Add new tutorial for deciphering viral populations using SNV and baculovirus isolates (Variant analysis)

[wennj]

Dear GTN Team,

I am a new contributor and have created a tutorial for analysing virus populations using SNV (and using the example of large dsDNA viruses). The workflow is based on published results and follows a scientific approach. I have followed the GTN instructions as closely as possible (and as I understand them). Please excuse any mistakes in using Github. The tutorial has also been reviewed by a student.

Key changes:

• Added a new step-by-step tutorial (variant-analysis/baculovirus-isolate-variation) for analysing viral populations on the example of baculovirus isolates
• CONTRIBUTORS.yaml was changed.

Checked:

• Images can be re-hosted.

Help:

• One image in the introduction is linked to ICTV.global. Allowed?

TODO:

• Tool bcftools mpileup / bcftools call are using DPR (deprecated but functional). Needs to be changed to AD in the future.
• Awaiting feedback and suggestions for improvement.
• Refine the tutorial based on reviewer comments.

Best,
JTW

[bgruening]

Very cool! Welcome @wennj!

I have added a few batch of comments, if you agree with them you can accept them from inside the github interface.

[bgruening]

Please do not change this file.

[bgruening]

Suggested change

	- Transform the output (VCF file) to a readable table format using tools available at Galaxy only.
	- Interpret the SNV data to analysis the intra-isolate variability of baculovirus isolates or samples.
	- Transform the output (VCF file) to a readable table format
	- Interpret the SNV data to analyse the intra-isolate variability of baculovirus isolates or samples.

Not sure if the transform step is really an objective.

[bgruening]

Suggested change

	- Some SNV positions occur only in certain isolates and are therefore specific for that isolate.
	- Some SNV positions occur only in certain isolates and are therefore specific to that isolate.

[bgruening]

Suggested change

	> It is the first fully sequenced genon of baculoviruses and today hundreds of genomes are fully sequenced
	> It is the first fully sequenced genome of baculoviruses and today hundreds of genomes are fully sequenced

[bgruening]

Suggested change

	The genome size makes it difficult to study genetic variation within a baculovirus population,
	especially since the most commonly used sequencing technique (Illumina sequencing)
	generates only short reads and requires genome assembly. Since genome assembly generates
	a consensus sequence that reflects the majority of a sequenced virus population,
	occuring genetic variation is masked. Tools for haplotype-sensitive assembly are
	available but so far have been establisehd for viruses with a relatively short genome
	but not for baculoviruses or other large dsDNA viruses (REFERENCES). Using Nanopore sequencing,
	it is possible to sequence signficiant fragments of baculovirus genomes to determine major
	haplotypes ({% cite Wennmann2024 %}).
	The genome size makes it difficult to study genetic variation within a baculovirus population,
	especially since the most commonly used sequencing technique (Illumina sequencing)
	generates only short reads and requires genome assembly. Since genome assembly generates
	a consensus sequence that reflects the majority of a sequenced virus population,
	occurring genetic variation is masked. Tools for haplotype-sensitive assembly are
	available but so far have been established for viruses with a relatively short genome
	but not for baculoviruses or other large dsDNA viruses (REFERENCES). Using Nanopore sequencing,
	it is possible to sequence significant fragments of baculovirus genomes to determine major
	haplotypes ({% cite Wennmann2024 %}).

[bgruening]

Suggested change

	One thing that stands out are the SAMPLE names, which were taken automatically from the NCBI SRA datasets. Since it is difficult to remember which virus isolate is behind which SRA number, we can replace the accession numbers with proper names. This makes the table even easier to read and later we can use the information directly to display the SNV positions.
	One thing that stands out is the SAMPLE names, which were taken automatically from the NCBI SRA datasets. Since it is difficult to remember which virus isolate is behind which SRA number, we can replace the accession numbers with proper names. This makes the table even easier to read and later we can use the information directly to display the SNV positions.

[bgruening]

Suggested change

	The final table should be much easier to read and contain all the information we need to perform an analysis of the intra-isolate specific variability. Here is an short overview about the final table (selected columns) and its first three SNV positions:
	The final table should be much easier to read and contain all the information we need to perform an analysis of the intra-isolate specific variability. Here is a short overview of the final table (selected columns) and its first three SNV positions:

[bgruening]

Suggested change

Most of the SNV positions specific for CpGV-E2 have a frequency around 0.5 (50%) (Fig. 5, SNV specificity: CpGV-E2). Looking at the positions specific for CpGV-S, we also see a SNV cloud at around 0.5 (50 %) (Fig. 5, SNV specificity: CpGV-S). From these two observations, we can conclude that CpGV-E2 and CpGV-S occur at a ratio of 50% each. As a control, we look at the markers (SNV positions) that are specific for both CpGV-E2 and CpGV-S (Fig. 5, SNV specificity: CpGV-E2 + CpGV-S). Here we see that most SNV positions have a relative frequency of ~1 (100%). This also makes sense, because if CpGV-V15 is a mixture of CpGV-E2 and CpGV-S, then the markers that indicate both isolates should be at 100%, which is the case. Based on the SNV analysis, it can be concluded that CpGV-V15 is mainly a mixture of CpGV-S and CpGV-E2.

Most of the SNV positions specific for CpGV-E2 have a frequency of around 0.5 (50%) (Fig. 5, SNV specificity: CpGV-E2). Looking at the positions specific to CpGV-S, we also see a SNV cloud at around 0.5 (50 %) (Fig. 5, SNV specificity: CpGV-S). From these two observations, we can conclude that CpGV-E2 and CpGV-S occur at a ratio of 50% each. As a control, we look at the markers (SNV positions) that are specific for both CpGV-E2 and CpGV-S (Fig. 5, SNV specificity: CpGV-E2 + CpGV-S). Here we see that most SNV positions have a relative frequency of ~1 (100%). This also makes sense, because if CpGV-V15 is a mixture of CpGV-E2 and CpGV-S, then the markers that indicate both isolates should be at 100%, which is the case. Based on the SNV analysis, it can be concluded that CpGV-V15 is mainly a mixture of CpGV-S and CpGV-E2.

[bgruening]

You can extend this for example by:

contributions:
authorship:
- wennj
editing:
- your-student

[bgruening]

Suggested change

	subtopic: ''
	subtopic: 'one-health'

Maybe?