Reconstruction of ancestral RNA sequences under multiple structural constraints

Area of Science:

• Bioinformatics
• Computational Biology
• RNA Biology

Background:

• Non-coding RNA (ncRNA) secondary structures are crucial for multiple sequence alignments.
• Accurate reconstruction of ancestral ncRNAs requires utilizing this structural signal.
• Inferring ancestors from a single consensus structure can introduce bias, deviating from true ancestral sequences.

Purpose of the Study:

• To introduce achARNement, a novel maximum parsimony approach for ancestral RNA sequence reconstruction.
• To simultaneously calculate ancestral RNA sequences for two homologous ncRNA families.

Main Methods:

• Implemented a maximum parsimony approach named achARNement.
• Input includes two alignments of homologous ncRNA families with consensus secondary structures and a phylogenetic tree.
• Simultaneously calculates ancestral RNA sequences for the two families.

Main Results:

• achARNement demonstrates superior accuracy compared to classical maximum parsimony methods on simulated data.
• The method significantly reduces the number of candidate ancestral sequences.
• Applied to Glm and FinP-traJ clans from Rfam, reconstructing high-quality candidate ancestors.

Conclusions:

• achARNement reconstructs small sets of high-quality candidate ancestors.
• The reconstructed ancestors show better agreement with target structures than those from classical approaches.
• The achARNement program is publicly available for research use.