RNA-seq: the future of transcriptomics
…….
?
Disclaimer: Tiago
Hori is not an expert
on RNA-seq
Wang et al., 2009
• RNA-seq or RNA-sequencing is not a complete novel idea.
• SAGE, long-SAGE, MPSS
• The recent developments in next-generation sequencing (NGS) have made whole
transcriptomic analyses more accessible.
• Does it work?
• Comparison with microarrays
• Advantages and disadvantages
• How does it work?
• Challenges
• Are microarrays going to go extinct?
Weapons of choice:
Marioni et al., 2008
There is a good correlation between microarray intensity
and count data.
There is also good correlation between Affymetrix foldchanges and Illumina-based RNA-seq fold-changes
The Pros and Cons of RNA-seq – do the benefits definitely outweigh the
problems?
Advantages:
• Allows for not only the identification of differentially expressed genes, but also identification
of differential allelic expression, SNPs, splice variants, new genes or isoforms.
• It is not limited to a set number of probes.
• It is NOT impacted by background signal or saturation that causes problems in studying highand low-expression transcripts.
Wang et al, 2009
The Pros and Cons of RNA-seq – do the benefits definitely outweigh the
problems?
Disadvantages:
• Cost
• Dependent on a reference genome or transcriptome. * see Trapnell et al., 2010 – Nature
Biotechnology (used 430 million paired-end reads to assemble a transcriptome de-novo
• Large amounts of data requiring large storage space and computational power
• Statistical methods are still in their infancy
How does it work?
A) Agilent polyA selection
B) NibleGen selection array
C) Generation of target cDNA
(sequence specific, e.g. for
allele discrimination)
D) Helicos sequencing
Ozsolak and Milos, 2011
How does it work?
Oshlack et al., 2010
Mapping
Challenges:
•Computational power required
•Exon junctions
•Alleles and SNPs
Two main methods:
Based on hash tables (local
alignment similar to BLAST)
Based on prefix/suffix trie
BFAST
Homer et al., 2009
BWA-SW
Li and Durbin et al., 2010
One of the biggest challenge with mapping is to reduce the “RAM footprint” of the
reference genome. This is accomplished by different ways of indexing the reference.
The other challenge is to map accurately while allowing for variable reads (e.g. SNPs or
error) to be mapped.
Data summarization:
There are 3 main ways of
summarizing your data:
1. Counts per exon
2. Counts per transcript
3. Counts per gene
(Oshlack et al., 2010)
Normalization:
Is RNA-seq data absolute mRNA count?
• Within libraries:
• Length bias
• Sequencing efficiency
• Between libraries:
• Sequencing depth
• Over-representation of highly-expressed transcripts
Differential Expression detection:
Challenges:
• Requires biological replication but perhaps not technical
replication.
• Count data is discrete rather than continuous.
• There is evidence the count data follow a negative
binomial distribution similar to the Poisson distribution.
• Accounting for type I error (False-Discovery)
Bioconductor packages:
edgeR: Developed for SAGE uses a modified Fisher exact test for dispersed data
(means and variance estimated using maximum likelihood)
DESeq: Similar to edgeR but uses a different model to estimate means and variance
(empirical estimation of mean-variance relationship)
BaySeq: Empirical Bayes inference to test of differential expression
Systems Biology:
DAVID and other microarray techniques used for GO enrichment
KEGG pathways
What do you do with data and what does it all mean?
Resources: