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Identify key transcription factors in cell fate determination using ANANSE
<p>The example data used in ANANSE protocol.</p>
Data from: Genetic footprints of adaptive divergence in the bracovirus of Cotesia sesamiae identified by targeted resequencing
The African parasitoid wasp Cotesia sesamiae is structured in contrasted populations showing differences in host range and the recent discovery of a specialist related species, C. typhae, provide a good framework to study the mechanisms that link the parasitoid and their host range. To investigate the genomic bases of divergence between these populations, we used a targeted sequencing approach on 24 samples. We targeted a specific genomic region encoding the bracovirus, which is deeply involved in the interaction with the host. High sequencing coverage was obtained for all samples allowing the study of genetic variations between wasp populations and species. Combining population genetic estimations, the diversity (π), the relative differentiation (FST) and the absolute differentiation (dxy), and branch-site dN/dS measures, we identified six divergent genes impacted by positive selection belonging to different gene families. These genes are potentially involved in host adaptation and in the specialization process. Fine scale analyses of the genetic variations also revealed deleterious mutations and large deletions on certain genes inducing pseudogenization and loss of function. These results highlight the crucial role of the bracovirus in the molecular interactions between the wasp and its hosts and in the evolutionary processes of specialization.
Data from: Identifying drivers of spatial variation in occupancy with limited replication camera trap data
Occupancy models are widely used in camera trap studies to analyze species presence, abundance, and geographic distribution, among other important ecological quantities. These models account for imperfect detection using a latent variable to distinguish between true presence/absence and observed detection of a species. Under certain experimental setups, parameter estimation in a latent variable framework can be challenging. Several studies have issued guidelines on the number of independent replicated observations (surveys) needed for each unchanging occupancy field (season) to ensure reliable estimation. In this paper we present a spatio-temporal occupancy model, and show through a simulation study that it can be fit to data obtained from a \textit{single} survey per season, so long as the number of seasons is sufficiently large. We include an application using camera-trap data on the Thomson's gazelle in the Serengeti in Tanzania.
Data from: Egg morphology fails to identify nests parasitized by conspecifics in common pochard: a test based on protein fingerprinting and including female relatedness
Conspecific brood parasites lay eggs in nests of other females of the same species. A variety of methods have been developed and used to detect conspecific brood parasitism (CBP). Traditional methods may be inaccurate in detecting CBP and in revealing its true frequency. On the other hand more accurate molecular methods are expensive and time consuming. Eadie developed a method for revealing CBP based on differences in egg morphology. That method is based on Euclidean distances calculated for pairs of eggs within a clutch using standardized egg measurements (length, width and weight). We tested the applicability of this method in the common pochard Aythya ferina using nests that were identified as parasitized (39 nests) or non-parasitized (16 nests) based on protein fingerprinting of eggs. We also analyzed whether we can distinguish between parasitic and host eggs in the nest. We found that variation in MED can be explained by parasitism but there was a huge overlap in MED between parasitized and non-parasitized nests. MED also increased with clutch size. Using discriminant function analysis (DFA) we found that only 76.4% of nests were correctly assigned as parasitized or nonparasitized and only 68.3% of eggs as parasitic or host eggs. Moreover we found that MED in parasitized nests increased with relatedness of the females that laid eggs in the nest. This finding was supported by positive correlation between MED and estimated relatedness in female–female pairs. Although variation in egg morphology is associated with CBP, it does not provide a reliable clue for distinguishing parasitized nests from non-parasitized nests in common pochard.
