Last updated: 2023-02-28

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Overview of the tutorial

Last time, you started sequencing of cattle genome using Nanopore MinION. For the following three sessions we will learn:

How to use Orion and conduct genome analysis
Quality check, Read filtering, mapping to the reference genome and variant calling
How to interpret summary statistics of Nanopore sequence data
How to interpret variant data

In this tutorial, we will investigate a small subset of bull genome sequence.

You will have an access to the whole datasets later for your reports as soon as the computation is done.

Also, Matthew and I prepared some questions in each section.

Please discuss and try the quizzes to deepen your understandig on Nanopore data.

Review the previous practice

DAY1

Overview.

Quality check -> Trimming of low quality reads -> Quality check

Connect to Orion and the preparation

Go to https://orion.nmbu.no/ at NMBU or with VPN.

In the Terminal/Command prompt, go to your directory. Review: the concept of current directry

cd your_directory

Let’s make a directory for analysis and enter in it.

mkdir bull_analysis # make directory "bull_analysis"
cd bull_analysis # set the current directory "bull_analysis"

Now, you will inspect the fastq file, which contains Nanopore read information.

Check the read quality by Nanoplot

Browse the inside of the read (fastq) file

Review: look into a file content in a command line

zcat /net/fs-2/scale/OrionStore/Courses/BIO326/EUK/bull_analysis/demo_data/bull_demodata_fastq.gz | more

How a fastq file looks.

Each entry in a FASTQ files consists of 4 lines:

A sequence identifier with information about the sequencing run. (run time, run ID, cflow cell id … )

2.The sequence (the base calls; A, C, T, G and N).

A separator, which is simply a plus (+) sign.
The base call quality scores. These are Phred +33 encoded, using ASCII characters to represent the numerical quality scores.” quality score sheet

by Illumina Knowledge

Get basic stats of the fastq file

“zcat”-> look inside

“wc” -> word count

“-l” -> line

zcat /net/fs-2/scale/OrionStore/Courses/BIO326/EUK/bull_analysis/demo_data/bull_demodata_fastq.gz | wc -l

Discussion Point

Now you got the number of lines in the fastq file.

How many sequence reads are in the fastq file?

Need Help?

We see that there are 96000 lines in the fastq file.

As we learned that “each entry in a FASTQ files consists of 4 lines”, one read is corresponding to four lines. So in this file we have 96000/4 = 24000 reads.

Run Nanoplot

The original fastq files may contain low quality reads. In this step, we will use “Nanoplot” to see the quality and lentgh of each read.

“Singularity” is a toolset on Orion to execute software. A variety of different bioinformatics tools are available in Singularity.

Make a slurm script like below and run it.

Review: make a slurm script and run it by sbatch



#!/bin/bash
#SBATCH --job-name=Nanoplot  # sensible name for the job
#SBATCH --mail-user=yourname@nmbu.no # Email me when job is done.
#SBATCH --mem=12G 
#SBATCH --ntasks=1   
#SBATCH --cpus-per-task=8
#SBATCH --mail-type=END


singularity exec /cvmfs/singularity.galaxyproject.org/all/nanoplot:1.41.0--pyhdfd78af_0 NanoPlot -t 8  --fastq /net/fs-2/scale/OrionStore/Courses/BIO326/EUK/bull_analysis/demo_data/bull_demodata_fastq.gz --plots dot   --no_supplementary --no_static --N50 -p before

Nanoplot will generate the result files, named “before”xxx. Lets look into them…


# taking too long?
qlogin 

cp /net/fs-2/scale/OrionStore/Courses/BIO326/EUK/bull_analysis/demo_data/beforeNanoPlot-report.html beforeNanoPlot-report.html

Open “beforeNanoPlot-report.html” on your local computer

Filtering by Nanofilt


#!/bin/bash
#SBATCH --job-name=Nanoplot  # sensible name for the job
#SBATCH --mail-user=yourname@nmbu.no # Email me when job is done.
#SBATCH --mem=12G 
#SBATCH --ntasks=1   
#SBATCH --mail-type=END

gunzip -c /net/fs-2/scale/OrionStore/Courses/BIO326/EUK/bull_analysis/demo_data/bull_demodata_fastq.gz | singularity exec  /cvmfs/singularity.galaxyproject.org/all/nanofilt:2.8.0--py_0 NanoFilt -q 12 -l 500 | gzip > cleaned.bull.fastq.gz

-l, Filter on a minimum read length

-q, Filter on a minimum average read quality score

In this case, we are removing reads lower than quality score 12 and shorter than 500 bases.

Compare the before and after cleaning sequences

Run Nanoplot again on the cleaned sequences.

Need help?


#!/bin/bash
#SBATCH --job-name=Nanoplot  # sensible name for the job
#SBATCH --mail-user=yourname@nmbu.no # Email me when job is done.
#SBATCH --mem=12G 
#SBATCH --ntasks=1   
#SBATCH --cpus-per-task=8
#SBATCH --mail-type=END

singularity exec /cvmfs/singularity.galaxyproject.org/all/nanoplot:1.41.0--pyhdfd78af_0 NanoPlot -t 8  --fastq cleaned.bull.fastq.gz  --N50  --no_supplementary --no_static  --plots dot   -p after

Open “afterNanoPlot-report.html” on your local computer.


