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Traditional taxonomy provides a hierarchical organization of bacteria and archaea across taxonomic ranks from kingdom to subspecies. More recently, bacterial taxonomy has been more robustly quantified using comparisons of sequenced genomes, as in the Genome Taxonomy Database (GTDB), resolving down to genera and species. Such taxonomies have proven useful in many contexts, yet lack the flexibility and resolution of a more fine-grained approach. We apply our Life Identification Number (LIN) approach as a common, quantitative framework to tie existing (and future) bacterial taxonomies together, increase the resolution of genome-based discrimination of taxa, and extend taxonomic identification below the species level in a principled way. We utilize our existing concept of a LINgroup as an organizational concept for microorganisms that are closely related by overall genomic similarity, to help resolve some of the confusions and unforeseen negative effects of nomenclature changes of microbes due to genome-based reclassification. Our results obtained from experimentation demonstrate the value of LINs and LINgroups in mapping between taxonomies, translating between different nomenclatures, and integrating them into a single taxonomic framework.

genomeRxiv is a newly-funded US-UK collaboration to provide a public, web-accessible database of public genome sequences, accurately catalogued and classified by whole-genome similarity independent of their taxonomic affiliation. Our goal is to supply the basic and applied research community with rapid, precise and accurate identification of unknown isolates based on genome sequence alone, and with molecular tools for environmental analysis.The DNA sequencing revolution enabled the use of cultured and uncultured microorganism genomes for fast and precise identification. However, precise identification is impossible without1. reference databases that precisely circumscribe classes of microorganisms, and label these with their uniquely-shared characteristics2. fast algorithms that can handle the volumes of genome dataOur approach integrates the highly-resolved classification framework of Life Identification Numbers (LINs) with the speed and computational efficiency of sourmash and k-mer hashing algorithms, and the precision and filtering of average nucleotide identity (ANI). We aim to construct a single genome-based indexing scheme that extends from phylum to strain, enabling the unique and consistent placement of any sequenced prokaryote genome.genomeRxiv includes protocols for confidentiality, allowing groups to identify and announce the identities of newly-sequenced organisms without sharing genome data directly. This protects communities working with commercially- and ethically-sensitive organisms (e.g. production engineering strains, potential bioweapons, and to enable benefit sharing with indigenous communities).genomeRxiv will also provide online capability to design molecular diagnostic tools for metabarcoding and qPCR, to enable tracking of specific groupings of bacteria directly in the environment.

