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opencc-by-4.0Aug 2021View details →

Supplementary Dataset for Deep learning based kcat prediction enables improved enzyme constrained model reconstruction

This dataset is the supplementary dataset for the paper "Deep learning based kcat prediction enables improved enzyme constrained model reconstruction". Protein sequence fasta files, deep learning predicted kcat values, classcial-ecGEMs, DL-ecGEMs and Posterior-mean-ecGEMs for 343 yeast/fungi species are available in this dataset.This repository also contains the computed results for reproducing the figures as model_build_files . The scripts can be found in Github (https://github.com/SysBioChalmers/DLKcat)

Fig. 3 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places

Fig. 3. Consensus results of species delimitation analyses. Phylogenetic distribution of hidden diversity estimated from strict consensus of delimitation results (SI Appendix, Table S1). Each silhouette represents a mammalian order with its shadow reflecting the ratio of predicted species to recognized species. Striped silhouettes represent orders with conflicting delimitation results that were not included in the predictive analysis. Phylogeny was adapted from ref. 31.

Fig. 4 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places

Fig. 4. Important predictors of hidden species in mammals. (A) From Top to Bottom, the 50 most important predictive variables (judged by MDA), for the consensus random forest classification model. In both plots, variables are color coded by life history, geographic, climatic, taxonomic, and environmental. (B) Boxplots representing values of the top predictive variables for species included in the consensus model. Values from species identified as hidden are shown at the Bottom of each plot (labeled "H"), and values from species not identified as hidden are shown Above (labeled "NH"). Outliers are excluded from boxplots.

Fig. 1 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places

Fig. 1. Predictive modeling workflow. The framework proposed for identifying named mammal species that are likely to contain hidden diversity utilizes barcoding gene sequences and machine learning models built from environmental, geographic, climatic, taxonomic, and life history variables.

Fig. 2 in Analysis of biodiversity data suggests that mammal species are hidden in predictable places

Fig. 2. Scope of the dataset. Genetic sequences for ∼70% of currently recognized mammalian species were obtained. All mammalian orders are represented, with 23 orders containing sequences from both COI and cytb and 4 having only sequences from cytb. (A) Circle plots reflect species representation for the COI and cytb genes in each order. Dark bars represent the species present in the dataset and light bars represent species for which no genetic data are available. (B) Blue bars represent the proportion of the sequence database represented by each order, and gray bars represent the proportion of recognized species in each order. (C) A total of 3,205,630 geographic occurrence records were obtained for species present in the genetic database.

opencc-zeroApr 2022View details →

dryad40/100

Dataset of habitat quality does not predict animal population abundance on frequently disturbed landscapes

The data presented here are related to the research article entitled "Habitat quality does not predict animal population abundance on frequently disturbed landscapes". Using an individual-based model, we simulated movement of theoretical individuals in a dynamically disturbed landscape and quantified the error of predicting population spatial relative abundance using an habitat model. This dataset provides the Earth Mover's Distance (EMD) as prediction error measure obtained in simulations with varying individual step length and disturbance frequency.

opencc-by-4.0Apr 2022View details →

Development of a machine learning model to predict non- durable response to anti-TNF therapy in Crohn's disease using transcriptome imputed from genotypes

This is the expression value predicted using PrediXcan version 7 to find a gene feature that can distinguish between patients with and without effect on infliximab. Among the various tissue models provided by PrediXcan v7, three models were selected and used: whole blood, Colon transverse, and terminal ileum of small intestine, and the predicted gene counts of each model were 6,294, 5,612 and 3,107. For each of the three models, predicted gene expression values and phenotype information per sample were submitted.

