PyamilySeq

v1.4.2 safe
3.0
Low Risk

PyamilySeq - A a tool to investigate sequence-based gene groups identified by clustering methods such as CD-HIT, DIAMOND, BLAST or MMseqs2.

🤖 AI Analysis

Final verdict: SAFE

The package shows minimal signs of risk with no network calls, no evidence of credential harvesting, and no obfuscation. The shell execution noted is not suspicious and likely serves a functional purpose within the package.

  • No network calls
  • Shell execution observed but benign
  • Incomplete author metadata
Per-check LLM notes
  • Network: No network calls detected, which is normal and expected.
  • Shell: Shell execution is observed but without suspicious arguments, suggesting it may be part of the package's functionality, though further investigation is recommended.
  • Obfuscation: No obfuscation patterns detected, suggesting low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The author's information is incomplete and they may be new or inactive, but there are no other red flags.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • options.verbose: subprocess.run(cdhit_command) else: subprocess.run(cdhi
  • nd) else: subprocess.run(cdhit_command, stdout=subprocess.DEVNULL, stderr=subprocess.
  • ns.verbose: ret = subprocess.run(cdhit_command) else: ret = subprocess.ru
  • else: ret = subprocess.run(cdhit_command, stdout=subprocess.DEVNULL, stderr=subprocess.
  • s executable try: subprocess.run([tool_name, '--version'], stdout=subprocess.PIPE, stderr=sub
  • ose", False): subprocess.run( ['mafft', '--auto', '--thread', str(opt
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: dimonaco.co.uk>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository NickJD/PyamilySeq appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • 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 PyamilySeq 
Develop a bioinformatics tool named 'GeneClusterAnalyzer' using the Python package 'PyamilySeq'. This tool aims to facilitate the analysis of sequence-based gene clusters identified through various clustering methods like CD-HIT, DIAMOND, BLAST, or MMseqs2. The primary goal is to provide researchers with an easy-to-use interface to explore, visualize, and interpret these gene clusters.

**Step-by-Step Application Development:**
1. **Setup Environment**: Ensure your development environment is equipped with Python and the necessary dependencies including PyamilySeq.
2. **Input Handling**: Design a user-friendly interface where users can upload their gene sequences in FASTA format and specify which clustering method was used.
3. **Processing**: Utilize PyamilySeq's core functionalities to process the uploaded sequences. Implement options for users to choose specific parameters for analysis based on the clustering method they selected.
4. **Visualization**: Integrate visual tools to display the results of the analysis. For example, create graphs showing the distribution of gene clusters, their sizes, and any other relevant metrics.
5. **Report Generation**: Allow users to generate comprehensive reports summarizing the findings from their analysis. Include sections for raw data, visual representations, and interpretation of the results.
6. **Export Options**: Provide functionality for exporting the results in different formats (CSV, PDF, etc.), allowing users to share or further analyze the data outside of the application.

**Suggested Features**:
- Real-time progress tracking during the processing phase.
- Interactive visualization tools for exploring cluster relationships.
- Customizable report templates with options to include or exclude specific sections.
- Integration with cloud storage services for saving and sharing reports.
- Support for batch processing multiple datasets simultaneously.

**Utilization of PyamilySeq**: Throughout the development, leverage PyamilySeq's capabilities to handle the complex tasks of identifying and analyzing gene clusters. Use its functions to perform detailed comparisons between sequences, extract meaningful insights, and prepare the data for visualization and reporting.