+ - 0:00:00
Notes for current slide
Notes for next slide

Introduction to Bioconductor

Pre-workshop Introduction to RNA-seq

R-Ladies Tunis team

2020-09-10

1 / 14

Introduction to Bioconductor

2 / 14

Introduction

3 / 14

Introduction

4 / 14

The Bioconductor Project

The Bioconductor project is an open-source repository for R packages, datasets, and workflows that are specific for analyzing biological data.

The broad goals of the Bioconductor project are:

  1. To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data.
5 / 14

The Bioconductor Project

The Bioconductor project is an open-source repository for R packages, datasets, and workflows that are specific for analyzing biological data.

The broad goals of the Bioconductor project are:

  1. To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data.

  2. To facilitate the inclusion of biological metadata in the analysis of genomic data, e.g. literature data from PubMed, annotation data from Entrez genes.

6 / 14

The Bioconductor Project

The Bioconductor project is an open-source repository for R packages, datasets, and workflows that are specific for analyzing biological data.

The broad goals of the Bioconductor project are:

  1. To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data.

  2. To facilitate the inclusion of biological metadata in the analysis of genomic data, e.g. literature data from PubMed, annotation data from Entrez genes.

  3. To provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software.
7 / 14

The Bioconductor Project

The Bioconductor project is an open-source repository for R packages, datasets, and workflows that are specific for analyzing biological data.

The broad goals of the Bioconductor project are:

  1. To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of genomic data.

  2. To facilitate the inclusion of biological metadata in the analysis of genomic data, e.g. literature data from PubMed, annotation data from Entrez genes.

  3. To provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software.

  4. To train researchers on computational and statistical methods for the analysis of genomic data.

8 / 14

How to install Bioconductor

The Bioconductor package collection forms its own repository and therefore is installed differently.

To install core Bioconductor packages you need to go to your Rstudio and type these lines of code in the console.

if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(version = "3.11")
## Bioconductor version 3.11 (BiocManager 1.30.10), R 4.0.2 (2020-06-22)
## Installation path not writeable, unable to update packages: callr, jsonlite,
## stringi, xfun, MASS
9 / 14

How to install Bioconductor(1)

When you'll type these lines of code, you'll get this in the console.

10 / 14

How to install Bioconductor(2)

11 / 14

How to install a Bioconductor package

To install a specific package, e.g., “DESeq2” we use the install() function from the BiocManager package.

BiocManager::install("DESeq2")

To install many packages:

BiocManager::install(c("airway","tximeta"))
12 / 14

Thanks!

14 / 14

Introduction to Bioconductor

2 / 14
Paused

Help

Keyboard shortcuts

, , Pg Up, k Go to previous slide
, , Pg Dn, Space, j Go to next slide
Home Go to first slide
End Go to last slide
Number + Return Go to specific slide
b / m / f Toggle blackout / mirrored / fullscreen mode
c Clone slideshow
p Toggle presenter mode
t Restart the presentation timer
?, h Toggle this help
Esc Back to slideshow