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Scientific topics: Data architecture, analysis... 

Reproducible analysis

Slides for the "Reproducible analysis" session of the "Best practices in research data management and stewardship" held regularly by ELIXIR-Luxembourg.

Scientific topics: Data architecture, analysis and design

Keywords: Workflows, Literate programming, Reproducible Science, Data analysis

Resource type: Presentation

Reproducible analysis http://tess.elixir-uk.org/materials/reproducible-analysis Slides for the "Reproducible analysis" session of the "Best practices in research data management and stewardship" held regularly by ELIXIR-Luxembourg. Pinar Alper Vilem Ded Data architecture, analysis and design Workflows, Literate programming, Reproducible Science, Data analysis PhD candidates Researchers
A common framework for designing portable federated pipelines

This video describes a common framework for designing portable federated pipelines

Scientific topics: Data architecture, analysis and design

Keywords: data access, federated data analysis

Resource type: Video

A common framework for designing portable federated pipelines http://tess.elixir-uk.org/materials/a-common-framework-for-designing-portable-federated-pipelines This video describes a common framework for designing portable federated pipelines Alvaro Gonzalez Data architecture, analysis and design data access, federated data analysis
Data Gravity in the Life Sciences: Lessons learned from the HCA and other federated data projects

CINECA webinar discussing when to bring compute to the data

Scientific topics: Data architecture, analysis and design

Keywords: Cloud computing, Data analysis, Standards, Translational research

Resource type: Video

Data Gravity in the Life Sciences: Lessons learned from the HCA and other federated data projects http://tess.elixir-uk.org/materials/data-gravity-in-the-life-sciences-lessons-learned-from-the-hca-and-other-federated-data-projects CINECA webinar discussing when to bring compute to the data Data architecture, analysis and design Cloud computing, Data analysis, Standards, Translational research
Reproducible data analysis with RStudio, github and Rmarkdown

Best practices for writing reproducible data-analysis Creating a reproducible and re-usable data-analysis environment with Rstudio Input: https://github.com/vibbits/RDM-LS Output: https://github.com/vibbits/RDM-LS-solution

Scientific topics: Data management, Data architecture, analysis and design

Keywords: Data analysis

Resource type: Presentation

Reproducible data analysis with RStudio, github and Rmarkdown http://tess.elixir-uk.org/materials/reproducible-data-analysis-with-rstudio-github-and-rmarkdown Best practices for writing reproducible data-analysis Creating a reproducible and re-usable data-analysis environment with Rstudio Input: https://github.com/vibbits/RDM-LS Output: https://github.com/vibbits/RDM-LS-solution Data management Data architecture, analysis and design Data analysis life scientists
High-throughput sequencing training materials repository

This repository includes training materials on the analysis of high-throughput sequencing (HTS) data, on the following topics: Introduction to HTS, RNA-seq, ChIP-seq and variant calling analysis. Materials have been annotated following the standards and guidelines proposed at the “Best practices...

Scientific topics: Data architecture, analysis and design, Bioinformatics

Keywords: High throughput sequencing analysis, Rna seq chip seq anayses, Variant calling

High-throughput sequencing training materials repository http://tess.elixir-uk.org/materials/high-throughput-sequencing-training-materials-repository This repository includes training materials on the analysis of high-throughput sequencing (HTS) data, on the following topics: Introduction to HTS, RNA-seq, ChIP-seq and variant calling analysis. Materials have been annotated following the standards and guidelines proposed at the “Best practices in next-generation sequencing data analysis” workshop which took place at the University of Cambridge, UK, on 13-14 January 2015. Following this workshop, a Git repository has been set up for sharing annotated materials. This repository uses Git, hence it is decentralized and self-managed by the community and can be forked/built-upon by all users. Data architecture, analysis and design Bioinformatics High throughput sequencing analysis, Rna seq chip seq anayses, Variant calling Life Science Researchers PhD students Trainers beginner bioinformaticians post-docs 2015-12-17 2017-10-09
RNA-seq data analysis: from raw reads to differentially expressed genes

This course material introduces the central concepts, analysis steps and file formats in RNA-seq data analysis. It covers the analysis from quality control to differential expression detection, and workflow construction and several data visualizations are also practised. The material consists of...

Scientific topics: Sequencing, RNA, Data architecture, analysis and design, Bioinformatics

Keywords: Bioinformatics, Differential expression, Ngs, Rna seq

RNA-seq data analysis: from raw reads to differentially expressed genes http://tess.elixir-uk.org/materials/rna-seq-data-analysis-from-raw-reads-to-differentially-expressed-genes This course material introduces the central concepts, analysis steps and file formats in RNA-seq data analysis. It covers the analysis from quality control to differential expression detection, and workflow construction and several data visualizations are also practised. The material consists of 10-30 minute lectures intertwined with hands-on exercises, and it can be accomplished in a day. As the user-friendly Chipster software is used in the exercises, no prior knowledge of R/Bioconductor or Unix ir required, and the course is thus suitable for everybody. Our book RNA-seq data analysis: A practical approach (CRC Press) can be used as background reading. The following topics and analysis tools are covered: 1. Introduction to the Chipster analysis platform 2. Quality control of raw reads (FastQC, PRINSEQ) 3. Preprocessing (Trimmomatic, PRINSEQ) 4. Alignment to reference genome (TopHat2) 5. Alignment level quality control (RseQC) 6. Quantitation (HTSeq) 7. Experiment level quality control with PCA and MDS plots 8. Differential expression analysis (DESeq2, edgeR) -normalization -dispersion estimation -statistical testing -controlling for batch effects, multifactor designs -filtering -multiple testing correction 9. Visualization of reads and results -genome browser -Venn diagram -volcano plot -plotting normalized counts for a gene -expression profiles 10. Experimental design Sequencing RNA Data architecture, analysis and design Bioinformatics Bioinformatics, Differential expression, Ngs, Rna seq Bench biologists Life Science Researchers 2015-12-04 2017-10-09