A survey of public honey bee sequence data was performed to detect infections by Lake Sinai Virus (LSV). The Sequence Read Archive of the National Center for Biotechnology Information (NCBI) was queried to identify accessions of RNA sequence data derived from honey bee. These were filtered as described below and then up to 50 million reads or read pairs were downloaded and searched against a reference database of conserved LSV sequence. Accessions with matches above a specified threshold were downloaded in their entirety and assembled into longer contiguous sequences (contigs). The result contigs were searched against each open reading frame (ORF) of the reference LSV genome present in the NCBI database (accession NC_032433.1) and matching regions from each contig. These ORF sequences were aligned with additional sequences identified in NCBI databases through the BLAST web service. These alignments provide the basis for computing phylogenetic trees, rates of nucleotide substitution, codon usage bias, and other evolutionary parameters.

Lake Sinai Viruses (LSV) are common ribonucleic acid (RNA) viruses of honey bees (Apis mellifera) that frequently reach high abundance but are not linked to overt disease. LSVs are genetically heterogeneous and collectively widespread, but despite frequent detection in surveys, the ecological and geographic factors structuring their distribution in A. mellifera are not understood. Even less is known about their distribution in other species. Better understanding of LSV prevalence and ecology have been hampered by high sequence diversity within the LSV clade. We developed a new genetic assay that detects all currently known lineages. We also performed pilot metagenetic sequencing to quantify the diversity of LSV sequences. The resulting data are summarized herein.

Deformed wing virus (DWV) is a major pathogen of concern to apiculture, and recent reports have indicated the local predominance and potential virulence of recombinants between DWV and a related virus, Varroa destructor virus 1 (VDV). However, little is known about the frequency and titer of VDV and recombinants relative to DWV generally. In this study, I assessed the relative occurrence and titer of DWV and VDV in public RNA-seq accessions of honey bee using a rapid, kmer-based approach. Three recombinant types were detectable graphically and corroborated by de novo assembly. Recombination breakpoints did not disrupt the capsid-encoding region, consistent with previous reports, and both VDV- and DWV-derived capsids were observed in recombinant backgrounds. High abundance of VDV kmers was largely restricted to recombinant forms. Non-metric multidimensional scaling identified genotypic clusters among DWV isolates, which was corroborated by read mapping and consensus generation. The recently described DWV-C lineage was not detected in the searched accessions. The data further highlight the utility of high-throughput sequencing to monitor viral polymorphisms and statistically test biological predictors of titer, and point to the need for consistent methodologies and sampling schemes.

Deformed wing virus (DWV) is a major pathogen of concern to apiculture, and recent reports have indicated the local predominance and potential virulence of recombinants between DWV and a related virus, Varroa destructor virus 1 (VDV). However, little is known about the frequency and titer of VDV and recombinants relative to DWV generally. In this study, I assessed the relative occurrence and titer of DWV and VDV in public RNA-seq accessions of honey bee using a rapid, kmer-based approach. Three recombinant types were detectable graphically and corroborated by de novo assembly. Recombination breakpoints did not disrupt the capsid-encoding region, consistent with previous reports, and both VDV- and DWV-derived capsids were observed in recombinant backgrounds. High abundance of VDV kmers was largely restricted to recombinant forms. Non-metric multidimensional scaling identified genotypic clusters among DWV isolates, which was corroborated by read mapping and consensus generation. The recently described DWV-C lineage was not detected in the searched accessions. The data further highlight the utility of high-throughput sequencing to monitor viral polymorphisms and statistically test biological predictors of titer, and point to the need for consistent methodologies and sampling schemes.

Honey bees (Apis mellifera), a critical agricultural pollinator in many areas, have a high rate of infection with a large DNA virus, Apis mellifera filamentous virus (AmFV), yet little is known about its ecology or impact on honey bee colonies, other than its ubiquity and apparent low virulence. This study scanned over 5,000 public data sets to detect AmFV sequences in honey bees as well as a parasitic mite of honey bees, Varroa destructor, that is a potential vector of AmFV. The data release consists of these files: 1. AmFV.genome.assemblies.aligned.fas, which contains new AmFV draft genome sequences generated by this study aligned with existing reference genome accessions downloaded from the National Center for Biotechnology Information (NCBI). 2. kmer.list.txt, a list of kmers that were extracted from reference sequences and searched for in Sequence Read Archive (SRA) accessions. 3. sample.metadata.txt, which lists all accessions of the SRA, and NCBI database of high-throughput sequence data, that were used in this study. The file also includes the raw occurrence counts of kmers listed in kmer.lists.txt, summed by category.

