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83 results for “landscape resistance”
Landscape layer for resistance
<p>This is raster file (base_cats_new3.asc) that was used to generate the environmental resistance surface with the ResistanceGA R package (Peterman, 2018) to evaluate models of environmental resistance to between-population movement of saltwater crocodiles <em>Crocodylus porosus</em> in the Northern Territory of Australia, represented by individual pairwise genetic distances among individuals. ResistanceGA models pairwise genetic distances in response to pairwise 'ecological distances' using linear mixed effects models with a maximum-likelihood population effects (MLPE) random effects structure (Clarke, Rothery, & Raybould, 2002), represented by individual ID in our models. We used Smouse and Peakall (1999) pairwise genetic distance as the response variable for this purpose.We estimated resistance surfaces that optimised random-walk commute distances (Etten, 2018) among the locations of sampled individuals as an explanatory variable in models of pairwise genetic distances among individuals. We ran a single surface optimisation in ResistanceGA (Peterman, 2018) to generate resistance values for the six environmental cover categories and stopped each model after 25 consecutive generations of no improvement in log-likelihood.</p>
Landscape layer for resistance
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Exploring the global metaplasmidome: unravelling plasmid landscapes and the spread of antibiotic resistance genes across diverse ecosystems
<p>Plasmid content was predicted from assembled data already publicly available or constructed from reads for this study. The assembled data supplied by Pasolli and colleagues (Pasolli <em>et al.</em>, 2019) , metasub consortium (Danko <em>et al.</em>, 2020) and TARA ocean (Tully <em>et al.</em>, 2018) were used for the human microbiome, the built environment and the marine ecosystem respectively. For assembly in the current study, reads from metagenomes were selected from two main databases. For the soil ecosystem, the metagenomes were selected from the dedicated curated database “TerrestrialMetagenomeDB” (Corrêa <em>et al.</em>, 2020). </p> <p>If the metagenomes were not assembled, reads were assembled by using megahit 1.2.9 with the metalarge option (Li <em>et al.</em>, 2015) after cleaning the data with bbduk2 (qtrim=rl trimq=28 minlen=25 maq=20 ktrim=r k=25 mink=11 and a list of adapters to remove) from the bbtools suite (<a href="https://jgi.doe.gov/data-and-tools/software-tools/bbtools/">https://jgi.doe.gov/data-and-tools/software-tools/bbtools/</a>).</p> <p>Plasmids were predicted for each assembly by using both reference-based and reference-free approaches as described in previous works (Hilpert <em>et al.</em>, 2021; Hennequin <em>et al.</em>, 2022) and available on the github website (https://github.com/meb-team/PlasSuite/). The databases used for the first approach included those for chromosomes (archaea and bacteria) and plasmids from RefSeq, as well as the MOB-suite tool (Robertson and Nash, 2018), SILVA (Quast <em>et al.</em>, 2013) and phylogenetic markers hosted by chromosomes (Wu <em>et al.</em>, 2013). The database created for this purpose is available at this address <a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-</a><a href="https://github.com/meb-team/PlasSuite/?tab=readme-ov-file#1-prepare-or-download-your-databases">databases</a>. Two reference-free methods were applied to contigs that were not affiliated with chromosomes (discarded) or plasmids (retained in the first step): PlasFlow (Krawczyk <em>et al.</em>, 2018) and PlasClass (Pellow <em>et al.</em>, 2020). Previously undetected viruses were removed by using ViralVerify (<a href="https://github.com/ablab/viralVerify">https://github.com/ablab/viralVerify</a>)(Antipov <em>et al.</em>, 2020) that provides in parallel plasmid/non-plasmid classification. This step would also remove potential plasmid-phage elements as described by Pfeifer <em>et al.</em> (Pfeifer <em>et al.</em>, 2021), but would minimise false positives. Eukaryotic contamination was removed by aligning the sequences against the NT database and human chromosomes (GRCh38) using minimap2 (Li, 2018) with -x asm5 option. Contigs mapping with 95% identity for at least 80% coverage were removed. The predicted plasmids, hereafter referred as plasmid-like sequences (PLSs), were grouped by "scientific names" (<em>i.e.</em> 27) such as defined in the SRA metadata (air, lake, wetland…) and subsequently named ecosystems. These ecosystems were grouped in 9 biomes (Tab Supplementary 4). The data were then dereplicated by ecosystems using cd-hit-est with a threshold of 99%. The dereplicated PLSs were then clustered using MMseqs2 (Steinegger and Söding, 2017) with 80% of coverage an 90% of identity (--min-seq-id 0.90 -c 0.8 --cov-mode 1 --cluster-mode 2 --alignment-mode 3 --kmer-per-seq-scale 0.2) to define plasmid-like clusters (PLCs).