RNAvigate

v1.1.2 suspicious
4.0
Medium Risk

RNA visualization and graphical analysis toolset

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some signs of potential obfuscation and incomplete metadata, raising concerns about its origin and intentions.

  • Obfuscation risk due to use of eval
  • Incomplete author information and single package from maintainer
Per-check LLM notes
  • Network: The observed network calls seem to be legitimate API interactions, possibly fetching necessary data for the RNA analysis tool.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The code appears to be using eval for operations on data attributes which could indicate obfuscation but may also be a normal part of the package's functionality.
  • Credentials: No patterns indicative of credential harvesting were found.
  • Metadata: The author information is incomplete and the maintainer has only one package, which could indicate a less experienced or potentially suspicious maintainer.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • rowPeak" ) response = requests.get(url_bed, headers=headers, timeout=21.1) # Load json resp
  • ) response2 = requests.get(url2, headers=headers, timeout=21.1) bed_dict = resp
  • ccession: bedgzfile = requests.get( f"https://www.encodeproject.org/files/{acc}/@@d
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • a["Diff_profile"] = self.data.eval("Norm_profile_1 - Norm_profile_2") self.data["Diff_s
  • ta["Diff_stderr"] = self.data.eval("Norm_stderr_1 + Norm_stderr_2") self.data["Smooth_d
  • ta["Smooth_diff"] = self.data.eval("Smooth_profile_1 - Smooth_profile_2") self.data["Sm
  • oth_diff_stderr"] = self.data.eval( "Smooth_stderr_1 + Smooth_stderr_2" )
  • nfidence_interval = self.data.eval( f"Smooth_stderr_1 + Smooth_stderr_2 * {zf_coeff
  • self.data.loc[self.data.eval("Significant & ~ Positive"), "Class"] = 1 self.data.
✓ 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: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Weeks-UNC/RNAvigate 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 RNAvigate 
Create a web-based application using Flask and RNAvigate to visualize and analyze RNA structures. The application should allow users to upload their own RNA sequences in FASTA format and then provide several tools to explore these sequences. Here are the key steps and features your application should include:

1. **User Interface Design**: Design a simple yet intuitive user interface where users can upload their RNA sequences and select from various analysis options.
2. **Sequence Upload**: Implement functionality for users to upload RNA sequences in FASTA format. Ensure the application checks if the uploaded file is valid before proceeding.
3. **Structure Visualization**: Utilize RNAvigate's visualization capabilities to display the secondary structure of the uploaded RNA sequence. This should include options for different visual representations such as dot-bracket notation, base-pair probability plots, and more.
4. **Analysis Tools**: Provide tools within the app to perform common RNA analyses such as predicting free energy changes upon mutation, identifying stable hairpin loops, and detecting pseudoknots. Use RNAvigate's analysis functions to calculate these metrics.
5. **Results Presentation**: Display the results of each analysis in a clear and understandable way, possibly including charts, graphs, or other visual aids provided by RNAvigate.
6. **Integration with RNAvigate**: Throughout the development process, make sure to integrate RNAvigate effectively by calling its functions for both visualization and analysis tasks. Refer to RNAvigate's documentation for examples on how to use its main features.
7. **Testing and Validation**: Before deploying the application, thoroughly test it with various RNA sequences to ensure accuracy and reliability of the visualizations and analysis results.
8. **Deployment**: Once tested and validated, deploy the application on a platform like Heroku or AWS so that it can be accessed online.

By following these steps, you'll create a powerful tool for researchers and students interested in exploring RNA structures and properties.