High quality gene models are necessary to expand the molecular and genetic tools available for a target organism, but these are available for only a handful of model organisms that have undergone extensive curation and experimental validation over the course of many years. The majority of gene models present in biological databases today have been identified in draft genome assemblies using automated annotation pipelines that are frequently based on orthologs from distantly related model organisms and usually have minor or major errors. Manual curation is time consuming and often requires substantial expertise, but is instrumental in improving gene model structure and identification. Manual annotation may seem to be a daunting and cost-prohibitive task for small research communities but involving undergraduates in community genome annotation consortiums can be mutually beneficial for both education and improved genomic resources. We outline a workflow for efficient manual annotation driven by a team of primarily undergraduate annotators. This model can be scaled to large teams and includes quality control processes through incremental evaluation. Moreover, it gives students an opportunity to increase their understanding of genome biology and to participate in scientific research in collaboration with peers and senior researchers at multiple institutions.

Presymptomatic detection of citrus trees infected with Candidatus Liberibacter asiaticus (CLas), the bacterial pathogen associated with Huanglongbing (HLB; citrus greening disease), is critical to controlling the spread of the disease. To test whether infected citrus trees produce systemic signals that may be used for indirect disease detection, lemon (Citrus limon) plants were graft-inoculated with either CLas-infected or control (CLas-) budwood, and leaf samples were longitudinally collected over 46 weeks and analyzed for plant changes associated with CLas infection. RNA, protein, and metabolite samples extracted from leaves were analyzed using RNA-Seq, mass spectrometry, and ¹H NMR spectroscopy, respectively. Significant differences in specific transcripts, proteins, and metabolites were observed between CLas-infected and control plants as early as 2 weeks post graft (wpg). The most dramatic differences between the transcriptome and proteome of CLas-infected and control plants were observed at 10 wpg, including coordinated increases in transcripts and proteins of citrus orthologs of known plant defense genes. This integrated approach to quantifying plant molecular changes in leaves of CLas-infected plants supports the development of diagnostic technology for presymptomatic or early disease detection as part of efforts to control the spread of HLB into uninfected citrus groves.

Huanglongbing (HLB) is a deadly, incurable citrus disease putatively caused by the unculturable bacterium, 'Candidatus Liberibacter asiaticus' (CLas), and transmitted by Diaphorina citri. Prior studies suggest D. citri transmits CLas in a circulative and propagative manner; however, the precise interactions necessary for CLas transmission remain unknown, and the impact of insect sex on D. citri-CLas interactions is poorly understood despite reports of sex-dependent susceptibilities to CLas. We analyzed the transcriptome, proteome, metabolome, and microbiome of male and female adult D. citri reared on healthy or CLas-infected Citrus medica to determine shared and sex-specific responses of D. citri and its endosymbionts to CLas exposure. More sex-specific than shared D. citri responses to CLas were observed, despite there being no difference between males and females in CLas density or relative abundance. CLas exposure altered the abundance of proteins involved in immunity and cellular and oxidative stress in a sex-dependent manner. CLas exposure impacted cuticular proteins and enzymes involved in chitin degradation, as well as energy metabolism and abundance of the endosymbiont 'Candidatus Profftella armatura' in both sexes similarly. Notably, diaphorin, a toxic Profftella-derived metabolite, was more abundant in both sexes with CLas exposure. The responses reported here resulted from a combination of CLas colonization of D. citri as well as the effect of CLas infection on C. medica. Elucidating these impacts on D. citri and their endosymbionts contributes to our understanding of the HLB pathosystem and identifies the responses potentially critical to limiting or promoting CLas acquisition and propagation in both sexes.

A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps; (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node; (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience; and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon.

Methods for assembly, taxonomic profiling and binning are key to interpreting metagenome data, but a lack of consensus about benchmarking complicates performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on highly complex and realistic data sets, generated from ∼700 newly sequenced microorganisms and ∼600 novel viruses and plasmids and representing common experimental setups. Assembly and genome binning programs performed well for species represented by individual genomes but were substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below family level. Parameter settings markedly affected performance, underscoring their importance for program reproducibility. The CAMI results highlight current challenges but also provide a roadmap for software selection to answer specific research questions.