AI Analysis
Final verdict: SAFE
The package SemiBin v2.3.0 exhibits low risks across all monitored categories except metadata, where it scores a moderate risk due to the maintainer's limited history with PyPI.
- No network calls detected
- Maintainer has only one package on PyPI
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution patterns detected, indicating the package does not execute external commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package on PyPI, which might indicate a new or less active account.
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: luispedro.org>
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://hmmer.org/
Git Repository History
Repository BigDataBiology/SemiBin appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Shaojun Pan" 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 SemiBin
Develop a mini-application named 'MetaBinner' that leverages the SemiBin Python package for metagenomic binning using siamese neural networks. This application will take as input raw metagenomic sequencing data from a microbial community and output refined bins of contigs that represent individual microbial genomes. Here’s a step-by-step guide on how to create MetaBinner: 1. **Setup**: Begin by setting up a virtual environment and installing necessary packages including SemiBin, as well as other dependencies such as numpy, pandas, scikit-learn, and matplotlib for data handling and visualization. 2. **Data Input**: Design a user-friendly interface where users can upload their raw metagenomic sequencing data files (typically in FASTQ format). 3. **Preprocessing**: Implement preprocessing steps within MetaBinner to clean and prepare the sequencing data for analysis. This might include quality control measures and trimming adapters. 4. **SemiBin Integration**: Utilize SemiBin to perform the core task of metagenomic binning. Ensure that your application configures SemiBin appropriately to leverage its siamese neural network capabilities effectively. 5. **Output Generation**: After processing, generate output files containing the binned contigs. Each bin should ideally represent a distinct genome from the microbial community. 6. **Visualization**: Include a feature to visualize the results. Use matplotlib or a similar library to plot graphs showing the distribution of contigs across different bins. 7. **Reporting**: Create a summary report for each bin, detailing key statistics like number of contigs, estimated genome size, GC content, etc. 8. **User Interface**: Develop a simple web-based interface for MetaBinner, allowing users to upload their data, view progress, and download the final results. 9. **Testing & Validation**: Rigorously test MetaBinner using publicly available datasets to ensure accuracy and reliability of the binning process. 10. **Documentation**: Provide comprehensive documentation explaining how to use MetaBinner, including setup instructions, usage examples, and expected outputs.