🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits low risk in terms of network, shell execution, and obfuscation, but its metadata quality and maintainer activity levels are concerning. This combination raises suspicion about potential supply-chain risks.

Low maintainer activity
Poor metadata quality

Per-check LLM notes

Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
Shell: No shell execution patterns detected, indicating the package does not execute system commands without user interaction.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package shows low maintainer activity and poor metadata quality, raising suspicion but not definitive 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

No author email provided

✓ 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

Author name is missing or very short
Author "" 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 TECSAS

Your task is to create a web-based mini-application using Python that leverages the 'TECSAS' package to predict chromatin structures from epigenetic data. This tool will serve as an educational and research aid for biologists and bioinformaticians who need to understand the relationship between epigenetic modifications and chromatin structure.

Step 1: Design the User Interface
- Develop a clean, user-friendly interface where users can upload their epigenetic data files (in common formats such as BED, WIG, or BigWig).
- Include a section to input parameters for the TECSAS model, such as the type of epigenetic modification being analyzed and any specific genomic regions of interest.

Step 2: Data Preprocessing
- Implement a backend process to preprocess uploaded data according to the requirements of the TECSAS package. This may include normalization, binning, or other transformations necessary for input into the TECSAS model.

Step 3: Model Integration
- Utilize the TECSAS package to predict chromatin structures based on the preprocessed epigenetic data. Ensure that the integration allows for customization of the TECSAS model parameters through the user interface.

Step 4: Visualization
- Provide interactive visualizations of the predicted chromatin structures. Users should be able to zoom in/out, view specific regions, and compare different predictions side-by-side.

Step 5: Export Results
- Allow users to export the results of their analysis in various formats, including downloadable images, tables, and raw data files suitable for further analysis in other tools.

Additional Features:
- Implement a feature that suggests potential biological interpretations based on the predicted chromatin structures, linking back to known literature or databases.
- Include a tutorial or documentation within the application to guide users through the process and help them understand the significance of the results.
- Consider adding a community aspect where users can share their findings or collaborate on projects.

Remember, the goal is to make the TECSAS package accessible and understandable to non-experts while providing powerful functionality for researchers.