The document provides information about a workshop on cancer genomic databases, including The Cancer Genome Atlas (TCGA), the International Cancer Genome Consortium (ICGC), and the Catalogue of Somatic Mutations in Cancer (COSMIC). It summarizes the goals, data access, and analysis tools available for each database. It also discusses controlled access vs open data and the process for applying for access to controlled TCGA and ICGC genomic and clinical data.
This document provides permissions for sharing and reusing the content of a presentation. It states that the presentation can be:
1) Copied, shared, adapted, or remixed.
2) Photographed, filmed, or broadcast.
3) Blogged about, live-blogged, or have videos posted.
As long as the work is attributed to its author and respects any rights and licenses associated with its components. One slide was created by Cameron Neylon and is available under a CC0 license. Social media icons were adapted from another source with permission.
International Cancer Genomics Consortium (ICGC) Data Coordinating CenterNeuro, McGill University
The document is a presentation slide deck for the International Cancer Genome Consortium (ICGC) Data Coordinating Center (DCC) given on November 14th 2013. It provides an overview of the ICGC, including its goals to catalog genomic abnormalities in 50 different cancer types using comprehensive genome, transcriptome, methylome, and clinical data analysis. It describes the activities of the ICGC DCC, which provides tools and infrastructure for data uploading, tracking, quality control, and distribution. The DCC aims to make ICGC data accessible and useful to researchers through search and analysis capabilities on its data portal.
Presentation at the Canadian Cancer Research Conference satellite bioinformatics.ca workshop. This one is an introduction to tcga, icgc and cosmic databases.
This document provides a status update and overview of the International Cancer Genomics Consortium (ICGC). The ICGC aims to sequence 500 tumor/normal pairs from 50 different cancer types to identify genome changes and make the data available for research. It coordinates cancer genome projects internationally to maximize data collection while minimizing duplication of efforts. The ICGC has established policies for data access, publication, and intellectual property. To date it has sequenced over 12,000 cancer genomes through 55 projects across 18 jurisdictions. The ICGC Data Coordination Center manages data submission and access and provides portals and tools for searching and accessing datasets.
Presentation to the Department of Biology at the University of Windsor, Windsor, Ontario. The description and update of activities related to the International Cancer Genome Consortium (ICGC)
Cancer genome databases & Ecological databases Waliullah Wali
Introduction
Biological databases are libraries of life sciences information, collected from scientific experiments, published literature, high-throughput experiment technology, and computational analysis.
Information contained in biological databases includes gene function, structure, localization, clinical effects of mutations as well as similarities of biological sequences and structures.
Cancer genome databases
COSMIC cancer database
COSMIC cancer database
COSMIC is an online database of somatically acquired mutations found in human cancer.
The database is freely available.
COSMIC cancer database
Types of data
Expert curation data
Genome-wide screen data
COSMIC cancer database
Expert curation data
Manually input by COSMIC expert curators.
Consists of comprehensive literature curation followed by subsequent updates.
Includes additional data points relevant to each disease and publication.
Provides accurate frequency data as mutation negative samples are specified.
COSMIC cancer database
Genome-wide screen data
Uploaded from publications reporting large scale genome screening data or imported from other databases such as TCGA and ICGC.
Provides unbiased molecular profiling of diseases while covering the whole genome.
Provides objective frequency data by interpreting non mutant genes across each genome.
Facilitates finding novel driver genes in cancer.
Enter into -
COSMIC cancer database
by typing http://cancer.sanger.ac.uk/cosmic
in the address bar of Browser
Searching Process
Examples
Examples
Examples
Examples
Ecological databases
Ecological databases
Ecological databases is a source for finding ecological datasets and quickly figuring out the best ways to use them.
BioOne
DataONE
GEOBASE
BioOne
BioOne is a nonprofit publisher that aims to make scientific research more accessible.
BioOne was established in 1999 in Washington, DC.
BioOne is Complete and open-access.
It serves a community of over 140 society and institutional publishers, 4,000 accessing institutions, and millions of researchers worldwide.
Enter into -
BioOne Ecological database
by typing http://www.bioone.org/
in the address bar of Browser
Biocuration activities for the International Cancer Genome Consortium (ICGC).Neuro, McGill University
The document discusses biocuration activities for the International Cancer Genome Consortium (ICGC). It provides information on the goals of ICGC including comprehensively analyzing 50 different cancer types/subtypes and making the genomic and clinical data publicly available. It describes the types of data being collected, standards being developed for data access and sharing, and current status of datasets released.
This document provides permissions for sharing and reusing the content of a presentation. It states that the presentation can be:
1) Copied, shared, adapted, or remixed.
2) Photographed, filmed, or broadcast.
3) Blogged about, live-blogged, or have videos posted.
As long as the work is attributed to its author and respects any rights and licenses associated with its components. One slide was created by Cameron Neylon and is available under a CC0 license. Social media icons were adapted from another source with permission.
International Cancer Genomics Consortium (ICGC) Data Coordinating CenterNeuro, McGill University
The document is a presentation slide deck for the International Cancer Genome Consortium (ICGC) Data Coordinating Center (DCC) given on November 14th 2013. It provides an overview of the ICGC, including its goals to catalog genomic abnormalities in 50 different cancer types using comprehensive genome, transcriptome, methylome, and clinical data analysis. It describes the activities of the ICGC DCC, which provides tools and infrastructure for data uploading, tracking, quality control, and distribution. The DCC aims to make ICGC data accessible and useful to researchers through search and analysis capabilities on its data portal.
Presentation at the Canadian Cancer Research Conference satellite bioinformatics.ca workshop. This one is an introduction to tcga, icgc and cosmic databases.
