🤖 AI Analysis

Final verdict: SAFE

The package RiboParser v0.1.15 presents minimal risks based on the analysis. It lacks network calls, shell executions, obfuscation, and credential harvesting patterns.

No network calls
No shell executions
No obfuscation patterns
No credential harvesting patterns
Metadata risk due to new/inactive maintainer account

Per-check LLM notes

Network: No network calls detected, which is typical for a parser tool unless it requires external data sources.
Shell: No shell executions detected, indicating the package does not execute system commands, which is normal for a parsing utility.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The maintainer has a new or inactive account and lacks a proper author name, which raises some concerns but not enough to definitively indicate malicious intent.

🔬 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: 163.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository renscq/RiboParser 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 RiboParser

Create a mini-application called 'RibosomeAnalyzer' that leverages the RiboParser package to streamline the analysis of ribosome profiling data. Your application should perform the following tasks:

1. **Data Importation**: Allow users to upload their raw ribosome profiling data files (e.g., FASTQ files).
2. **Quality Control**: Implement basic quality control checks on the imported data to ensure it meets the necessary standards for analysis.
3. **Mapping**: Use RiboParser to map the reads to a reference genome. Ensure the user can specify the reference genome to be used.
4. **Feature Extraction**: Extract key features from the mapped reads such as codon occupancy profiles and ribosome density.
5. **Visualization**: Provide graphical representations of the extracted features, including heatmaps and line plots.
6. **Report Generation**: Automatically generate a comprehensive report summarizing the findings, including visualizations and statistical summaries.
7. **User Interface**: Develop a simple yet intuitive web-based UI using Flask or Django where users can interact with the application.

Suggested Features:
- Support for multiple file formats commonly used in RNA sequencing.
- Ability to handle large datasets efficiently.
- Integration of real-time progress updates during data processing.
- Customizable parameters for mapping and feature extraction processes.
- Export options for reports and visualizations in various formats (PDF, PNG, CSV).

How to Utilize RiboParser:
- Import RiboParser at the beginning of your script and utilize its functions for mapping reads and extracting features.
- For example, use `ribo.parser.map_reads()` to map reads and `ribo.parser.extract_features()` to derive meaningful insights from the mapped data.
- Ensure you document how each function from RiboParser is being used within your application.