferation and undermine cellular defense mechanisms such as apoptosis or detection by the immune system. However, the strategies by which this is achieved in different cancers and sometimes even in different patients of the same cancer is very heterogeneous, which hinders the design of general treatment options. Recently, there has been an ongoing effort to study this phenomenon on a genomic scale in order to understand the causality underlying the disease. Hence, current “omics” technologies have contributed to identify and monitor different biological pieces at different biological levels, such as genes, proteins or metabolites. These technological capacities have provided us with vast amounts of clinical data where a single patient may often give rise to various tissue samples, each of them being characterized in detail by genomescale data on the sequence, expression, proteome and metabolome level. Data with such detail poses the imminent problem of extracting meaningful interpretations and translating them into specific treatment options. To this purpose, Systems Biology provides a set of promising computational tools in order to decipher the mechanisms driving a healthy cell’s metabolism into a cancerous one. However, this enterprise requires bridging the gap between large data resources, mathematical analysis and modeling specifically designed to work with the available data. This is by no means trivial and requires high levels of communication and adaptation between the experimental and theoretical side of research. |
| tcga rna-seq data: RNA Sequencing in Clinical Oncology for Metabolism and Immunity Ye Wang, Xiaoming Xing, Anton A. Buzdin, Xinmin Li, 2022-06-01 |
| tcga rna-seq data: Statistical Genomics Ewy Mathé, Sean Davis, 2016-03-24 This volume expands on statistical analysis of genomic data by discussing cross-cutting groundwork material, public data repositories, common applications, and representative tools for operating on genomic data. Statistical Genomics: Methods and Protocols is divided into four sections. The first section discusses overview material and resources that can be applied across topics mentioned throughout the book. The second section covers prominent public repositories for genomic data. The third section presents several different biological applications of statistical genomics, and the fourth section highlights software tools that can be used to facilitate ad-hoc analysis and data integration. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, step-by-step, readily reproducible analysis protocols, and tips on troubleshooting and avoiding known pitfalls. Through and practical, Statistical Genomics: Methods and Protocols, explores a range of both applications and tools and is ideal for anyone interested in the statistical analysis of genomic data. |
| tcga rna-seq data: 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6) Toi Vo Van, Thanh An Nguyen Le, Thang Nguyen Duc, 2017-09-21 Under the motto “Healthcare Technology for Developing Countries” this book publishes many topics which are crucial for the health care systems in upcoming countries. The topics include Cyber Medical Systems Medical Instrumentation Nanomedicine and Drug Delivery Systems Public Health Entrepreneurship This proceedings volume offers the scientific results of the 6th International Conference on the Development of Biomedical Engineering in Vietnam, held in June 2016 at Ho Chi Minh City. |
| tcga rna-seq data: Collaborative Genomics Projects: A Comprehensive Guide Margi Sheth, Julia Zhang, Jean C Zenklusen, 2016-02-24 Collaborative Genomics Projects: A Comprehensive Guide contains operational procedures, policy considerations, and the many lessons learned by The Cancer Genome Atlas Project. This book guides the reader through methods in patient sample acquisition, the establishment of data generation and analysis pipelines, data storage and dissemination, quality control, auditing, and reporting. This book is essential for those looking to set up or collaborate within a large-scale genomics research project. All authors are contributors to The Cancer Genome Atlas (TCGA) Program, a NIH- funded effort to generate a comprehensive catalog of genomic alterations in more than 35 cancer types. As the cost of genomic sequencing is decreasing, more and more researchers are leveraging genomic data to inform the biology of disease. The amount of genomic data generated is growing exponentially, and protocols need to be established for the long-term storage, dissemination, and regulation of this data for research. The book's authors create a complete handbook on the management of research projects involving genomic data as learned through the evolution of the TCGA program, a project that was primarily carried out in the US, but whose impact and lessons learned can be applied to international audiences. - Establishes a framework for managing large-scale genomic research projects involving multiple collaborators - Describes lessons learned through TCGA to prepare for potential roadblocks - Evaluates policy considerations that are needed to avoid pitfalls - Recommends strategies to make project management more efficient |
