Back

PhyloForge: Unifying micro and macro evolution with comprehensive genomic signals

Wang, Y.; Dong, W.; Liang, Y.; Chen, F.

2024-03-07 molecular biology
10.1101/2024.03.06.583656 bioRxiv
Show abstract

With the explosive growth of biological data, the dimensions of phylogenetic research have expanded to encompass various aspects, including the study of large-scale populations at the microevolutionary level and comparisons between different species or taxonomic units at the macroevolutionary level. Traditional phylogenetic tools often struggle to handle the diverse and complex data required for these different evolutionary scales. In response to this challenge, we introduce PhyloForge-a robust tool designed to seamlessly integrate the demands of both micro- and macro-evolution, comprehensively utilizing diverse phylogenomic signals, such as genes, SNPs, structural variations, as well as mitochondrial and chloroplast genomes. PhyloForges groundbreaking innovation lies in its capability to seamlessly integrate multiple phylogenomic signals, enabling unified analysis of multidimensional genomic data. This unique feature empowers researchers to gain a more comprehensive understanding of diverse aspects of biological evolution. PhyloForge not only provides highly customizable analysis tools for experienced researchers but also features an intuitively designed interface, facilitating effortless phylogenetic analysis for beginners. Extensive testing across various domains, including animals, plants, and fungi, attests to its broad applicability in the field of phylogenetics. In summary, the developmental background and innovative features of PhyloForge position it with significant potential in the era of large-scale genomics, offering a new perspective and toolset for a deeper understanding of the evolution of life.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.