Nanopore cDNA tutorial for differential gene expression analysis. The aim of this tutorial is to perform differential gene expression analysis based on replicated cDNA data. This workflow is for fastq sequence datasets where a reference genome sequence, and its gene annotation, is available.
Sufficient information is provided in the tutorial, and example data, such that the workflow can be replicated with a study design to address gene expression questions such as
Methods utilised within this tutorial include
condafor management of bioinformatics software
snakemakefor managing the bioinformatics workflow
minimap2for mapping sequence reads to reference genome
samtoolsfor SAM/BAM handling and mapping statistics
DESeq2for differential expression analysis
Computational requirements for this tutorial include
There are four goals for this tutorial:
This tutorial aims to identify, where possible, a set of differentially expressed gene transcripts from the long read sequence data. This tutorial does not aim to provide an exhaustive analysis or annotation of the differentially expressed genes. The tutorial specifies some available reference data so that a workflow can be reproduced with a known outcome.
This tutorial requires that we are working at a computer workstation
running a Linux operating system. An update for MacOS will be released.
The workflow described has been tested using
Centos 7, and
Ubuntu 18_04. This tutorial is written in the
Rmarkdown file format. This merges both markdown (and
easy-to-write plain text format as used in many Wiki systems). See
Allaire et al. (2018) for more information about
rmarkdown. The document template contains chunks of embedded
R code (and some linux bash commands). The included workflow
makes extensive use of the
conda package management and the
snakemake workflow software. These software packages and the
Rmarkdown provide the source for a rich,
reproducible, and extensible tutorial document.
The workflow contained within this Tutorial performs a real
bioinformatics analysis and uses the whole human genome as an example.
There are some considerations in terms of memory and processor
requirement. Indexing the whole human genome for sequence read mapping
minimap2 for example will use at least
18Gb of memory.
The minimal recommended hardware setup for this tutorial is therefore an
8 threaded computer with at least 16Gb of RAM and 15Gb of storage space.
If you have modest amounts of RAM (<24Gb) then please consider
increasing the amount of swap
available for indexing reference genomes and holding genome index in
memory whilst mapping.
There are a few dependencies that need to be installed at the system
level - please refer to the section below on system requirements. The
conda package management software will coordinate the installation
of key software and dependencies in user space - this is dependent on a
robust internet connection.
As a best practice this tutorial will separate primary DNA sequence data
(the base-called fastq files) from the
Rmarkdown source, and the
genome reference data. The analysis results and figures will again be
placed in a separate working directory. The required layout for the
primary data is shown in figure . This minimal structure will be
prepared over the next few sections of this tutorial - it is recommended
to follow this tutorial with the provided working data before attempting
to run the tutorial with your own data. Following the tutorial,
preparing the computer, downloading data, and running the analysis
should take approximately 90 minutes.
This tutorial requires a small amount of interaction with the computer
console. The bulk of the analysis will be managed through the GUI
provided by the
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