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Related Experiment Video

Updated: Jun 18, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

Incorporating Sequence-Dependent DNA Shape and Dynamics into Transcriptome Data Analysis.

Manisha Kalsan¹, Almas Jabeen¹, Shandar Ahmad²

¹School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India.

Methods in Molecular Biology (Clifton, N.J.)

|July 27, 2024

Summary

This summary is machine-generated.

This study reveals that DNA shape and dynamics, not just sequence motifs, drive gene co-regulation. We show how to detect these signatures from gene expression data using tools like DynaSeq.

Keywords:

DNA conformational dynamics DNA shape Gene expression Sequence motifs Transcription factor

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Last Updated: Jun 18, 2025

RNA Secondary Structure Prediction Using High-throughput SHAPE

RNA Secondary Structure Prediction Using High-throughput SHAPE

Published on: May 31, 2013

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

An Approach to Study Shape-Dependent Transcriptomics at a Single Cell Level

Published on: November 2, 2020

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

Published on: January 30, 2019

Area of Science:

Genomics
Computational Biology
Molecular Biology

Background:

Gene co-regulation by transcription factors (TFs) is crucial in cellular processes.
Detecting TF-mediated co-regulation is challenging, especially with single-cell expression data and limited TF binding information.
Traditional motif enrichment analysis may miss co-regulation driven by DNA shape and dynamics.

Purpose of the Study:

To explore alternative mechanisms of gene co-regulation beyond sequence motifs.
To demonstrate methods for detecting DNA shape and dynamics signatures from gene expression data.
To introduce DynaSeq as a tool for analyzing sequence-dependent DNA shape features.

Main Methods:

Analysis of gene expression data.
Utilizing computational tools for motif enrichment analysis.
Employing DynaSeq for predicting sequence-dependent DNA shape features and dynamics.

Main Results:

Identified DNA shape and dynamics as significant factors in gene co-regulation.
Demonstrated the feasibility of detecting these signatures from gene expression data.
Showcased the utility of DynaSeq in uncovering these non-motif-based regulatory mechanisms.

Conclusions:

Gene co-regulation can be driven by DNA shape and dynamics, offering a new perspective beyond sequence motifs.
Computational approaches, including DynaSeq, can effectively identify these regulatory signatures.
This work provides valuable insights for understanding gene regulation at a deeper mechanistic level.