| tcga rna-seq data: Early Detection and Diagnosis of Cancer Jian-Bing Fan, Jin Jen, Neeraj S. Salathia, Youping Deng, Rahul Kumar, Lei Wei, 2022-03-31 Jian-Bing Fan is a professor at Southern Medical University, China, and founder of AnchorDx. Jin Jen has a joint appointment with the Mayo Clinic and Bristol-Myers Squibb. Neeraj Salathia is employed by Bristol-Myers Squibb. All other Topic Editors declare no competing interests with regard to the Research Topic subject. |
| tcga rna-seq data: Bioinformatics Tools (and Web Server) for Cancer Biomarker Development Xiangqian Guo, Liuyang Wang, Wan Zhu, Longxiang Xie, Jing Zhao, 2020-12-23 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. |
| tcga rna-seq data: Methodologies of Multi-Omics Data Integration and Data Mining Kang Ning, 2023-01-15 This book features multi-omics big-data integration and data-mining techniques. In the omics age, paramount of multi-omics data from various sources is the new challenge we are facing, but it also provides clues for several biomedical or clinical applications. This book focuses on data integration and data mining methods for multi-omics research, which explains in detail and with supportive examples the “What”, “Why” and “How” of the topic. The contents are organized into eight chapters, out of which one is for the introduction, followed by four chapters dedicated for omics integration techniques focusing on several omics data resources and data-mining methods, and three chapters dedicated for applications of multi-omics analyses with application being demonstrated by several data mining methods. This book is an attempt to bridge the gap between the biomedical multi-omics big data and the data-mining techniques for the best practice of contemporary bioinformatics and the in-depth insights for the biomedical questions. It would be of interests for the researchers and practitioners who want to conduct the multi-omics studies in cancer, inflammation disease, and microbiome researches. |
| tcga rna-seq data: The Interconnection Between the Tumor Microenvironment and Immunotherapy in Brain Tumors Quan Cheng, Wen Cheng, Junxia Zhang, Longbo Zhang, 2023-06-08 |
| tcga rna-seq data: Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research, Volume II Lixin Cheng, Hongwei Wang, Shibiao Wan, 2023-09-05 This Research Topic is part of a series with, Bioinformatics Analysis of Omics Data for Biomarker Identification in Clinical Research - Volume I (https://www.frontiersin.org/research-topics/13816/bioinformatics-analysis-of-omics-data-for-biomarker-identification-in-clinical-research) The advances and the decreasing cost of omics data enable profiling of disease molecular features at different levels, including bulk tissues, animal models, and single cells. Large volumes of omics data enhance the ability to search for information for preclinical study and provide the opportunity to leverage them to understand disease mechanisms, identify molecular targets for therapy, and detect biomarkers of treatment response. Identification of stable, predictive, and interpretable biomarkers is a significant step towards personalized medicine and therapy. Omics data from genomics, transcriptomics, proteomics, epigenomics, metagenomics, and metabolomics help to determine biomarkers for prognostic and diagnostic applications. Preprocessing of omics data is of vital importance as it aims to eliminate systematic experimental bias and technical variation while preserving biological variation. Dozens of normalization methods for correcting experimental variation and bias in omics data have been developed during the last two decades, while only a few consider the skewness between different sample states, such as the extensive over-repression of genes in cancers. The choice of normalization methods determines the fate of identified biomarkers or molecular signatures. From these considerations, the development of appropriate normalization methods or preprocessing strategies may promote biomarker identification and facilitate clinical decision-making. |
