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Christoph Urs Mayer [et al.], Target DNA enrichment combined with high-throughput sequencing technologies is a powerful approach to probing a large number of loci in genomes of interest. However, software algorithms that explicitly consider nucleotide sequence information of target loci in multiple reference species for optimizing design of target enrichment baits to be applicable across a wide range of species have not been developed. Here we present an algorithm that infers target DNA enrichment baits from multiple nucleotide sequence alignments. By applying clustering methods and the combinatorial 1-center sequence optimization to bait design, we are able to minimize the total number of baits required to efficiently probe target loci in multiple species. Consequently, more loci can be probed across species with a given number of baits. Using transcript sequences of 24 apoid wasps (Hymenoptera: Crabronidae, Sphecidae) from the 1KITE project and the gene models of Nasonia vitripennis, we inferred 57,650, 120-bp-long baits for capturing 378 coding sequence sections of 282 genes in apoid wasps. Illumina reduced-representation library sequencing confirmed successful enrichment of the target DNA when applying these baits to DNA of various apoid wasps. The designed baits furthermore enriched a major fraction of the target DNA in distantly related Hymenoptera, such as Formicidae and Chalcidoidea, highlighting the baits' broad taxonomic applicability. The availability of baits with broad taxonomic applicability is of major interest in numerous disciplines, ranging from phylogenetics to biodiversity monitoring. We implemented our new approach in a software package, called BaitFisher, which is open source and freely available at https://github.com/cmayer/BaitFisher-package.git., C.B. is a “Ramon y Cajal” fellow supported by the Spanish Ministry of Science and Education (MEC) (RYC-2014-15615).

Phylogenetic relationships of the primarily wingless insects are still considered unresolved. Even the most comprehensive phylogenomic studies that addressed this question did not yield congruent results. To get a grip on these problems, we here analyzed the sources of incongruence in these phylogenomic studies by using an extended transcriptome data set. Our analyses showed that unevenly distributed missing data can be severely misleading by inflating node support despite the absence of phylogenetic signal. In consequence, only decisive data sets should be used which exclusively comprise data blocks containing all taxa whose relationships are addressed. Additionally, we used Four-cluster Likelihood Mapping (FcLM) to measure the degree of congruence among genes of a data set, as a measure of support alternative to bootstrap. FcLM showed incongruent signal among genes, which in our case is correlated neither with functional class assignment of these genes nor with model misspecification due to unpartitioned analyses. The herein analyzed data set is the currently largest data set covering primarily wingless insects, but failed to elucidate their interordinal phylogenetic relationships. Although this is unsatisfying from a phylogenetic perspective, we try to show that the analyses of structure and signal within phylogenomic data can protect us from biased phylogenetic inferences due to analytical artifacts.