July 2021
DNA Sequencing
Genome Assembly
SNPs/SVs/CNVs
DNA methylation
DNA-protein interactions (ChIPseq)
Chromatin Modification (ATAC-seq/ChIPseq)
Transcriptome Assembly
Differential Gene Expression
Fusion Genes
Splice variants
Single-Cell
RNA/DNA
Low-level RNA/DNA detection
Cell-type classification
Dissection of heterogenous cell populations
Experimental Design
Library Preparation
Sequencing
Bioinformatics Analysis
Image adapted from: Wang, Z., et al. (2009), Nature Reviews Genetics, 10, 57–63.
Have clear objectives
Have sufficient power
Be amenable to statisical analysis
Be reproducible
More on experimental design later
Coverage: how many reads?
Read length & structure: Long or short reads? Paired or Single end?
Library preparation method: Poly-A, Ribominus, other?
Controlling for batch effects
The coverage is defined as:
\(\frac{Read\,Length\;\times\;Number\,of\,Reads}{Length\,of\,Target\,Sequence}\)
The amount of sequencing needed for a given sample is determined by the goals of the experiment and the nature of the RNA sample.
The answer depends on the experiment:
Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
Batch effects are problematic if they are confounded with the experimental variable.
Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
Batch effects are problematic if they are confounded with the experimental variable.
Batch effects that are randomly distributed across experimental variables can be controlled for.
Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
Batch effects are problematic if they are confounded with the experimental variable.
Batch effects that are randomly distributed across experimental variables can be controlled for.
Randomise all technical steps in data generation in order to avoid batch effects.
Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
Batch effects are problematic if they are confounded with the experimental variable.
Batch effects that are randomly distributed across experimental variables can be controlled for.
Randomise all technical steps in data generation in order to avoid batch effects.
Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
Batch effects are problematic if they are confounded with the experimental variable.
Batch effects that are randomly distributed across experimental variables can be controlled for.
Randomise all technical steps in data generation in order to avoid batch effects.
Batch effects are sub-groups of measurements that have qualitatively different behavior across conditions and are unrelated to the biological or scientific variables in a study.
Batch effects are problematic if they are confounded with the experimental variable.
Batch effects that are randomly distributed across experimental variables can be controlled for.
Randomise all technical steps in data generation in order to avoid batch effects
Record everything: Age, sex, litter, cell passage ..
Experimental Design
Library Preparation
Sequencing
Bioinformatics Analysis
Image adapted from: Wang, Z., et al. (2009), Nature Reviews Genetics, 10, 57–63.
- Ribosomal RNA
- Poly-A transcripts
- Other RNAs e.g. tRNA, miRNA etc.
Total RNA extraction
Poly-A Selection
Poly-A transcripts e.g.:
Ribominus selection
Poly-A transcripts + Other mRNAs e.g.:
Experimental Design
Library Preparation
Sequencing
Bioinformatics Analysis
Image adapted from: Wang, Z., et al. (2009), Nature Reviews Genetics, 10, 57–63.
Experimental Design
Library Preparation
Sequencing
Bioinformatics Analysis
Image adapted from: Wang, Z., et al. (2009), Nature Reviews Genetics, 10, 57–63.
Quantification estimates the relative read counts for each gene
Does this accurately represent the original population of RNAs?
The relationship between counts and RNA expression is not the same for all genes across all samples
Library Size
Differing sequencing depth
Gene properties
Length, GC content, sequence
Library composition
Highly expressed genes overrepresented at the cost of lowly expressed genes
“Composition Bias”
Comparing feature abundance under different conditions
Assumes linearity of signal
When feature=gene, well-established pre- and post-analysis strategies exist
Mortazavi, A. et al (2008) Nature Methods
Simple difference in means
Replication introduces variation
Normal (Gaussian) Distribution - t-test
Two parameters - \(mean\) and \(sd\) (\(sd^2 = variance\))
Suitable for microarray data but not for RNAseq data
Count data - Poisson distribution
One parameter - \(mean\) \((\lambda)\)
\(variance\) = \(mean\)
Use the Negative Binomial distribution
In the NB distribution \(mean\) not equal to \(variance\)
Two paramenters - \(mean\) and \(dispersion\)
\(dispersion\) describes how \(variance\) changes with \(mean\)
Anders, S. & Huber, W. (2010) Genome Biology
Calculate coefficients describing change in gene expression
Linear Model \(\rightarrow\) Generalized Linear Model
Calculate coefficients describing change in gene expression
Linear Model \(\rightarrow\) General Linear Model
Calculate coefficients describing change in gene expression
Linear Model \(\rightarrow\) General Linear Model
http://software.broadinstitute.org/gsea