| tcga rna-seq data: Translational Medicine in the Diagnosis and Treatment of Cancer based on Oncogenetics: From Bench to Bedside Changjing Cai, Rui Cao, Jiao Hu, Ying Han, Changsheng Xing, 2023-12-07 Translational medicine was first mentioned in 1992 by Choi D. W and has since become a rapidly expanding area within biomedical research. It is based on the ‘bench to bedside’ approach, which describes it’s relationship between basic science and clinical practice. In 2008, Drs Conway and Dougherty provided a 3 step process to translational medicine and how it should be implemented to transform healthcare systems. The first step is the translation of basic science into clinical research (T1). The second step (T2) focuses on making healthcare more patient specific, basing itself on providing ‘the right treatment for the right patient in the right way at the right time’. T2 also looks for this science to be translated into practice guidelines for clinicians, policy makers and the patients themselves. The final step (T3) addresses the ‘how’ of implementing these ideas, so that high quality healthcare can be delivered reliably to all patients in all settings of care. T3 activities would include policy changes that could serve to bring about meaningful change towards this goal. Cancer is a major public health problem worldwide. Clarifying the etiology and pathogenesis of cancer is of great significance to the prevention, diagnosis, and treatment of the disease. The bench to bedside pattern can be a useful method for cancer-related studies. This Research Topic aims to highlight the emerging role of oncogenetics in cancer, and discuss potential challenges of diagnosis and treatment, from the bench to the bedside. |
| tcga rna-seq data: Epigenetic drugs and therapeutic resistance for epithelial malignancies Zhiqian Zhang, Fangfang Tao, Wanjin Hong, 2023-06-05 |
| tcga rna-seq data: Biomedical Image or Genomic Data Characterization and Radiogenomic/Image-omics Ming Fan, Jiangning Song, Zhaowen Qiu, 2022-09-27 Much of the emphasis in discussions about personalized medicine has been focused on the molecular characterization of tissue samples using microarray technology. However, as genetic differ between and within tumors and are quite heterogeneous, these techniques are limited. Imaging is noninvasive and is often used in routine clinical practice for disease diagnosis, treatment, and prognosis. Imaging is useful to guide disease therapy by providing a more comprehensive view of the entire lesion and it can be used on an ongoing basis to monitor lesion growth and progression or its response to treatment. The imaging includes but not limited to ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET). Radiomics refers to the conversion of images to high dimensional data and the subsequent mining for characterization of biology and ultimately to improve disease management for patients. Radiogenomics investigates relationships between imaging features and genomics, which represents the correlation between the anatomical-histological level to the genomic level. With advanced artificial intelligence methods, especially deep learning, data processing, feature extraction and data integration have been greatly improved. The topic is about artificial intelligence methods in biomedical images and genomics data for disease diagnosis, treatment, and prognosis, as listed here: • Biomarker identification from biomedical images to predict disease diagnosis, treatment, and prognosis • Radiogenomics/image-omics in identifying imaging biomarkers associated with molecular characteristics of the disease. • Machine learning/deep learning methods in biomedical imaging or genomics for disease detection and precision medicine. • Prediction of histological characteristics of disease based on biomedical imaging. • Integration of radiomics and genomics features for disease diagnosis, prognosis, and prediction medicine • Multimodality images or multi-omics data integration methods |
| tcga rna-seq data: Cellular Dormancy: State Determination and Plasticity Guang Yao, Alexis Ruth Barr, Jyotsna Dhawan, 2022-09-15 |
| tcga rna-seq data: Pattern Recognition and Machine Intelligence Bhabesh Deka, Pradipta Maji, Sushmita Mitra, Dhruba Kumar Bhattacharyya, Prabin Kumar Bora, Sankar Kumar Pal, 2019-11-25 The two-volume set of LNCS 11941 and 11942 constitutes the refereed proceedings of the 8th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2019, held in Tezpur, India, in December 2019. The 131 revised full papers presented were carefully reviewed and selected from 341 submissions. They are organized in topical sections named: Pattern Recognition; Machine Learning; Deep Learning; Soft and Evolutionary Computing; Image Processing; Medical Image Processing; Bioinformatics and Biomedical Signal Processing; Information Retrieval; Remote Sensing; Signal and Video Processing; and Smart and Intelligent Sensors. |
| tcga rna-seq data: Bioinformatics of Genome Regulation and Systems Biology Yuriy L. Orlov, Ancha Baranova, 2020-09-17 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. |
