GOBLET Talk at INCoB 2014
Supporting Trainers to Improve Bioinformatics Education Globally
Speaker: Dr. Michelle Brazas
A needs assessment isn’t necessary to realize that across the globe, there is a high demand for quality bioinformatics training in all domains of life science. Delivering on this demand however is not...
GOBLET Talk at INCoB 2014
https://www.mygoblet.org/training-portal/materials/goblet-talk-incob-2014
https://tess.elixir-europe.org/materials/goblet-talk-at-incob-2014
Supporting Trainers to Improve Bioinformatics Education Globally
Speaker: Dr. Michelle Brazas
A needs assessment isn’t necessary to realize that across the globe, there is a high demand for quality bioinformatics training in all domains of life science. Delivering on this demand however is not trivial. In addition to computational infrastructure and software tools, quality bioinformatics training depends upon excellent trainers and training resources.
With a focus on the trainer in the learning equation, the Global Organization for Bioinformatics Learning, Education and Training (GOBLET) aims to facilitate the advancement of bioinformatics education globally by training and supporting a network of bioinformatics trainers. Activities include coordinating training efforts, sharing data sets and teaching materials, discussing best practices and building up teaching standards and teaching recognition. Examples to improve your bioinformatics training programs were provided. Through support and development of trainer excellence, GOBLET is working to improve the global landscape in bioinformatics education.
Michelle Brazas
Goblet
General Interest
2014-08-14
2017-10-09
Workshop on Education in Bioinformatics 2014 - ISMB 2014 - Benjamin Good
Online Gaming in Bioinformatics: The ‘Fun’ Model to Online Bioinformatics Education
Benjamin Good, Senior Staff Scientist, Department of Molecular and Experimental Medicine, The Scripps Research Institute.
Dr. Good’s research focuses on games with a purpose in crowdsourcing biological knowledge....
Scientific topics: Bioinformatics
Keywords: Bioinformatics, Gaming, Online learning
Workshop on Education in Bioinformatics 2014 - ISMB 2014 - Benjamin Good
https://www.mygoblet.org/training-portal/materials/workshop-education-bioinformatics-2014-ismb-2014-benjamin-good
https://tess.elixir-europe.org/materials/workshop-on-education-in-bioinformatics-2014-ismb-2014-benjamin-good
Online Gaming in Bioinformatics: The ‘Fun’ Model to Online Bioinformatics Education
Benjamin Good, Senior Staff Scientist, Department of Molecular and Experimental Medicine, The Scripps Research Institute.
Dr. Good’s research focuses on games with a purpose in crowdsourcing biological knowledge. To this end, his research has contributed to the development of a suite of interactive video games that allow for play, learning and contributions to science: genegames.org. Dr. Good spoke on online games for bioinformatics and how they can (or cannot) be used to teach bioinformatics at WEB 2014 at ISMB 2014.
Michelle Brazas
Bioinformatics
Bioinformatics, Gaming, Online learning
General Interest
2014-08-14
2017-10-09
Workshop on Education in Bioinformatics 2014 - ISMB 2014 - David Searls
Self-directed Learning: Does online learning in bioinformatics work?
Proposed Speaker: (CONFIRMED) David Searls, Adjunct Associate Professor, Perleman School of Medicine, University of Pennsylvania.
Dr. Searls is an independent consultant in bioinformatics and former Senior VP of Bioinformatics...
Keywords: Bioinformatics, Mooc, Online learning
Workshop on Education in Bioinformatics 2014 - ISMB 2014 - David Searls
https://www.mygoblet.org/training-portal/materials/workshop-education-bioinformatics-2014-ismb-2014-david-searls
https://tess.elixir-europe.org/materials/workshop-on-education-in-bioinformatics-2014-ismb-2014-david-searls
Self-directed Learning: Does online learning in bioinformatics work?
Proposed Speaker: (CONFIRMED) David Searls, Adjunct Associate Professor, Perleman School of Medicine, University of Pennsylvania.
Dr. Searls is an independent consultant in bioinformatics and former Senior VP of Bioinformatics at GlaxoSmithKline Pharmaceuticals. He recently authored “Ten Simple Rules for Online Learning” and “An Online Bioinformatics Curriculum”. Dr. Searls spoke on the opportunities and challenges of online learning in bioinformatics from a learner's perspective at WEB 2014 at ISMB 2014.
Michelle Brazas
Bioinformatics, Mooc, Online learning
General Interest
2014-08-14
2017-10-09
Workshop on Education in Bioinformatics 2014 - ISMB 2014 - Michael Love
MOOCs in Bioinformatics: The Online Course Model to Online Bioinformatics Education
Dr. Michael Love, edX Faculty and post-doctoral fellow in Dept. Biostatistics, Dana Farber Cancer Institute and Harvard School of Public Health.
