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20 materials found

Keywords: Cross domain  or Statistical-model 


EMBL-EBI Train Online

If you work in the life sciences, you may find that you’re spending less time doing experiments and more time analysing huge amounts of biological data. Train online is here to help you do this quickly and efficiently.

Scientific topics: Bioinformatics

Keywords: Bioinformatics, Chemical biology, Cross domain, Dna rna, Gene expression, Literature, Ontologies, Proteins, Structures, Systems

EMBL-EBI Train Online https://tess.elixir-europe.org/materials/embl-ebi-train-online If you work in the life sciences, you may find that you’re spending less time doing experiments and more time analysing huge amounts of biological data. Train online is here to help you do this quickly and efficiently. Bioinformatics Bioinformatics, Chemical biology, Cross domain, Dna rna, Gene expression, Literature, Ontologies, Proteins, Structures, Systems Beginner informatics Life sciences 2018-05-11
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
Bioinformatics for the terrified

Bioinformatics for the terrified from http://www.ebi.ac.uk/training/online/course/bioinformatics-terrified.

Keywords: Cross domain

Bioinformatics for the terrified https://tess.elixir-europe.org/materials/bioinformatics-for-the-terrified Bioinformatics for the terrified from http://www.ebi.ac.uk/training/online/course/bioinformatics-terrified. Cross domain 2016-06-14
EMBL-EBI resources: An introduction

EMBL-EBI resources: An introduction from http://www.ebi.ac.uk/training/online/course/introduction-embl-ebi-resources-webinar.

Keywords: Cross domain

EMBL-EBI resources: An introduction https://tess.elixir-europe.org/materials/embl-ebi-resources-an-introduction EMBL-EBI resources: An introduction from http://www.ebi.ac.uk/training/online/course/introduction-embl-ebi-resources-webinar. Cross domain 2016-06-14
Cellular Microscopy Phenotype Ontology (CMPO): Quick tour

Cellular Microscopy Phenotype Ontology (CMPO): Quick tour from http://www.ebi.ac.uk/training/online/course/cellular-microscopy-phenotype-ontology-cmpo-quick.

Keywords: Ontologies, Cross domain

Cellular Microscopy Phenotype Ontology (CMPO): Quick tour https://tess.elixir-europe.org/materials/cellular-microscopy-phenotype-ontology-cmpo-quick-tour Cellular Microscopy Phenotype Ontology (CMPO): Quick tour from http://www.ebi.ac.uk/training/online/course/cellular-microscopy-phenotype-ontology-cmpo-quick. Ontologies, Cross domain 2016-06-14
Biomedical data: Ethical, legal and social implications

Biomedical data: Ethical, legal and social implications from http://www.ebi.ac.uk/training/online/course/biomedical-data-ethical-legal-and-social-implicati.

Keywords: Cross domain

Biomedical data: Ethical, legal and social implications https://tess.elixir-europe.org/materials/biomedical-data-ethical-legal-and-social-implications Biomedical data: Ethical, legal and social implications from http://www.ebi.ac.uk/training/online/course/biomedical-data-ethical-legal-and-social-implicati. Cross domain 2016-06-14
Biocuration: An introduction

Biocuration: An introduction from http://www.ebi.ac.uk/training/online/course/biocuration-introduction.

Keywords: Proteins, Cross domain

Biocuration: An introduction https://tess.elixir-europe.org/materials/biocuration-an-introduction Biocuration: An introduction from http://www.ebi.ac.uk/training/online/course/biocuration-introduction. Proteins, Cross domain 2016-06-14
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
Differential expression analysis

This lecture covers the process from count matrix to statistical analysis results (differential expression). More specifically, it covers experimental design, normalization, statistical modeling and parameter estimation, multiple hypothesis testing and a more detailed look at some of the most...

Keywords: Differential-expression, Statistical-model

Differential expression analysis https://tess.elixir-europe.org/materials/differential-expression-analysis This lecture covers the process from count matrix to statistical analysis results (differential expression). More specifically, it covers experimental design, normalization, statistical modeling and parameter estimation, multiple hypothesis testing and a more detailed look at some of the most common differential expression methods as well as a comparison between them. Differential-expression, Statistical-model
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
Differential expression analysis on the Robinson, Delhomme et al. dataset.

A differential expression analysis conducted on the **[Robinson, Delhomme et al., dataset](https://microasp.upsc.se/ngs_trainers/Materials/blob/master/Datasets/Robinson-Delhomme-Populus-tremula-shows-no-evidence-of-sexual-dimorphism.md)**. The dataset has 17 samples and 2 important meta-data: the...

Scientific topics: RNA-Seq

Keywords: RNA-Seq, Differential-expression, R-programming, Statistical-model

Differential expression analysis on the Robinson, Delhomme et al. dataset. https://tess.elixir-europe.org/materials/differential-expression-analysis-on-the-robinson-delhomme-et-al-dataset A differential expression analysis conducted on the **[Robinson, Delhomme et al., dataset](https://microasp.upsc.se/ngs_trainers/Materials/blob/master/Datasets/Robinson-Delhomme-Populus-tremula-shows-no-evidence-of-sexual-dimorphism.md)**. The dataset has 17 samples and 2 important meta-data: the sample sex and year of collection. The goal is to test whether genes are involved in different processes based on the sex of the tree; _i.e._ is there a sexual dimorphism in _Populus tremula_ trees. It has indeed been hypothesized that male tree should be taller so as to spread their pollen further, whereas female would be more resistant to pests and diseases. The existing literature is contradictory, however it resulted from studies where plants were grown in controlled environment. In the present dataset, plant samples were collected in the wild, at a 2 years interval. The latter is a very important factor in the analysis as the 'year effect' is a strong confounding factor that hides the 'sex effect'. The present tutorial, hence, introduces a differential-expression analysis, but goes further by adressing confounding factors and how to _block_ them in an analysis. It is a good dataset to remind trainees that they should always be critical towards the conclusion they draw from their data. RNA-Seq RNA-Seq, Differential-expression, R-programming, Statistical-model
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
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