| tcga rna-seq data: Application of Bioinformatics in Cancers Chad Brenner, 2019-11-20 This collection of 25 research papers comprised of 22 original articles and 3 reviews is brought together from international leaders in bioinformatics and biostatistics. The collection highlights recent computational advances that improve the ability to analyze highly complex data sets to identify factors critical to cancer biology. Novel deep learning algorithms represent an emerging and highly valuable approach for collecting, characterizing and predicting clinical outcomes data. The collection highlights several of these approaches that are likely to become the foundation of research and clinical practice in the future. In fact, many of these technologies reveal new insights about basic cancer mechanisms by integrating data sets and structures that were previously immiscible. Accordingly, the series presented here bring forward a wide range of artificial intelligence approaches and statistical methods that can be applied to imaging and genomics data sets to identify previously unrecognized features that are critical for cancer. Our hope is that these articles will serve as a foundation for future research as the field of cancer biology transitions to integrating electronic health record, imaging, genomics and other complex datasets in order to develop new strategies that improve the overall health of individual patients. |
| tcga rna-seq data: Omic Association Studies with R and Bioconductor Juan R. González, Alejandro Cáceres, 2019-06-14 After the great expansion of genome-wide association studies, their scientific methodology and, notably, their data analysis has matured in recent years, and they are a keystone in large epidemiological studies. Newcomers to the field are confronted with a wealth of data, resources and methods. This book presents current methods to perform informative analyses using real and illustrative data with established bioinformatics tools and guides the reader through the use of publicly available data. Includes clear, readable programming codes for readers to reproduce and adapt to their own data. Emphasises extracting biologically meaningful associations between traits of interest and genomic, transcriptomic and epigenomic data Uses up-to-date methods to exploit omic data Presents methods through specific examples and computing sessions Supplemented by a website, including code, datasets, and solutions |
| tcga rna-seq data: New Technologies in Cancer Diagnostics and Therapeutics Dong-Hua Yang, Pascale Cohen, Haichang Li, Yingyan Yu, 2021-10-21 |
| tcga rna-seq data: Cancer Bioinformatics Alexander Krasnitz, Pascal Belleau, 2025-06-25 This second volume covers state-of-the-art cancer-related methods and tools for data analysis and interpretation. Chapters detail methods on cancer-related software repositories, databases, cloud computing resources, genomic alterations caused by cancer, methods on evaluate findings from liquid biopsies, and prognostic tools for immunotherapies. Written in the highly successful Methods in Molecular Biology series format, the chapters include brief introductions to the material, lists of necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and a Notes section which highlights tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Cancer Bioinformatics, Second Edition aims to be comprehensive guide for researchers in the field. |
| tcga rna-seq data: Applications of RNA-Seq and Omics Strategies Fabio Marchi, Priscila Cirillo, Elvis Cueva Mateo, 2017-09-13 The large potential of RNA sequencing and other omics techniques has contributed to the production of a huge amount of data pursuing to answer many different questions that surround the science's great unknowns. This book presents an overview about powerful and cost-efficient methods for a comprehensive analysis of RNA-Seq data, introducing and revising advanced concepts in data analysis using the most current algorithms. A holistic view about the entire context where transcriptome is inserted is also discussed here encompassing biological areas with remarkable technological advances in the study of systems biology, from microorganisms to precision medicine. |
| tcga rna-seq data: Predicting High-Risk Individuals for Common Diseases Using Multi-Omics and Epidemiological Data Lu Zhang, Bailiang Li, Xin Zhou, Yuanwei Zhang, 2021-10-01 |
