April 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?
Controlling for batch effects
Library preparation method: Poly-A, Ribominus, other?
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:
Measures the biological variations between individuals
Accounts for sampling bias
Measures the variation in response quantification due to imprecision in the technique
Accounts for technical noise
Each replicate is from an indepent biological individual
In Vivo:
In Vitro:
Replicates are from the same individual but processed separately
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
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
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.
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