</p> <div> <p>The PLC sequences are included in the file "predicted_PLC.fasta" and the main features are dercribed in the file "metadata_PLC.tsv"</p> <ul> <li>fasta_id: fasta identification of the PLC</li> <li>ecosystem: ecosystem from which the PLC originates</li> <li>biome: biome of the ecosystem</li> <li>latitude, longitude: GPS coordinate of the ecosystem</li> <li>length: PLC length</li> <li>map_markers: plasmid marker genes detected by PlasSuite (Hilpert et al., 2021)</li> <li>map_ncbi: PLCs present in the RefSeq plasmid database(Hilpert et al., 2021)</li> <li>nb_genes: Number of genes detected by Prokka implemented in PlasSuite</li> <li>nb_args: ARGs detected by PlasSuite</li> <li>plascad: results from plascad (Che et al., 2021)</li> </ul> <p> </p> <p> </p> <p>Antipov, D., Raiko, M., Lapidus, A., and Pevzner, P.A. (2020) MetaviralSPAdes: assembly of viruses from metagenomic data. <em>Bioinformatics</em> <strong>36</strong>: 4126–4129.</p> <p>Che, Y., Yang, Y., Xu, X., Břinda, K., Polz, M.F., Hanage, W.P., and Zhang, T. (2021) Conjugative plasmids interact with insertion sequences to shape the horizontal transfer of antimicrobial resistance genes. Proceedings of the National Academy of Sciences 118: e2008731118.</p> <p>Corrêa, F.B., Saraiva, J.P., Stadler, P.F., and da Rocha, U.N. (2020) TerrestrialMetagenomeDB: a public repository of curated and standardized metadata for terrestrial metagenomes. <em>Nucleic Acids Res</em> <strong>48</strong>: D626–D632.</p> <p>Danko, D., Bezdan, D., Afshinnekoo, E., Ahsanuddin, S., Bhattacharya, C., Butler, D.J., et al. (2020) Global Genetic Cartography of Urban Metagenomes and Anti-Microbial Resistance. <em>bioRxiv</em> 724526.</p> <p>Hennequin, C., Forestier, C., Traore, O., Debroas, D., and Bricheux, G. (2022) Plasmidome analysis of a hospital effluent biofilm: Status of antibiotic resistance. <em>Plasmid</em> <strong>122</strong>: 102638.</p> <p>Hilpert, C., Bricheux, G., and Debroas, D. (2021) Reconstruction of plasmids by shotgun sequencing from environmental DNA: which bioinformatic workflow? <em>Briefings in Bioinformatics</em> <strong>22</strong>: bbaa059.</p> <p>Krawczyk, P.S., Lipinski, L., and Dziembowski, A. (2018) PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. <em>Nucleic Acids Res</em> <strong>46</strong>: e35.</p> <p>Li, D., Liu, C.-M., Luo, R., Sadakane, K., and Lam, T.-W. (2015) MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. <em>Bioinformatics</em> <strong>31</strong>: 1674–1676.</p> <p>Li, H. (2018) Minimap2: pairwise alignment for nucleotide sequences. <em>Bioinformatics</em> <strong>34</strong>: 3094–3100.</p> <p>Pasolli, E., Asnicar, F., Manara, S., Zolfo, M., Karcher, N., Armanini, F., et al. (2019) Extensive Unexplored Human Microbiome Diversity Revealed by Over 150,000 Genomes from Metagenomes Spanning Age, Geography, and Lifestyle. <em>Cell</em> <strong>176</strong>: 649-662.e20.</p> <p>Pellow, D., Mizrahi, I., and Shamir, R. (2020) PlasClass improves plasmid sequence classification. <em>PLOS Computational Biology</em> <strong>16</strong>: e1007781.</p> <p>Pfeifer, E., Moura de Sousa, J.A., Touchon, M., and Rocha, E.P.C. (2021) Bacteria have numerous distinctive groups of phage–plasmids with conserved phage and variable plasmid gene repertoires. <em>Nucleic Acids Res</em> <strong>49</strong>: 2655–2673.</p> <p>Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al. (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. <em>Nucleic Acids Res</em> <strong>41</strong>: D590–D596.</p> <p>Robertson, J. and Nash, J.H.E. (2018) MOB-suite: software tools for clustering, reconstruction and typing of plasmids from draft assemblies. <em>Microbial Genomics</em> <strong>4</strong>:.</p> <p>Steinegger, M. and Söding, J. (2017) MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. <em>Nature Biotechnology</em>.</p> <p>Tully, B.J., Graham, E.D., and Heidelberg, J.F. (2018) The reconstruction of 2,631 draft metagenome-assembled genomes from the global oceans. <em>Scientific Data</em> <strong>5</strong>: 170203.</p> <p>Wu, D., Jospin, G., and Eisen, J.A. (2013) Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. <em>PLoS One</em> <strong>8</strong>:.</p> </div> <p> </p>
Raw experiment data from: Homogeneity of agriculture landscape promotes insecticide resistance in the ground beetle Poecilus cupreus
<p>This dataset is raw research data related to the paper published in PLOS ONE: Sowa et al. 2022, Homogeneity of agriculture landscape promotes insecticide resistance in the ground beetle <em>Poecilus cupreus</em>; <a href="https://doi.org/10.1371/journal.pone.0266453">https://doi.org/10.1371/journal.pone.0266453</a></p>
Data from: Aridity and forest age mediate landscape scale patterns of tropical forest resistance to cyclonic storms
