,The generic placement of the strawberry leaf blight fungus, formerly known as Phomopsis obscurans has always been subject to uncertainty. These datasets provide the phylogenetic evidence based on four DNA markers (28S rDNA/LSU, ITS, TEF1 and RPB2) that support the establishment of a monotypic new fungal genus Paraphomopsis. Datasets include the single gene sequence alignments for the LSU, ITS, TEF1 and RPB2 markers, and the complete combined phylogenetic dataset and phylogenetic tree files for each single gene and combined analysis. The updated multi-gene datasets and trees for the Diaporthales provide the evidence to distinguish the leaf blight pathogen (Paraphomopsis obscurans) from the taxa associated with leaf blotch (Gnomoniopsis fragariae) and petiole blight and root rot (Paragnomonia fragariae).,,

This study generated genetic 'metabarcode' data using high-throughput sequencing to characterize pollen foraging behavior of the endangered rusty-patched bumblebee, Bombus affinis. Pollen samples were collected primarily from forest meadow habitats in the Appalachian piedmont of the eastern United States, specifically within the states of Virginia and West Virginia. Three additional samples from the upper Midwest were also included for comparison. This data release consists of three tab-delimited files and a file of DNA sequences: 1) sample.metadata.txt includes sample identifiers and the accessions they have been assigned by the National Center for Biotechnology Information (NCBI), the authoritative repository for publicly funded genetic data in the United States. These accessions can be used individually to obtain raw sequencing data or sample information at www.ncbi.nlm.nih.gov. Alternatively, the BioProject accession PRJNA1235776 can be searched to retrieve the full set of data and sample accessions listed in the file. Entity and attribute metadata are provided for this file herein. 2) ITS1.raw.pollen.counts.txt includes the inferred taxon counts at the internal transcribed spacer 1 (ITS1) genetic locus, i.e. number of ITS1 sequences in a sample attributable to each identified taxon in each sample. Taxa are in rows and sequencing libraries are in columns. Taxa are listed by scientific name and the taxonomic rank of that scientific name. A numeric taxonomic identifier used by NCBI for each taxon is also provided, as the taxonomic identifier is unique in the NCBI databases whereas scientific names may not be. Entity and attribute data are not provided for this file due to its size and repetitive content. 3) ITS2.raw.pollen.counts.txt includes the inferred taxon counts at the internal transcribed spacer 2 (ITS2) genetic locus, i.e. number of ITS2 sequences in a sample attributable to each identified taxon in each sample. The file is identical in structure to the ITS1 file. 4) reference.db.fas contains the plant reference DNA sequences used for taxonomic assignment of the pollen sample sequences.

,The objective was to generate a high-quality draft assembly of the whole genome as a resource for future applications such as temporal and spatial dispersal studies of the pathogen and to investigate genotype diversity relevant in fungicide resistance studies and cypress breeding programs. We report here the genome sequence of Passalora sequoiae 9LC2 that was isolated from Leyland cypress 'Leighton Green' (Cupressocyparis leylandii) in 2017 in southern Mississippi. The draft genome was obtained using Pacific Biosciences (PacBio) SMRT and Illumina HiSeq 2500 sequencing. Illumina reads were mapped to PacBio assembled contigs to determine base call consistency. Based on a total of 44 contigs with 722 kilobase (kb) average length (range 9.4 kb to 3.4 Mb), the whole genome size was estimated at 31,768,716 bp. Mapping of Illumina reads to PacBio contigs resulted in a 1000 x coverage and were used to confirm accuracy of the consensus sequences.,The figures and methods files are documentation in support of a BMC Data Notes publication 'Draft Genome Assembly of Passalora sequoiae a Needle Blight Pathogen on Leyland Cypress'. The images illustrate,.,

,Protein predictions using Augustus web for the fungi Neonectria coccinea and N. faginata, as well as protein prediction of closely related species N. ditissima, and Corinectria fuckeliana.,,

