VARGRAM submission

[cjpalpallatoc]

Submitting Author: (@cjpalpallatoc)
All current maintainers: (@cjpalpallatoc)
Package Name: VARGRAM
One-Line Description of Package: A Python visualization tool for genomic surveillance
Repository Link: https://github.com/pgcbioinfo/vargram
Version submitted: 0.3.0
EiC: @coatless
Editor: TBD
Reviewer 1: TBD
Reviewer 2: TBD
Archive: TBD
JOSS DOI: TBD
Version accepted: TBD
Date accepted (month/day/year): TBD

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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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  • Data retrieval
  • Data extraction
  • Data processing/munging
  • Data deposition
  • Data validation and testing
  • Data visualization¹
  • Workflow automation
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  • Scientific software wrappers
  • Database interoperability

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• Geospatial
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  • Who is the target audience and what are 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 the same thing? If so, how does yours differ?
    The closest we're aware of is snipit. The main difference is that VARGRAM provides a visual comparison of mutation profiles between groups or within a population of samples. There are also additional features such as grouping mutations per gene, adding multiple sets of reference mutations, and other customizations. We also plan to expand the package to provide other types of visualization relevant to genomic surveillance.
    We're also aware of 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.

  • If you made a pre-submission enquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted:
    VARGRAM #225

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[coatless]

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Editor comments

Hi, thanks for submittiing the package.

Regarding the examples and quick start, please include a quick command, e.g. setup_test_data(), that can be used to obtain the required data from the repository so that the package's example can be run.

The following addresses this first step discussed under Example.

• VARGRAM EIC Notebook on Colab
• VARGRAM EIC Notebook on GitHub

Sample implementation for `setup_test_data()`

import requests
import zipfile
import os
import tempfile
import shutil
from pathlib import Path
from typing import Optional, Union, Tuple

def setup_test_data(
    repo: str = "pgcbioinfo/vargram",
    source_path: str = "tests/test_data",
    target_dir: Optional[Union[str, Path]] = None
) -> Path:
    """
    Download the latest release of a GitHub repository and extract a specific directory's
    contents to a target directory.
    
    This function is useful for setting up test data for a package. It fetches the latest
    release from GitHub, extracts the specified directory's contents, and places them
    directly in the target directory.
    
    Parameters
    ----------
    repo : str, default "pgcbioinfo/vargram"
        The GitHub repository in format "owner/repo".
    source_path : str, default "tests/test_data"
        Path within the repository to extract. This directory's contents will be
        placed in the target directory.
    target_dir : str or Path, optional
        Directory where the contents should be extracted. If None, the current
        working directory is used.
        
    Returns
    -------
    Path
        A Path object pointing to the target directory where files were extracted.
        
    Raises
    ------
    requests.HTTPError
        If the API request to GitHub fails.
    FileNotFoundError
        If the specified source_path doesn't exist in the repository.
    ValueError
        If the repository format is invalid.
        
    Examples
    --------
    >>> # Extract to current directory
    >>> data_path = setup_test_data()
    >>> 
    >>> # Extract to a specific directory
    >>> data_path = setup_test_data(target_dir="./my_test_data")
    >>> 
    >>> # Extract a different path from a different repo
    >>> data_path = setup_test_data(
    ...     repo="username/repo",
    ...     source_path="data/examples",
    ...     target_dir="./examples"
    ... )
    """
    # Validate repo format
    if not repo or "/" not in repo:
        raise ValueError(f"Invalid repository format: {repo}. Expected format: 'owner/repo'")
    
    # Convert target_dir to Path if specified, otherwise use current directory
    if target_dir is None:
        target_dir = Path.cwd()
    else:
        target_dir = Path(target_dir)
        target_dir.mkdir(parents=True, exist_ok=True)
    
    # Step 1: Get the latest release information
    print(f"Getting latest release for {repo}...")
    response = requests.get(f"https://api.github.com/repos/{repo}/releases/latest")
    response.raise_for_status()  # Raise an exception for HTTP errors
    release_data = response.json()
    zipball_url = release_data["zipball_url"]
    release_tag = release_data["tag_name"]
    print(f"Found release: {release_tag}")
    
    # Step 2: Download the release zip file
    print(f"Downloading release from {zipball_url}...")
    zip_response = requests.get(zipball_url, stream=True)
    zip_response.raise_for_status()
    
    # Create temporary files
    with tempfile.NamedTemporaryFile(delete=False, suffix='.zip') as temp_zip:
        # Write the downloaded zip to the temporary file
        for chunk in zip_response.iter_content(chunk_size=8192):
            temp_zip.write(chunk)
        temp_zip_path = temp_zip.name
    
    temp_dir = tempfile.mkdtemp()
    
    try:
        # Step 3: Extract the archive
        print("Extracting the release...")
        with zipfile.ZipFile(temp_zip_path, 'r') as zip_ref:
            zip_ref.extractall(temp_dir)
        
        # Step 4: Find the extracted directory
        extracted_dir = next(Path(temp_dir).iterdir())  # Get the first (and only) directory
        
        # Step 5: Check if the source path directory exists
        source_data_path = extracted_dir / source_path
        if not source_data_path.exists():
            raise FileNotFoundError(f"{source_path} directory not found in the release")
        
        # Step 6: Copy each item from source directory directly to the target directory
        print(f"Copying contents of {source_path} to {target_dir}...")
        for item in source_data_path.iterdir():
            dest_path = target_dir / item.name
            
            # If it's a directory, copy the entire directory tree
            if item.is_dir():
                if dest_path.exists():
                    shutil.rmtree(dest_path)
                shutil.copytree(item, dest_path)
            # If it's a file, just copy the file
            else:
                if dest_path.exists():
                    os.remove(dest_path)
                shutil.copy2(item, dest_path)
                
        print(f"Successfully extracted {source_path} contents to {target_dir}")
        
        return target_dir
    
    finally:
        # Step 7: Clean up temporary files
        os.unlink(temp_zip_path)
        shutil.rmtree(temp_dir)

setup_test_data()

An alternative approach would be to create a tar of all the data files and, then, provide a series of shell commands that download and extract the data into the working directory.

With this in mind, there are two different data locations with different data files. Some data is used, some is not, and some is missing:

1. test/test_data
   • analysis/
   • keys/
   • sequences/
   • summary/
   • README.md
   • sc2.gff
   • sc2_wuhan_2019.fasta
2. docs/assets/data
   • BA1_summary_data.csv
   • omicron_summary_data.csv
   • omicron_summary_data_with_keys.csv

For the later example with mutation profiles, there lacks a discussion about where nextclade_analysis.csv or covid_samples/* data can be obtained from once Nextclade CLI is installed.

I'll kick start the review process on the package as the reviews can use the above script in the interim to explore the initial example.

[cjpalpallatoc]

Thanks for the initial comments @coatless . I just have an important deadline to meet this coming week, but I'll work on your suggestions as soon as I can. Looking forward to the review.

[coatless]

@cjpalpallatoc I'm happy to share we have an editor for the review!

@ctb will be the editor for this package review. I'll let him introduce himself.

Note

We're moving the review along despite the data note given above as the sample data for examples is reachable via:

https://github.com/coatless/pyopensci-eic/blob/main/i243-vargram.ipynb

This will likely get picked up in later individual reviews if the issue still persists.