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33 materials found

Authors: Jared Simpson  or Sorana Morrissy  or David Wishart  or Jüri Reimand  or Nicolas Delhomme @delhomme 


Informatics and Statistics for Metabolomics 2018 Module 6-Future of Metabolomics

Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases.

Informatics and Statistics for Metabolomics 2018 Module 6-Future of Metabolomics https://tess.elixir-europe.org/materials/informatics-and-statistics-for-metabolomics-2018-module-6-future-of-metabolomics Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases. Researchers Post-Doctoral Fellows Graduate students Biologists, Genomicists, Computer Scientists
Informatics and Statistics for Metabolomics 2018 Module 3-Databases for Chemical, Spectral, and Biological Data

Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases.

Informatics and Statistics for Metabolomics 2018 Module 3-Databases for Chemical, Spectral, and Biological Data https://tess.elixir-europe.org/materials/informatics-and-statistics-for-metabolomics-2018-module-3-databases-for-chemical-spectral-and-biological-data Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases. Researchers Post-Doctoral Fellows Graduate Students Biologists, Genomicists, Computer Scientists
Informatics and Statistics for Metabolomics 2018 Module 2-Metabolite Identification and Annotation

Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases.

Informatics and Statistics for Metabolomics 2018 Module 2-Metabolite Identification and Annotation https://tess.elixir-europe.org/materials/informatics-and-statistics-for-metabolomics-2018-module-2-metabolite-identification-and-annotation Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases. Researchers Graduate students Post-Doctoral Fellows Biologists, Genomicists, Computer Scientists
Informatics and Statistics for Metabolomics 2018 Module 1-Introduction to Metabolomics

Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases.

Informatics and Statistics for Metabolomics 2018 Module 1-Introduction to Metabolomics https://tess.elixir-europe.org/materials/informatics-and-statistics-for-metabolomics-2018-module-1-introduction-to-metabolomics Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, using pathway databases, performing pathway analysis, conducting univariate and multivariate statistics, working with metabolomics databases, and exploring chemical databases. Researchers Graduate Students Post-Doctoral Fellows Biologists, Genomicists, Computer Scientists
Informatics on High-Throughput Sequencing Data 2018 Module 6-De Novo Assmebly

Course covers the bioinformatics tools available for managing and interpreting high-throughput sequencing data, where the focus is on Illumina reads although information is applicable to all sequencer reads.

Informatics on High-Throughput Sequencing Data 2018 Module 6-De Novo Assmebly https://tess.elixir-europe.org/materials/informatics-on-high-throughput-sequencing-data-2018-module-6-de-novo-assmebly Course covers the bioinformatics tools available for managing and interpreting high-throughput sequencing data, where the focus is on Illumina reads although information is applicable to all sequencer reads. Researchers Post-Doctoral Fellows Biologists, Genomicists, Computer Scientists Graduate students
Informatics on High-Throughput Sequencing Data 2018 Module 1-Introduction to High-Throughput Sequencing

Course covers the bioinformatics tools available for managing and interpreting high-throughput sequencing data, where the focus is on Illumina reads although information is applicable to all sequencer reads.

Informatics on High-Throughput Sequencing Data 2018 Module 1-Introduction to High-Throughput Sequencing https://tess.elixir-europe.org/materials/informatics-on-high-throughput-sequencing-data-2018-module-1-introduction-to-high-throughput-sequencing Course covers the bioinformatics tools available for managing and interpreting high-throughput sequencing data, where the focus is on Illumina reads although information is applicable to all sequencer reads. Researchers Graduate students Post-Doctoral Fellows Biologists, Genomicists, Computer Scientists
Bioinformatics for Cancer Genomics 2018 Module 13-Genes to Pathways

Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud.

Bioinformatics for Cancer Genomics 2018 Module 13-Genes to Pathways https://tess.elixir-europe.org/materials/bioinformatics-for-cancer-genomics-2018-module-13-genes-to-pathways Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud. Researchers Post-Doctoral Fellows Biologists, Genomicists, Computer Scientists Graduate Students
Bioinformatics for Cancer Genomics 2018 Module 7-Somatic Mutations and Annotations

Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud.

