In the wake of constant improvements in sequencing technologies, numerous insect genomes have been sequenced. Currently, 1219 insect genome-sequencing projects have been registered with the National Center for Biotechnology Information, including 401 that have genome assemblies and 155 with an official gene set of annotated protein-coding genes. Comparative genomics analysis showed that the expansion or contraction of gene families was associated with well-studied physiological traits such as immune system, metabolic detoxification, parasitism and polyphagy in insects. Here, we summarize the progress of insect genome sequencing, with an emphasis on how this impacts research on pest control. We begin with a brief introduction to the basic concepts of genome assembly, annotation and metrics for evaluating the quality of draft assemblies. We then provide an overview of genome information for numerous insect species, highlighting examples from prominent model organisms, agricultural pests and disease vectors. We also introduce the major insect genome databases. The increasing availability of insect genomic resources is beneficial for developing alternative pest control methods. However, many opportunities remain for developing data-mining tools that make maximal use of the available insect genome resources. Although rapid progress has been achieved, many challenges remain in the field of insect genomics. © 2019 The Royal Entomological Society.
Several algorithms have been developed that use high-throughput sequencing technology to characterize structural variations (SVs). Most of the existing approaches focus on detecting relatively simple types of SVs such as insertions, deletions and short inversions. In fact, complex SVs are of crucial importance and several have been associated with genomic disorders. To better understand the contribution of complex SVs to human disease, we need new algorithms to accurately discover and genotype such variants. Additionally, due to similar sequencing signatures, inverted duplications or gene conversion events that include inverted segmental duplications are often characterized as simple inversions, likewise, duplications and gene conversions in direct orientation may be called as simple deletions. Therefore, there is still a need for accurate algorithms to fully characterize complex SVs and thus improve calling accuracy of more simple variants.We developed novel algorithms to accurately characterize tandem, direct and inverted interspersed segmental duplications using short read whole genome sequencing datasets. We integrated these methods to our TARDIS tool, which is now capable of detecting various types of SVs using multiple sequence signatures such as read pair, read depth and split read. We evaluated the prediction performance of our algorithms through several experiments using both simulated and real datasets. In the simulation experiments, using a 30× coverage TARDIS achieved 96% sensitivity with only 4% false discovery rate. For experiments that involve real data, we used two haploid genomes (CHM1 and CHM13) and one human genome (NA12878) from the Illumina Platinum Genomes set. Comparison of our results with orthogonal PacBio call sets from the same genomes revealed higher accuracy for TARDIS than state-of-the-art methods. Furthermore, we showed a surprisingly low false discovery rate of our approach for discovery of tandem, direct and inverted interspersed segmental duplications prediction on CHM1 (<5% for the top 50 predictions).TARDIS source code is available at https://github.com/BilkentCompGen/tardis, and a corresponding Docker image is available at https://hub.docker.com/r/alkanlab/tardis/.Supplementary data are available at Bioinformatics online. © The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org.
Wild almond species accumulate the bitter and toxic cyanogenic diglucoside amygdalin. Almond domestication was enabled by the selection of genotypes harboring sweet kernels. We report the completion of the almond reference genome. Map-based cloning using an F1 population segregating for kernel taste led to the identification of a 46-kilobase gene cluster encoding five basic helix-loop-helix transcription factors, bHLH1 to bHLH5. Functional characterization demonstrated that bHLH2 controls transcription of the P450 monooxygenase-encoding genes PdCYP79D16 and PdCYP71AN24, which are involved in the amygdalin biosynthetic pathway. A nonsynonymous point mutation (Leu to Phe) in the dimerization domain of bHLH2 prevents transcription of the two cytochrome P450 genes, resulting in the sweet kernel trait. Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.