- Biologie
- Biomédical
- Informatique
BiRD brings together life-science and digital-science research laboratories in Nantes. It supports researchers in managing, integrating, analyzing, and modeling experimental data by providing large-scale storage and computing infrastructure, as well as (bio)informatics tools, standardized analysis pipelines, and training. By pooling human resources, expertise, software, and datasets, BiRD aims to strengthen and sustain bio-analysis capabilities, facilitate the analysis and reuse of heterogeneous data, scale up methods developed by partner teams, and contribute to training in life and digital sciences.
- NGS Data Analysis
- Metagenomics
- Metabolic Network Modelling
- Ontologies
- Transcriptomics
- Variant analysis
- Interoperability
- Integration of heterogeneous data
- Knowledge representation
- Workflow development
- DifficultyLevel
- Novice
- OpenTo
- Everyone
- more
- ...
Objectives
- Understand the principles and advantages of the Linux system
- Know and use the main bash commands. Ability to chain multiple commands using pipes
- Launch programs with arguments
- Gain independence to perform command line analyses
Pedagogical Content
- Introduction to the Linux system.
- File system: directory structure, paths, home directory, file and directory management.
- Principle of protections: reading file attributes, access rights, management of user groups.
- Shell usage: command reminders, input/output redirection, history, completion, launching programs with arguments.
- Commands relevant to bioinformatics: grep, cut, sed, sort, more, etc.
- Connection (ssh) - how to start a session from Linux or Windows PowerShell
Environments and best practices for using the BiRD cluster
- DifficultyLevel
- OpenTo
- Everyone
- more
- ...
Objectives
- Understand and implement the principles of reproducible science in analysis and development projects
- Acquire basic commands necessary for optimal use of the cluster
Pedagogical Content
- Introduction to reproducibility
- Best practices on code history and sharing: Git
- Conda environment
- Presentation of the computing cluster
- Introduction to workflows using Snakemake
- DifficultyLevel
- OpenTo
- Everyone
- more
- ...
Objectives
- Understand the key steps in RNASeq data analysis for a differential expression study
- Know how to perform command-line analysis using Snakemake.
Pedagogical Content
Day 1
- Principle of RNASeq technology: objectives and experimental design.
- Data quality assessment (FastQC, MultiQC).
- Sequence alignment to a reference genome (STAR).
Day 2
- Differential gene expression analysis (HTSeqCount, DESeq2).
- Functional annotation (GO, Kegg).
- Using the Snakemake workflow system.
- Comparison between RNASeq and 3’SRP methods.
The theoretical part is followed by a pipeline run step-by-step on a test dataset.
It will be possible to start an analysis on your own data.
Objectives
- Understand the principles and advantages of the Linux system
- Know and use the main bash commands. Ability to chain multiple commands using pipes
- Launch programs with arguments
- Gain independence to perform command line analyses
Pedagogical Content
- Introduction to the Linux system.
- File system: directory structure, paths, home directory, file and directory management.
- Principle of protections: reading file attributes, access rights, management of user groups.
- Shell usage: command reminders, input/output redirection, history, completion, launching programs with arguments.
- Commands relevant to bioinformatics: grep, cut, sed, sort, more, etc.
- Connection (ssh) - how to start a session from Linux or Windows PowerShell
Environments and best practices for using the BiRD cluster
Objectives
- Understand and implement the principles of reproducible science in analysis and development projects
- Acquire basic commands necessary for optimal use of the cluster
Pedagogical Content
- Introduction to reproducibility
- Best practices on code history and sharing: Git
- Conda environment
- Presentation of the computing cluster
- Introduction to workflows using Snakemake
Objectives
- Understand the key steps in RNASeq data analysis for a differential expression study
- Know how to perform command-line analysis using Snakemake.
Pedagogical Content
Day 1
- Principle of RNASeq technology: objectives and experimental design.
- Data quality assessment (FastQC, MultiQC).
- Sequence alignment to a reference genome (STAR).
Day 2
- Differential gene expression analysis (HTSeqCount, DESeq2).
- Functional annotation (GO, Kegg).
- Using the Snakemake workflow system.
- Comparison between RNASeq and 3’SRP methods.
The theoretical part is followed by a pipeline run step-by-step on a test dataset.
It will be possible to start an analysis on your own data.
Objectives
- Understand the principles and advantages of the Linux system
- Know and use the main bash commands. Ability to chain multiple commands using pipes
- Launch programs with arguments
- Gain independence to perform command line analyses
Pedagogical Content
- Introduction to the Linux system.
- File system: directory structure, paths, home directory, file and directory management.
- Principle of protections: reading file attributes, access rights, management of user groups.
- Shell usage: command reminders, input/output redirection, history, completion, launching programs with arguments.
- Commands relevant to bioinformatics: grep, cut, sed, sort, more, etc.
- Connection (ssh) - how to start a session from Linux or Windows PowerShell
Objectives
- Understand the key steps in RNASeq data analysis for a differential expression study
- Know how to perform command-line analysis using Snakemake.
Pedagogical Content
Day 1
- Principle of RNASeq technology: objectives and experimental design.
- Data quality assessment (FastQC, MultiQC).
- Sequence alignment to a reference genome (STAR).
Day 2
- Differential gene expression analysis (HTSeqCount, DESeq2).
- Functional annotation (GO, Kegg).
- Using the Snakemake workflow system.
- Comparison between RNASeq and 3’SRP methods.
The theoretical part is followed by a pipeline run step-by-step on a test dataset.
It will be possible to start an analysis on your own data.
Environments and best practices for using the BiRD cluster
Objectives
- Understand and implement the principles of reproducible science in analysis and development projects
- Acquire basic commands necessary for optimal use of the cluster
Pedagogical Content
- Introduction to reproducibility
- Best practices on code history and sharing: Git
- Conda environment
- Presentation of the computing cluster
- Introduction to workflows using Snakemake
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