Bioinformatics for Cancer Genomics 2018 Module 7-Somatic Mutations and Annotations https://tess.elixir-europe.org/materials/bioinformatics-for-cancer-genomics-2018-module-7-somatic-mutations-and-annotations Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Bioinformatics for Cancer Genomics 2018 Module 6-Copy Number Variants

Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud.

Bioinformatics for Cancer Genomics 2018 Module 6-Copy Number Variants https://tess.elixir-europe.org/materials/bioinformatics-for-cancer-genomics-2018-module-6-copy-number-variants Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Bioinformatics for Cancer Genomics 2018 Module 5-Genome Assembly

Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud.

Bioinformatics for Cancer Genomics 2018 Module 5-Genome Assembly https://tess.elixir-europe.org/materials/bioinformatics-for-cancer-genomics-2018-module-5-genome-assembly Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Bioinformatics for Cancer Genomics 2018 Module 4-Genome Alignment

Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud.

Bioinformatics for Cancer Genomics 2018 Module 4-Genome Alignment https://tess.elixir-europe.org/materials/bioinformatics-for-cancer-genomics-2018-module-4-genome-alignment Course covers the key bioinformatics concepts and tools required to analyze cancer genomic data sets and access and work with data sets in the cloud. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Informatics and Statistics for Metabolomics 2017 Module 6-Future of Metabolomics

Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.

Informatics and Statistics for Metabolomics 2017 Module 6-Future of Metabolomics https://tess.elixir-europe.org/materials/informatics-and-statistics-for-metabolomics-2017-module-6-future-of-metabolomics Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Informatics and Statistics for Metabolomics 2017 Module 5-MetaboAnalyst

Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.

Informatics and Statistics for Metabolomics 2017 Module 5-MetaboAnalyst https://tess.elixir-europe.org/materials/informatics-and-statistics-for-metabolomics-2017-module-5-metaboanalyst Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases. Researchers Graduate Students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Informatics and Statistics for Metabolomics 2017 Module 3-Databases for Chemical, Spectral, and Biological Data

Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.

Informatics and Statistics for Metabolomics 2017 Module 3-Databases for Chemical, Spectral, and Biological Data https://tess.elixir-europe.org/materials/informatics-and-statistics-for-metabolomics-2017-module-3-databases-for-chemical-spectral-and-biological-data Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases. Researchers Graduate Students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Informatics and Statistics for Metabolomics 2017 Module 2-Metabolite Identification and Annotation

Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.

Informatics and Statistics for Metabolomics 2017 Module 2-Metabolite Identification and Annotation https://tess.elixir-europe.org/materials/informatics-and-statistics-for-metabolomics-2017-module-2-metabolite-identification-and-annotation Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases. Researchers Graduate students Post-Doctoral Fellows Biologists, Genomicists, Computer Scientists
Informatics and Statistics for Metabolomics 2017 Module 1-Introduction to Metabolomics

Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases.

Informatics and Statistics for Metabolomics 2017 Module 1-Introduction to Metabolomics https://tess.elixir-europe.org/materials/informatics-and-statistics-for-metabolomics-2017-module-1-introduction-to-metabolomics Course covers many topics ranging from understanding metabolomics technologies, data collection and analysis, conducting univariate and multivariate statistics, working with metabolomic databases, and exploring chemical databases. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Bioinformatics for Cancer Genomics 2017 Module 5-Somatic Mutations and Annotations

Course covers the bioinformatics tools required to analyze genomic data sets.

Bioinformatics for Cancer Genomics 2017 Module 5-Somatic Mutations and Annotations https://tess.elixir-europe.org/materials/bioinformatics-for-cancer-genomics-2017-module-5-somatic-mutations-and-annotations Course covers the bioinformatics tools required to analyze genomic data sets. Researchers Graduate Students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Bioinformatics for Cancer Genomics 2017 Module 4-Copy Number Variants

Course covers the bioinformatics tools required to analyze genomic data sets.

Bioinformatics for Cancer Genomics 2017 Module 4-Copy Number Variants https://tess.elixir-europe.org/materials/bioinformatics-for-cancer-genomics-2017-module Course covers the bioinformatics tools required to analyze genomic data sets. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Bioinformatics for Cancer Genomics 2017 Module 3-Genome Alignment and Assembly

Course covers the bioinformatics tools required to analyze genomic data sets.

