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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 Sébastien 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 Sébastien 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 | ||