,Organisms living in honey bees and honey bee colonies form large associative holobiont communities that are integral to bee biology. High-throughput sequencing approaches to characterize these holobiont communities from honey bees in various states of health and disease are now commonplace, producing large amounts of nucleotide sequence data that must be accurately and consistently analyzed in order to produce reliable and comparable reports. In addition, new species designations and revisions are actively being made from honey bee holobiont communities, complicating nomenclature in larger databases where taxonomic descriptions associated with archived sequences can quickly become outdated and misleading.,To improve the accuracy and consistency of honey bee holobiont research, we have developed HoloBee: a curated database of publicly accessioned nucleotide sequences from the honey bee holobiont community. Except in rare and noted exceptions made by curators, sequences used in HoloBee were obtained from, or in association with, Apis mellifera (Western honey bee) as well as other honey bee species where available (e.g. Apis cerana, Apis dorsata, Apis laboriosa, Apis koschevnikovi, Apis florea, Apis andreniformis and Apis nigrocincta). Sources include: within or on the surface of honey bees (adult, pupae, larvae, egg), corbicular pollen, bee bread, royal jelly, honey, comb, hive surfaces (e.g. bottom board debris, frames, landing platforms), and isolates of microbes, parasites and pathogens from honey bees. HoloBee contains two non-overlapping sets of sequence data, HoloBee-Barcode and HoloBee-Mop, each of which have distinct intended uses.,HoloBee-Barcode is a non-redundant database of taxonomically informative barcoding loci for all viruses, bacteria, fungi, protozoans and metazoans associated with honey bees (Apis spp.). It was created from an exhaustive master sequence archive of all valid holobiont sequences. Redundancy was removed from this master archive using a clustering algorithm that grouped sequences with ≥ 99% identity and retained the longest sequence from each cluster as the representative accession for that sequence type (“centroid”). These centroid sequences were concatenated into a fasta formatted file to create the HoloBee-Barcode database. Associated taxonomy for each centroid, including Superkingdom through Species and Strain/Isolate, was individually reviewed and corrected when necessary by a curator. Cross reference tables (separated according to 5 major taxonomic groups) provide a user-friendly outline of information for each centroid accession within HoloBee-Barcode including taxonomy, gene/product name, sequence length, the unaltered NCBI definition line, the number and identity of redundant sequences clustered within each centroid, and any additional information provided by the curator. HoloBee-Barcode centroid counts are: Viruses = 86; Bacteria = 496; Fungi = 41; Protozoa = 4; Metazoa = 60.,HoloBee-Barcode is intended to improve and standardize quantitative and qualitative metagenomic descriptions of holobiont communities associated with honey bees by providing a curated set of barcode sequences. The goal of genetic barcoding is to associate a nucleotide sequence sample to a taxonomically valid species. Genomic regions targeted for such barcoding purposes varied by taxonomic group. The small subunit (SSU) ribosomal RNA, or 16S rRNA, is the most commonly used barcode for bacteria and is used in HB-Barcode. These 16S rRNA sequences will support the analysis of data generated with the widely used approach of amplicon-based 16S rRNA deep sequencing to study microbiota communities. Although barcode markers for fungi are less definitive than bacteria, HB-Barcode defaults to the ribosomal RNA internal transcribed spacer region (ITS), which typically includes ITS-1, 5.8S, and ITS-2. For some clades that cannot be resolved by this region, other barcode markers were selected. The majority of barcodes for

,Boxwood plants are affected by many different diseases caused by fungi. Some boxwood diseases are deadly and quickly kill the infected plants, but with others, the plant can survive and even thrive when infected. The fungus that causes volutella blight is the most common of these weak boxwood pathogens. Even the healthiest boxwood plants are infected by the volutella fungus, and often there are no signs that the plants are hurt by the infection. In order to understand why the volutella blight fungus is such a weak pathogen and to understand the genetic mechanisms it uses to interact with boxwood, the complete genome of the volutella fungus was sequenced and characterized. These datasets are generated from the genome sequence of Pseudonectria foliicola, strain ATCC13545, the fungus responsible for volutella disease of boxwood. Datasets include the nuclear genome and mitochondrial genome assemblies (sequenced using Illumina technology), the predicted gene model dataset generated using MAKER, the multiple sequence alignment of single-copy orthologs used for phylogenetic analysis, CMAP files generated from SimpleSynteny analysis of mitogenomes, and high quality photographic images.,,

,Phenotypic Data A subset of 264 lines from the National Small Grains Collection global hexaploid winter wheat germplasm collection was evaluated under controlled growth chamber conditions for reaction to the pathogens Parastagonospora nodorum and Pyrenophora tritici-repentis. Both infiltrations and inoculations were performed on plants planted in plastic cones and when seedlings were at the second leaf stage. Plants were infiltrated with the P. nodorum necrotrophic effectors (NEs) SnTox1, SnToxA, SnTox3, SnTox267, and SnTox5; and the P. tritici-repentis NE Ptr ToxB. The scoring system was 0-3, with reaction types of 2 and 3 considered sensitive and 0 to 1 were insensitive. Plants were inoculated with the P. nodorum isolates Sn4, Sn2000, AR2-1, SnIr05H71a, and NOR4 and P. tritici-repentis isolates Pti2, 86-124, DW5, and AR CrossB10. After inoculation, plants were placed in a 100 % humidity growth chamber at 21 °C for 24 hours under constant light, then moved to a controlled growth chamber at 21 °C with a 12 h photoperiod. Plants were scored at 7 days post inoculation. For P. nodorum, plants were scored using a 0 to 5 scale, with 0 being highly resistant and 5 being highly susceptible. For P. tritici-repentis, plants were scored using a 1 to 5 scale, with 1 being highly resistance and 5 being highly susceptible. Three homogeneous replicates (determined by Bartlett’s chi squared analysis) were used to calculate an average value for each trait. This value was used for the rest of the analysis.,Genotypic Data DNA of the winter wheat panel was extracted and genotyped using the Illumina iSelect 90k wheat SNP array. Clustering data was analyzed using GenomeStudio 2.0.5 from Illumina, Inc. SNPs were ordered based on their physical position in the Chinese Spring IWGSC RefSeq v2.0. In TASSEL v5.2, SNP markers were filtered with a minor allele frequency greater than 0.01 and missing data less than 50%. For the remaining markers, missing values were imputed using the LD-KNNi method.,Genome-wide association analysis data Association mapping was conducted using the R package GAPIT v.3. The filtered hapmap file was used for the association mapping, along with the average value for each phenotypic trait. The models GLM, MLM, MLMM, FarmCPU, and Blink were run on the averages for each trait. ** Resources in this dataset:,