Bioinformatics for Cancer Genomics 2017 Module 3-Genome Alignment and Assembly https://tess.elixir-europe.org/materials/bioinformatics-for-cancer-genomics-2017-module-3-genome-alignment-and-assembly Course covers the bioinformatics tools required to analyze genomic data sets. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
Informatics on High-Throughput Sequencing Data 2017 Module 6-De Novo Assembly

Course covers the bioinformatics tools available for managing and interpreting high-throughput sequencing data with a focus on Illumina reads.

Informatics on High-Throughput Sequencing Data 2017 Module 6-De Novo Assembly https://tess.elixir-europe.org/materials/informatics-on-high-throughput-sequencing-data-2017-module-6-de-novo-assembly Course covers the bioinformatics tools available for managing and interpreting high-throughput sequencing data with a focus on Illumina reads. Researchers Graduate Students Post-Doctoral Fellows Biologists, Genomicists, Computer Scientists
Informatics on High-Throughput Sequencing Data 2017 Module 1-Introduction to High-Throughput Sequencing

Course covers the bioinformatics tools available for managing and interpreting high-throughput sequencing data with a focus on Illumina reads.

Informatics on High-Throughput Sequencing Data 2017 Module 1-Introduction to High-Throughput Sequencing https://tess.elixir-europe.org/materials/informatics-on-high-throughput-sequencing-data-module-1-introduction-to-high-throughput-sequencing Course covers the bioinformatics tools available for managing and interpreting high-throughput sequencing data with a focus on Illumina reads. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
High-Throughput Biology 2017 Module 13-Finding Over-Represented Pathways

Course covers the key bioinformatics concepts and tools required to analyze DNA- and RNA- sequence reads using a reference genome.

High-Throughput Biology 2017 Module 13-Finding Over-Represented Pathways https://tess.elixir-europe.org/materials/high-throughput-biology-2017-module-13-finding-over-represented-pathways Course covers the key bioinformatics concepts and tools required to analyze DNA- and RNA- sequence reads using a reference genome. Graduate Students Researchers Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
High-Throughput Biology 2017 Module 12-Introduction to Pathway and Network Analysis

Course covers the key bioinformatics concepts and tools required to analyze DNA- and RNA- sequence reads using a reference genome.

High-Throughput Biology 2017 Module 12-Introduction to Pathway and Network Analysis https://tess.elixir-europe.org/materials/high-throughput-biology-2017-module-12-introduction-to-pathway-and-network-analysis Course covers the key bioinformatics concepts and tools required to analyze DNA- and RNA- sequence reads using a reference genome. Graduate students Researchers Post-Doctoral Fellows Biologists, Genomicists, Computer Scientists
High-Throughput Biology 2017 Module 6-De Novo Assembly

Course covers the key bioinformatics concepts and tools required to analyze DNA- and RNA- sequence reads using a reference genome.

High-Throughput Biology 2017 Module 6-De Novo Assembly https://tess.elixir-europe.org/materials/high-throughput-biology-2017-module-6-de-novo-assembly Course covers the key bioinformatics concepts and tools required to analyze DNA- and RNA- sequence reads using a reference genome. Researchers Graduate students Biologists, Genomicists, Computer Scientists Post-Doctoral Fellows
High-Throughput Biology 2017 Module 1-Introduction to High-Throughput Sequencing

Course covers the key bioinformatics concepts and tools required to analyze DNA- and RNA- sequence reads using a reference genome.

High-Throughput Biology 2017 Module 1-Introduction to High-Throughput Sequencing https://tess.elixir-europe.org/materials/high-throughput-biology-2017-module-1-introduction-to-high-throughput-sequencing Course covers the key bioinformatics concepts and tools required to analyze DNA- and RNA- sequence reads using a reference genome. Graduate students Post-Doctoral Fellows Researchers Biologists, Genomicists, Computer Scientists
Expression estimation

This introduces how to summarise short read alignments by the annotation of interest to obtain a count-table; i.e. the structure necessary to most downstream expression based analyses. Here, the focus is put on gene-expression, but the aspects of transcript-expression are briefly addressed.

