annoreport

v0.1.2 suspicious
4.0
Medium Risk

Summary and visualization tool for MAG gene annotation workflows

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to potential network communication with external servers and a lack of active maintenance and community engagement.

  • network risk due to external API calls
  • low maintainer activity and community engagement
Per-check LLM notes
  • Network: The network call pattern suggests the package might be communicating with an external server to fetch or send data, which is not inherently malicious but requires scrutiny of the purpose and destination.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer seems new or inactive, and the repository lacks community engagement.

📦 Package Quality Overall: Low (2.2/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7276 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
○ Low Type Annotations 1.0

No type annotations detected

  • No type annotations, py.typed marker, or stub files detected
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 13 commits in keplerridge/annoreport
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • earch?{params}" req = urllib.request.Request(url, headers={'User-Agent': 'annotation_report/2.0'}
  • on_report/2.0'}) with urllib.request.urlopen(req, timeout=10) as response: data = jso
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "keplerridge" 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 annoreport 
Create a gene annotation analysis mini-app using the 'annoreport' Python package. This app will serve as a powerful tool for researchers working with MAG (Metagenome-Assembled Genome) data. The goal is to provide users with an intuitive interface to analyze, visualize, and interpret their gene annotation results efficiently.

### Project Overview:
- **Name**: MAG Annotation Explorer
- **Objective**: To offer an interactive environment where users can load, summarize, and visualize gene annotation data from MAGs.

### Key Features:
1. **Data Import**: Allow users to upload their gene annotation files (e.g., GFF, BED).
2. **Summary Report Generation**: Utilize 'annoreport' to automatically generate comprehensive summary reports of the uploaded annotations.
3. **Interactive Visualization**: Implement interactive plots using libraries like Plotly or Bokeh to visualize key statistics derived from the summary reports.
4. **Customization Options**: Enable users to customize visualizations based on specific criteria (e.g., gene function, taxonomy).
5. **Export Functionality**: Provide options for exporting both the summary report and visualizations in various formats (CSV, PNG, PDF).

### Steps to Build the Mini-App:
1. **Setup Environment**:
   - Install necessary packages including 'annoreport', 'pandas', 'matplotlib', 'plotly', and 'streamlit'.
2. **Data Handling**:
   - Develop functions to parse different types of annotation files and store them in a structured format (e.g., DataFrame).
3. **Summary Report Creation**:
   - Use 'annoreport' to process the structured data and generate detailed summary reports.
4. **Visualization Development**:
   - Create customizable charts and graphs using Plotly or Bokeh, ensuring they are interactive and informative.
5. **UI Design**:
   - Design an easy-to-use interface using Streamlit, allowing users to interactively explore their data through filters and controls.
6. **Testing & Validation**:
   - Validate the app with sample datasets and ensure all features work as expected.
7. **Deployment**:
   - Deploy the app either locally or online for broader accessibility.

### How 'annoreport' is Utilized:
- For generating summaries and reports of the gene annotations. 'annoreport' simplifies the complex task of analyzing large-scale genomic data by providing high-level insights into the annotations, which can then be further explored through the app's visualizations.

💬 Discussion Feed

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