aviary-genome

v0.13.0 suspicious
4.0
Medium Risk

aviary - metagenomics pipeline using long and short reads

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some unusual behavior with shell executions that need further investigation. While there are no immediate signs of malicious activity, the non-HTTPS link and limited author history add to the suspicion.

  • Unusual shell executions
  • Non-HTTPS link in metadata
Per-check LLM notes
  • Network: No network calls detected, which is typical for benign packages.
  • Shell: Shell executions appear to be related to package installation and configuration, but the use of 'pixi' commands might warrant further investigation into their legitimacy within the context of 'aviary-genome'.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, suggesting no immediate risk of secret or sensitive information being stolen.
  • Metadata: The author has only one package, and there's a non-HTTPS link, but no other suspicious activities are noted.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 9 test file(s) found

  • 9 test file(s) detected (e.g. test_annotate.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (9387 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 57 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in rhysnewell/aviary
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • as manifest_path: subprocess.run(f"pixi config set --local run-post-link-scripts insecure --m
  • gs.build_gpu: subprocess.run(f"pixi install -a --frozen --manifest-path {manifest_path}".
  • ith("-gpu")]) subprocess.run(f"pixi install --frozen --manifest-path {manifest_path} {env
  • environ[variable] = value subprocess.run(f"{pixi_run} conda env config vars set {variable}={value}".s
  • output=True) try: subprocess.run(f"pixi run --frozen conda env config vars set {variable}={va
  • s.CalledProcessError: subprocess.run(f"conda env config vars set {variable}={value}".split(), che
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://bioconda.github.io/recipes/aviary/README.html
✓ Git Repository History

Repository rhysnewell/aviary appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Rhys Newell" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aviary-genome 
Create a bioinformatics tool called 'AviaryExplorer' that leverages the 'aviary-genome' package to analyze mixed genetic samples from bird populations. This tool will help researchers understand the genetic diversity within and between different bird species by processing both long and short DNA sequencing reads.

Steps to build AviaryExplorer:
1. Set up a virtual environment and install 'aviary-genome'.
2. Design a user-friendly command-line interface (CLI) where users can input their sample files (FASTQ format).
3. Implement functionality to preprocess the input data, including quality control checks and trimming of adapters.
4. Use 'aviary-genome' to assemble the genetic material from the input reads into contigs.
5. Integrate a feature to annotate the assembled contigs using a reference database of known bird genes.
6. Develop a report generator that summarizes the genetic findings, such as the presence of specific genes or genetic variations, and outputs them in a readable format like HTML or PDF.
7. Ensure the application logs all actions performed and stores them for future reference.
8. Test the application with various bird sample datasets to ensure accuracy and reliability.
9. Document the installation process, usage instructions, and API documentation if applicable.

Suggested Features:
- Support for multiple input file formats beyond FASTQ.
- Integration with cloud storage services for large datasets.
- Visualization tools to display genetic sequences and annotations graphically.
- User authentication and authorization for multi-user environments.
- Automated workflows for continuous processing of new datasets.

How 'aviary-genome' is Utilized:
- For assembling the mixed genetic samples into contigs, 'aviary-genome' provides powerful algorithms optimized for handling both long and short read data, which is crucial for accurately reconstructing the genetic landscape of bird populations. Additionally, it simplifies the complex processes involved in metagenomics analysis, making it easier for biologists without extensive programming knowledge to perform sophisticated genetic studies.

💬 Discussion Feed

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