This document provides a status update and overview of the International Cancer Genomics Consortium (ICGC). The ICGC aims to sequence 500 tumor/normal pairs from 50 different cancer types to identify genome changes and make the data available for research. It coordinates cancer genome projects internationally to maximize data collection while minimizing duplication of efforts. The ICGC has established policies for data access, publication, and intellectual property. To date it has sequenced over 12,000 cancer genomes through 55 projects across 18 jurisdictions. The ICGC Data Coordination Center manages data submission and access and provides portals and tools for searching and accessing datasets.
Presentation to the Department of Biology at the University of Windsor, Windsor, Ontario. The description and update of activities related to the International Cancer Genome Consortium (ICGC)
Cancer genome databases & Ecological databases Waliullah Wali
Introduction
Biological databases are libraries of life sciences information, collected from scientific experiments, published literature, high-throughput experiment technology, and computational analysis.
Information contained in biological databases includes gene function, structure, localization, clinical effects of mutations as well as similarities of biological sequences and structures.
Cancer genome databases
COSMIC cancer database
COSMIC cancer database
COSMIC is an online database of somatically acquired mutations found in human cancer.
The database is freely available.
COSMIC cancer database
Types of data
Expert curation data
Genome-wide screen data
COSMIC cancer database
Expert curation data
Manually input by COSMIC expert curators.
Consists of comprehensive literature curation followed by subsequent updates.
Includes additional data points relevant to each disease and publication.
Provides accurate frequency data as mutation negative samples are specified.
COSMIC cancer database
Genome-wide screen data
Uploaded from publications reporting large scale genome screening data or imported from other databases such as TCGA and ICGC.
Provides unbiased molecular profiling of diseases while covering the whole genome.
Provides objective frequency data by interpreting non mutant genes across each genome.
Facilitates finding novel driver genes in cancer.
Enter into -
COSMIC cancer database
by typing http://cancer.sanger.ac.uk/cosmic
in the address bar of Browser
Searching Process
Examples
Examples
Examples
Examples
Ecological databases
Ecological databases
Ecological databases is a source for finding ecological datasets and quickly figuring out the best ways to use them.
BioOne
DataONE
GEOBASE
BioOne
BioOne is a nonprofit publisher that aims to make scientific research more accessible.
BioOne was established in 1999 in Washington, DC.
BioOne is Complete and open-access.
It serves a community of over 140 society and institutional publishers, 4,000 accessing institutions, and millions of researchers worldwide.
Enter into -
BioOne Ecological database
by typing http://www.bioone.org/
in the address bar of Browser
Biocuration activities for the International Cancer Genome Consortium (ICGC).Neuro, McGill University
The document discusses biocuration activities for the International Cancer Genome Consortium (ICGC). It provides information on the goals of ICGC including comprehensively analyzing 50 different cancer types/subtypes and making the genomic and clinical data publicly available. It describes the types of data being collected, standards being developed for data access and sharing, and current status of datasets released.
Presentation for teaching faculty about resources, data, issues, and strategies for including personal genomics in the classroom, within the context of precision medicine as an overarching theme.
GenomeTrakr: Perspectives on linking internationally - Canada and IRIDA.cafionabrinkman
Talk at GenomeTrakr network meeting Sept 23 2015 in Washington DC. On Canada's open source Integrated Rapid Infectious Disease Analysis (IRIDA) bioinformatics platform - aiding genomic epidemiology analysis for public health agencies with planned open data release and linkage to GenomeTrakr. Discussed perspectives, challenges, solutions for getting more GenomeTrakr participation internationally.
Free webinar-introduction to bioinformatics - biologist-1Elia Brodsky
The Omics Logic Introduction to Bioinformatics program is a one-month online training program that provides an introduction to the field of bioinformatics for beginners. The program consists of six sessions taught by an international team of experts, covering topics like genomics, transcriptomics, statistical analysis, machine learning, and a final bioinformatics project. Participants will learn data analysis skills in Python and R and how to extract insights from multi-omics datasets with applications in biomedicine. The goal is to prepare students for data-driven research in life sciences through interactive lessons, coding exercises, and independent projects.
This document provides an overview of the November 2000 issue of JALA (Journal of Analytical Laboratories Automation). It describes the development of a novel robotic system for the New York Cancer Project biorepository in collaboration with the Medical Automation Research Center. The biorepository receives 50-100 blood samples per day which are processed robotically to extract, quantify, aliquot and store DNA, plasma and RNA to be accessible to investigators. The robotic system aims to provide rapid random access to the hundreds of thousands of DNA samples stored for high-throughput analysis in studies of gene-environment interactions and cancer risk.
A machine learning and bioinformatics approach was used to identify non-invasive miRNA biomarkers for early detection of non-small cell lung cancer (NSCLC). 13 miRNAs were found to be consistently underexpressed in NSCLC tissue, blood and serum across 4 datasets. Kaplan-Meier analysis showed 6 miRNAs had prognostic power. A random forest model identified a 3-miRNA panel (miR-320e, miR-103a, miR-526b) that detected NSCLC with 91.5% accuracy. These miRNAs were also prognostic for lung adenocarcinoma survival. An online tool called BiomarkerGenie was created to automate biomarker selection from omics data.
The document outlines plans to transition the cBioPortal cancer genomics platform to an open source model with coordinated development between Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, and Princess Margaret Cancer Centre. It discusses expanding usage, new features, funding options, and establishing an advisory committee. The goal is to build a sustainable open source community through collaborative development, additional funding, and engagement with users and potential contributors.
The Global Micorbial Identifier (GMI) initiative - and its working groupsExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
The GMI initiative - and its working groups. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management -23-25 May 2016, Rome, Italy.
