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Bacterial Transcriptomics Towards Functional Characterisation of Genes   Date : 26 – 27 June 2013 Time : 9 am – 5 pm Venue : MGRC Training Centre 37-5 Level 5 Boulevard Signature Office Mid Valley City 59200 Kuala Lumpur Fee : RM2,000 per person Download Brochure : Bacterial Transcriptomics Workshop

Course Outcomes

You will learn to:

•             Use transcript browsers such as Artemis to identify novel transcripts

•             Identify differentially expressed genes between samples

•             Annotate and analyse differentially expressed gene sets for function

•             Assemble transcriptomes de novo for non-coding RNA discovery

•             Design experiments to discover genes related to particular strain phenotypes

•             Analyse metatranscriptome datasets

 

Requirements

You should have:

•             Basic computer competency

•             Basic experience in the UNIX Operating System

•             A background in biology, preferably microbial biology

 

Workshop Agenda

An overview of bacterial transcriptomes

     -      Structure of bacterial transcriptomes

     -      mRNAs and other RNAs

     -      Read preprocessing and mapping

     -      Use of Artemis and other browsers

     -      Discovery of non-coding RNAs and other novel features

     -      Worked examples

Differential expression analysis

     -      Read count based expression such as FPKM and TPM

     -      Use of Excel, Matlab, R, RSEM and other applications for expression analysis

     -      Experimental design for gene function discovery and annotation

     -      Microarray data

     -      Annotation and analyses of sets of differentially expressed genes

     -      Worked examples

Transcript assembly

     -      Transcript assembly

     -      Comparison with genome annotation and discovery of novel features

     -      ncRNAs, overlapping genes and other events

     -      Worked examples

Analysis of transcriptome data

     -      Metatranscriptome datasets and metabolic analysis

     -      Use of multiple data sources for improved analysis

     -      Worked examples