EMBO High Throughput Sequencing Data Analysis, Cambridge, UK, 2014

Keywords

FASTQ, GFF3, BAM, Populus-tremula, RNA-Seq, Pre-processing, QC, Alignment, Annotation, Expression-estimation, Differential-expression, R-programming

Details

The material described here further corresponds to the 2nd and the 3rd Day of the course. They focus on:

pre-processing RNA-Seq data
manipulating the resulting files using R/Bioconductor
manipulating genic annotation using R/Bioconductor
extracting a count-table (at the gene level)
performing a gene differential expression analysis

Venue

• EBI Campus, Welcome Trust Center, Hinxton, UK, 20-26th October 2014

Organisation

• Organizer: Gabriella Rustici (@gabriella)
• Trainers: Nicolas Delhomme (@delhomme), Bastian Schiffthaler (@bastian)

| Keywords | Details | Objectives | Content | Data |

Global learning objectives

1. Introduction
2. Quality Control
3. Alignment
4. Annotation
5. Expression-estimation
6. Differential-expression
   • What is a differential expression analysis
   • How to assess the quality (the biological meaningfulness) of the count data
   • What is a confounding factor
   • How to identify a confounding factor
   • How to block a confounding factor

| Keywords | Details | Objectives | Content | Data |

Content

• EMBO October 2014 - Tutorial
• EMBO October 2014 - Introduction
• EMBO October 2014 - QC
• EMBO October 2014 - Alignment
• EMBO October 2014 - Annotation
• EMBO October 2014 - Expression-estimate
• EMBO October 2014 - Differential-expression

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Data

• Uses the dataset Robinson, Delhomme et al., 2014

| Keywords | Details | Objectives | Content | Data |