| tcga rna-seq data: Statistical Analysis of Next Generation Sequencing Data Somnath Datta, Dan Nettleton, 2014-07-03 Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today. To extract signals from high-dimensional NGS data and make valid statistical inferences and predictions, novel data analytic and statistical techniques are needed. This book contains 20 chapters written by prominent statisticians working with NGS data. The topics range from basic preprocessing and analysis with NGS data to more complex genomic applications such as copy number variation and isoform expression detection. Research statisticians who want to learn about this growing and exciting area will find this book useful. In addition, many chapters from this book could be included in graduate-level classes in statistical bioinformatics for training future biostatisticians who will be expected to deal with genomic data in basic biomedical research, genomic clinical trials and personalized medicine. About the editors: Somnath Datta is Professor and Vice Chair of Bioinformatics and Biostatistics at the University of Louisville. He is Fellow of the American Statistical Association, Fellow of the Institute of Mathematical Statistics and Elected Member of the International Statistical Institute. He has contributed to numerous research areas in Statistics, Biostatistics and Bioinformatics. Dan Nettleton is Professor and Laurence H. Baker Endowed Chair of Biological Statistics in the Department of Statistics at Iowa State University. He is Fellow of the American Statistical Association and has published research on a variety of topics in statistics, biology and bioinformatics. |
| tcga rna-seq data: Advances in Mathematical and Computational Oncology Doron Levy, George Bebis, Russell C. Rockne, Ernesto Augusto Bueno Da Fonseca Lima, Katharina Jahn, Panayiotis V. Benos, 2022-05-05 |
| tcga rna-seq data: High-throughput Sequencing-based Investigation of Chronic Disease Markers and Mechanisms. Hua Li, Wen-Lian Chen, Yuriy L. Orlov, Guoshuai Cai, 2022-07-12 |
| tcga rna-seq data: NK cell modifications to advance their anti-tumor activities Ye Li, Hind Rafei, Thomas Walle, Dimitrios Laurin Wagner, May Daher, 2023-09-08 |
| tcga rna-seq data: Tumor Microenvironment (TME) and Tumor Immune Microenvironment (TIME): New Perspectives for Prognosis and Therapy Ana Karina de Oliveira, Jay William Fox, Mariane Tami Amano, Adriana Franco Paes Leme, Rodrigo Nalio Ramos, 2022-09-30 |
| tcga rna-seq data: Detection and Characterization of Gastrointestinal (Early) Cancer Andrej Wagner, Valeria Barresi, Simona Gurzu, Yuming Jiang, 2022-12-16 |
| tcga rna-seq data: Exploiting DNA Damage Response in the Era of Precision Oncology Yitzhak Zimmer, Christian Reinhardt, Michaela Medová, 2020-12-11 Topic Editor Christian Reinhardt has received funding from companies Gilead, and lecture fees from Abbvie, Merck, and AstraZeneca. All other topic editors declare no competing interests with regards to the Research Topic subject. |
| tcga rna-seq data: Multi-Omics Analysis of the Human Microbiome Indra Mani, Vijai Singh, 2024-05-29 This book introduces the rapidly evolving field of multi-omics in understanding the human microbiome. The book focuses on the technology used to generate multi-omics data, including advances in next-generation sequencing and other high-throughput methods. It also covers the application of artificial intelligence and machine learning algorithms to the analysis of multi-omics data, providing readers with an overview of the powerful computational tools that are driving innovation in this field. The chapter also explores the various bioinformatics databases and tools available for the analysis of multi-omics data. The book also delves into the application of multi-omics technology to the study of microbial diversity, including metagenomics, metatranscriptomics, and metaproteomics. The book also explores the use of these techniques to identify and characterize microbial communities in different environments, from the gut and oral microbiome to the skin microbiome and beyond. Towards theend, it focuses on the use of multi-omics in the study of microbial consortia, including mycology and the viral microbiome. The book also explores the potential of multi-omics to identify genes of biotechnological importance, providing readers with an understanding of the role that this technology could play in advancing biotech research. Finally, the book concludes with a discussion of the clinical applications of multi-omics technology, including its potential to identify disease biomarkers and develop personalized medicine approaches. Overall, this book provides readers with a comprehensive overview of this exciting field, highlighting the potential for multi-omics to transform our understanding of the microbial world. |
| tcga rna-seq data: Ferroptosis in cancer and beyond, volume II Yanqing Liu, Guo Chen, Chaoyun Pan, Xin Wang, 2023-09-29 |
| tcga rna-seq data: Identification of Therapeutic Targets and Novel Biomarkers in Prostate Cancer Shashwat Sharad, Hua Li, Alagarsamy Srinivasan, Suman Kapur, 2023-02-08 |
| tcga rna-seq data: Omics Data Integration towards Mining of Phenotype Specific Biomarkers in Cancers and Diseases Liang Cheng, Lei Deng, Chuan-Xing Li, Mingxiang Teng, Yan Zhang, 2022-02-16 |
| tcga rna-seq data: Characterizing the Multi-faceted Dynamics of Tumor Cell Plasticity Satyendra Chandra Tripathi, Mohit Kumar Jolly, Herbert Levine, Sendurai A. Mani, 2021-03-01 |
