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r_symposium [2017/04/29 11:27]
vincent_fugere
r_symposium [2017/05/09 12:16] (current)
sebastien.renaut
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 **Intro to gene expression analysis in R** //by S├ębastien Renaut//. Next generation sequencing has promised cheap DNA sequences to the masses. While this may be true, the bottleneck has now shifted from generating data to analyzing it. Here, I will use transcriptome sequencing data (RNAseq) to quantify gene expression. I will introduce data formats commonly used in genomics (e.g.: .fastq,​.bam,​.sam) and I will use the R programming language to identify differentially expressed genes (e.g. DESeq2, edgeR packages), cluster samples based on gene expression, detects gene ontology categories which are over/under represented (goseq) and present various graphics to illustrate results. **Intro to gene expression analysis in R** //by S├ębastien Renaut//. Next generation sequencing has promised cheap DNA sequences to the masses. While this may be true, the bottleneck has now shifted from generating data to analyzing it. Here, I will use transcriptome sequencing data (RNAseq) to quantify gene expression. I will introduce data formats commonly used in genomics (e.g.: .fastq,​.bam,​.sam) and I will use the R programming language to identify differentially expressed genes (e.g. DESeq2, edgeR packages), cluster samples based on gene expression, detects gene ontology categories which are over/under represented (goseq) and present various graphics to illustrate results.
  
-[[http://sebastien.renaut.com/​rnaseq_workshop/Rcode/]]+[[https://github.com/seb951/​rnaseq_workshop]]
  
 **Predicting species geographical distribution using R** //by Pedro Henrique P. Braga and Julia Nordlund//. Species distribution models (SDM) have been widely applied to address many questions in **Predicting species geographical distribution using R** //by Pedro Henrique P. Braga and Julia Nordlund//. Species distribution models (SDM) have been widely applied to address many questions in