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...Elia Brodsky
This workshop will address critical issues related to Transcriptomics data:
Processing raw Next Generation Sequencing (NGS) data:
1. Next Generation Sequencing data preprocessing:
Trimming technical sequences
Removing PCR duplicates
2. RNA-seq based quantification of expression levels:
Conventional pipelines (looking at known transcripts)
Identification of novel isoforms
Analysis of Expression Data Using Machine Learning:
3. Unsupervised analysis of expression data:
Principal Component Analysis
Clustering
4. Supervised analysis:
Differential expression analysis
Classification, gene signature construction
5. Gene set enrichment analysis
The workshop will include hands-on exercises utilizing public domain datasets:
breast cancer cell lines transcriptomic profiles (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r110),
patient-derived xenograft (PDX) mouse model of tumor and stroma transcriptomic profiles (http://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=view&path[]=8014&path[]=23533), and
processed data from The Cancer Genome Atlas samples (https://cancergenome.nih.gov/).
Team: The workshops are designed by the researchers at the Tauber Bioinformatics Research Center at University of Haifa, Israel in collaboration with academic centers across the US. Technical support for the workshops is provided by the Pine Biotech team. https://edu.t-bio.info/a-critical-approach-to-transcriptomic-data-analysis/
Personal Genomes: what can I do with my data?Melanie Swan
Biology evolved to be just good enough to survive and genomics provides the critical next-generation toolkit for its greater exploitation. Genomics is already starting to be medically actionable and is likely to become increasingly useful over time. This presentation discusses how your genetic information is already useful today,
This document provides an introduction to bioinformatics. It defines bioinformatics as the analysis of large amounts of biological data, such as DNA sequences, using computer programs. It discusses how next-generation sequencing technologies are generating terabytes of nucleotide sequence data that is analyzed by automated computer programs. The document then provides examples of the types of biological data that is analyzed in bioinformatics, including DNA, RNA, protein sequences and their interactions. It also discusses some common programming languages and analysis techniques used in bioinformatics.
Introduction
Definition
History
Principle
Components of bioinformatics
Bioinformatics databases
Tools of bioinformatics
Applications of bioinformatics
Molecular medicine
Microbial genomics
Plant genomics
Animal genomics
Human genomics
Drug and vaccine designing
Proteomics
For studying biomolecular structures
In- silico testing
Conclusion
References
Slides for the afternoon session on "Introduction to Bioinformatics", delivered at the James Hutton Institute, 29th, 20th May and 5th June 2014, by Leighton Pritchard and Peter Cock.
Slides cover introductory guidance and links to resources, theory and use of BLAST tools, and a workshop featuring some common tools and tasks.
DNA Testing: Living Longer Via Personal GenomicsMelanie Swan
This document summarizes a presentation on direct-to-consumer DNA testing and personal genomics. It discusses numerous applications of genomics including ancestry, health, athletic performance and aging. It also summarizes several direct-to-consumer genetic testing services and compares their costs, conditions analyzed, and data access. The presentation concludes by discussing future improvements in DNA sequencing technologies that could enable more affordable personal genome sequencing.
This document introduces bioinformatics and discusses some of its key concepts and applications. It defines bioinformatics as an interdisciplinary field that combines computer science, statistics and engineering to study and process biological data. It describes some basic cell components like DNA, RNA and proteins, and how genetics and the genetic code work. It also provides a brief history of bioinformatics, highlighting projects like the Human Genome Project. Finally, it outlines several applications of bioinformatics like phylogenetic analysis, drug design, microarray analysis and protein-protein interaction networks.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
Introducing Bioinformatics
Bioinformatics in the Big Data Era
How to get into Bioinformatics?
How to learn and practice Bioinformatics?
Bioinformatics Careers and Salaries Worldwide
Applications of Bioinformatics
Take-Home Messages
Bioinformatics can be applied to climate smart horticulture in several ways:
1) It allows for crop improvement through comparative genomics between crop plants and model species to identify important genes.
2) It facilitates plant breeding by providing tools for genome analysis, marker identification, and rational gene annotation.
3) Stress-tolerant varieties can be developed by using bioinformatics databases like KEGG to identify pathways and genes involved in drought resistance.
Increasing demand and the use of high-quality samples, data and services place biobanks at the center of basic and applied research. The BBMRI-ERIC Quality Management Service (BBMRI.QM) is designed to help biobanks and researchers meet the highest quality standards for their research and meet the needs of their clients. This webinar will give you an insight into the service portfolio of BBMRI.QM and an overview of relevant European and international standards useful for research on human specimens.
CORBEL (http://www.corbel-project.eu) is an initiative of eleven new biological and medical research infrastructures (BMS RIs), which together will create a platform for harmonised user access to biological and medical technologies, biological samples and data services required by cutting-edge biomedical research. CORBEL will boost the efficiency, productivity and impact of European biomedical research.
This webinar took place on 6th December 2018 and is part of the CORBEL webinar series. A recording of the webinar is available through the CORBEL website:
https://www.corbel-project.eu/webinars/bbmri-eric-quality-management-services.html
For previous and upcoming CORBEL webinars see:
http://www.corbel-project.eu/webinars
Presentation for teaching faculty about resources, data, issues, and strategies for including personal genomics in the classroom, within the context of precision medicine as an overarching theme.
GenomeTrakr: Perspectives on linking internationally - Canada and IRIDA.cafionabrinkman
Talk at GenomeTrakr network meeting Sept 23 2015 in Washington DC. On Canada's open source Integrated Rapid Infectious Disease Analysis (IRIDA) bioinformatics platform - aiding genomic epidemiology analysis for public health agencies with planned open data release and linkage to GenomeTrakr. Discussed perspectives, challenges, solutions for getting more GenomeTrakr participation internationally.
