A novel phylogenomics pipeline reveals complex pattern of reticulate evolution in Cucurbitales

A diverse range of high-throughput sequencing data, such as target capture, RNA-Seq, genome skimming, and high-depth whole genome sequencing, are used for phylogenomic analyses but the integration of such mixed data types into a single phylogenomic dataset requires a number of bioinformatic tools and significant computational resources. Here, we present a novel pipeline, CO_SCPLOWAPTUSC_SCPLOW, to analyze mixed data in a fast and efficient way. CO_SCPLOWAPTUSC_SCPLOW assembles these data types, allows searching of the assemblies for loci of interest, and finally produces alignments filtered for paralogs. If reference target loci are not available for the studied taxon, CO_SCPLOWAPTUSC_SCPLOW can also be used to discover new putative homologs via sequence clustering. Compared to other software, CO_SCPLOWAPTUSC_SCPLOW allows the recovery of a greater number of more complete loci across a larger number of species. We apply CO_SCPLOWAPTUSC_SCPLOW to assemble a comprehensive mixed dataset, comprising the four types of sequencing data for the angiosperm order Cucurbitales, a clade of about 3,100 species in eight mainly tropical plant families, including begonias (Begoniaceae) and gourds (Cucurbitaceae). Our phylogenomic results support the currently accepted circumscription of Cucurbitales except for the position of the holoparasitic Apodanthaceae, which group with Rafflesiaceae in Malpighiales. A subset of mitochondrial gene regions supports the earlier position of Apodanthaceae in Cucurbitales. However, the nuclear regions and majority of mitochondrial regions place Apodanthaceae in Malpighiales. Within Cucurbitaceae, we confirm the monophyly of all currently accepted tribes but also reveal deep reticulation patterns both in Cucurbitales and within Cucurbitaceae. We show that contradicting results among earlier phylogenetic studies in Cucurbitales can be reconciled when accounting for gene tree conflict and demonstrate the efficiency of CO_SCPLOWAPTUSC_SCPLOW for complex datasets.