ask-ecdna

v0.1.1 safe
3.0
Low Risk

AmpliconSeeK: a Python toolkit for detecting amplified genomic structures and candidate extrachromosomal DNA from sequencing data

⚠ Tarball exceeded 25 MB — source code analysis was limited to package metadata only.

🤖 AI Analysis

Final verdict: SAFE

The package ask-ecdna v0.1.1 is deemed safe based on the analysis notes, showing no signs of network or shell risks, obfuscation, or credential harvesting. The metadata risk is minor.

  • No network calls detected
  • No shell execution detected
  • No obfuscation patterns found
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution detected, indicating the package does not execute external commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: The maintainer has only one package and lacks PyPI classifiers, suggesting low effort or a new/inactive account.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (27862 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
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 20 commits in nanawei11/AmpliconSeeK
  • Single author but highly active (20 commits)

🔬 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: 163.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository nanawei11/AmpliconSeeK appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "Nana Wei" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ask-ecdna 
Create a mini-application called 'AmpliconSeeker' that leverages the 'ask-ecdna' Python package to analyze sequencing data for detecting amplified genomic structures and candidate extrachromosomal DNA. This application will serve as a user-friendly interface for researchers to upload their sequencing data and receive detailed analysis reports. Here’s a step-by-step guide on how to develop this application:

1. **Project Setup**: Start by setting up a new Python environment and installing the necessary packages including 'ask-ecdna'. Additionally, include libraries like pandas for data manipulation and matplotlib/seaborn for visualization.
2. **Data Upload Interface**: Develop a simple web interface using Flask or Django where users can upload their FASTQ or BAM files. Ensure the interface includes options for file format selection and metadata input such as sample ID.
3. **Data Preprocessing**: Implement a backend process that preprocesses the uploaded sequencing data. This might involve quality control checks, adapter trimming, and alignment if raw reads are provided. Use 'ask-ecdna' for any specific preprocessing steps required for accurate amplification detection.
4. **Amplification Detection**: Utilize 'ask-ecdna' to perform the core task of identifying amplified regions within the genomic data. Configure the application to allow users to specify parameters such as minimum amplification threshold and region of interest.
5. **Visualization and Reporting**: Create visualizations of the detected amplifications using matplotlib or seaborn. Provide a downloadable report that summarizes the findings, including tables and graphs. Ensure the report is easily understandable for non-expert users.
6. **Integration with External Tools**: Optionally, integrate the application with external databases or tools for further analysis or validation of the detected amplifications. For example, linking to a database of known genetic variations could help contextualize the findings.
7. **User Feedback and Support**: Include a feedback mechanism in the application where users can provide comments or questions about their results. This will also allow you to gather valuable insights for future improvements.

Suggested Features:
- User authentication for secure data handling.
- Real-time progress tracking for long-running analyses.
- Customizable output formats for the final reports.
- Integration with cloud storage services for large datasets.

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