Improved Core Genes Prediction for Constructing well-supported Phylogenetic Trees in large sets of Plant Species

TL;DR

This paper introduces an improved method for predicting core genes by combining multiple annotation tools, enhancing the accuracy of phylogenetic trees in plant species by producing well-supported, reliable core gene sets.

Contribution

It presents a novel quality test approach (QTA) that integrates NCBI and DOGMA annotations to improve core gene prediction for phylogenetic analysis.

Findings

Enhanced core gene quality leads to more accurate phylogenetic trees.

Well-defined core genes produce subtrees with strong bootstrap support.

Merged supertrees are more reliably supported in evolutionary studies.

Abstract

The way to infer well-supported phylogenetic trees that precisely reflect the evolutionary process is a challenging task that completely depends on the way the related core genes have been found. In previous computational biology studies, many similarity based algorithms, mainly dependent on calculating sequence alignment matrices, have been proposed to find them. In these kinds of approaches, a significantly high similarity score between two coding sequences extracted from a given annotation tool means that one has the same genes. In a previous work article, we presented a quality test approach (QTA) that improves the core genes quality by combining two annotation tools (namely NCBI, a partially human-curated database, and DOGMA, an efficient annotation algorithm for chloroplasts). This method takes the advantages from both sequence similarity and gene features to guarantee that the core genome contains correct and well-clustered coding sequences (\emph{i.e.}, genes). We then show in this article how useful are such well-defined core genes for biomolecular phylogenetic reconstructions, by investigating various subsets of core genes at various family or genus levels, leading to subtrees with strong bootstraps that are finally merged in a well-supported supertree.