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November 30 & December 5, 2017

Transcriptomics



Oshlak A, Robinson MD, Young MD (2010) From RNA-seq reads to differential expression results. BMC Genome Biology 11:220. http://genomebiology.com/2010/11/12/220.

Trapnell, C et al. (2012) Differential gene expression and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols 7:562-578. doi:10.1038/nprot.2012.016.

Cresko Lab RNA-seqlopedia http://rnaseq.uoregon.edu/

Thiru, P RNA-seq: Methods and Applications. http://jura.wi.mit.edu/bio/education/hot_topics/RNAseq/RNA_Seq.pdf


A. Overview

1. Types of data
2. What we are trying to learn

B. Microarrays have largely been superseded by RNAseq

C. Experimental considerations for RNAseq

1. Sources of experimental variation
2. Experimental design
3. RNA
4. Sequencing technologies

D. Transcriptomic Data pipelines

1. de-novo assemblies vs. assembly by read mapping
2. Normalization
3. Which genes show a "significant" difference between treatments?
4. Differential expression




A. Overview

1) Types  of data

Transcriptomic studies tend to generate two different types of data. Studies in which two or more conditions are compared at a time generate discrete state data. Often it is critical to follow the expression of a gene over time after a treatment. In timecourse experiments, the expression of each gene in response to two or more treatments is measured over time. For example, in the timecourse at right, the solid blue and red dashed curves might represent the expression levels for a gene in response to two different drugs.



There is a whole family of problems in normalization of data and controlling for components of experimental variation.

To put things into perspective, if the experiment was repeated 4 times, the timecourse above represents
2 treatments x 6 times x 4 replicates = 48 labeled RNA populations to be sequenced
to generate the data. Although the data for each replicate are averaged, there is often a great deal of variation  in the results, which can potentially negate any meaning. Therefore, extraordinary measures must be taken to minimize experimental variation at each step in the procedure, to minimize the overall variation.

2. What are we trying to learn from transcriptomics?

The primary goal of transcriptomic experiments is to generate expression information for every gene in the array, under some set of condittions. Expression may be studied in The kind of results that are sought in transcriptomic experiments can be illustrated as follows:

In the example, timecourse data are generated for each transcript in an RNA population. The raw data consists of a series of expression curves for timecourses, or histograms where other types of treatments are being compared. The goal is usually to find which groups of genes have the most similar expression patterns. In the example, two genes (hatched background) show a gradual induction over the period of the timecourse. Two other genes (shaded background) show a biphasic response with two distinct periods of strong expression.
 

Key questions:
  • Which genes are expressed  differentially, between condition A and condition B?
  • How can genes be grouped according to similarities in expression patterns?

B. Microarrays have largely been superseded by RNAseq

RNA-seq has become the method of choice for transcriptomics.because RNA-seq directly counts cDNA copies of mRNAs, it has fewer sources of experimental variance than microarrays.  Because RNA-seq has now become cost-competitive with microarrays, and the costs of sequencing keep going down, RNA-seq is rapidly replacing microarrays.

Comparison of sources of experimental error
Microarrays
RNA-seq
requires previously sequenced and annotated genome
NA
error in quantitation of RNA can affect ratios of expression compared between two treatments 
NA
cDNA synthesis/labeling
NA
quality of array
NA
hybridization
NA
washing
NA
measurement of signal
NA
NA - not applicable



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