17 materials found

Keywords:

RNA-seq module Frederik Coppens

Material concerning RNA-seq analysis, with an emphasis on the statistical aspects.

Keywords: FASTQ, Alignment, BAM, Differential-expression, Statistical-model, Exploratory-analysis

RNA-seq module Frederik Coppens https://tess.elixir-europe.org/materials/rna-seq-module-frederik-coppens-4a328fa8-0bef-471c-84e4-b0d89d216128 Material concerning RNA-seq analysis, with an emphasis on the statistical aspects. FASTQ, Alignment, BAM, Differential-expression, Statistical-model, Exploratory-analysis
Poisson process

Poisson process from Khan Academy Statistics

Keywords: Random variables and probability distributions

Poisson process https://tess.elixir-europe.org/materials/poisson-process Poisson process from Khan Academy Statistics Random variables and probability distributions 2016-08-14
Binomial distribution

Binomial distribution from Khan Academy Statistics

Keywords: Random variables and probability distributions

Binomial distribution https://tess.elixir-europe.org/materials/binomial-distribution Binomial distribution from Khan Academy Statistics Random variables and probability distributions 2016-08-14
Expected value

Now that we know what a random variable is, we can think about expected value. As we'll see, it can be viewed as a probability-weighted average of possible outcomes!

Keywords: Random variables and probability distributions

Expected value https://tess.elixir-europe.org/materials/expected-value Now that we know what a random variable is, we can think about expected value. As we'll see, it can be viewed as a probability-weighted average of possible outcomes! Random variables and probability distributions 2016-08-14
Random variables and probability distributions

Random variables and probability distributions from Khan Academy Statistics

Keywords: Random variables and probability distributions

Random variables and probability distributions https://tess.elixir-europe.org/materials/random-variables-and-probability-distributions Random variables and probability distributions from Khan Academy Statistics Random variables and probability distributions 2016-08-14
Variant-calling

No description available

Keywords: Alignment, Annotation, BAM, BCF, De-novo-transcriptome-assembly, Exploratory-analysis, FASTQ, Pre-processing, QC, Statistical-model, Variant-calling, VCF

Variant-calling https://tess.elixir-europe.org/materials/variant-calling No description available Alignment, Annotation, BAM, BCF, De-novo-transcriptome-assembly, Exploratory-analysis, FASTQ, Pre-processing, QC, Statistical-model, Variant-calling, VCF
Day 4 - RNA-Seq Analysis

Day 4 focuses on the final steps after production of significant gene lists, including gene clustering, visualization, and annotation.

Keywords: Exploratory-analysis, Differential-expression, Statistical-model, Annotation

Day 4 - RNA-Seq Analysis https://tess.elixir-europe.org/materials/day-4-rna-seq-analysis Day 4 focuses on the final steps after production of significant gene lists, including gene clustering, visualization, and annotation. Exploratory-analysis, Differential-expression, Statistical-model, Annotation
Statistics and RNA-Seq

This lecture and optional practical introduce the relevant statistical concepts along with its computational counterparts used on a common RNA-Seq data analysis workflow.

Keywords: FASTQ, Alignment, BAM, Differential-expression, Statistical-model, Exploratory-analysis

Statistics and RNA-Seq https://tess.elixir-europe.org/materials/statistics-and-rna-seq This lecture and optional practical introduce the relevant statistical concepts along with its computational counterparts used on a common RNA-Seq data analysis workflow. FASTQ, Alignment, BAM, Differential-expression, Statistical-model, Exploratory-analysis
Material for the course RNA-seq data analysis with Chipster

This material covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis.

Scientific topics: RNA-Seq

Keywords: RNA-Seq, FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis

Material for the course RNA-seq data analysis with Chipster https://tess.elixir-europe.org/materials/material-for-the-course-rna-seq-data-analysis-with-chipster This material covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis. RNA-Seq RNA-Seq, FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis
Day 3 - RNA-Seq Analysis

Day 3 focuses on statistical analysis of RNA-Seq data and identification of differentiall expressed genes in multiple comparisons.

