G2PInsight

v1.0.0 safe
3.0
Low Risk

Genome-wide association analysis toolkit

🤖 AI Analysis

Final verdict: SAFE

The package appears to serve its intended purpose without significant malicious indicators. The presence of pickle.loads is noted but does not alone suggest malice, and there is no clear evidence of credential risk.

  • Use of pickle.loads, potential for obfuscation
  • No detected credential harvesting
Per-check LLM notes
  • Obfuscation: The use of pickle.loads suggests an attempt to deserialize data, which could be obfuscation but is also common in legitimate scenarios like restoring complex objects.
  • Credentials: No clear evidence of credential harvesting patterns detected.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • result['cv_results'] = pickle.loads(cv_results_bytes) else: result['cv_results'] = N
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • {cmd_str}") result = subprocess.run( cmd, stdout=subprocess.PIPE, stderr
  • try: result = subprocess.run( cmd, capture_output=True,
  • try: result = subprocess.run( plink_cmd, capture_
  • s) try: result = subprocess.run( plink_cmd, capture_output=True,
  • ] try: subprocess.run(plink_cmd, capture_output=True, text=True, check=True)
  • try: result = subprocess.run( plink_cmd, capture_output=True,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: stu.ynu.edu.cn>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author name is missing or very short
  • Author "" 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 G2PInsight 
Create a genome-wide association study (GWAS) mini-app using the Python package 'G2PInsight'. This app will allow researchers to upload their own GWAS data, perform basic association analyses, and visualize the results. Here’s a detailed breakdown of what your app should include:

1. **Data Upload**: Users should be able to upload their GWAS dataset in a common format such as CSV or TSV. Ensure the app checks for the presence of necessary columns like SNP ID, chromosome, position, and p-values.

2. **Association Analysis**: Utilize G2PInsight's functions to perform association analysis on the uploaded data. This includes identifying significant associations between genetic variants and phenotypes based on predefined thresholds for p-values.

3. **Visualization Tools**: Integrate visualization capabilities into your app to help users better understand the results of the association analysis. For instance, create Manhattan plots to display the distribution of p-values across different SNPs, and QQ-plots to compare observed vs expected p-values.

4. **Result Export**: Allow users to export their analysis results in formats such as PDF or Excel for further review and sharing.

5. **User Interface**: Develop a simple yet intuitive web interface using Flask or Django. The UI should guide users through the process of uploading data, selecting analysis parameters, viewing results, and exporting outputs.

6. **Documentation**: Provide comprehensive documentation explaining how to use the app, including sample datasets that users can try out.

To utilize G2PInsight effectively, ensure you explore its key functionalities such as loading genomic data, performing statistical tests, and generating summary statistics. Additionally, consider incorporating user feedback mechanisms to continuously improve the app.