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Data Visualisation in UCSC Genome Browser

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ARTbio/startbio

Home
Interactive Online Companionship (IOC)
Interactive Online Companionship (IOC)
- IOC on bulk RNAseq analysis
  IOC on bulk RNAseq analysis
  - Provisional Program / Schedule
  - Week 0
    Week 0
    
    Introduction - Week-0
    
    Week 0 exercises
  - Week 1
    Week 1
    
    Review on week-0 work
    
    Analysis Overwiew
    Analysis Overwiew
    
    The Key idea
    
    RNAseq librairies
    RNAseq librairies
    
    cDNA synthesis
    
    Inserts and sequencing strategies
    
    Sequence Quality and Read Filtering
    
    Transcript Quantification
    
    Statistical Analysis of Differential expression
    
    Understanding of all your experimental procedures
    
    Week 1 exercises
    Week 1 exercises
    
    Use case PRJNA630433
    Use case PRJNA630433
    
    Presentation
    
    Data upload
    
    Quality Control
  - Week 2
    Week 2
    
    Review on week-1 work
    
    Read Mapping overview
    Read Mapping overview
    
    Reference genomes
    
    Aligners
    
    Splice-aware alignment
    
    Week 2 exercices
    Week 2 exercices
    
    Library strandness
    
    HiSAT2
    
    STAR
    
    UCSC visualisation
  - Week 3
    Week 3
    
    Review on week-2 work
    
    Counting reads or fragments
    
    Week 3 exercices
    Week 3 exercices
    
    FeatureCounts
    
    HTSeq_counts
    
    Count with STAR
  - Week 4
    Week 4
    
    Review on week-3 work
    
    Differential Gene Expression Analysis
    
    Week 4 exercices
    Week 4 exercices
    
    DESeq2
    
    edgeR
    
    limma
    
    DE_manipulations
    DE_manipulations
    
    DESeq2
    
    edgeR
    
    limma
    
    DEseq/edgeR/limma comparison
    
    Volcano plots
  - Week 5
    Week 5
    
    Review on week-4 work
    
    Gene Ontology Enrichment Analysis
    
    Week 5 exercices
    Week 5 exercices
    
    GOseq
  - Week 6
    Week 6
    
    Review on week-5 work
    
    Gene Set Enrichment Analysis (GSEA)
    
    Week 6 exercices
    Week 6 exercices
    
    Intro to week 6 exercices
    
    fGSEA
    
    EGSEA
  - Week 7
    Week 7
    
    Review on week-6 work
    
    Galaxy Workflows
    Galaxy Workflows
    
    Introduction
    
    Week 7 exercices
    Week 7 exercices
    
    Build your workflow
  - Week 8
    Week 8
    
    Review on week-7 work
    
    Recap
    
    20min presentations
    20min presentations
    
    Trainee 1
    
    Trainee 2
    
    Trainne 3
  - External Materials
- IOC on R
  IOC on R
  - Provisional Program
    Provisional Program
    
    Schedule
    
    Week 0 - Introduction
    
    Week 1 - First steps
    
    Week 2 - Learning vectors and more
    
    Week 3 - Learning Lists
    
    Week 4 - Two-dimensional objects
    
    Week 5 - Level up your code
    
    Week 6 - Tidyverse
    
    Week 7 - Data visualisation
  - Reference Manual
    Reference Manual
    
    Variables
    
    Vectors
    
    Operators
    
    Functions
    
    Best Practices
    
    Lists
    
    Data Frames and Matrices
    
    Data Import and Export
    
    Apply & Co
    
    Conditions
    
    Manipulating data with tidyverse
    
    Visualization
  - Storage room
- Single-cell RNAseq analysis using Seurat in R
  Single-cell RNAseq analysis using Seurat in R
  - Introduction
  - Prerequisites
  - Initialization of R analysis
  - Import data
    Import data
    
