A 4-lineage statistical suite to evaluate the support of large-scale retrotransposon insertion data to reconstruct evolutionary trees

Retrophylogenomics makes use of genome-wide retrotransposon presence/absence insertion patterns to resolve questions in phylogeny and population genetics. In the genomics era, evaluating high-throughput data requires the associated development of appropriately powerful statistical tools. The currently used KKSC 3-lineage statistical test for estimating the significance of retrophylogenomic data is limited by the number of possible tree topologies it can assess in one step. To improve on this, we have extended the analysis to simultaneously compare 4-lineages, enabling us to evaluate ten distinct presence/absence insertion patterns for 26 possible tree topologies plus 129 trees with different incidences of hybridization or introgression. The new tool provides statistics for cases involving multiple ancestral hybridizations/introgressions, ancestral incomplete lineage sorting, bifurcation, and polytomy. The test is embedded in a user-friendly web R-application (http://retrogenomics.uni-mu..., , , # Data from: A 4-lineage statistical suite to evaluate the support of large-scale retrotransposon insertion data to reconstruct evolutionary trees

**Appendix_1.pdf:** Detailed description of mathematical models

**Appendix_2.csv:** Supplementary Tables S1-S7

**S1:** Comparison of the *stepwise* method with chi-square distribution against a 50% chi-square distribution with a 50% point mass 0 mixture criterion for frequency of correct recognition of models in the Poisson randomized sets.

**S2:** Comparison of the *reverse* method and chi-square distribution against binary chi-square distribution according to degrees of freedom plus point mass 0 mixture and binary chi-square distribution according to degrees of freedom plus point mass 1 mixture criteria for frequency of correct recognition of models in the Poisson randomized set.

**S3:** Testing the *reverse* method and chi-square distribution criterion for frequency of correct recognition of models in the Poisson randomized set.

**S4...,