# taking too long?
qlogin 

cp /net/fs-2/scale/OrionStore/Courses/BIO326/EUK/bull_analysis/demo_data/afterNanoPlot-report.html afterNanoPlot-report.html

Discussion Point

Did you see the difference of read and quality distribution between before and after the filtering?

The end of Day1. Well done!

DAY2

Overview

Map the reads to the reference genome -> Detect variants (difference from the reference genome)

Find out where “minimap2” is in Singularity

minimap2
find /cvmfs/singularity.galaxyproject.org/all/  -name minimap2*  


/cvmfs/singularity.galaxyproject.org/all/minimap2:2.24--h7132678_1

run Minimap and map the reads to the reference genome



#!/bin/bash
#SBATCH --job-name=Nanoplot  # sensible name for the job
#SBATCH --mail-user=yourname@nmbu.no # Email me when job is done.
#SBATCH --mem=12G 
#SBATCH --ntasks=1   
#SBATCH --cpus-per-task=8
#SBATCH --mail-type=END

singularity exec /cvmfs/singularity.galaxyproject.org/all/minimap2:2.24--h7132678_1 minimap2 -t 8 -a Bos_taurus.fa.gz cleaned.bull.fastq.gz > bull.sam

Browse the mapped file

cat bull.sam | head bull.sam


# taking too long?
qlogin 

cp /net/fs-2/scale/OrionStore/Courses/BIO326/EUK/bull_analysis/demo_data/bull.sam bull.sam

File format conversion


#!/bin/bash
#SBATCH --job-name=Nanoplot  # sensible name for the job
#SBATCH --mail-user=yourname@nmbu.no # Email me when job is done.
#SBATCH --mem=12G 
#SBATCH --ntasks=1   
#SBATCH --cpus-per-task=8
#SBATCH --mail-type=END


# convert the sam file to bam format
singularity exec  /cvmfs/singularity.galaxyproject.org/all/samtools:1.16.1--h6899075_1 samtools view -S -b bull.sam > bull0.bam

## sort the bam file
singularity exec  /cvmfs/singularity.galaxyproject.org/all/samtools:1.16.1--h6899075_1 samtools sort bull0.bam -o bull.bam

# index the bam file
singularity exec  /cvmfs/singularity.galaxyproject.org/all/samtools:1.16.1--h6899075_1 samtools index -M  bull.bam

Error correction with Pilon/Medaka

(Skip this time as it takes time… You will learn the error correction in the prokaryotic part. )

Variant Calling using Sniffles

#!/bin/bash
#SBATCH --job-name=Nanoplot  # sensible name for the job
#SBATCH --mail-user=yourname@nmbu.no # Email me when job is done.
#SBATCH --mem=12G 
#SBATCH --ntasks=1   
#SBATCH --mail-type=END

singularity exec /cvmfs/singularity.galaxyproject.org/all/sniffles:2.0.7--pyhdfd78af_0 sniffles --input  bull.bam --vcf bull.vcf

Now you got the variant file!

resulting vcf file

Inspect variants

more -s 2255 bull.vcf
grep -v '^##' bull.vcf | head

DAY3

Now you have variants! Lets see what genes are affected by the variants.

sessionInfo()

R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.10      compiler_4.2.2   pillar_1.8.1     bslib_0.4.2     
 [5] later_1.3.0      git2r_0.31.0     jquerylib_0.1.4  tools_4.2.2     
 [9] getPass_0.2-2    digest_0.6.31    jsonlite_1.8.4   evaluate_0.20   
[13] lifecycle_1.0.3  tibble_3.1.8     pkgconfig_2.0.3  rlang_1.0.6     
[17] cli_3.6.0        rstudioapi_0.14  yaml_2.3.7       xfun_0.37       
[21] fastmap_1.1.0    httr_1.4.4       stringr_1.5.0    knitr_1.42      
[25] fs_1.6.1         vctrs_0.5.2      sass_0.4.5       rprojroot_2.0.3 
[29] glue_1.6.2       R6_2.5.1         processx_3.8.0   fansi_1.0.4     
[33] rmarkdown_2.20   callr_3.7.3      magrittr_2.0.3   whisker_0.4.1   
[37] ps_1.7.2         promises_1.2.0.1 htmltools_0.5.4  httpuv_1.6.8    
[41] utf8_1.2.3       stringi_1.7.12   cachem_1.0.6

Bio326_2023

Marie Saitou

2023-01-30

Overview of the tutorial

DAY1

Connect to Orion and the preparation

Check the read quality by Nanoplot

Browse the inside of the read (fastq) file

Get basic stats of the fastq file

Discussion Point

Run Nanoplot

Filtering by Nanofilt

Compare the before and after cleaning sequences

Discussion Point

The end of Day1. Well done!

DAY2

Find out where “minimap2” is in Singularity

run Minimap and map the reads to the reference genome

Browse the mapped file

File format conversion

Variant Calling using Sniffles

Inspect variants

DAY3