Dr. Love teaches the Data Analysis for Genomics course offered...
Keywords: Bioinformatics, Mooc
Workshop on Education in Bioinformatics 2014 - ISMB 2014 - Michael Love
https://www.mygoblet.org/training-portal/materials/workshop-education-bioinformatics-2014-ismb-2014-michael-love
https://tess.elixir-europe.org/materials/workshop-on-education-in-bioinformatics-2014-ismb-2014-michael-love
MOOCs in Bioinformatics: The Online Course Model to Online Bioinformatics Education
Dr. Michael Love, edX Faculty and post-doctoral fellow in Dept. Biostatistics, Dana Farber Cancer Institute and Harvard School of Public Health.
Dr. Love teaches the Data Analysis for Genomics course offered through edX. His research focuses on inferring biologically meaningful patterns from high-throughput sequencing read counts. He also develops open-source statistical software for the analysis of exome sequencing and RNA sequencing experiments for the Bioconductor Project. Dr. Love spoke on how open online courses operate and how they can (or cannot) be used to teach bioinformatics at WEB 2014 at ISMB 2014.
Michelle Brazas
Bioinformatics, Mooc
2014-08-14
2017-10-09
GOBLET Poster at ISMB 2014
This is a PDF of the GOBLET poster presentation at ISMB 2014 in Boston.
Keywords: Goblet survey life scientists
GOBLET Poster at ISMB 2014
https://www.mygoblet.org/training-portal/materials/goblet-poster-ismb-2014
https://tess.elixir-europe.org/materials/goblet-poster-at-ismb-2014
This is a PDF of the GOBLET poster presentation at ISMB 2014 in Boston.
Michelle Brazas
Goblet survey life scientists
2014-07-07
2017-10-09
RNA-seq Analysis 2014 Module 4 - Isoform discovery and alternative expression
Explore use of Cufflinks in reference annotation based transcript (RABT) assembly mode and ‘de novo’ assembly mode. Both modes require a reference genome sequence.
Scientific topics: RNA
Keywords: Alternative expression, Transcript isoforms
RNA-seq Analysis 2014 Module 4 - Isoform discovery and alternative expression
https://www.mygoblet.org/training-portal/materials/rna-seq-analysis-2014-module-4-isoform-discovery-and-alternative
https://tess.elixir-europe.org/materials/rna-seq-analysis-2014-module-4-isoform-discovery-and-alternative-expression
Explore use of Cufflinks in reference annotation based transcript (RABT) assembly mode and ‘de novo’ assembly mode. Both modes require a reference genome sequence.
Michelle Brazas
RNA
Alternative expression, Transcript isoforms
Biologists, Genomicists, Computer Scientists
Graduate Students
Post-Doctoral Fellows
Researchers
2014-06-27
2017-10-09
RNA-seq Analysis 2014 Module 3 - Expression and Differential Expression
Get FPKM style expression estimates using Cufflinks
Perform differential expression analysis with Cuffdiff
Perform summary analysis with CummeRbund
Downstream interpretation of expression analysis (multiple testing, clustering, heatmaps, classification, pathway analysis, etc.)
Keywords: Differential expression, Transript expression
RNA-seq Analysis 2014 Module 3 - Expression and Differential Expression
https://www.mygoblet.org/training-portal/materials/rna-seq-analysis-2014-module-3-expression-and-differential-expression
https://tess.elixir-europe.org/materials/rna-seq-analysis-2014-module-3-expression-and-differential-expression
Get FPKM style expression estimates using Cufflinks
Perform differential expression analysis with Cuffdiff
Perform summary analysis with CummeRbund
Downstream interpretation of expression analysis (multiple testing, clustering, heatmaps, classification, pathway analysis, etc.)
Michelle Brazas
Differential expression, Transript expression
Biologists, Genomicists, Computer Scientists
Graduate Students
Post-Doctoral Fellows
Researchers
2014-06-27
2017-10-09
RNA-seq Analysis 2014 Module 2 - Alignment and Visualization
Use of Bowtie/TopHat
‘Regular mode’ vs. ‘Fusion mode’
Introduction to the BAM format
Basic manipulation of BAMs with samtools, etc.
