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Updated: Oct 7, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

A framework for mutational signature analysis based on DNA shape parameters.

Aleksandra Karolak^1,2, Jurica Levatić¹, Fran Supek^1,3

¹Genome Data Science, Institute for Research in Biomedicine (IRB Barcelona), Barcelona Institute of Science and Technology, Barcelona, Spain.

|January 11, 2022

Summary

This summary is machine-generated.

DNA mutation risk is linked to oligonucleotide structure. New methods using DNA shape features improve the analysis of mutational signatures, offering better insights into cancer mechanisms and selection.

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Last Updated: Oct 7, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

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Published on: April 26, 2013

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Mutation risk is influenced by DNA sequence context.
Individual differences in mutagen exposure and DNA repair affect mutation rates.
Mutational signatures are mathematical models of mutation frequency spectra.

Purpose of the Study:

To enhance methods for inferring mutational signatures using DNA structural features.
To improve the analysis of sparse mutation data for biological insights.
To provide accurate mutation rate baselines for selection inference.

Main Methods:

Representing oligonucleotides using DNA conformational descriptors (base pair, step, minor groove width).
Predicting mutation occurrence based on DNA structural parameters.
Applying nonnegative matrix factorization to mutation spectra stratified by DNA structural features.

Main Results:

DNA structural features accurately predict mutation occurrence from various mutagens and repair failures.
Mutation frequency, classified by structural features, captures mutagenesis variability across diverse human tumors.
Novel mutational signatures were extracted, and known signatures were linked to DNA structural descriptor space.

Conclusions:

DNA shape features enhance the power of sequence motif-based mutational signature analysis.
This approach aids in interpreting mutational signatures and provides mechanistic insights.
The findings offer a more accurate understanding of mutagenesis and its variability.