Additional file 1 : Supplemental Figure S1: Comparing evaluations of short read assemblies to long read assemblies. Supplemental Figure S2: Assembly ranking correlation matrices. Supplemental Figure S3: Filtering out non-Arthropod, contaminating reads using Taxonomy- annotated GC plots. Supplemental Figure S4: Length Distributions for Illumina Scaffolds, PacBio Reads and MinION Molecules. Supplemental Figure S5: Percent identity of MinION reads compared to a PacBio-only assembly. Supplemental Figure S6: Evaluations across Quiver polishing rounds. Supplemental Figure S7: Blended assemblies with both PacBio and MinION data tended to receive better ranks than PacBio-alone assemblies. Supplemental Figure S8: Metrics comparing assemblies after scaffolding. Supplemental Figure S9: Aligning chosen and discarded scaffolds from each assembler (Canu and Falcon). Supplemental Figure S10: BlobTools analysis of the Canu scaffolds. Supplemental Figure S11: BlobTools analysis and anchoring Falcon scaffolds. Supplemental Figure S12: The single locus that contains the full-length Escribá insert. Supplemental Figure S13: Pairwise comparisons of final Canu and Falcon scaffolds. Supplemental Figure S14: Pairwise comparisons of final Canu and Falcon annotations. Supplemental Figure S15: Dosage compensation of X-linked genes in Sciara coprophila. Supplemental Figure S16: Distribution of DNA modifications across Sciara genome (PacBio analysis). Supplemental Figure S17: Position weighted 7-mer motifs learned from different filtering and different subsets of the genome sequence (PacBio analysis). Supplemental Figure S18: MEME motifs in the PacBio and MinION analyses. Supplemental Figure S19: MinION signal distributions for 6mers defined by motifs learned in the PacBio analysis and negative controls. Supplemental Figure S20: The GCG trimer is depleted in the genome and transcriptome compared to expectation. Supplemental Figure S21: Distribution of distances between adjacent DNA modifications (PacBio analysis) on the same strand shows enrichment of short distances, a 10 bp periodicity, and a spike of enrichment at mono-nucleosome lengths of ~ 175 bp. Supplemental Figure S22: Distribution of distances between adjacent DNA modifications (PacBio analysis) on either strand also shows enrichment of short distances, a 10 bp periodicity, and a spike of enrichment at mono-nucleosome lengths of ~ 175 bp for 6mA and 5mC. Supplemental Table S1 A-E: Expected genome and chromosome sizes. Supplemental Table S2: RNA-seq samples spanning both sexes and 4 life cycle stages. Supplemental Table S3: Short read assembly size statistics. Supplemental Table S4: Long read assembly size statistics. Supplemental Table S5: Pairwise comparisons of size statistics of hybrid scaffolds from pair of Canu or Falcon assemblies. Supplemental Table S6: Size statistics of Canu C3.2 across the work flow. Supplemental Table S7: Size statistics of Falcon F9 across the work flow. Supplementary Table 8 A-C: Gap size statistics. Supplemental Table S9: Bacterial contig statistics in each assembly. Supplemental Table S10: Sciara (Bradysia) coprophila repeat family classes from RepeatModeler. Supplemental Table S11: Sub-classification of classified Repeat Families found in Sciara coprophila genome with RepeatModeler. Supplemental Table S12 A-B: Repeat Masking on Canu. Supplemental Table S13 A-B: Repeat Masking on Falcon. Supplemental Table S14: Transcriptome Evaluations. Supplemental Table S15 A-B: Maker Annotation Transcript Evaluations on Canu and Falcon. Supplemental Table S16: Additional characterization and comparisons of the final annotations of Canu and Falcon assemblies. Supplemental Table S17 A-C: Putative Sciara homologs for proteins involved in reading, writing, and erasing DNA methylation marks for adenine and cytosine. Supplemental Table S18 A-F: DNA modification percentages in male embryonic genomic DNA. Supplemental Table S19: Which dimers are observed with modifications more often than expected?. Supplemental Table S20: Which trimers are observed with modifications more often than expected?. Supplemental Table S21 A-F: Binomial tests for enrichment or depletion of DNA modifications in various genomic features. The Supplement also contains detailed experimental and bioinformatic methods sections, as well as software versions and supplemental references.

With the demand and abundance of information increasing over the last two decades, generations of computer scientists are trying to improve the whole process of information searching, retrieval, and storage. With the diversification of the information sources, users' demand for various requirements of the data has also changed drastically both in terms of usability and performance. Due to the growth of the source material and requirements, correctly sorting, filtering, and storing has given rise to many new challenges in the field. With the help of all four other teams on this project, we are developing an information retrieval, analysis, and storage system to retrieve data from Virginia Tech's Electronic Thesis and Dissertation (ETD), Twitter, and Web Page archives. We seek to provide an appropriate data research and management tool to the users to access specific data. The system will also give certain users the authority to manage and add more data to the system. This project's deliverable will be combined with four others to produce a system usable by Virginia Tech's library system to manage, maintain, and analyze these archives. This report attempts to introduce the system components and design decisions regarding how it has been planned and implemented. Our team has developed a front end web interface that is able to search, retrieve, and manage three important content collection types: ETDs, tweets, and web pages. The interface incorporates a simple hierarchical user permission system, providing different levels of access to its users. In order to facilitate the workflow with other teams, we have containerized this system and made it available on the Virginia Tech cloud server. The system also makes use of a dynamic workflow system using a KnowledgeGraph and Apache Airflow, providing high levels of functional extensibility to the system. This allows curators and researchers to use containerised services for crawling, pre-processing, parsing, and indexing their custom corpora and collections that are available to them in the system. NSF: CMMI-1638207 IMLS: LG-37-19-0078-19 NSF: OAC-1835660 CS5604F2020FEreport.zip: Project documentation in LaTeX CS5604F2020FEreport.pdf: Project documentation CS5604F2020FEpresentation.pdf: Presentation slides CS5604F2020FEpresentation.pptx: Presentation slides CS5604F2020FEpresentation-demo.mp4: Video of the prototype demonstration

