Searching for sequence features that control DNA flexibility

TL;DR

This paper introduces a correlation function-based method, inspired by statistical physics, to identify DNA sequence features that influence DNA flexibility, demonstrating its effectiveness with experimental data.

Contribution

It proposes a novel, simple correlation function approach to analyze DNA flexibility from genomic data, connecting physics concepts to genomics.

Findings

Correlation functions effectively identify DNA flexibility features

Method yields predictions comparable to complex models

Approach links DNA analysis to sensory neuron stimulus analysis

Abstract

Modern genomics experiments measure functional behaviors for many thousands of DNA sequences. We suggest that, especially when these sequences are chosen at random, it is natural to compute correlation functions between sequences and measured behaviors. In simple models for the dependence of DNA flexibility on sequence, for example, correlation functions can be interpreted directly as interaction parameters. Analysis of recent experiments shows that this is surprisingly effective, leading directly to extraction of distinct features for DNA flexibility and predictions that are as accurate as more complex models. This approach follows the conventional use of correlation functions in statistical physics and connects the search for relevant DNA sequence features to the search for relevant stimulus features in the analysis of sensory neurons.