aviralg / ont_tutorial_basicqc

A bioinformatics tutorial demonstrating a best-practice workflow to review a flowcell's sequence_summary.txt

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1. Introduction:

Overview:

The Summary Statistics and QC tutorial is intended as a functional guide to help assess the quality characteristics of a single Nanopore sequence run. This tutorial aims to enable an objective assessment of the performance of a Nanopore flowcell run and to assess the sequence characteristics to benchmark quality.

Features:

Sufficient information is provided in the tutorial such that the workflow can be tested, validated, and replicated. The tutorial is provided with an example dataset from a barcoded sequence library. The tutorial is intended to address important questions;

• how many reads (and how many gigabases) were sequenced?
• what fraction of my sequence collection is good quality?
• how are longer sequence reads represented in my sample?
• how uniform is the representation of different barcodes?

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2. Getting Started:

Input and Output:

This tutorial uses the R markdown contained within this Github repository, a sequence_summary.txt file from the Guppy base-calling software, and optionally a barcoding_summary.txt file from Guppy barcoding as input. Example summary files are included within the repository. The result of the tutorial will be a tutorial document in html format. This workflow can also process the sequence_summary.txt file prepared by the albacore base calling software.

Dependencies:

This tutorial requires a computer running Linux (Centos7, Ubuntu 18_10, Fedora 29) - 8Gb of memory would be recommended. The tutorial has been tested on minimal server installs of these operating systems.

Other dependencies include

• Conda is required by this tutorial and orchestrates and manages the installation of other required software
• R is a statistical analysis software and is used for the analysis and reporting of the sequence summary data
• Rstudio is a graphical user interface to R and provides much of the required reporting framework
• git packages for downloading the tutorial from Github repository.
• git-lfs is required to download the sequence and metadata files provided with the tutorial.

Installation:

1. Most software dependecies are managed though conda, install as described at
   https://conda.io/docs/install/quick.html.

    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
    bash Miniconda3-latest-Linux-x86_64.sh
    bash

2. Download Nanopore QC tutorial & example files into a folder named QCTutorial. This tutorial requires the git-lfs large file support capabilities; this should be installed first through conda

    conda install -c conda-forge git-lfs
    git lfs install
    git clone https://github.com/nanoporetech/ont_tutorial_basicqc.git QCTutorial

3. Change working directory into the new QCTutorial folder

    cd QCTutorial

4. Install conda software dependencies with

    conda env create --name BasicQC --file environment.yaml

5. Initialise conda environment with

    source activate BasicQC

Compilation From Source

This tutorial does not contain software that requires compilation.

Usage:

In your Conda environment, and in the tutorial working directory,

1. optional edit the provided config.yaml file to match your own study design
2. Render the tutorial report using the command

    R --slave -e 'rmarkdown::render("Nanopore_SumStatQC_Tutorial.Rmd", "html_document")'

The provided Rmarkdown tutorial script can also be opened directly in Rstudio

rstudio Nanopore_SumStatQC_Tutorial.Rmd

The report can be prepared by "knit" from the GUI as shown in the figure

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3. Results

This tutorial workflow will produce a rich description of your sequence characteristics as observed from the starting sequence_summary.txt file. Please visit the tutorial page at https://community.nanoporetech.com/knowledge/bioinformatics for more information

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4. Help:

Licence and Copyright:

© 2019 Oxford Nanopore Technologies Ltd.

Bioinformatics-Tutorials are distributed by Oxford Nanopore Technologies under the terms of the MPL-2.0 license.

FAQs:

Abbreviations:

• knit is the command to render an Rmarkdown file. The knitr package is used to embed code, the results of R analyses and their figures within the typeset text from the document.

• L50 the number of sequences (or contigs etc) that are longer than, or equal to, the N50 length and therefore include half the bases of the assembly

• N50 length such that sequences (or contigs etc) of this length or longer include half the bases of the sequence collection

• Rmarkdown is an extension to markdown. Functional R code can be embedded in a plain-text document and subsequently rendered to other formats including the PDF format of this report.

• QV the quality value - -log10(p) that any given base is incorrect. QV may be either at the individual base level, or may be averaged across whole sequences

• sequencing_summary.txt a summary file describing sequence characteristics following base calling with the Guppy / Albacore software.

References and Supporting Information:

• https://community.nanoporetech.com/knowledge/bioinformatics
• https://www.r-project.org/
• https://snakemake.readthedocs.io/en/stable/
• https://bioconda.github.io/