Robust and Efficient Confidence Limits for Phylogenomic Inference of Organismal Relationships

Phylogenomic data are indispensable for establishing reliable relationships needed to build a robust Tree of Life. The superalignment approach concatenates hundreds or thousands of genomic segments, providing a straightforward, computationally efficient, and effective means of inferring phylogenies. However, the standard bootstrap method can produce overly confident support for incorrect inferences based on superalignments. It fails to account for the heterogeneity in phylogenetic signals across the data, which is caused by incomplete lineage sorting (ILS), data errors, and other biological processes. To detect such erroneous inferences, researchers need to produce and deliberate on the concordance of inferences derived from many complex and computationally demanding analyses that require knowledge of data partitions. This study demonstrates that analyzing phylogenomic subsamples with bootstrap upsampling overcomes the overconfidence drawback of the superalignment approach. We found that bootstrapping multiple small, randomly selected site subsets can detect the presence of phylogeny variation signals across the dataset, similar to that detected using data partitions. We present the Net Bootstrap Support (NBS) approach that accounts for this phylogenetic variation in the estimates of bootstrap confidence. NBS values showed comparable performance to multispecies coalescent analyses in the presence of ILS and surpassed it for datasets simulated with gene tree estimation errors. NBS analyses of phylogenomic data from rodents, fungi, and carnivorous plants corroborated the performance observed in simulated datasets and even mitigated overconfidence resulting from some data errors. NBS calculations are computationally efficient, with low memory consumption and high computational time savings, making the NBS approach well suited for big data molecular phylogenetics on both desktops and high-performance computing systems.