Ralstonia solanacearum phylotype II sequevar 1 (RsII-1, formerly race 3 biovar 2) causes tomato bacterial wilt, potato brown rot, and Southern wilt of geranium. Strains in RsII-1 cause wilting in potato and tomato at cooler temperatures than tropical lowland R. solanacearum strains. Although periodically introduced, RsII-1 has not established in the United States. This pathogen is of quarantine concern and listed as a Federal Select Agent. We report a rapidly sequenced (RsII-1 isolate introduced to the United States in geranium cuttings in spring 2020. UW848 belongs to the near-clonal cluster of RsII-1 global pandemic strains.

Painful processing procedures in piglets such as tail docking, castration, and teeth clipping are an emerging animal welfare concern. We hypothesized that transmammary delivery of a nonsteroidal anti-inflammatory drug, firocoxib, would reduce pain associated with processing in piglets. This study compared the pharmacokinetics, efficacy, safety, and tissue residue concentrations of 4 doses of firocoxib (0.5, 1.0, 1.5, or 2.0 mg/kg) administered to sows and delivered to nursing piglets prior to processing. Sixteen sows, 5 ± 2 d postpartum, were randomly assigned to 1 of 4 treatment groups. On day 0, sows received a single intramuscular dose of firocoxib at 7 ± 1 h before piglet surgical castration, tail docking, and teeth clipping (males) or sham handling (females). Firocoxib and cortisol concentrations were determined from selected samples collected from sows and 3 piglets per litter (2 barrows and 1 gilt) at 0, 2, 4, 6, 8, 12, 24, 48, 72, 96, and 120 h after drug administration. On day 21, piglets were weighed and all animals were euthanized and necropsied. Tissues were collected from 3 piglets per litter for histological examination and drug residue analysis. Mean (±SEM) peak plasma firocoxib concentrations (C(max)) were 107.90 ± 15.18, 157.50 ± 24.91, 343.68 ± 78.89, and 452.83 ± 90.27 ng/mL in sows receiving 0.5, 1.0, 1.5, and 2.0 mg/kg firocoxib, respectively, and 9.53 ± 1.21, 31.04 ± 6.79, 53.30 ± 11.1, and 44.03 ± 7.47 ng/mL in their respective piglets. Mean plasma terminal half-life values ranged from 26 to 31 h in sows and 30 to 48 h in piglets. Barrows nursing sows that received 2.0 mg/kg firocoxib had a lower mean plasma cortisol concentration at 1 ± 1 h after processing compared with barrows nursing sows that received 1.0 mg/kg (P = 0.0416) and 0.5 mg/kg of firocoxib (P = 0.0397). From processing to weaning, litters of sows receiving 2.0 mg/kg firocoxib gained more weight than litters of sows that received 0.5 mg/kg (P = 0.008) or 1.0 mg/kg (P = 0.005). No signs of nonsteroidal anti-inflammatory drug toxicity were observed on examination of the kidney, liver, stomach, and small intestine, and concentrations of firocoxib and the descyclopropylmethyl metabolite were below the limit of detection (0.01 µg/g) in all tissues examined from sows and piglets. These findings indicate that maternal delivery of firocoxib to suckling piglets before tail docking and castration may safely reduce processing-induced stress and enhance production by increasing weaning weights.