Free webinar-introduction to bioinformatics - biologist-1Elia Brodsky
The Omics Logic Introduction to Bioinformatics program is a one-month online training program that provides an introduction to the field of bioinformatics for beginners. The program consists of six sessions taught by an international team of experts, covering topics like genomics, transcriptomics, statistical analysis, machine learning, and a final bioinformatics project. Participants will learn data analysis skills in Python and R and how to extract insights from multi-omics datasets with applications in biomedicine. The goal is to prepare students for data-driven research in life sciences through interactive lessons, coding exercises, and independent projects.
This document provides an overview of the November 2000 issue of JALA (Journal of Analytical Laboratories Automation). It describes the development of a novel robotic system for the New York Cancer Project biorepository in collaboration with the Medical Automation Research Center. The biorepository receives 50-100 blood samples per day which are processed robotically to extract, quantify, aliquot and store DNA, plasma and RNA to be accessible to investigators. The robotic system aims to provide rapid random access to the hundreds of thousands of DNA samples stored for high-throughput analysis in studies of gene-environment interactions and cancer risk.
A machine learning and bioinformatics approach was used to identify non-invasive miRNA biomarkers for early detection of non-small cell lung cancer (NSCLC). 13 miRNAs were found to be consistently underexpressed in NSCLC tissue, blood and serum across 4 datasets. Kaplan-Meier analysis showed 6 miRNAs had prognostic power. A random forest model identified a 3-miRNA panel (miR-320e, miR-103a, miR-526b) that detected NSCLC with 91.5% accuracy. These miRNAs were also prognostic for lung adenocarcinoma survival. An online tool called BiomarkerGenie was created to automate biomarker selection from omics data.
The document outlines plans to transition the cBioPortal cancer genomics platform to an open source model with coordinated development between Memorial Sloan Kettering Cancer Center, Dana-Farber Cancer Institute, and Princess Margaret Cancer Centre. It discusses expanding usage, new features, funding options, and establishing an advisory committee. The goal is to build a sustainable open source community through collaborative development, additional funding, and engagement with users and potential contributors.
The Global Micorbial Identifier (GMI) initiative - and its working groupsExternalEvents
http://www.fao.org/about/meetings/wgs-on-food-safety-management/en/
The GMI initiative - and its working groups. Presentation from the Technical Meeting on the impact of Whole Genome Sequencing (WGS) on food safety management -23-25 May 2016, Rome, Italy.
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...Elia Brodsky
This workshop will address critical issues related to Transcriptomics data:
Processing raw Next Generation Sequencing (NGS) data:
1. Next Generation Sequencing data preprocessing:
Trimming technical sequences
Removing PCR duplicates
2. RNA-seq based quantification of expression levels:
Conventional pipelines (looking at known transcripts)
Identification of novel isoforms
Analysis of Expression Data Using Machine Learning:
3. Unsupervised analysis of expression data:
Principal Component Analysis
Clustering
4. Supervised analysis:
Differential expression analysis
Classification, gene signature construction
5. Gene set enrichment analysis
The workshop will include hands-on exercises utilizing public domain datasets:
breast cancer cell lines transcriptomic profiles (https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-10-r110),
patient-derived xenograft (PDX) mouse model of tumor and stroma transcriptomic profiles (http://www.oncotarget.com/index.php?journal=oncotarget&page=article&op=view&path[]=8014&path[]=23533), and
processed data from The Cancer Genome Atlas samples (https://cancergenome.nih.gov/).
Team: The workshops are designed by the researchers at the Tauber Bioinformatics Research Center at University of Haifa, Israel in collaboration with academic centers across the US. Technical support for the workshops is provided by the Pine Biotech team. https://edu.t-bio.info/a-critical-approach-to-transcriptomic-data-analysis/
Personal Genomes: what can I do with my data?Melanie Swan
Biology evolved to be just good enough to survive and genomics provides the critical next-generation toolkit for its greater exploitation. Genomics is already starting to be medically actionable and is likely to become increasingly useful over time. This presentation discusses how your genetic information is already useful today,
This document provides an introduction to bioinformatics. It defines bioinformatics as the analysis of large amounts of biological data, such as DNA sequences, using computer programs. It discusses how next-generation sequencing technologies are generating terabytes of nucleotide sequence data that is analyzed by automated computer programs. The document then provides examples of the types of biological data that is analyzed in bioinformatics, including DNA, RNA, protein sequences and their interactions. It also discusses some common programming languages and analysis techniques used in bioinformatics.
Introduction
Definition
History
Principle
Components of bioinformatics
Bioinformatics databases
Tools of bioinformatics
Applications of bioinformatics
Molecular medicine
Microbial genomics
Plant genomics
Animal genomics
Human genomics
Drug and vaccine designing
Proteomics
For studying biomolecular structures
In- silico testing
Conclusion
References
Slides for the afternoon session on "Introduction to Bioinformatics", delivered at the James Hutton Institute, 29th, 20th May and 5th June 2014, by Leighton Pritchard and Peter Cock.
Slides cover introductory guidance and links to resources, theory and use of BLAST tools, and a workshop featuring some common tools and tasks.
DNA Testing: Living Longer Via Personal GenomicsMelanie Swan
This document summarizes a presentation on direct-to-consumer DNA testing and personal genomics. It discusses numerous applications of genomics including ancestry, health, athletic performance and aging. It also summarizes several direct-to-consumer genetic testing services and compares their costs, conditions analyzed, and data access. The presentation concludes by discussing future improvements in DNA sequencing technologies that could enable more affordable personal genome sequencing.
