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Updated: May 19, 2026
11:13
Mass Spectrometry-Guided Genome Mining as a Tool to Uncover Novel Natural Products
Published on: March 12, 2020
Data mining as a discovery tool for imprinted genes
Chelsea Brideau1, Paul Soloway
1Nuffield Department of Surgical Sciences, Gray Institute for Radiation Oncology and Biology, The University of Oxford, Oxford, UK.
Methods in Molecular Biology (Clifton, N.J.)
|August 22, 2012
Summary
This chapter introduces using genome-wide sequence and epigenomic data to train generalized linear models (GLM) for predicting imprinted status. It covers basic computational methods and Perl programming examples for genomic region analysis.
Area of Science:
- Genomics
- Epigenetics
- Computational Biology
Background:
- Genome-wide sequence and epigenomic data are crucial for understanding gene regulation.
- Predicting imprinted status requires sophisticated analytical methods.
Purpose of the Study:
- To introduce methods for collecting and analyzing genome-wide data.
- To demonstrate training generalized linear models (GLM) for predicting imprinted status.
- To provide straightforward examples using genomic regions and epigenetic features.
Main Methods:
- Collection of genome-wide sequence and epigenomic data.
- Training generalized linear models (GLM).
- Utilizing Perl programming for data analysis and modeling.
Main Results:
- Demonstration of straightforward examples for predicting imprinted status.
- Exploration of various genomic regions (gene body, introns, exons, UTRs, flanking regions).
- Potential for building models using combinations of genomic regions and epigenetic features.
Conclusions:
- Genome-wide data and GLM provide a powerful framework for predicting imprinted status.
- The chapter offers a foundational understanding for computational analysis in epigenetics.
- Further model complexity can be achieved by combining different genomic and epigenetic features.
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