March 2021

HTS Applications - Overview

DNA Sequencing

  • Genome Assembly

  • SNPs/SVs/CNVs

  • DNA methylation

  • DNA-protein interactions (ChIPseq)

  • Chromatin Modification (ATAC-seq/ChIPseq)

RNA Sequencing

  • 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

RNAseq Workflow

Experimental Design

Library Preparation

Sequencing

Bioinformatics Analysis

Image adapted from: Wang, Z., et al. (2009), Nature Reviews Genetics, 10, 57–63.

Designing the right experiment

Practical considerations for RNAseq

  • Coverage

  • Read length

  • Library preparation method

Designing the right experiment

Coverage: How many reads do we need?


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.

  • For a general view of differential expression: 5–25 million reads per sample
  • For alternative splicing and lowly expressed genes: 30–60 million reads per sample.
  • In-depth view of the transcriptome/assemble new transcripts: 100–200 million reads
  • Targeted RNA expression requires fewer reads.
  • miRNA-Seq or Small RNA Analysis require even fewer reads.

Designing the right experiment

Read length: long or short reads? Paired or Single end?

The answer depends on the experiment:

  • Gene expression – typically just a short read e.g. 50/75 bp; SE or PE.
  • kmer-based quantification of Gene Expression (Salmon etc.) - benefits from PE.
  • Transcriptome Analysis – longer paired-end reads (such as 2 x 75 bp).
  • Small RNA Analysis – short single read, e.f. SE50 - will need trimming.

Library preparation

- Ribosomal RNA

- Poly-A transcripts

- Other RNAs e.g. tRNA, miRNA etc.

Total RNA extraction

Library preparation

Poly-A Selection

Poly-A transcripts e.g.:

  • mRNAs
  • immature miRNAs
  • snoRNA

Ribominus selection

Poly-A transcripts + Other mRNAs e.g.:

  • tRNAs
  • mature miRNAs
  • piRNAs

Capturing Variance - Replication

Biological Replication

  • Measures the biological variations between individuals

  • Accounts for sampling bias

Technical Replication

  • Measures the variation in response quantification due to imprecision in the technique

  • Accounts for technical noise

Capturing Variance - Replication

Biological Replication

Each replicate is from an indepent biological individual

  • In Vivo:

    • Patients
    • Mice

  • In Vitro:

    • Different cell lines
    • Different passages

Capturing Variance - Replication

Technical Replication

Replicates are from the same individual but processed separately

  • Experimental protocol
  • Measurement platform

Controlling 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.

Controlling batch effects

Controlling batch effects

Controlling 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

Controlling 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

Controlling 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

Controlling 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

Controlling batch effects

Multiplexing

Hidden Confounding variables

  • Think deeply about the samples you are collecting

  • This will be covered in more detail tomorrow

Library preparation

Library preparation

Digitising Oligonucleotides

Differential Gene Expression Analysis Workflow