This document introduces bioinformatics and discusses some of its key concepts and applications. It defines bioinformatics as an interdisciplinary field that combines computer science, statistics and engineering to study and process biological data. It describes some basic cell components like DNA, RNA and proteins, and how genetics and the genetic code work. It also provides a brief history of bioinformatics, highlighting projects like the Human Genome Project. Finally, it outlines several applications of bioinformatics like phylogenetic analysis, drug design, microarray analysis and protein-protein interaction networks.
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
Introducing Bioinformatics
Bioinformatics in the Big Data Era
How to get into Bioinformatics?
How to learn and practice Bioinformatics?
Bioinformatics Careers and Salaries Worldwide
Applications of Bioinformatics
Take-Home Messages
Bioinformatics can be applied to climate smart horticulture in several ways:
1) It allows for crop improvement through comparative genomics between crop plants and model species to identify important genes.
2) It facilitates plant breeding by providing tools for genome analysis, marker identification, and rational gene annotation.
3) Stress-tolerant varieties can be developed by using bioinformatics databases like KEGG to identify pathways and genes involved in drought resistance.
Increasing demand and the use of high-quality samples, data and services place biobanks at the center of basic and applied research. The BBMRI-ERIC Quality Management Service (BBMRI.QM) is designed to help biobanks and researchers meet the highest quality standards for their research and meet the needs of their clients. This webinar will give you an insight into the service portfolio of BBMRI.QM and an overview of relevant European and international standards useful for research on human specimens.
CORBEL (http://www.corbel-project.eu) is an initiative of eleven new biological and medical research infrastructures (BMS RIs), which together will create a platform for harmonised user access to biological and medical technologies, biological samples and data services required by cutting-edge biomedical research. CORBEL will boost the efficiency, productivity and impact of European biomedical research.
This webinar took place on 6th December 2018 and is part of the CORBEL webinar series. A recording of the webinar is available through the CORBEL website:
https://www.corbel-project.eu/webinars/bbmri-eric-quality-management-services.html
For previous and upcoming CORBEL webinars see:
http://www.corbel-project.eu/webinars
This presentation summarizes the advancements towards the completing the work described in GBIF Work Programme Update 2016.
It was composed by different members from the GBIF Secretariat. This particular version was shared during the European Nodes Meeting in Lisbon the 19 April 2016.
CINECA webinar slides: Data Gravity in the Life Sciences: Lessons learned fro...CINECAProject
We live in an era of cloud computing. Many of the services in the life sciences are keenly planning cloud transformations, seeking to create globally distributed ecosystems of harmonised data based on standards from organisations like GA4GH. CINECA faces similar challenges, gathering cohort datasets from all over the globe, many of which are pinned in place, due to their size, legal restrictions, or other considerations. But is “bringing compute to the data” always the right choice? In this webinar, based on experiences from the Human Cell Atlas Data Coordination Platform and other projects from EMBL-EBI, we will explore the concept of “data gravity”: The idea that whilst there are forces that may hold data in one place, there are others that require it to be mobile. We’ll consider how effectively planning a cloud strategy requires consideration of the gravity of datasets, and the impact it may have on team skills required, incentives for good practice, and storage and compute costs.
The CINECA webinar series aims to discuss ways to address common challenges and share best practices in the field of cohort data analysis, as well as distribute CINECA project results. All CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions. Please note that all webinars are recorded and available for posterior viewing. CINECA webinars include an audience Q&A session during which attendees can ask questions and make suggestions.
This webinar took place on 12th November 2020 and is part of the CINECA webinar series.
For previous and upcoming CINECA webinars see:
https://www.cineca-project.eu/webinars
This document discusses opportunities for using the open source cBioPortal platform in a commercial setting. It summarizes The Hyve's experiences supporting cBioPortal for the Center for Translational Molecular Medicine's TraIT project. The Hyve provides professional support for open source bioinformatics software like cBioPortal through software development, data services, consultancy, and hosting. For translation projects, The Hyve employs a phased approach including definition, pilot, implementation, and evaluation phases to implement cBioPortal and demonstrate its capabilities for data integration and analysis.
IRIDA: Canada’s federated platform for genomic epidemiology, ABPHM 2015 WHsiaoIRIDA_community
This document summarizes the IRIDA platform, a federated genomic epidemiology platform for Canada. IRIDA aims to bridge gaps between advances in genomic epidemiology and real-time application in public health. It is developing solutions such as building a user-friendly analysis platform, implementing security and role-based sharing of genomic data, and using ontologies to standardize inconsistent information representation and address the complexity of genomic data interpretation. The IRIDA platform is in beta testing and plans continued development and training workshops.
GCAT Update June 2013 @ The Clinical Genome ConferenceDavid Mittelman
GCAT (Genome Comparison and Analytic Testing) is a free online platform for benchmarking NGS methods and developing standards and metrics. It allows users to process sequencing data through different analysis tools and compare results. Since launching in April 2013, GCAT has been viewed over 20,000 times and has processed large amounts of sequencing data. New features continue to be added, including comparing variant calls to validation datasets and support for additional sequencing applications like RNAseq and de novo assembly. The goal is to accelerate adoption of NGS technologies by providing a common system for experimentation and validation of analysis methods.
ELIXIR Pilot Actions launched in 2014: Integration of BILS-ProteomeXchange us...Juan Antonio Vizcaino
This is a report of the ELIXIR pilot project performed by the EMBL-EBI (PRIDE and System teams), BILS and EUDAT. The title of the pilot project was: "Integration of BILS-ProteomeXchange using EUDAT resources".
