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The course participants will learn how to use R/BioConductor software and Illumina BaseSpace Cloud apps. The course covers various topics using graphical interfaces. Furthermore everyday there will be, in morning and afternoon, a brief session on R scripting to provide the basics knowledge to move from the use of the graphical environment to command line. During the last day of the course some of the workflows used in the first three days will be dissected and executed using command line approach. All computers for hands-on exercises will be provided along with demo data sets.
This course was developed for biologists as the intended audience, particularly for those already with basic knowledge of massively parallel sequencing (MPS). However, R-coding skills and deep knowledge of statistics are not necessary for attendance.
• Tools for RNA-seq data analysis
• Experimental design
• Quality control
• Normalisation, batch effect correction and data reformatting
• Basic Statistics
• Selecting differentially regulated genes/microRNAs/non-coding RNAs
• Biological interpretation
• Transcriptome analysis in absence of genomic annotation information.
• Detecting alternative splicing events
• Single cell RNAseq data analysis
The students will learn the importance of experimental design in order to ask sensible biological questions, as well as the ability to assess the quality of data. Perform normalisation and reformatting procedures. Also covered will be complete basic statistical tests on next generation sequencing (NGS) data, various problems encountered when analysing data and the basics of R scripting.