Scientific topics: RNA-Seq

Keywords: GFF3, BAM, Populus-tremula, RNA-Seq, Expression-estimation

Expression estimation https://tess.elixir-europe.org/materials/expression-estimation This introduces how to summarise short read alignments by the annotation of interest to obtain a count-table; i.e. the structure necessary to most downstream expression based analyses. Here, the focus is put on gene-expression, but the aspects of transcript-expression are briefly addressed. RNA-Seq GFF3, BAM, Populus-tremula, RNA-Seq, Expression-estimation
Tutorial

This file describes the main tutorial PDF file. Almost all tutorials and hands-on practices are indeed collated in a single document. In addition to this PDF, R code excerpts and installation instructions are also provided.

Scientific topics: RNA-Seq

Keywords: FASTQ, GFF3, BAM, Populus-tremula, RNA-Seq, Pre-processing, QC, Alignment, Annotation, Expression-estimation, R-programming

Tutorial https://tess.elixir-europe.org/materials/tutorial This file describes the main tutorial PDF file. Almost all tutorials and hands-on practices are indeed collated in a single document. In addition to this PDF, R code excerpts and installation instructions are also provided. RNA-Seq FASTQ, GFF3, BAM, Populus-tremula, RNA-Seq, Pre-processing, QC, Alignment, Annotation, Expression-estimation, R-programming
Populus tremula shows no evidence of sexual dimorphism

**Background:** Although the majority of plant species are co-sexual, being either monoecious or hermaphroditic, a significant number are dioecious, having separate male and female individuals. Evolutionary theory suggests that males and females may develop sexually dimorphic phenotypic and...

Scientific topics: RNA-Seq

Keywords: FASTQ, GFF3, BAM, Populus-tremula, RNA-Seq, Pre-processing, QC, Alignment, Annotation, Expression-estimation, Differential-expression

Populus tremula shows no evidence of sexual dimorphism https://tess.elixir-europe.org/materials/populus-tremula-shows-no-evidence-of-sexual-dimorphism **Background:** Although the majority of plant species are co-sexual, being either monoecious or hermaphroditic, a significant number are dioecious, having separate male and female individuals. Evolutionary theory suggests that males and females may develop sexually dimorphic phenotypic and biochemical traits concordant with each sex having different optimal strategies of resource investment to maximise reproductive success and fitness. The establishment of such sexual dimorphism would result in changes in gene expression patterns in non-floral organs. RNA-Seq FASTQ, GFF3, BAM, Populus-tremula, RNA-Seq, Pre-processing, QC, Alignment, Annotation, Expression-estimation, Differential-expression
Nicolas Delhomme - Bastian Schiffthaler - October 2014 EMBO course material

Material for the course held on EBI Campus, Welcome Trust Center, Hinxton, UK on 20-26th, October 2014. The material cover general RNA-Seq data pre-processing as described in these [guidelines](http://www.epigenesys.eu/en/protocols/bio-informatics/1283-guidelines-for-rna-seq-data-analysis) and...

Scientific topics: RNA-Seq

Keywords: FASTQ, GFF3, BAM, Populus-tremula, RNA-Seq, Pre-processing, QC, Alignment, Annotation, Expression-estimation, Differential-expression, R-programming

Nicolas Delhomme - Bastian Schiffthaler - October 2014 EMBO course material https://tess.elixir-europe.org/materials/nicolas-delhomme-bastian-schiffthaler-october-2014-embo-course-material Material for the course held on EBI Campus, Welcome Trust Center, Hinxton, UK on 20-26th, October 2014. The material cover general RNA-Seq data pre-processing as described in these [guidelines](http://www.epigenesys.eu/en/protocols/bio-informatics/1283-guidelines-for-rna-seq-data-analysis) and reproduces the Differential Expression analysis conducted in Robinson, Delhomme et al., 2014. RNA-Seq FASTQ, GFF3, BAM, Populus-tremula, RNA-Seq, Pre-processing, QC, Alignment, Annotation, Expression-estimation, Differential-expression, R-programming
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