Honey bees (Apis mellifera), a critical agricultural pollinator in many areas, have a high rate of infection with a large DNA virus, Apis mellifera filamentous virus (AmFV), yet little is known about its ecology or impact on honey bee colonies, other than its ubiquity and apparent low virulence. This study scanned over 5,000 public data sets to detect AmFV sequences in honey bees as well as a parasitic mite of honey bees, Varroa destructor, that is a potential vector of AmFV. The data release consists of these files: 1. AmFV.genome.assemblies.aligned.fas, which contains new AmFV draft genome sequences generated by this study aligned with existing reference genome accessions downloaded from the National Center for Biotechnology Information (NCBI). 2. kmer.list.txt, a list of kmers that were extracted from reference sequences and searched for in Sequence Read Archive (SRA) accessions. 3. sample.metadata.txt, which lists all accessions of the SRA, and NCBI database of high-throughput sequence data, that were used in this study. The file also includes the raw occurrence counts of kmers listed in kmer.lists.txt, summed by category.

- 본 데이터는 소비 촬영기, 소문 촬영기, 플레이트 촬영기를 사용하여 꿀벌 이미지의 질병을 분류하고 벌통 내부 환경 데이터(온도, 습도, 이산화탄소)를 꿀벌 이미지와 쌍으로 수집한 데이터

High-throughput methods for identification of pollinator taxa are desirable to improve our understanding of pollinator distributions, population trends, and ecology. Genetic sequencing of taxonomically informative 'barcode' loci is one high-throughput strategy, which can be applied to individual specimens using Sanger technology and to complex mixtures using metabarcoding technology. This study generated Sanger sequencing data from morphologically identified specimens and metabarcoding data derived from pooled tissues. This data release consists of several files: 1. sample.metadata.txt, which contains sampling metadata and identifiers linking to sequence data that has been deposited in the Sequence Read Archive of the National Center for Biotechnology Information (NCBI). This database is authoritative and comprehensive for sharing high-throughput sequence data produced with public funds. All NCBI-derived accessions listed in the file can be searched at www.ncbi.nlm.nih.gov to retrieve sample and sequence information 2. raw.counts.txt, which is a tab-delimited table of counts of individual metabarcode sequences that are attributed to each detected bee genus. 3. BeeSangerSequencesFASTA.txt, which is a text document in FASTA format containing the DNA sequences obtained from the individual specimens using Sanger sequencing technology. 4. reference.sequences.fasta, which is a text document in FASTA format containing the DNA sequences obtained from public databases for taxonomic assignment of metabarcode sequences as described in the process steps.

,Supplemental information from a project describing the transcriptome of a beetle parasite of honey bees,The small hive beetle (SHB), Aethina tumida, is a major pest of managed honey bee (Apis mellifera) colonies in the United States and Australia, and an emergent threat in Europe. While strong honey bee colonies generally keep SHB populations in check, weak or stressed colonies can succumb to infestations. This parasite has spread from a sub-Saharan Africa to three continents, leading to immense management and regulatory costs. We performed a transcriptomic analysis involving deep sequencing of multiple life stages and both sexes of this species. The assembled transcriptome appears to be nearly complete, as judged by conserved insect orthologs and the ability to find plausible homologs for 11,952 proteins described from the genome of the red flour beetle. Expressed genes include each of the major metabolic, developmental and sensory groups, along with genes for proteins involved with immune defenses and insecticide resistance. We also present a total of 23,085 high-quality SNP's for the assembled contigs. We highlight potential differences between this beetle and its honey bee hosts, and suggest mechanisms of future research into the biology and control of this species. SNP resources will allow functional genetic analyses and analyses of dispersal for this invasive pest.,,