Visualization of RNA-seq alignments - IGV
BAM read counting and determination of variant allele expression status
Keywords: Data visualization, Rna seq alignment
RNA-seq Analysis 2014 Module 2 - Alignment and Visualization
https://www.mygoblet.org/training-portal/materials/rna-seq-analysis-2014-module-2-alignment-and-visualization
https://tess.elixir-europe.org/materials/rna-seq-analysis-2014-module-2-alignment-and-visualization
Use of Bowtie/TopHat
‘Regular mode’ vs. ‘Fusion mode’
Introduction to the BAM format
Basic manipulation of BAMs with samtools, etc.
Visualization of RNA-seq alignments - IGV
BAM read counting and determination of variant allele expression status
Michelle Brazas
Data visualization, Rna seq alignment
Biologists, Genomicists, Computer Scientists
Graduate Students
Post-Doctoral Fellows
Researchers
2014-06-27
2017-10-09
RNA-seq Analysis 2014 Module 1 - Introduction to RNA-seq Analysis
Basic introduction to biology of RNA-seq
Experimental design considerations
Commonly asked questions
Data visualization using IGV
Keywords: Experimental design rna seq, Igv, Introduction rna seq analysis
RNA-seq Analysis 2014 Module 1 - Introduction to RNA-seq Analysis
https://www.mygoblet.org/training-portal/materials/rna-seq-analysis-2014-module-1-introduction-rna-seq-analysis
https://tess.elixir-europe.org/materials/rna-seq-analysis-2014-module-1-introduction-to-rna-seq-analysis
Basic introduction to biology of RNA-seq
Experimental design considerations
Commonly asked questions
Data visualization using IGV
Michelle Brazas
Experimental design rna seq, Igv, Introduction rna seq analysis
Biologists, Genomicists, Computer Scientists
Graduate Students
Post-Doctoral Fellows
Researchers
2014-06-27
2017-10-09
Pathway and Network Analysis 2014 Module 5 - Network Visualization
Introduction to network visualization
Visualizing omics data on a network or pathway
Active modules in Cytoscape
Keywords: Cytoscape, Network visualization
Pathway and Network Analysis 2014 Module 5 - Network Visualization
https://www.mygoblet.org/training-portal/materials/pathway-and-network-analysis-2014-module-5-network-visualization
https://tess.elixir-europe.org/materials/pathway-and-network-analysis-2014-module-5-network-visualization
Introduction to network visualization
Visualizing omics data on a network or pathway
Active modules in Cytoscape
Michelle Brazas
Cytoscape, Network visualization
Biologists, Genomicists, Computer Scientists
Graduate Students
Post-Doctoral Fellows
Researchers
2014-06-26
2017-10-09
Pathway and Network Analysis 2014 Module 4 - Pathway and Network Analysis
Introduction to pathway and network analysis
Basic network concepts
Types of pathway and network information
Pathway Databases: Reactome, KEGG, etc.
Pathway analysis of large scale genomics data sets, including cancer genomics
Keywords: Biological networks, Kegg, Pathway analysis, Reactome
Pathway and Network Analysis 2014 Module 4 - Pathway and Network Analysis
https://www.mygoblet.org/training-portal/materials/pathway-and-network-analysis-2014-module-4-pathway-and-network-analysis
https://tess.elixir-europe.org/materials/pathway-and-network-analysis-2014-module-4-pathway-and-network-analysis
Introduction to pathway and network analysis
Basic network concepts
Types of pathway and network information
Pathway Databases: Reactome, KEGG, etc.