| tcga rna-seq data: Emerging Biomarkers for NSCLC: Recent Advances in Diagnosis and Therapy Umberto Malapelle, Etienne Giroux Leprieur, Christian Rolfo, Paul Takam Kamga, Marius Tresor Chiasseu, 2021-06-28 |
| tcga rna-seq data: Insights in Cancer Genetics and Oncogenomics: 2022 Anton A. Buzdin, Tugba Önal-Süzek, Xin Hong, 2023-10-27 |
| tcga rna-seq data: Computational Intelligence Methods for Bioinformatics and Biostatistics Paolo Cazzaniga, Daniela Besozzi, Ivan Merelli, Luca Manzoni, 2020-12-09 This book constitutes revised selected papers from the 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019, which was held in Bergamo, Italy, during September 4-6, 2019. The 28 full papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are grouped in topical sections as follows: Computational Intelligence Methods for Bioinformatics and Biostatistics; Algebraic and Computational Methods for the Study of RNA Behaviour; Intelligence methods for molecular characterization medicine; Machine Learning in Healthcare Informatics and Medical Biology; Modeling and Simulation Methods for Computational Biology and Systems Medicine. |
| tcga rna-seq data: Multivariate Biomarker Discovery Darius M. Dziuda, 2024-05-31 A concise guide to all aspects of predictive modeling for biomarker discovery for medical diagnosis, prognosis, and personalized medicine. |
The Cancer Genome Atlas Program (TCGA) - NCI
The Cancer Genome Atlas (TCGA) is a landmark cancer genomics program that sequenced and molecularly characterized over 11,000 cases of primary cancer samples. Learn more about …
GDC Data Portal Homepage
May 7, 2025 · High-quality Datasets From Foundational Cancer Genomic Studies. High-quality datasets spanning cases from cancer genomic studies such as The Cancer Genomic Atlas …
TCGA | NCI Genomic Data Commons - Cancer
Feb 10, 2021 · The GDC supports the submission of clinical and biospecimen supplements. Supplemental files can be downloaded from the GDC by searching for the Data Type "Clinical …
TCGA Resources | NCI Genomic Data Commons - Cancer
The GDC provides access to multiple contributed datasets, including data from The Cancer Genome Atlas (TCGA), a landmark cancer genomics program that molecularly characterized …
TCGA - PanCanAtlas Publications | NCI Genomic Data …
The Pan-Cancer Atlas (PanCanAtlas) initiative aims to answer big, overarching questions about cancer by examining the full set of tumors characterized in the robust TCGA dataset.. Program …
The Cancer Genome Atlas (TCGA)—A Living Legacy for Cancer …
Apr 25, 2024 · If you’re studying the genes underlying cancer, you’re likely familiar with The Cancer Genome Atlas (TCGA). This landmark collection maps the genomic profiles of 33 …
TCGA Study Abbreviations | NCI Genomic Data Commons - Cancer
Study Abbreviation Study Name; LAML: Acute Myeloid Leukemia: ACC: Adrenocortical carcinoma: BLCA: Bladder Urothelial Carcinoma: LGG: Brain Lower Grade Glioma: BRCA
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Attention GDC Users: The GDC network will be undergoing maintenance today June 9, 2025 from 4:00PM to 9:00PM CT. During this time, intermittent connectivity loss to the GDC websites is …
Access Data | NCI Genomic Data Commons - Cancer
The GDC Data Portal is a groundbreaking tool that enables a better understanding of cancer biology by allowing researchers to: Search and query genomic data
Accessing and Downloading TCGA Data - BTEP Coding Club
The primary hub for TCGA data including supplemental data and associated files from resulting publications is the Genomic Data Commons. There is also a suite of other tools that have …
The Cancer Genome Atlas Program (TCGA) - NCI
The Cancer Genome Atlas (TCGA) is a landmark cancer genomics program that sequenced and molecularly characterized over 11,000 cases of primary cancer samples. Learn more about how the program …
GDC Data Portal Homepage
May 7, 2025 · High-quality Datasets From Foundational Cancer Genomic Studies. High-quality datasets spanning cases from cancer genomic studies such as The Cancer Genomic Atlas (TCGA), Human Cancer Models …
TCGA | NCI Genomic Data Commons - Cancer
Feb 10, 2021 · The GDC supports the submission of clinical and biospecimen supplements. Supplemental files can be downloaded from the GDC by searching for the Data Type "Clinical Supplement" or …
TCGA Resources | NCI Genomic Data Commons - Cancer
The GDC provides access to multiple contributed datasets, including data from The Cancer Genome Atlas (TCGA), a landmark cancer genomics program that molecularly characterized over 20,000 primary cancer …
TCGA - PanCanAtlas Publications | NCI Genomic Data Commons - Ca…
The Pan-Cancer Atlas (PanCanAtlas) initiative aims to answer big, overarching questions about cancer by examining the full set of tumors characterized in the robust TCGA dataset.. Program Description. The Cancer …
