VARGRAM

[cjpalpallatoc]

Submitting Author: C. J. Palpal-latoc (@cjpalpallatoc)
Package Name: VARGRAM
One-Line Description of Package: A Python visualization tool for genomic surveillance
Repository Link (if existing): https://github.com/pgcbioinfo/vargram
EiC: @coatless

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Description

• Include a brief paragraph describing what your package does:

During a viral outbreak, the diversity of sampled sequences often needs to be quickly determined to understand the evolution of a pathogen. VARGRAM (Visual ARrays for GRaphical Analysis of Mutations) empowers researchers to quickly generate a mutation profile to compare batches of sequences against each other and against a reference set of mutations. A publication-ready profile can be generated in a couple lines of code by providing sequence files (FASTA, GFF3) or tabular data (CSV, TSV, Pandas DataFrame). When sequence files are provided, VARGRAM leverages Nextclade CLI to perform mutation calling. We have user-friendly installation instructions and tutorials on our documentation website.

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Scope

• Please indicate which category or categories this package falls under:

  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization
  • Workflow automation
  • Citation management and bibliometrics
  • Scientific software wrappers
  • Database interoperability

Domain Specific

• Geospatial
• Education

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• Explain how and why the package falls under these categories (briefly, 1-2 sentences). For community partnerships, check also their specific guidelines as documented in the links above. Please note any areas you are unsure of:
  VARGRAM falls under data processing as the user-provided input cannot be plotted immediately. When sequence files are provided, an external tool (Nextclade) will also be called and its output needs to be transformed. It falls under data visualization as the main output is a figure which provides insights not accessible by reading the input alone.

• Who is the target audience and what are the scientific applications of this package?
  We hope that VARGRAM would be useful for researchers, analysts, and students in the field of molecular epidemiology/genomic surveillance. During the pandemic, we've used an early mutation profile script to characterize emergent variants and potential recombinants.

• Are there other Python packages that accomplish similar things? If so, how does yours differ?
  There's no Python package that we are aware of that is similar to VARGRAM which is also how we've come to create the package in the first place. There are packages like Marsilea that can in principle be used to make a profile, but these are more general in scope and would require more work for the user than if they used VARGRAM. Outside Python, we've seen researchers create mutation profiles with custom scripts (in R) and there are also web tools available like Nextclade. VARGRAM differs by making the process substantially convenient in terms of generation and customization of the figure.

• Any other questions or issues we should be aware of:
  We envision VARGRAM to be a visualization library for common use cases in genomic surveillance. In the coming months, we hope to gradually add more features. But rather than wait for all those features to be include, we believe that we can benefit more from the pyOpenSci community if we submit now while the package is young (but already useful).

P.S. Have feedback/comments about our review process? Leave a comment here

[coatless]

Hi C. J. Palpal-latoc (@cjpalpallatoc) thank you for making this presubmission inquiry. Sorry we haven't gotten back to you sooner as we were on break and undergoing an EiC transition.

I think this is a perfect package to be added underneath the Data Visualization category and Scientific Software wrappers (due to Nextclade). There is a clear scientific specialization

One question and two quick comments:

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Was the underlying methodology for creating the gene graph reviewed? I see two linked papers to biorxiv (Fig 5) and medrxiv (Fig 2). I'm asking because we cannot validate the underlying methodology; but, we can validate the software standards per:

https://www.pyopensci.org/software-peer-review/about/package-scope.html#scope-for-packages-that-support-analytics-statistics-and-modeling

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Before submitting for the official review, please consider adding a blurb that discusses what is being viewed in the Example graphs.

https://pgcbioinfo.github.io/vargram/basics/#example

Please consider showing a small sample of the GFF file used for annotations in the more complex example.

[cjpalpallatoc]

Hi @coatless, no worries about the delay and thank you for your comments!

Was the underlying methodology for creating the gene graph reviewed? I see two linked papers to biorxiv (Fig 5) and medrxiv (Fig 2). I'm asking because we cannot validate the underlying methodology; but, we can validate the software standards per:

https://www.pyopensci.org/software-peer-review/about/package-scope.html#scope-for-packages-that-support-analytics-statistics-and-modeling

I'm assuming you're referring to the detection and identification of mutations for visualization? To clarify, mutation calling usually follows a standard wet lab + dry lab tandem workflow with peer-reviewed bioinformatics tools (e.g. iVar, Nextclade). For example, this workflow was followed in the preprints I cited.

VARGRAM only picks up the output of this process for visualization (although it helps in the last step by wrapping around Nextclade). So it can hopefully be reviewed without worry about how the mutations were called.

Before submitting for the official review, please consider adding a blurb that discusses what is being viewed in the Example graphs.

https://pgcbioinfo.github.io/vargram/basics/#example

I've now added short descriptions below each figure in https://pgcbioinfo.github.io/vargram/basics/#example.

Please consider showing a small sample of the GFF file used for annotations in the more complex example.

I've added a preview of the GFF file in the ordered example and also in https://pgcbioinfo.github.io/vargram/mutation_profile/#providing-sequence-files.