TEtranscripts

v2.2.4 suspicious
5.0
Medium Risk

Tools for estimating differential enrichment of Transposable Elements and other highly repetitive regions

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential misuse through shell commands and has metadata that suggests an account that may not be well-maintained, raising concerns about its reliability and potential for supply-chain attacks.

  • High Shell risk due to use of os.system and subprocess.Popen
  • Metadata risk due to non-HTTPS links and potentially inactive maintainer account
Per-check LLM notes
  • Network: No network calls detected, which is normal and does not indicate any risk.
  • Shell: Use of os.system and subprocess.Popen indicates potential execution of external commands which could be risky if not properly sanitized or controlled.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity related to code obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The package has no typosquatting or email domain flags, but it contains non-HTTPS links and the maintainer history suggests a new or inactive account.

🔬 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)

  • end try: os.system("bamToBED -ed -i sample >res") res = __assignWei
  • se : try: os.system("bamToBED -bedpe -i sample >res") res = __assign
  • f i != 0 : #msf = subprocess.check_output(["Rscript","bin_corr.r",tmpfname,outfname]) msf
  • ,outfname]) msf = subprocess.Popen(["Rscript","bin_corr.r",tmpfname,outfname],stdout=subprocess
  • msf = 1 # subprocess.call(["rm","-f",tmpfname]) if min_sf > msf :
  • [i],tmpfname) #msf = subprocess.check_output(["Rscript","bin_corr.r",tmpfname,outfname]) msf = su
✓ 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 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://daehwankimlab.github.io/hisat2/
  • Non-HTTPS external link: http://www.gnu.org/licenses/
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Ying Jin, Eric Paniagua, Talitha Forcier, Oliver Tam, Molly Hammell" 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 TEtranscripts 
Create a web-based application using Flask (a Python micro web framework) that integrates the TEtranscripts package to analyze differential enrichment of Transposable Elements (TEs) across different genomic samples. This application will serve as a user-friendly tool for researchers to upload their genomic data, process it through TEtranscripts' algorithms, and visualize the results. Here are the key steps and features for building this application:

1. **Setup Environment**: Ensure Python 3.x is installed along with necessary libraries such as Flask, TEtranscripts, and any visualization libraries like Plotly or Matplotlib.
2. **User Interface**: Develop a simple yet effective user interface where users can upload their FASTQ files or pre-processed BED files containing TEs annotations. Provide options for selecting between paired-end and single-end reads if applicable.
3. **Data Processing**: Implement backend functionality using TEtranscripts to process uploaded data. This includes mapping reads to the reference genome, identifying TEs, and calculating differential enrichment scores between different conditions or samples.
4. **Visualization**: Create interactive visualizations of the processed data. For instance, use bar charts to show the abundance of specific TEs across samples, heatmaps to display differential enrichment scores, and scatter plots to compare expression levels.
5. **Results Download**: Allow users to download the processed data and visualizations in formats like CSV or PNG.
6. **Documentation**: Write comprehensive documentation explaining how to use the application, including example datasets and expected outputs.
7. **Testing**: Conduct thorough testing on various datasets to ensure accuracy and reliability of the application.
8. **Deployment**: Deploy the application on a cloud platform like Heroku or AWS, making it accessible to a wide audience of researchers.

This project aims to bridge the gap between complex bioinformatics analysis and user-friendly tools, empowering researchers to easily explore the role of TEs in their studies.