AI Analysis
Final verdict: SUSPICIOUS
The package shows no immediate signs of malicious activity, but the metadata risk score raises concerns about the maintainer's credibility.
- Maintainer has a new or inactive account
- Lack of proper author name
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package requires internet access for its functionality.
- Shell: No shell execution detected, indicating the package does not execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
- Metadata: The maintainer has a new or inactive account and lacks a proper author name, raising some suspicion but not conclusive evidence of malice.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
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
Email domain looks legitimate: ccmb.res.in>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository sowpatilab/ATaRVa appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 ATaRVa
Create a bioinformatics mini-app called 'TandemRepeatsAnalyzer' that leverages the ATaRVa package to analyze tandem repeat variations within DNA sequences. This app will serve as a user-friendly tool for researchers and students interested in studying genetic variations caused by tandem repeats. Here’s a step-by-step guide on how to build this application: 1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python 3.x installed along with the necessary packages like ATaRVa, Biopython, and Flask for web development. 2. **User Interface Design**: Develop a simple yet intuitive web interface using Flask. The UI should allow users to upload their DNA sequence files (in FASTA format) and select various analysis options such as repeat unit length, minimum repeat count, and type of variation analysis (e.g., insertion, deletion). 3. **Data Processing**: Use ATaRVa to process the uploaded DNA sequences. Implement functions that utilize ATaRVa's core functionalities to identify tandem repeats, analyze variations within these repeats, and generate statistical summaries about the identified repeats. 4. **Visualization**: Integrate visualization capabilities into your app to help users better understand the results. Use libraries like Plotly or Matplotlib to create graphs showing the distribution of tandem repeats across the genome, frequency of different types of variations, etc. 5. **Results Presentation**: Display the processed data back to the user in a clear and concise manner. Include tables summarizing key findings, such as the number of identified repeats, their positions, lengths, and types of variations observed. 6. **Additional Features**: Consider adding extra features like saving the results to a file, exporting visualizations, and providing links to relevant scientific articles about tandem repeats. 7. **Testing and Documentation**: Before deployment, thoroughly test the application with various input scenarios to ensure reliability and accuracy. Write comprehensive documentation detailing how to use the app effectively and interpret its outputs. 8. **Deployment**: Finally, deploy your application to a cloud platform like Heroku or AWS so it can be accessed by anyone with an internet connection. By following these steps, you'll create a powerful yet accessible tool for exploring tandem repeat variations in DNA sequences.