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Data from: Land cover, individual’s age and spatial sorting shape landscape resistance in the invasive frog Xenopus laevis
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Data from: Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal
We evaluated the effect of habitat and landscape characteristics on the population genetic structure of the white-footed mouse. We develop a new approach that uses numerical optimization to define a model that combines site differences and landscape resistance to explain the genetic differentiation between mouse populations inhabiting forest patches in southern Québec. We used ecological distance computed from resistance surfaces with Circuitscape to infer the effect of the landscape matrix on gene flow. We calculated site differences using a site index of habitat characteristics. A model that combined site differences and resistance distances explained a high proportion of the variance in genetic differentiation and outperformed models that used geographical distance alone. Urban and agriculture related land uses were, respectively, the most and the least resistant landscape features influencing gene flow. Our method detected the effect of rivers and highways as highly resistant linear barriers. The density of grass and shrubs on the ground best explained the variation in the site index of habitat characteristics. Our model indicates that movement of white-footed mouse in this region is constrained along routes of low resistance. Our approach can generate models that may improve predictions of future northward range expansion of this small mammal.
Fig. 6 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 6 Skulls and jaw for Neusticomys vossi sp. nov. (a QCAZ 7830 and b AMNH 244609) and N. monticolus (c AMNH 46574 and d AMNH 64626). a, c are the respective tupe specimens. All skulls are from adult females with closed cranial sutures except for c which is a juvenile male with open sutures
Fig. 2 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 2 Phulocenetic tree of sicmodontine rodents based on Cytb (a) and Rbp3 (b) DNA sequences. Bauesian posterior probabilitu values creater than 0.95 are represented bu asterisks placed above the branch
Fig. 1 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 1 Graphical representation of the PCA analuses (a, b) and LDA analusis (c, d). Vectors labeled as in Table 1. a, c Samples identified bu localitu and ace. Eastern samples (N. vossi sp. nov.: V) are light gray, western samples (N. monticolus: M) are dark gray, specimens from Antioquia (N. monticolus: M1) are mid-gray. Adults (fused craniosutures) are circles, and subadults (closed craniosutures) are triangles. b, d Samples identified bu localitu and sex. Location sumbols same as above, females—circles, males—triangles
Fig. 3 in Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Fig. 3 Map of recordinc localities of specimens of Neusticomys analuzed in the present studu. Eastern samples (N. vossi sp. nov.: V) are circles, western samples (N. monticolus: M) are squares, and specimens from Antioquia (N. monticolus: M1) are triangles
Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
Landscape genetics lacks explicit methods for dealing with the uncertainty in landscape resistance estimation, which is particularly problematic when sample sizes of individuals are small. Unless uncertainty can be quantified, valuable but small datasets may be rendered unusable for conservation purposes. We offer a method to quantify uncertainty in landscape resistance estimates using multi-model inference as an improvement over single-model based inference. We illustrate the approach empirically using co-occurring, woodland-preferring Australian marsupials within a common study area: two arboreal gliders (Petaurus breviceps, and Petaurus norfolcensis) and one ground-dwelling Antechinus (Antechinus flavipes). First, we use maximum-likelihood and a bootstrap procedure to identify the best-supported isolation by resistance (IBR) model out of 56 models defined by linear and non-linear resistance functions. We then quantify uncertainty in resistance estimates by examining parameter selection probabilities from the bootstrapped data. The selection probabilities provide estimates of uncertainty in the parameters that drive the relationships between landscape features and resistance. We then validate our method for quantifying uncertainty using simulated genetic and landscape data showing that for most parameter combinations it provides sensible estimates of uncertainty. We conclude that small datasets can be informative in landscape genetic analyses provided uncertainty can be explicitly quantified. Being explicit about uncertainty in landscape genetic models will make results more interpretable and useful for conservation decision-making, where dealing with uncertainty is critical.
Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia: scRNA-seq and CyTOF processed datasets
<p><strong>This data was generated as part of the Tumor Profiler study. If you use it in your research, please cite:</strong></p> <p>Wegmann, R., Bonilla, X., Casanova, R. <em>et al.</em> Single-cell landscape of innate and acquired drug resistance in acute myeloid leukemia. <em>Nat Commun</em> 15, 9402 (2024). https://doi.org/10.1038/s41467-024-53535-4</p> <p><strong>Derived data - scRNA-seq</strong></p> <p>This is an R data set (.RDS) containing a SingleCellExperiment object with the following slots:</p> <div> <ul> <li>Assays: <ul> <li>counts: raw counts</li> </ul> </li> </ul> </div> <div> <ul> <li>colData: Cell-level metadata <ul> <li> barcodes: The cell barcode</li> <li> fractionMT: Fraction mitochondrial genes per cell</li> <li> n_umi: Total number of UMIs per cell</li> <li> n_gene: Total number of genes per cell</li> <li> log_umi: log10 total number of UMIs per cell</li> <li> g2m_score: Cell cycle phase score for G2M</li> <li>s_score: Cell cycle phase score for S</li> <li>cycle_phase: predicted cell cycle phase</li> <li>celltype_major_full_ct_name: Major cell type full name</li> <li>celltype_major: Major cell type short name</li> <li>celltype_final_full_ct_name: Cell subtype full name</li> <li>celltype_final: Cell subtype short name </li> <li>sample_id </li> </ul> </li> </ul> </div> <div> <ul> <li>rowData: Gene-level metadata <ul> <li>gene_ids</li> <li>gene_names</li> </ul> </li> </ul> </div> <p><strong>Derived data - CyTOF</strong></p> <p>This is an R data set (.RDS) containing a SingleCellExperiment object with the following slots:</p> <ul> <li>Assays:<br> <ul> <li>counts_raw: signal intensity based on CyTOF dual counts</li> <li>exprs_raw: arcsinh transformed raw counts (cofactor 5)</li> <li>counts: batch corrected raw counts (linear scaling based on a quantile)</li> <li>exprs: arcsin transformed counts (cofactor 5)</li> <li>scaled: 0-1 normalized exprs (clipped to the 99.95th percentile)</li> </ul> </li> <li>colData (cell metadata) <ul> <li>bc_id: barcode of the sample during staining </li> <li>run: CyTOF experiment batch, named after the first sample of the batch</li> <li>type: Sample type (blood or bone marrow)</li> <li>sample_id: TuPro sample ID</li> <li>pred_id: Predicted cell type [char]</li> <li>pred_n: Predicted cell type [integer]</li> </ul> </li> <li>rowData (marker metadata) <ul> <li>channel_name: Name and isotopic mass of the metal ion corresponding to this marker</li> <li>marker_name: Protein name</li> <li>channel_group, channel_group_integer: Biological processes the channel identifies, e.g. specific cell type, signalling, cell death</li> <li>tsne_channel: Logical - use this channel for dimensionality reduction?</li> <li>channel_order: Define the order of channels for plotting</li> <li>cluster_channel: Logical - use this channel for clustering?</li> </ul> </li> </ul>
Data from: Isolation by distance, resistance and/or clusters? Lessons learned from a forest-dwelling carnivore inhabiting a heterogeneous landscape
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Data from: Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal
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Data from: The effect of cost surface parameterization on landscape resistance estimates
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Data from: Dealing with uncertainty in landscape genetic resistance models: a case of three co-occurring marsupials
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Genetic diversity, gene flow, and landscape resistance in a pond-breeding amphibian in agricultural and natural forested landscapes in Norway
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Beyond the landscape: resistance modelling infers physical and behavioural gene flow barriers to a mobile carnivore across a metropolitan area
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Data from: Modelling landscape connectivity for greater horseshoe bat using an empirical quantification of resistance
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