autobarcoder

v1.0.2 safe
4.0
Medium Risk

Demultiplex and cluster RNA barcodes from 96-well-plate sequencing reads.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be safe with no network calls, shell executions, or obfuscations detected. However, the metadata contains a non-HTTPS link and the maintainer has only one package listed, which slightly raises suspicion but does not strongly indicate malicious intent.

  • No network calls detected
  • Maintainer has only one package
  • Non-HTTPS link present in metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function properly.
  • Shell: No shell execution patterns detected, indicating the package does not execute external commands, which is expected and safe.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The presence of a non-HTTPS link and the maintainer having only one package suggest potential risks, but not strong evidence of malicious intent.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. test_clustering.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4895 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 8 type-annotated function signatures (partial)
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 11 commits in abachu2005/AutoBarcoder-OS-
  • Two distinct contributors found

🔬 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8000
✓ Git Repository History

Repository abachu2005/AutoBarcoder-OS- appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "Abhinav Bachu" 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 autobarcoder 
Create a user-friendly command-line tool called 'RNASeqAnalyzer' that leverages the 'autobarcoder' Python package to process RNA sequencing data from 96-well plates. The tool should automate the demultiplexing and clustering of RNA barcodes from sequencing reads, making it easier for researchers to analyze their data. Here’s a step-by-step guide on what your application should do:

1. **User Input Handling**: The tool should accept input files containing raw sequencing reads from a 96-well plate. It should also allow users to specify the barcode sequences used in the experiment.
2. **Data Processing**: Utilize the 'autobarcoder' package to demultiplex the reads based on the specified barcodes. This involves identifying which reads belong to which samples based on their barcodes.
3. **Barcode Clustering**: After demultiplexing, cluster similar barcodes together to correct minor sequencing errors and improve the accuracy of sample assignment.
4. **Output Generation**: Produce output files that contain the demultiplexed and corrected reads for each sample. Optionally, include summary statistics about the processing steps.
5. **Error Reporting**: Implement error handling to manage issues such as missing input files, incorrect barcode specifications, or other common problems encountered during processing.
6. **Customization Options**: Allow users to customize certain parameters like the minimum quality score required for a read to be included in the analysis, or the distance threshold for barcode clustering.
7. **Help and Documentation**: Provide comprehensive help documentation that explains how to use the tool, including examples of input files and expected outputs.

To utilize the 'autobarcoder' package effectively, ensure you integrate its key functions for demultiplexing and clustering into your workflow. Additionally, consider adding features that enhance usability and reliability, such as progress indicators during long-running processes or support for different file formats commonly used in sequencing experiments.

💬 Discussion Feed

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