A new computational tool enhances genetic data analysis, allowing for easier and quicker exploration of the evolutionary connections among species.
Researchers from Hokkaido University have created a new computational tool designed to assist evolutionary biologists in examining complex genetic information to piece together the evolutionary history and relationships among various species. This tool, named PsiPartition, optimizes both the efficiency of data analysis and the precision of the phylogenetic trees that represent these connections. The innovative method has been detailed in the Journal of Molecular Evolution.
By studying genetic variations among species, evolutionary biologists can deduce their relatedness and outline their evolutionary timeline. This analysis culminates in a phylogenetic tree, which visually depicts the evolution process, illustrating shared ancestors and the divergence paths that contribute to the vast diversity of life seen today.
With modern sequencing technologies generating vast amounts of genomic data for phylogenetic studies, researchers face challenges since different genomic regions evolve at varying rates. Certain genes may change more rapidly, and specific areas of the genome exhibit distinct evolutionary trends. This issue, referred to as site heterogeneity, complicates the accurate modeling of evolution with existing methods that often struggle with efficiency and precision.
“PsiPartition is a handy tool that streamlines DNA data analysis by grouping it into segments, or partitions, to address the differing evolutionary rates of various DNA sections,” states Shijie Xu, the study’s lead author from the Graduate School of Environmental Science at Hokkaido University. “What sets PsiPartition apart is its capability to swiftly and accurately assess evolutionary rates utilizing advanced algorithms. It also automatically determines the ideal number of partitions needed, which saves time and minimizes errors typically associated with conventional techniques.”
When tested with both real and simulated datasets, PsiPartition showed remarkable performance. It significantly sped up processing times, especially for extensive datasets, and excelled in managing complex, highly variable information. Notably, in the case of the moth family Noctuidae, it enhanced the accuracy of the reconstructed phylogenetic trees, showing strong bootstrap support for the branches. Thus, the trees generated by PsiPartition offer a fresh and potentially more accurate evolutionary depiction of these species.
“Our tool will enable evolutionary biologists to conduct their studies with greater accuracy and efficiency,” says Professor Akira Onoda, the lead author and member of the Faculty of Environmental Earth Science at Hokkaido University. “By simplifying the handling of large, intricate genomic datasets, PsiPartition emerges as a powerful new resource for evolutionary research.”