Extended Data Figure 7 in Host and viral traits predict zoonotic spillover from mammals

Extended Data Figure 7 | Global distribution of viral and host species richness for primates (order Primates). a, Observed total viral richness (for n = 71 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 73);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3e); g, global host species richness for Primates (n = 400); h, host species richness for Primates in our database (n = 98);i, primate species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in primates. Red/pink colours in panel i highlight areas with poor viral surveillance in primate species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 4 in Host and viral traits predict zoonotic spillover from mammals

Extended Data Figure 4 | Global distribution of viral and host species richness for wild carnivores (order Carnivora). a, Observed total viral richness (for n = 55 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 55); e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3b); g, global host species richness for Carnivora (n = 276);h, host species richness for Carnivora in our database (n = 79); i, species of the order Carnivora with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in carnivores. Red/pink colours in panel i highlight areas with poor viral surveillance in carnivore species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 3 in Host and viral traits predict zoonotic spillover from mammals

Extended Data Figure 3 | Global distribution of viral and host species richness for all wild mammals. a, Observed total viral richness (for n = 576 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 584);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3a); g, global mammal species richness (n = 5,290); h, mammal richness for species in our database (n = 753);i, mammal species with no described viruses in the literature. Warmer colours (larger values) in panels c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in mammals. Red/pink colours in panel i highlight areas with poor viral surveillance in mammal species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Figure 4 in Host and viral traits predict zoonotic spillover from mammals

Figure 4 | Traits that predict zoonotic potential of a virus. a, Box plot of maximum phylogenetic host breadth per virus (PHB, see methods) for each of 586 mammalian viruses, aggregated by 28 viral families. Individual points represent viral species, colour-coded by zoonotic status. Box plots coloured and sorted by the proportion of zoonoses in each viral family. b–d, Partial effect plots for the best-fit GAM to predict the zoonotic potential of a virus. b, Maximum PHB. Viruses that infect a phylogenetically broader range of hosts are more likely to be zoonotic. c, Research effort (log, number of PubMed citations per viral species). d, Whether or not a virus replicates in the cytoplasm or is vector-borne. Viral genome length and whether or not a virus is enveloped improved the overall predictive power but were non-significant and are not shown (see Extended Data Table 1).

Extended Data Figure 2 in Host and viral traits predict zoonotic spillover from mammals

Extended Data Figure 2 | Heat map of observed total viral richness by mammalian order and viral family. Dataset includes 754 mammalian species and 586 unique ICTV recognized viral species. Heat map aggregated by rows and columns to group taxa with similar levels of observed viral richness.

Figure 3 in Host and viral traits predict zoonotic spillover from mammals

Figure 3 | Global distribution of the predicted number of 'missing zoonoses' by order. Warmer colours highlight areas predicted to be of greatest value for discovering novel zoonotic viruses. a, All wild mammals (n = 584 spp. included in the best-fit model). b, Carnivores (order Carnivora, n = 55).c, Even-toed ungulates (order Cetartiodactyla, n = 70). d, Bats (order Chiroptera, n = 157).e, Primates (order Primates, n = 73). f, Rodents (order Rodentia, n = 183). Hatched regions represent areas where model predictions deviate systematically for the assemblage of species in that grid cell (approximately 18 km × 18 km, see Methods). Animal silhouettes from PhyloPic.

Extended Data Figure 6 in Host and viral traits predict zoonotic spillover from mammals

Extended Data Figure 6 | Global distribution of viral and host species richness for bats (order Chiroptera). a, Observed total viral richness (for n = 156 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 157);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3d); g, global host species richness for Chiroptera (n = 1117);h, host species richness for Chiroptera in our database (n = 192);i, species of the order Chiroptera with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in bats. Red/pink colours in panel i highlight areas with poor viral surveillance in bat species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 1 in Host and viral traits predict zoonotic spillover from mammals

Extended Data Figure 1 | Conceptual model of zoonotic spillover, viral richness, and summary of models. a, Conceptual model of zoonotic spillover showing primary risk factors examined, colour-coded according to generalized additive models used. b, Conceptual model of observed, predicted, and actual viral richness in mammals. c, GAMs used in our study to address specific components of a and b, colour-coded by model. Variables listed with 'or' under each GAM covaried and were provided as competing terms in model selection, and those in bold were included in the best-fit model using all host–virus associations. Significant variables from each best-fit GAM are noted with an asterisk. Zoonotic viral spillover first depends on the underlying total viral richness in mammal populations and the ecological, taxonomic, and life-history traits that govern this diversity (GAM 1). Second, host- and virus-specific factors may facilitate viral spillover. We examine the relative importance of host phylogenetic distance to humans, ecological opportunity for contact, or other species-specific life-history and taxonomic traits (GAM 2), and identify viral traits associated with a higher likelihood of an observed virus being zoonotic (GAM 3). We estimate the total and zoonotic viral richness per host species using GAMs 1 and 2, and calculate the missing viruses and missing zoonoses under a scenario of increased research effort (b, Methods). Owing to imperfect surveillance in both humans and wildlife and biases in viral detection, there may be uncertainty in the exact proportion of viruses that are zoonotic (b, light grey), and also between the actual, or true, viral richness (dotted lines) and the predicted maximum viral richness per host (dashed line).

