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README file for packageDescribes all files in package and provides external links to additional documentation.SILVA-to-NCBI mappingContains a mapping of Genbank accession numbers to NCBI taxon ids and names. The accessions are those that are the reference sequences for SILVA clusters.accessions.tgzOTT amendmentsThe taxonomy amendments used in preparation of OTT. Includes all amendments in https://github.com/OpenTreeOfLife/amendments-1 at the time that we built the version of OTT described in the article. Each amendment is a json file that includes the change being proposed as well as sources that support the change.amendments.tgzmapping of source ids to OTT idsCumulative mapping of identifiers in sources to OTT identifiers. Format of each line is source:sourceid,OTTid.by_qid.tgzOTT version2.10This is the previous version of the Open Tree Taxonomy. It is included because we use the previous version during identifier assignment (ott2.10 required to build ott3.0). See the enclosed README for details on files and formats.ott2.10.tgzOTT version 3.0Version of the Open Tree Taxonomy described in the manuscript. See the enclosed README for details on files and formats.ott3.0.tgzseparation taxonomyThe 'separation taxonomy' described in the manuscript. Contains a list of taxa and a list of synonyms.separation.tgz Taxonomy and nomenclature data are critical for any project that synthesizes biodiversity data, as most biodiversity data sets use taxonomic names to identify taxa. Open Tree of Life is one such project, synthesizing sets of published phylogenetic trees into comprehensive summary trees. No single published taxonomy met the taxonomic and nomenclatural needs of the project. Here we describe a system for reproducibly combining several source taxonomies into a synthetic taxonomy, and we discuss the challenges of taxonomic and nomenclatural synthesis for downstream biodiversity projects.

Related Article: Paul A. Goddard, John Singleton, Isabel Franke, Johannes S. Möller, Tom Lancaster, Andrew J. Steele, Craig V. Topping, Stephen J. Blundell, Francis L. Pratt, C. Baines, Jesper Bendix, Ross D. McDonald, Jamie Brambleby, Martin R. Lees, Saul H. Lapidus, Peter W. Stephens, Brendan W. Twamley, Marianne M. Conner, Kylee Funk, Jordan F. Corbey, Hope E. Tran, J. A. Schlueter, Jamie L. Manson|2016|Phys.Rev.B|93|094430|doi:10.1103/PhysRevB.93.094430

Simulation of a black-hole binary system evolved by the SpEC code.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures. Related Article: H.P. Yennawar, L.J. Silverberg, K. Cannon, D. Gandla, S.K. Kondaveeti, M.J. Zdilla, A. Nuriye|2018|Acta Crystallogr.,Sect.E:Cryst.Commun.|74|1695|doi:10.1107/S2056989018015098

Abstract Background Populus trichocarpa is an important forest tree species for the generation of lignocellulosic ethanol. Understanding the genomic basis of biomass production and chemical composition of wood is fundamental in supporting genetic improvement programs. Considerable variation has been observed in this species for complex traits related to growth, phenology, ecophysiology and wood chemistry. Those traits are influenced by both polygenic control and environmental effects, and their genome architecture and regulation are only partially understood. Genome wide association studies (GWAS) represent an approach to advance that aim using thousands of single nucleotide polymorphisms (SNPs). Genotyping using exome capture methodologies represent an efficient approach to identify specific functional regions of genomes underlying phenotypic variation. Results We identified 813 K SNPs, which were utilized for genotyping 461 P. trichocarpa clones, representing 101 provenances collected from Oregon and Washington, and established in California. A GWAS performed on 20 traits, considering single SNP-marker tests identified a variable number of significant SNPs (p-value

Characterizing transcriptomes in non-model organisms has resulted in a massive increase in our understanding of biological phenomena. This boon, largely made possible via high-throughput sequencing, means that studies of functional, evolutionary and population genomics are now being done by hundreds or even thousands of labs around the world. For many, these studies begin with a de novo transcriptome assembly, which is a technically complicated process involving several discrete steps. The Oyster River Protocol (ORP), described here, implements a standardized and benchmarked set of bioinformatic processes, resulting in an assembly with enhanced qualities over other standard assembly methods. Specifically, ORP produced assemblies have higher Detonate and TransRate scores and mapping rates, which is largely a product of the fact that it leverages a multi-assembler and kmer assembly process, thereby bypassing the shortcomings of any one approach. These improvements are important, as previously unassembled transcripts are included in ORP assemblies, resulting in a significant enhancement of the power of downstream analysis. Further, as part of this study, I show that assembly quality is unrelated with the number of reads generated, above 30 million reads. Code Availability: The version controlled open-source code is available at https://github.com/macmanes-lab/Oyster_River_Protocol. Instructions for software installation and use, and other details are available at http://oyster-river-protocol.rtfd.org/.

Additional file 12. Stomoxys metabolic detoxification gene family member amino acid sequences.

CERN-LHC. Experimental measurements of the correlations between the elliptic or triangular flow coefficients, $v_m$ ($m=2$ or 3), and other flow harmonics, $v_n$ ($n=2$ to 5) in lead-lead collisions. The data tables are linked to the corresponding figure number in the paper as well as additional plots in reference. $v_{4}$ data for various $q_3$ bins, Centrality 0-5%.

Diverse bacterial communities are found on every surface of macro-organisms, and they play important roles in maintaining normal physiological functions in their hosts. While the study of microbiomes has expanded with the influx of data enabled by recent technological advances, microbiome research in reptiles lags behind other organisms. We sequenced the nasal microbiomes in a sample of four North American tortoise species, and we found differing community compositions among tortoise species and sampling sites, with higher richness and diversity in Texas and Sonoran desert tortoises. Using these data, we investigated the prevalence and operational taxonomic unit (OTU) diversity of the potential pathogen Pasteurella testudinis, and found it to be common, abundant, and highly diverse. However, the presence of this bacterium was not associated with differences in bacterial community composition within host species. We also found that the presence of nasal discharge from tortoises at the time of sampling was associated with a decline in diversity and a change in microbiome composition, which we posit is due to the harsh epithelial environment associated with immune responses. Repeated sampling across seasons, and at different points of pathogen colonisation, should contribute to our understanding of the causes and consequences of different bacterial communities in these long-lived hosts. OTU biom tableOTU biom table of bacteria in the upper respiratory tracts of 146 North American tortoises. Biom table includes only bacterial OTUs and excludes OTUs represented by only one sequence.otu_bacteriaonly.biomTortoise sample data fileData file including information on each tortoise sampled, presence or absence of pathogens in the samples, and barcode sequences for pyrosequencing. Includes sheet of column descriptions.WeitzmanTortoise454mapping.xlsxTortoise Nasal Bacterial SequencesSequences from 454-pyrosequencing of nasal flush samples from North American tortoises. Sequences are demultiplexed. Mapping file provides information on host species, site, and sex; sampling year; presence of mucus at sampling time; presence of two Mycoplasma spp. in the samples from quantitative PCR; and sequencing information such as barcode sequences.tortoise_nasal_seqs.fna