assay-blast

v2.4 suspicious
5.0
Medium Risk

AssayBLAST - for in silico analysis of PCR oligos

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its use of shell executions, which could potentially be exploited. However, there are no clear signs of malicious intent or credential harvesting.

  • High shell risk due to os.system and subprocess usage
  • Low risk in network, obfuscation, and credential areas
Per-check LLM notes
  • Network: The network calls seem to be for downloading necessary files for the package's operation.
  • Shell: The shell executions may indicate potential security risks due to the use of os.system and subprocess which can execute arbitrary commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The authors have only one package, which could indicate a new or less active account, but no other suspicious activities were detected.

📦 Package Quality Overall: Low (3.2/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. assay_test.py)
○ Low Documentation 1.0

No documentation detected

  • No documentation URL, doc files, or meaningful description found
○ 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 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in rnajena/AssayBLAST
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • Download file {url}') urllib.request.urlretrieve(url, fname) return fname def _call(cmd):
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • ' print(call) os.system(call) else: print(f'Use BLAST database at {db}')
  • ) print(call) os.system(call) # this takes long for large genomes #s
  • ) print(call) os.system(call) _filter_outfmt0(out2) t2 = time.time()
  • d') print(call) out = subprocess.check_output(call.split(), text=True) source_ids = [line.strip().spli
  • cmd -db {db} -info' out = subprocess.check_output(call.split(), text=True) match = re.search(r'sequences;
✓ 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

Repository rnajena/AssayBLAST appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Tom Eulenfeld, Max Collatz" 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 assay-blast 
Create a mini-application called 'PCR Assay Analyzer' using the Python package 'assay-blast'. This application will serve as a tool for biologists and researchers to perform in silico analysis on PCR primers and probes. The goal is to help users understand potential issues such as primer-dimer formation, hairpin structures, and melting temperatures of their oligonucleotides before conducting actual experiments.

### Features:
1. **Input Section**: Users can input sequences of PCR primers and probes directly into the application. Each sequence should have a unique identifier for easy reference.
2. **Analysis Section**: Utilize 'assay-blast' to analyze each oligo for potential issues including but not limited to primer-dimer formations, hairpin structures, and GC content. Additionally, calculate the melting temperature (Tm) for each oligo.
3. **Report Generation**: After analyzing all provided sequences, generate a comprehensive report summarizing the findings. The report should include details like whether any primer-dimers were detected, hairpin structures identified, GC content, and Tm values for each oligo.
4. **Visualization**: Implement simple visualizations such as graphs showing the distribution of Tm values across different oligos or bar charts depicting GC content.
5. **User Interface**: Develop a user-friendly interface using a web framework like Flask or Django. The UI should allow users to easily input sequences, view results, and download reports.
6. **Error Handling**: Ensure robust error handling is implemented to manage cases where invalid sequences are entered or if there are issues during the analysis process.
7. **Documentation**: Provide clear documentation on how to use the application, including setup instructions and examples of valid input sequences.

### How 'assay-blast' is Utilized:
- For detecting primer-dimers, utilize 'assay-blast' functions that compare sequences against each other.
- To identify hairpin structures, use relevant functions within 'assay-blast' designed to predict secondary structures.
- Calculate GC content and Tm values using appropriate functions from 'assay-blast', ensuring accuracy and reliability of these calculations.

This mini-application aims to streamline the initial testing phase of PCR assays, helping researchers save time and resources by identifying problematic sequences early in the process.

💬 Discussion Feed

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