This document summarizes Yves Sucaet's presentation on whole slide imaging and digital pathology. It discusses the history of digital pathology, how digital pathology can improve biobanks by allowing remote querying and analysis of virtual slides, and the future of intelligent querying of biobanks using digital pathology and bioinformatics tools. The presentation concludes by encouraging attendees to implement digital pathology workflows and continue the conversation around computational pathology.
IRIDA: Canada’s federated platform for genomic epidemiology William Hsiao
This document summarizes the IRIDA platform, a federated genomic epidemiology platform for Canada. IRIDA aims to (1) build a user-friendly analysis platform to process genomic data, (2) enable more efficient information sharing between public health agencies, and (3) standardize inconsistent information representation through the use of ontologies. The platform is a partnership between various public health and academic institutions to bridge gaps between genomic research and applications in public health outbreak investigations.
Digital pathology and its importance as an omics data layerYves Sucaet
Bioinformatics and pathology are obvious scientific partners. Bioinformatics often takes places at the most basic (almost chemical, or even physical) level of life, but much of its procedures to obtain data are destructive. Pathology on the other hand takes place at a much more coarse level of data acquisition (usually where the physical properties of visible light end), but has the advantage of being rooted in the tradition of medicine. The traditional paradigm of pathology is "tissue is the issue". Morphology (exactly the component that often gets overlooked in bioinformatics) plays a large role and helps millions of patients each year around the world. Pathology is proven technology, bioinformatics is limited to niche applications.
With the development of whole slide imaging technology some twenty years ago, digital pathology became possible. Observations that used to be for the eyes of the pathologist only, could now be captured and translated into high-resolution pixels, and studied by and communicated to many. Many began to dream of automated tissue evaluation systems and AI-pathology, some even going as far as to suggest the replacement of the pathologist by intelligent computer systems.
Meanwhile in several areas of bioinformatics, new limits are being hit. Yes, we can do high-throughput experiments, but noisy datasets are often the results, (inter- and even intra-observer) replicability is difficult, and statistics only offer limited relief.
The goal of this introductory lecture is to highlight the problems as well as opportunities for both fields of study, and how exchange of experiences, and (in a later stadium) integration of techniques close the scientific gap that still exists in a great many areas.
There is no lack of pathology-centric workshops that offer insights into the world of algorithms. With the CPW event however, we take another approach. We want to bring together the most advanced groups in digital pathology, with the bioinformatics community, to explore the opportunities that exist on both sides of the fence.
We start by explaining the basic data types that are introduced by digital pathology. We also explain where they come from, and why this presents unique challenges when it comes to data mining and image analysis. Finally, we introduce PMA.start, a free software environment that can be used to universally gain access to digital pathology (imaging) data.
Bioinformatics groups can help quantify, model, and reduce morphological whole tissue data. Pathologists can help interpret and explain heterogeneous high-throughput datasets. And the first seeds of such collaboration can be planted right here, in Athens.
PRIDE is a proteomics database at EMBL-EBI that stores mass spectrometry-based proteomics data, including peptide and protein identifications and quantifications. It is part of the ProteomeXchange consortium, which aims to facilitate standardized data submission and dissemination between proteomics repositories. The document outlines the types of data stored in PRIDE, how to access and submit data, and tools for data conversion and visualization like PRIDE Converter 2 and PRIDE Inspector.
CIP has implemented GRIN-Global to manage its large potato and sweetpotato genebank. It has migrated passport and characterization data for over 50,000 accessions. The new system provides public search access and supports internal distribution management. CIP developers created custom wizards and tools to improve usability and enable tasks like batch updates. Remaining challenges include improving documentation, supporting mobile access, and continuing data curation during the migration process.
This document provides updates on several collaborative health IT projects in New Zealand, including:
1) The e-Labs project which aims to enable electronic ordering and sharing of lab test results between primary and secondary care.
2) The Consumer Health Portal proof of concept which created a personal health record for patients that integrated various online health tools and services.
3) The announcement of a new "Collaborate 2 Innovate" initiative seeking proposals for innovative collaborative health IT projects in New Zealand.
This presentation was provided by Violeta Ilik of Northwestern University during the NISO Virtual Conference held on Feb 15, 2017, entitled Institutional Repositories: Ensuring Yours is Populated, Useful and Thriving. The DOI for this presentation is http://dx.doi.org/10.18131/G3VP6R
This document discusses the use of linked data in industry. It provides examples of how the BBC, Volkswagen, and various government agencies are publishing open data using linked data approaches. It also discusses the potential for linked data in life sciences and healthcare, including a translational medicine platform for Alzheimer's disease. Semantic web projects in these domains aim to integrate data from distributed sources to answer complex queries. The challenges of big data in genomics are also mentioned, as well as the role of "data marketplaces" and platforms that enable access and integration of diverse biomedical datasets.
This document provides an overview of next generation sequencing (NGS) analysis. It discusses various NGS platforms such as Illumina, Roche 454, PacBio, and Ion Torrent. It also covers common file formats for sequencing data like FASTQ, quality control measures to assess data quality, and applications of NGS such as RNA-seq and ChIP-seq. The document aims to introduce researchers to basic concepts in NGS analysis and highlights available resources for storing and analyzing large sequencing datasets.
In this talk I'll discuss work in biomedical image and volume segmentation and classification, as well as outcome prediction modeling from insurance claims data that I've pursued at LifeOmic here in the Triangle. In the former case datasets include radiological image volumes, retinal fundus images, and cell images created with fluorescent microscopy. The latter includes MIMIC-III data represented as FHIR objects. I'll discuss the relative challenges and advantages of doing ML locally vs. on a cloud-based platform.
