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Reconstructing Ancestral Non-Coding RNAs of Multiple Families Using Sequence and Structural Information with Tree

Songdi Hu¹, Vladimir Reinharz², Olivier Tremblay-Savard¹

¹Department of Computer Science, University of Manitoba, Winnipeg, Manitoba, Canada.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|March 10, 2026

Summary

This summary is machine-generated.

Reconstructing ancestral RNA sequences is difficult due to structural conservation. This study introduces an improved method using tree decomposition to accurately infer ancestral non-coding RNA (ncRNA) sequences, even with multiple ancestral structures.

Keywords:

algorithms ancestral inference ncRNA phylogeny secondary structure tree decomposition

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Novel Sequence Discovery by Subtractive Genomics

Novel Sequence Discovery by Subtractive Genomics

Published on: January 25, 2019

Area of Science:

Computational Biology
Bioinformatics
Molecular Evolution

Background:

Ancestral non-coding RNA (ncRNA) sequence reconstruction is challenging because evolutionary pressure favors structural, not sequence, conservation.
Previous methods often produce ancestral sequences that are more energetically stable than their descendants, creating a contradiction.
RNA families may evolve from ancestral molecules with multiple functions and multistable structures, followed by duplication and subspecialization.

Purpose of the Study:

To improve the accuracy and reduce ambiguity in ancestral ncRNA sequence reconstruction.
To address the limitations of methods that assume a single conserved structure for ancestral ncRNAs.
To develop a method capable of inferring ancestral sequences from related ncRNA families that originated from a single ancestral molecule with multiple functions.

Main Methods:

Developed an improved ancestral reconstruction approach leveraging tree decomposition algorithms.
Incorporated more constraints and positions into the reconstruction process.
Utilized a more realistic energetic model for sequence evaluation.

Main Results:

Demonstrated significant improvements in ancestral sequence inference accuracy on simulated datasets.
Reduced the number of optimal ancestral sequences inferred by several orders of magnitude.
On real datasets (RFam clans Glm and FinP-traJ), the new approach inferred fewer, more structurally fit ancestral sequences compared to previous methods.

Conclusions:

The enhanced tree decomposition approach offers a more accurate and less ambiguous method for reconstructing ancestral ncRNA sequences.
This method effectively handles scenarios where ancestral ncRNAs possessed multiple functions and structures.
The findings have implications for understanding the evolutionary history of ncRNA families and their functional diversification.