Extended Data Figure 9 in Host and viral traits predict zoonotic spillover from mammals

Extended Data Figure 9 | Order-level phylogenies showing residuals from zoonoses model. a–e, Subtrees from cytochrome b maximum likelihood phylogeny for 558 mammal species (constrained to order-level topology of mammal supertree) for bats (a), carnivores (b), even-toed ungulates (c), rodents (d) and primates (e). Species included have at least one described virus association and available genetic data. Wildlife species names and terminal branches are colour-coded by the residuals (predicted minus observed) from the best-fit GAM to predict the number of zoonotic viruses using all data. Species with residual values between −1 and 1 (black) are accurately predicted within one virus. Warm colours represent species with positive residuals (orange>1 to 3; red>3). Cool colours represent species with negative residuals (green <−1 to−3; blue<−3). Marine mammals, domestic animals, and species with missing data and not included in the best-fit models are shown in grey.

Figure 2 in Host and viral traits predict zoonotic spillover from mammals

Figure 2 | Host traits that predict total viral richness (top row) and proportion of zoonotic viruses (bottom row) per wild mammal species. Partial effect plots show the relative effect of each variable included in the best-fit GAM, given the effect of the other variables. Shaded circles represent partial residuals; shaded areas, 95% confidence intervals around mean partial effect. a–e, Best model for total viral richness includes: a, number of disease-related citations per host species (research effort, log); b, phylogenetic eigenvector regression (PVR) of body mass (log); c, geographic range area of each species (log km2); d, number of sympatric mammal species overlapping with at least 20% area of target species range; and e, mammalian orders. f–i, Best model for proportion of zoonoses includes: f, research effort (log); g, phylogenetic distance from humans (cytochrome b tree constrained to the topology of the mammal supertree28); h, ratio of urban to rural human population within species range; and i, three mammalian orders. Bats are the only order with a significantly larger proportion of zoonotic viruses than would be predicted by the other variables in the all-data model. Three additional mammalian orders, and whether or not a species is hunted, improved the overall predictive power of the best zoonotic virus model but were non-significant and are not shown (see Extended Data Table 1).

Extended Data Figure 8 in Host and viral traits predict zoonotic spillover from mammals

Extended Data Figure 8 | Global distribution of viral and host species richness for rodents (order Rodentia). a, Observed total viral richness (for n = 178 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 183);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3f); g, global host species richness for Rodentia (n = 2206);h, host species richness for Rodentia in our database (n = 221); i, rodent species with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in wild rodents. Red/pink colours in panel i highlight areas with poor viral surveillance in rodent species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).

Extended Data Figure 5 in Host and viral traits predict zoonotic spillover from mammals

Extended Data Figure 5 | Global distribution of viral and host species richness for wild even-toed ungulates (order Cetartiodactyla). a, Observed total viral richness (for n = 70 host spp.); b, predicted total viral richness given maximum research effort; c, missing viruses or predicted minus observed total viral richness; d, observed zoonotic viral richness (n = 70);e, predicted zoonotic viral richness given maximum research effort; f, missing zoonoses or predicted minus observed zoonotic viral richness (same as included in Fig. 3c); g, global host species richness for Cetartiodactyla (n = 229);h, host species richness for Cetartiodactyla in our database (n = 105);i, species of the order Cetartiodactyla with no described viruses in the literature. Warmer colours (larger values) in c and f highlight areas predicted to be of greatest value for discovering novel viruses or novel viral zoonoses, respectively, in even-toed ungulates. Red/pink colours in panel i highlight areas with poor viral surveillance in even-toed ungulates species to date. Hatched regions represent areas where model predictions deviate systematically for the collection of species in that grid cell (see Methods).