Keywords: QC, Exploratory-analysis, Differential-expression, Statistical-model, Pre-processing

Day 3 - RNA-Seq Analysis https://tess.elixir-europe.org/materials/day-3-rna-seq-analysis Day 3 focuses on statistical analysis of RNA-Seq data and identification of differentiall expressed genes in multiple comparisons. QC, Exploratory-analysis, Differential-expression, Statistical-model, Pre-processing
RNA-Seq Analysis with Biocluster and R

Sequencing of RNA (RNA-Seq) is the latest method to assess global gene expression because it

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Alignment, Annotation, BAM, Differential-expression, Exploratory-analysis, Expression-estimation, FASTA, FASTQ, Feature-summarisation, Pre-processing, QC, Statistical-model

RNA-Seq Analysis with Biocluster and R https://tess.elixir-europe.org/materials/rna-seq-analysis-with-biocluster-and-r Sequencing of RNA (RNA-Seq) is the latest method to assess global gene expression because it RNA-Seq RNA-Seq, Alignment, Annotation, BAM, Differential-expression, Exploratory-analysis, Expression-estimation, FASTA, FASTQ, Feature-summarisation, Pre-processing, QC, Statistical-model
Material provided by Charlotte Soneson

This folder contains material provided by Charlotte Soneson. The following material is included:

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Differential-expression, Statistical-model, Exploratory-analysis

Material provided by Charlotte Soneson https://tess.elixir-europe.org/materials/material-provided-by-charlotte-soneson This folder contains material provided by Charlotte Soneson. The following material is included: RNA-Seq RNA-Seq, Differential-expression, Statistical-model, Exploratory-analysis
RNA-seq module Eija Korpelainen

All material concerning RNA-seq data analysis with Chipster

Keywords: FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis

RNA-seq module Eija Korpelainen https://tess.elixir-europe.org/materials/rna-seq-module-eija-korpelainen All material concerning RNA-seq data analysis with Chipster FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis
Exercises for the course RNA-seq data analysis with Chipster

This practical covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis, using the free Chipster software. Material updated in Dec 2015.

Keywords: FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis

Exercises for the course RNA-seq data analysis with Chipster https://tess.elixir-europe.org/materials/exercises-for-the-course-rna-seq-data-analysis-with-chipster This practical covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis, using the free Chipster software. Material updated in Dec 2015. FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis
Day 2 - RNA-Seq Analysis

Day 2 continues throught the steps in a typical RNA-Seq experiment from alignment to sample QC and count normalization, including a brief overview of the IGV Genome Browser.

Keywords: Alignment, BAM, FASTA, FASTQ, QC, Exploratory-analysis, Feature-summarisation, Pre-processing

Day 2 - RNA-Seq Analysis https://tess.elixir-europe.org/materials/day-2-rna-seq-analysis Day 2 continues throught the steps in a typical RNA-Seq experiment from alignment to sample QC and count normalization, including a brief overview of the IGV Genome Browser. Alignment, BAM, FASTA, FASTQ, QC, Exploratory-analysis, Feature-summarisation, Pre-processing
Lecture slides for the course RNA-seq data analysis with Chipster

This material covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis. It discusses also experimental design. Material updated in Dec 2015.

Keywords: FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis

Lecture slides for the course RNA-seq data analysis with Chipster https://tess.elixir-europe.org/materials/lecture-slides-for-the-course-rna-seq-data-analysis-with-chipster This material covers the whole RNA-seq data analysis pipeline, from quality control of raw reads to differential expression analysis. It discusses also experimental design. Material updated in Dec 2015. FASTQ, QC, Pre-processing, Alignment, BAM, Expression-estimation, Feature-summarisation, Differential-expression, Statistical-model, Exploratory-analysis
Exploratory analysis and downstream analysis

This lecture gives an overview of exploratory analysis (clustering) and supervised analysis (prediction/classification), as well as visualization methods (heatmaps/PCA) and gene set analysis. It also shows how to transform count data to make it more suitable to apply the traditional methods...

Keywords: Statistical-model, Exploratory-analysis

Exploratory analysis and downstream analysis https://tess.elixir-europe.org/materials/exploratory-analysis-and-downstream-analysis This lecture gives an overview of exploratory analysis (clustering) and supervised analysis (prediction/classification), as well as visualization methods (heatmaps/PCA) and gene set analysis. It also shows how to transform count data to make it more suitable to apply the traditional methods developed (e.g.) for microarray data. Statistical-model, Exploratory-analysis