    Import data
    
    Exercices
  - Preprocessing
    Preprocessing
    
    Preprocessing
    
    Exercices
  - Reduction of dimensionality
  - Clustering
  - Cell population identification
    Cell population identification
    
    Introduction and markers identification
    
    Markers Annotation
    Markers Annotation
    
    Presentation of annotation methods
    
    Over-representation analysis
    
    Gene Set Enrichment Analysis
    
    Cluster annotation
  - Comparison between 2 populations
  - Differents visualizations of Seurat
  - References
- Introduction to Deep-seq Analysis
  Introduction to Deep-seq Analysis
1Omin2
1Omin2
- Grep and Regex (1)
- Python Decorators
Galaxy Trainings
Galaxy Trainings
- Analyse des Génomes (2023)
  Analyse des Génomes (2023)
- Run your Galaxy server
  Run your Galaxy server
- Run a Galaxy workflow for somatic mutation detection in cancers
  Run a Galaxy workflow for somatic mutation detection in cancers
  - INTRODUCTION
- Search for chomosome translocations
  Search for chomosome translocations
  - Introduction
  - Sequencing Protocol
  - Lumpy approach
  - Trainee connection
  - Prepare the input data history
  - Align reads to the human genome
  - lumpy analysis
  - Reformat VCF files for visualization in the UCSC genome browser
  - Compute the genome read coverage
  - Data Visualisation in UCSC Genome Browser Data Visualisation in UCSC Genome Browser
    Table of contents
    
    Visualisation of vcf files and genome read coverage for patients A & B
  - Generate workflow from an history
  - Run the Lumpy workflow
- Reference-based RNAseq analysis
  Reference-based RNAseq analysis
  - Introduction
  - Pretreatments
    Pretreatments
    
    Data upload
    
    Quality control
  - Outline and general concepts for the RNAseq analysis
    Outline and general concepts for the RNAseq analysis
    
    RNAseq librairies - cDNA synthesis
    
    RNAseq librairies - Inserts and sequencing strategies
    
    The key idea in Reference-base Expression analysis
    
    Reflecting on quality control & “filtering” in RNAseq analysis
    
    Transcript Quantification
    
    Statistical Analysis of Differential expression
    
    Experimental procedures affect downstream analyses
  - Quality filtering
    Quality filtering
    
    Optional filtering of reads on sequence quality
  - Mapping
    Mapping
    
    Mapping
    
    RNA STAR
    
    HiSAT2
    
    BAM inspection
    
    Visualisation of read mapping
  - Analysis of the differential gene expression
    Analysis of the differential gene expression
    
    Introduction to read counting
    
    Estimation of the strandness
    
    Count the number of reads per annotated gene
    
    Identification of the differentially expressed features - DESeq2
    
    DESeq2 use
    
    Volcano Plot
    
    Visualization of the differentially expressed genes
    
    Analysis of functional enrichment among the differentially expressed genes
    Analysis of functional enrichment among the differentially expressed genes
    
    Introduction
    
    GO
  - Galaxy Workflows
    Galaxy Workflows
    
    Introduction
    
    Example
- Viruses Metagenomics
  Viruses Metagenomics
  - Metavisitor
  - Prepare Metavisitor Galaxy instance for analyses
  - Prepare input data histories
  - Use Cases 1-1 to 1-4
  - Use Cases 2-1 and 2-2
  - Use Case 3-1
  - Use Case 3-2
  - Use Case 3-3
  - Install_Metavisitor
    Install_Metavisitor
    
    Intro
    
    GalaxyKickStart
    
    Docker
    
    Access and Control Metavisitor Galaxy instance
- Download TARGET-AML Data in Galaxy
  Download TARGET-AML Data in Galaxy
Code Snippets
Code Snippets
Links

Table of contents

Visualisation of vcf files and genome read coverage for patients A & B

Data Visualisation in UCSC Genome Browser

Visualisation of vcf files and genome read coverage for patients A & B¶

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