A global integrative ecosystem for digital pathology: how can we get there?Yves Sucaet
Digital pathology has many faces. Its stakeholders can roughly be classified into four categories: education, research, clinical, and clinical research. We come together at events like Pathology Informatics or Pathology Visions, and discuss the evolution of the field.
While progression is being made, it sometimes appears that around every corner are more challenges and forks in the road. New applications and scenarios emerge at a rapid pace, and it is clear that a single one-size-fits-all type of software is unlikely to satisfy most participants in this space, if any.
At the institutional level, ecosystems of digital pathology have already been established. At a national level, attempts are being made. At a global level, this is still a wide open question, but one very much worth exploring.
Digital pathology comes with some unique properties, like the data it generates and the pace at which this happens. This guest lecture then will examine the solutions that already exist, and what an inclusive global scalable digital pathology ecosystem may look like in the future.
Similar to Cancer uk 2015_module1_ouellette_ver02 (20)
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Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
Authoring a personal GPT for your research and practice: How we created the Q...Leonel Morgado
Thematic analysis in qualitative research is a time-consuming and systematic task, typically done using teams. Team members must ground their activities on common understandings of the major concepts underlying the thematic analysis, and define criteria for its development. However, conceptual misunderstandings, equivocations, and lack of adherence to criteria are challenges to the quality and speed of this process. Given the distributed and uncertain nature of this process, we wondered if the tasks in thematic analysis could be supported by readily available artificial intelligence chatbots. Our early efforts point to potential benefits: not just saving time in the coding process but better adherence to criteria and grounding, by increasing triangulation between humans and artificial intelligence. This tutorial will provide a description and demonstration of the process we followed, as two academic researchers, to develop a custom ChatGPT to assist with qualitative coding in the thematic data analysis process of immersive learning accounts in a survey of the academic literature: QUAL-E Immersive Learning Thematic Analysis Helper. In the hands-on time, participants will try out QUAL-E and develop their ideas for their own qualitative coding ChatGPT. Participants that have the paid ChatGPT Plus subscription can create a draft of their assistants. The organizers will provide course materials and slide deck that participants will be able to utilize to continue development of their custom GPT. The paid subscription to ChatGPT Plus is not required to participate in this workshop, just for trying out personal GPTs during it.
Anti-Universe And Emergent Gravity and the Dark UniverseSérgio Sacani
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Cancer uk 2015_module1_ouellette_ver02
1. Provided to you by the
Canadian Bioinformatics
Workshop series
www.bioinformatics.ca
NCRI Cancer Conference:
Cancer data and its analysis
practical workshop
November 1, 2015
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You are free to:
Copy, share, adapt, or re-mix;
Photograph, film, or broadcast;
Blog, live-blog, or post video of;
This presentation. Provided that:
You attribute the work to its author and
respect the rights and licenses associated
with its components.
Slide Concept by Cameron Neylon, who has waived all copyright and related or neighbouring rights. This slide only ccZero.
Social Media Icons adapted with permission from originals by Christopher Ross. Original images are available under GPL at;
http://www.thisismyurl.com/free-downloads/15-free-speech-bubble-icons-for-popular-websites
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Schedule for Module 1:
Cancer Genomic Databases
• Introduction to the Canadian Bioinformatics
Workshop series.
• The Databases:
– The Cancer Genome Atlas (TCGA)
– The International Cancer Genome Consortium (ICGC)
• Data Access: human genomes and security and
privacy issues:
Open Data vs. Controlled Access data
• Another Database:
– The Catalogue of Somatic Mutations in Cancer (COSMIC)
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Workshops planned for 2016:
http://bioinformatics.ca/workshops
1. Bioinformatics for Cancer Genomics
2. High-throughput Biology: From Sequence to Networks (2017 - CSHL)
3. Introduction to R
4. Exploratory Analysis of Biological Data using R
5. Informatics for RNA-sequence Analysis
6. Informatics on High Throughput Sequencing Data
7. Pathway and Network Analysis of -omics Data
8. Informatics and Statistics for Metabolomics
9. Analysis of Metagenomic Data
10. How to Work in the Cloud: Computing on Human Genome Data
11. Epigenomic Data Analysis
12. Big Data in Precision Genomics
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Soap-Box time!
• Open Access, Open Data and Open Source are essential for good
Science.
• Openness is a responsibility, an obligation, and something that comes
with the privilege of doing publicly funded work.
Open Access
Open Source
Open Data
Opencourseware
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Cancer therapy is like
beating the dog with
a stick to get rid of
his fleas.
- Anna Deavere Smith,
Let me down easy
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The revolution in cancer
research can summed up
in a single sentence:
cancer is in essence,
a genetic disease.
- Bert Vogelstein
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Cancer: a Disease of the Genome
Challenge in Treating Cancer:
Every tumour is different
Every cancer patient is different
22. NCRI Workshop 2015 – Module 1 bioinformatics.ca
TCGA
The Cancer Genome Atlas is a
comprehensive and coordinated
effort to accelerate our
understanding of the molecular
basis of cancer through the
application of genome analysis
technologies, including large-
scale genome sequencing.
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About the TCGA
• National Cancer Institute (NCI)
• National Human Genome Research Institute
(NHGRI)
• Phased Structure:
– Three-year pilot in 2006 with an investment of $50 million
from each
– TCGA will collect and characterize more than 20 additional
tumour types
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Division of Labour
• Biospecimen Core Resource (BCR)
– centre where samples are carefully catalogued, processed, qualitychecked
and stored along with participant clinical information
• Genome Sequencing Centre (GSC)
– uses high-throughput methods to identify changes to DNA sequences that are
associated with specific cancer types
• Genome Characterization Centre (GCC)
– uses high-throughput technologies to analyze genomic changes involved in cancer
• Genome Data Analysis Centre (GDAC)
– provides novel informatics tools to the research community
– provides analysis results using TCGA data.
