AI Analysis
Final verdict: SAFE
The package RNAlysis v4.2.0 shows minimal risk indicators with no network calls, shell executions, or obfuscation techniques observed. The metadata risk is slightly elevated due to the maintainer having only one package, but there are no other red flags.
- No network calls or shell executions
- Low risk of obfuscation or credential harvesting
- Maintainer has only one package
Per-check LLM notes
- Network: No network calls detected, which is typical and safe for most packages unless network interaction is expected.
- Shell: No shell execution patterns detected, reducing the likelihood of executing harmful commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, suggesting the package is not attempting to steal secrets.
- Metadata: The maintainer has only one package, indicating possible new or less active status, but no other red flags are present.
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: gmail.com
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://numba.pydata.org/
Git Repository History
Repository GuyTeichman/RNAlysis appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Guy Teichman" 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 RNAlysis
Develop a mini-application called 'RNA Explorer' that leverages the RNAlysis package to provide comprehensive analysis and visualization of RNA sequencing data. The application should enable users to upload their RNA sequencing datasets, perform various analyses, and generate visual representations of the results. Here’s a detailed step-by-step guide on what the application should include: 1. **Data Upload Interface**: Create an intuitive interface where users can upload their RNA sequencing datasets. Ensure that the application supports common file formats such as FASTQ, BAM, and CSV. 2. **Data Preprocessing**: Implement basic preprocessing steps using RNAlysis, including quality control checks, adapter trimming, and read alignment if necessary. 3. **Expression Analysis**: Utilize RNAlysis to quantify gene expression levels from the uploaded data. Provide options to normalize the data using different methods (e.g., TMM, RPKM). 4. **Differential Expression Analysis**: Offer the capability to compare gene expression levels between different conditions or samples. Use RNAlysis to identify differentially expressed genes and provide p-values and fold changes. 5. **Visualization Tools**: Integrate RNAlysis to generate various types of plots such as volcano plots for differential expression analysis, heatmaps for hierarchical clustering, and boxplots for comparing sample groups. 6. **Interactive Exploration**: Allow users to interactively explore the data by filtering genes based on criteria like log-fold change, p-value, and gene ontology annotations. 7. **Results Export**: Enable users to export their analysis results in various formats, including tables and images, for further use or sharing. 8. **User Documentation**: Provide clear documentation explaining each feature of the application and how RNAlysis is utilized in the background to perform these tasks. Throughout the development process, focus on making the application user-friendly and accessible to researchers without extensive bioinformatics expertise.