BiRD

A Snakemake-based pipeline for 3' sequencing RNA profiling data analysis. This 3’ Digital gene expression sequencing technique allows a precise and low-cost transcriptome profiling.

The main steps of the pipeline are:
- Samples demultiplexing transform the raw paired-end fastq files into a single-end fastq file for each sample.
- Alignment on refseq reference transcriptome is performed using bwa.
- Aligned reads are parsed and UMI are counted for each gene in each sample to create an expression matrix.
If secondary analysis has been asked (providing a comparisons file), the expression matrix is normalized and differentially expressed genes (DEG) are searched using deseq2.
- If DEG are found, annotation is performed using the database GO and KEGG.
- A report is provided listing the main quality controls performed and the results found.

Cite : https://dx.doi.org/10.21203/rs.3.pex-1336/v1

Analysis pipeline using Snakemake for RNAseq analysis in order to find differentially expressed genes.

FAIR-Checker is a tool aimed at assessing FAIR principles and empowering data provider to enhance the quality of their digital resources. Data providers and consumers can check how FAIR are web resources. Developers can explore and inspect metadata exposed in web resources.

An automated workflow for genome-resolved metagenomics

An interactive web application for multi-omics data exploration and integration.

A web application for multi-omics analysis.

This workflow provides an automated microbiome data analysis, starting with sequenced taxonomic markers (such as 16SrRNA) and using the standard QIIME2 toolbox to produce an abundance table and preliminary diversity, phylogeny and taxonomy analysis.

From a list of genes, pyBRAvo produces upstream regulation or signaling networks. pyBRAvo internally leverages the BioPAX ontolgy, the PathwayCommons knowledge graph, and the SPARQL semantic web query language. pyBRAvo can be used through either a Jupyter notebook, or a command line interface.

• Biomédical
• Biologie
• 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

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

Course 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.
- Useful commands for file manipulation
- Redirection operators (command input/output)
- Creating and running a bash script
Introduction to environment variables
Connecting to a remote server via a terminal or via WSL

Objectives
- Understand and implement the principles of reproducible science in analysis and development projects
- Acquire basic commands necessary for optimal use of a HPC cluster

Course Content
- Introduction to reproducibility
- Best practices on code history and sharing: Git
- Software environment : conda/mamba
- Presentation of a HPC 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 infrastructure of GLiCID HPC cluster
- understand the different types of storage and computing nodes
- launch computing tasks via the Slurm scheduler

Course Content
- configuration of your account and connection with ssh on GLiCID (ssh keys)
- navigate through the storage spaces
- use slurm to launch a job
- manage the software environments (micromamba, guix, modules)
- use workflow managers on GLiCID

Objectives
- Understand basic R commands
- Learn how to use the RStudio interface
- Understand the use of R functions
- Be able to perform simple data manipulations
- Be able to create basic visualizations

Course Content
I. Introduction
- Getting started with the RStudio environment
- Programming best practices
- Different types and classes of variables
- Functions

II. Data manipulation with the tidyverse
- Logical operators
- Working with data frames

III. Visualization with ggplot2
- Principles
- Simple examples

Les plateformes de bioinformatique du réseau Biogenouest (ABiMS, BiRD, GenOuest et SeBiMER) vous proposent une formation “FAIR-bioinfo” à destination des bioinformaticien.ne.s, bioanalystes et biostatisticien.ne.s.

Lors de cette formation, nous vous présenterons les principes “FAIR” (Facile à trouver, Accessible, Interopérable, Réutilisable) et leur application dans les projets d’analyse et de développement.
Des présentations théoriques suivies d’utilisations pratiques de plusieurs outils permettant d’améliorer la reproductibilité des analyses seront proposées.

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 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.

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 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

Les plateformes de bioinformatique du réseau Biogenouest (ABiMS, BiRD, GenOuest et SeBiMER) vous proposent une formation “FAIR-bioinfo” à destination des bioinformaticien.ne.s, bioanalystes et biostatisticien.ne.s.

Lors de cette formation, nous vous présenterons les principes “FAIR” (Facile à trouver, Accessible, Interopérable, Réutilisable) et leur application dans les projets d’analyse et de développement.
Des présentations théoriques suivies d’utilisations pratiques de plusieurs outils permettant d’améliorer la reproductibilité des analyses seront proposées.

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

Course 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.
- Useful commands for file manipulation
- Redirection operators (command input/output)
- Creating and running a bash script
Introduction to environment variables
Connecting to a remote server via a terminal or via WSL

Objectives
- Understand and implement the principles of reproducible science in analysis and development projects
- Acquire basic commands necessary for optimal use of a HPC cluster

Course Content
- Introduction to reproducibility
- Best practices on code history and sharing: Git
- Software environment : conda/mamba
- Presentation of a HPC cluster
- Introduction to workflows using Snakemake

Objectives
- Understand basic R commands
- Learn how to use the RStudio interface
- Understand the use of R functions
- Be able to perform simple data manipulations
- Be able to create basic visualizations

Course Content
I. Introduction
- Getting started with the RStudio environment
- Programming best practices
- Different types and classes of variables
- Functions

II. Data manipulation with the tidyverse
- Logical operators
- Working with data frames

III. Visualization with ggplot2
- Principles
- Simple examples

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 infrastructure of GLiCID HPC cluster
- understand the different types of storage and computing nodes
- launch computing tasks via the Slurm scheduler

Course Content
- configuration of your account and connection with ssh on GLiCID (ssh keys)
- navigate through the storage spaces
- use slurm to launch a job
- manage the software environments (micromamba, guix, modules)
- use workflow managers on GLiCID

Objectives
- Understand and learn the main steps of scRNA-seq data analysis, up to marker gene detection and cell type identification.
- Be able to use the Seurat package on a small test dataset, from count matrices to clustering and cluster annotation.
- Understand the basics of the analysis in order to apply them to one’s own dataset.

Course Content
I. Introduction
- Single-cell RNA sequencing
- From raw sequencing data to count matrices
- Software tools

II. Preprocessing of the expression matrix (Theory and Practice)
- Quality control
- Normalization
- Dimensionality reduction (HVG, PCA, UMAP)
- Detection of expression biases

III. Annotation (Theory and Practice)
- Clustering
- Marker genes
- Cell type identification
- Analysis of marker gene lists with the R package ClusterProfiler

IV. Practical Workshop “Bring your own data”
- Semi-autonomous execution of primary analysis on learners’ own data