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Published on: February 11, 2019
PAP: a comprehensive workbench for mammalian transcriptional regulatory sequence analysis
Li-Wei Chang1, Burr R Fontaine, Gary D Stormo
1Department of Biomedical Engineering, Washington University, St. Louis, MO 63130, USA.
Determining transcriptional regulation of co-expressed genes is crucial for understanding biological pathways and diseases. The Promoter Analysis Pipeline (PAP) is a new, user-friendly web tool that aids experimental biologists in this complex analysis.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Microarray technology enables genome-wide expression monitoring, correlating changes to biological processes and diseases.
- Deciphering the transcriptional regulation of co-expressed genes is essential for understanding underlying genetic networks.
- Existing computational approaches lack integration and user-friendliness for experimental biologists, especially in higher eukaryotes.
Purpose of the Study:
- To extend a previously reported statistical model for mammalian transcriptional regulatory sequence analysis.
- To develop a comprehensive and user-friendly web application suite for promoter analysis.
- To provide experimental biologists with an accessible tool for deciphering transcriptional regulation.
Main Methods:
- Systematic, statistical modeling of mammalian transcriptional regulatory sequences.
- Development of a web application suite named Promoter Analysis Pipeline (PAP).
- Extensions made to an existing statistical model for enhanced functionality.
Main Results:
- A comprehensive, user-friendly web application suite (PAP) has been developed.
- The PAP integrates advanced statistical models for promoter analysis.
- The tool facilitates the determination of transcriptional regulation in higher eukaryotes.
Conclusions:
- The Promoter Analysis Pipeline (PAP) addresses the need for an integrated, user-friendly tool for analyzing transcriptional regulation.
- PAP empowers experimental biologists to investigate gene regulatory networks more effectively.
- This web application advances the study of gene expression in relation to biological processes and disease states.
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