Pathway analysis of large scale genomics data sets, including cancer genomics
Michelle Brazas
Biological networks, Kegg, Pathway analysis, Reactome
Biologists, Genomicists, Computer Scientists
Graduate Students
Post-Doctoral Fellows
Researchers
2014-06-26
2017-10-09
Pathway and Network Analysis 2014 Module 3 - Gene Regulation Analysis
Overview of transcription
Data sources for regulatory data - Chromatin IP, DNA hypersensitivity, other chip data
Basics of processing data
Pattern discovery
Keywords: Pattern discovery, Transcriptional regulation
Pathway and Network Analysis 2014 Module 3 - Gene Regulation Analysis
https://www.mygoblet.org/training-portal/materials/pathway-and-network-analysis-2014-module-3-gene-regulation-analysis
https://tess.elixir-europe.org/materials/pathway-and-network-analysis-2014-module-3-gene-regulation-analysis
Overview of transcription
Data sources for regulatory data - Chromatin IP, DNA hypersensitivity, other chip data
Basics of processing data
Pattern discovery
Michelle Brazas
Pattern discovery, Transcriptional regulation
Biologists, Genomicists, Computer Scientists
Graduate Students
Post-Doctoral Fellows
Researchers
2014-06-26
2017-10-09
Pathway and Network Analysis 2014 Module 2 - Finding Over-represented Pathways
Over-representation analysis (ORA)
Statistics for detecting over-representation e.g. hypergeometric test, GSEA
Multiple testing correction: Bonferroni, Benjamini-Hochberg FDR
Filtering Gene Ontology e.g. using evidence codes
Keywords: Gsea, Over representation analysis
Pathway and Network Analysis 2014 Module 2 - Finding Over-represented Pathways
https://www.mygoblet.org/training-portal/materials/pathway-and-network-analysis-2014-module-2-finding-over-represented
https://tess.elixir-europe.org/materials/pathway-and-network-analysis-2014-module-2-finding-over-represented-pathways
Over-representation analysis (ORA)
Statistics for detecting over-representation e.g. hypergeometric test, GSEA
Multiple testing correction: Bonferroni, Benjamini-Hochberg FDR
Filtering Gene Ontology e.g. using evidence codes
Michelle Brazas
Gsea, Over representation analysis
Biologists, Genomicists, Computer Scientists
Graduate Students
Post-Doctoral Fellows
Researchers
2014-06-26
2017-10-09
Pathway and Network Analysis 2014 Module 1 - Introduction to Gene Lists
Gene list analysis overview: Workflow of concepts and tools from gene list to pathway analysis
Where do gene lists come from?
Working with gene function information
Keywords: Gene lists, Gene ontology
Pathway and Network Analysis 2014 Module 1 - Introduction to Gene Lists
https://www.mygoblet.org/training-portal/materials/pathway-and-network-analysis-2014-module-1-introduction-gene-lists
https://tess.elixir-europe.org/materials/pathway-and-network-analysis-2014-module-1-introduction-to-gene-lists
Gene list analysis overview: Workflow of concepts and tools from gene list to pathway analysis
Where do gene lists come from?
Working with gene function information
Michelle Brazas
Gene lists, Gene ontology
Biologists, Genomicists, Computer Scientists
Graduate Students
Post-Doctoral Fellows
Researchers
2014-06-26
2017-10-09
Flow Cytometry 2013 Module 4 - 1D Static gating
How to create a constant gate for the whole data set for one channel by first creating a sample representative of the whole data set
How to apply a static gate to all samples and count the proportions of cells lying on either side of the gate
Visualizing the proportions using a density...
Keywords: 1d static gating, Flow cytometry data
Flow Cytometry 2013 Module 4 - 1D Static gating
https://www.mygoblet.org/training-portal/materials/flow-cytometry-2013-module-4-1d-static-gating
https://tess.elixir-europe.org/materials/flow-cytometry-2013-module-4-1d-static-gating
How to create a constant gate for the whole data set for one channel by first creating a sample representative of the whole data set
How to apply a static gate to all samples and count the proportions of cells lying on either side of the gate
Visualizing the proportions using a density plot, histogram, bean/violin plot
Automating a sequential gating strategy in R to obtain cell proportions for a desired phenotype
Michelle Brazas
1d static gating, Flow cytometry data
2013-06-26
2017-10-09
Flow Cytometry 2013 Module 6 - Clustering and Additional FCM Tools
Lecture on FlowCAP (Flow Cytometry: Critical Assessment of Population Identification Methods) project
K-means explained
flowMeans: smart k-means for FCM data
flowClust3.0, SPADE, flowBin, flowFP, SamSPECTRAL clustering
Biomarker discovery: flowType and RchyOptimyx, advanced tools used...
Keywords: Biomarker discovery, Flow cytometry data, Flowcap
Flow Cytometry 2013 Module 6 - Clustering and Additional FCM Tools
https://www.mygoblet.org/training-portal/materials/flow-cytometry-2013-module-6-clustering-and-additional-fcm-tools
https://tess.elixir-europe.org/materials/flow-cytometry-2013-module-6-clustering-and-additional-fcm-tools
Lecture on FlowCAP (Flow Cytometry: Critical Assessment of Population Identification Methods) project
K-means explained
flowMeans: smart k-means for FCM data
flowClust3.0, SPADE, flowBin, flowFP, SamSPECTRAL clustering
Biomarker discovery: flowType and RchyOptimyx, advanced tools used to explore novel phenotypes and find ones which correlate with a clinical diagnosis
More R: Bioconductor.org, an open source software project full of bioinformatics packages created for R, including sample work flows
GenePattern.org: web-interface for running analysis modules such as sample deidentication, extracting specic keywords, quality control, normalization, clustering, classification
FlowRepository.org: online FCM data repository to share public or private data sets with collaborators while providing detailed descriptions of the experiment set up and each FCS file
Michelle Brazas
Biomarker discovery, Flow cytometry data, Flowcap
2013-06-26
2017-10-09
Flow Cytometry 2013 Module 5 - 1D Dynamic gating
Using quantiles to set a gate based on a negative control
Using median and standard deviation to set a gate based on a negative control
Exploring rangeGate: an automated way to set a 1D gate for each sample individually
Recording proportions of cells in subpopulations of interest by...
