1. Home
  2. Materials
20 materials found

Authors: Michelle Brazas 

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...

Keywords: Goblet

GOBLET Talk at 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. 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://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. 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://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. 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://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. 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://tess.elixir-europe.org/materials/goblet-poster-at-ismb-2014 This is a PDF of the GOBLET poster presentation at ISMB 2014 in Boston. 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://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.   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://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.)   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://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     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://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   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://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 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://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 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://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 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://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 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://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 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://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 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://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 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://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 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://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 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://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 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://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 Introduction flow cytometry, Review r 2013-06-25 2017-10-09