• Data Coordinating Centre (DCC)
– Central provider of TCGA data.
– Standardizes data formats and validates submitted data.
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TCGA Data
• Sequence reads from newer sequencing
technologies are available at the Cancer Genome
Hub: https://cghub.ucsc.edu/
• Higher level sequence data (variation calls and
abundance measures) are available at the TCGA
Portal: http://cancergenome.nih.gov/
• Also integrated with ICGC data (more on this later)
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Data Coordinating Centre
• Play a central role
– Receiving data from BCR, GSC and GCC sites
– Providing access to users
– Performing analysis of data
• Responsibilities:
– Protecting participant privacy and confidentiality
– Developing data standards and controlled vocabularies
– Establishing informatics pipelines for data flow
– Developing new analytical and visualization technologies
to facilitate data analysis, for all audiences
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TCGA DCC Data Portal
• Provides a platform to search, download and
analyze TCGA data sets
• Two data access tiers: Open and Controlled
• Analytic tools include: Cancer Molecular Analysis
and Cancer Genome Workbench (NCBIB),
Integrative Genomics Viewer (Broad) and
CancerGenomics Analysis (MSKCC).
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TCGA Data Browser
https://tcga-data.nci.nih.gov/tcga/
Query TCGA
data online
using the
TCGA Data
Browser
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The International Cancer Genome Consortium (ICGC)
• http://www.icgc.org/
• “ICGC was launched
to coordinate large-
scale cancer genome
studies in tumours
from 50 different
cancer types and/or
subtypes that are of
clinical and societal
importance across
the globe”
43. NCRI Workshop 2015 – Module 1 bioinformatics.ca
ICG
C
TCGA
Differences between ICGC & TCGA
• Different tumour types
• Different geographic rules
• Many countries vs one jurisdiction
• Different definitions of what is controlled
• Different data access rules
44. NCRI Workshop 2015 – Module 1 bioinformatics.ca
• Detailed Phenotype and Outcome data
• Gene Expression (probe-level data)
• Raw genotype calls
• Gene-sample identifier links
• Genome sequence files
• Germ line variants
ICGC Controlled
Access Datasets
• Cancer Pathology
Histologic type or subtype
Histologic nuclear grade
• Patient/Person
Gender, Age range,
Vital status, Survival time
Relapse type, Status at follow-up
• Gene Expression (normalized)
• DNA methylation
•Computed Copy Number and
Loss of Heterozygosity
• Somatic variants from Exome or WGS
ICGC Open
Access Datasets
http://goo.gl/w4mrV
45. NCRI Workshop 2015 – Module 1 bioinformatics.ca
• Primary sequence data
(BAM and FASTQ files)
• SNP6 array level 1 and level 2 data
• Exon array level 1 and level 2 data
• Somatic variants from whole
genome sequencing
• Certain information in MAFs
• A full list of controlled-access
data types can be found at:
http://goo.gl/K1h7zu
TCGA Controlled
Access Datasets
• De-identified clinical and
demographic data
• Gene expression data
• Copy number alterations in regions
of the genome
• Epigenetic data
• Summaries of data compiled across
individuals
• Anonymized single amplicon DNA
sequence data
• Somatic variants from scrubbed
exome sequencing
TCGA Open
Access Datasets
http://goo.gl/A1rMRB
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TCGA/ICGC users agreed:
• … to keep all computer systems on which controlled
access data reside, or which provide access to such
data, up to date with respect to software and
security patches.
• … to protect Controlled Access Data against
disclosure to unauthorized individuals.
• … to monitor and control which individuals have
access to Controlled Access Data.
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TCGA/ICGC users agreed:
• … to destroy all copies of controlled access data
after controlled access privileges expires.
• ... to only use secure transfer protocols:
e.g. https and sftp
• … to encrypt Controlled Access data in transfers
and storage
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What does it mean for this file?
simple_somatic_mutation.aggregated.vcf.gz
https://dcc.icgc.org/repository/icgc/release_19/Summary
51. NCRI Workshop 2015 – Module 1 bioinformatics.ca
Identify
yourself
Fill out detail form which
includes:
• Contact and Project
Information
•Information Technology
details and procedures
for keeping data secure
•Data Access Agreement
All of these
documents are
put into a PDF
file that you
print and get your
institution to sign
off on your behalf
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DACO/DCC User Data Access Process
• Users approved through DACO are now automatically granted access to
ICGC controlled access datasets available through the ICGC Data Portal and
the EBI’s EGA repository
DACO Web
Application
DCC User
Registry
DCC Data
Portal
EBI EGA
application
approved
by DACO
user
accounts
activated
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Catalogue of Somatic Mutations in Cancer
(COSMIC) • http://cancer.sanger.ac.uk/cancerg
enome/projects/cosmic/
• COSMIC is designed
to store and display
somatic mutation
information and
related details and
contains information
relating to human
cancers.
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In closing
• Remember all these sites have great amounts of
documentation
• The field is changing quickly, and so are the portals.
• New features are planned as we speak, and so you
need to use the sites, and keep coming back.
• Don’t be afraid to explore
• Interested in learning more after today? Consider
one of the bioinformatics.ca workshops!
72. NCRI Workshop 2015 – Module 1 bioinformatics.ca
Acknowledgements:
the CBW gang
Michelle Brazas
Michael
Stromberg
Marc
Fiume
Michael
Brudno