Keywords: 1d dynamic gating, Flow cytometry data
Flow Cytometry 2013 Module 5 - 1D Dynamic gating
https://www.mygoblet.org/training-portal/materials/flow-cytometry-2013-module-5-1d-dynamic-gating
https://tess.elixir-europe.org/materials/flow-cytometry-2013-module-5-1d-dynamic-gating
Using quantiles to set a gate based on a negative control
Using median and standard deviation to set a gate based on a negative control
Exploring rangeGate: an automated way to set a 1D gate for each sample individually
Recording proportions of cells in subpopulations of interest by creating a spreadsheet file
Recording a visual record of the gating results by creating JPEG image files
Michelle Brazas
1d dynamic gating, Flow cytometry data
2013-06-26
2017-10-09
Flow Cytometry 2013 Module 3 - Preprocessing and Quality Assurance of FCM Data
Preprocessing
Removing margin events
Data transformation: log vs. biexponential
Data normalization
Quality Assurance
Overview of quality assurance concepts: total raw/viable cell count, margin event count, outlier detection based on density of common...
Keywords: Flow cytometry data, Preprocessing, Quality assurance
Flow Cytometry 2013 Module 3 - Preprocessing and Quality Assurance of FCM Data
https://www.mygoblet.org/training-portal/materials/flow-cytometry-2013-module-3-preprocessing-and-quality-assurance-fcm-data
https://tess.elixir-europe.org/materials/flow-cytometry-2013-module-3-preprocessing-and-quality-assurance-of-fcm-data
Preprocessing
Removing margin events
Data transformation: log vs. biexponential
Data normalization
Quality Assurance
Overview of quality assurance concepts: total raw/viable cell count, margin event count, outlier detection based on density of common parameters
Building quality assurance objects using flowQ and generating summary HTML reports
Michelle Brazas
Flow cytometry data, Preprocessing, Quality assurance
2013-06-26
2017-10-09
Flow Cytometry 2013 Module 2 - Exploring FCM data in R
Loading a single or groups of FCS files into R
flowFrame and flowSet objects and their attributes
Exploring sample annotation and keywords stored within the FCS file format, searching for specic samples (such as controls) using grep, highlighting the importance of correct sample annotation...
Keywords: Fcs files, Plotting data, R
Flow Cytometry 2013 Module 2 - Exploring FCM data in R
https://www.mygoblet.org/training-portal/materials/flow-cytometry-2013-module-2-exploring-fcm-data-r
https://tess.elixir-europe.org/materials/flow-cytometry-2013-module-2-exploring-fcm-data-in-r
Loading a single or groups of FCS files into R
flowFrame and flowSet objects and their attributes
Exploring sample annotation and keywords stored within the FCS file format, searching for specic samples (such as controls) using grep, highlighting the importance of correct sample annotation at point of acquisition
Simple dot plots and density plots
Michelle Brazas
Fcs files, Plotting data, R
2013-06-26
2017-10-09
Flow Cytometry 2013 Module 1 - Introduction to Flow Cytometry Analysis in R
Lecture on how tools to be demonstrated have been used in research and clinical applications
Description of data and underlying biological problem
Summary of a typical work flow to be covered in the workshop: transformation, margin event removal, quality assurance, gating, recording and...
Keywords: Introduction flow cytometry, Review r
Flow Cytometry 2013 Module 1 - Introduction to Flow Cytometry Analysis in R
https://www.mygoblet.org/training-portal/materials/flow-cytometry-2013-module-1-introduction-flow-cytometry-analysis-r
https://tess.elixir-europe.org/materials/flow-cytometry-2013-module-1-introduction-to-flow-cytometry-analysis-in-r
Lecture on how tools to be demonstrated have been used in research and clinical applications
Description of data and underlying biological problem
Summary of a typical work flow to be covered in the workshop: transformation, margin event removal, quality assurance, gating, recording and visualizing results
Short R refresher of tools to be used
Variable assignment, simple arithmetic
Vectors, matrices, lists, names
Functions, apply, sapply, lapply
Simple plotting functions
Michelle Brazas
Introduction flow cytometry, Review r
2013-06-25
2017-10-09