Data from: Genotype by sequencing identifies natural selection as a driver of intraspecific divergence in Atlantic populations of the high dispersal marine invertebrate, Macoma petalum
Mitochondrial DNA analyses indicate that the Bay of Fundy population of the intertidal tellinid bivalve Macoma petalum is genetically divergent from coastal populations in the Gulf of Maine and Nova Scotia. To further examine the evolutionary forces driving this genetic break, we performed double digest genotype by sequencing (GBS) to survey the nuclear genome for evidence of both neutral and selective processes shaping this pattern. The resulting reads were mapped to a partial transcriptome of its sister species, M. balthica, to identify single nucleotide polymorphisms (SNPs) in protein-coding genes. Population assignment tests, principle components analyses, analysis of molecular variance, and outlier tests all support differentiation between the Bay of Fundy genotype and the genotypes of the Gulf of Maine, Gulf of St. Lawrence, and Nova Scotia. Although both neutral and non-neutral patterns of genetic subdivision were significant, genetic structure among the regions was nearly 20 times higher for loci putatively under selection, suggesting a strong role for natural selection as a driver of genetic diversity in this species. Genetic differences were the greatest between the Bay of Fundy and all other population samples, and some outlier proteins were involved in immunity-related processes. Our results suggest that in combination with limited gene flow across the mouth of the Bay of Fundy, local adaptation is an important driver of intraspecific genetic variation in this marine species with high dispersal potential.
Data from: A genome-wide association study identifies a region strongly associated with symmetrical onychomadesis on chromosome 12 in dogs
Symmetrical onychomadesis causes periodic loss of claws in otherwise healthy dogs. Genome-wide association analysis in 225 Gordon Setters identified a single region associated with symmetrical onychomadesis on chromosome 12 (spanning about 3.3 mb). A meta-analysis including also English Setters indicated that this genomic region predisposes for symmetrical onychomadesis in English Setters as well. The associated region spans most of the major histocompatibility complex and nearly 1 Mb downstream. Like many other autoimmune diseases, associations of symmetrical onychomadesis with DLA class II alleles have been reported. In this study, no associated markers were revealed within any of the DLA-DRB1, -DQA1 or -DQB1 genes, and the odds for symmetrical onychomadesis in the Gordon Setters were much higher, carrying significant single nucleotide polymorphisms compared to the odds of any of the recorded DLA-DRB1/DQA1/DQB1 haplotypes. We noticed that some of the associated DLA haplotypes were different between the English Setters and the Gordon Setters. Interestingly, associated SNP chip markers showed a more consistent pattern of allelic variants related to cases or controls regardless of breed. In conclusion, the associated genetic markers identified in this study hold the potential to aid in selection of breeding animals to reduce the frequency of symmetrical onychomadesis in the dog.
Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding
Purpose: This retrospective cross-sectional study aimed to identify opportunities for improvement in food and nutrition research by examining risk of bias (ROB) domains. Methods: Rating were extracted from critical appraisal records for 5675 studies used in systematic reviews conducted by three organizations. Variables were as follows: ROB domains defined by the Cochrane Collaboration (Selection, Performance, Detection, Attrition, and Reporting), publication year, research type (intervention or observation) and specific design, funder, and overall quality rating (positive, neutral, or negative). Appraisal instrument questions were mapped to ROB domains. The kappa statistic was used to determine consistency when multiple ROB ratings were available. Binary logistic regression and multinomial logistic regression were used to predict overall quality and ROB domains. Findings: Studies represented a wide variety of research topics (clinical nutrition, food safety, dietary patterns, and dietary supplements) among 15 different research designs with a balance of intervention (49%) and observation (51%) types, published between 1930 and 2015 (64% between 2000-2009). Duplicate ratings (10%) were consistent (k=0.86-0.94). Selection and Performance domain criteria were least likely to be met (57.9% to 60.1%). Selection, Detection, and Performance ROB ratings predicted neutral or negative quality compared to positive quality (p<0.001). Funder, year, and research design were significant predictors of ROB. Some sources of funding predicted increased ROB (p<0.001) for Selection (Interventional: industry only and none/not reported; Observational: other only and none/not reported) and Reporting (Observational: university only and other only). Reduced ROB was predicted by combined and other-only funding for intervention research (p<0.005). Performance ROB domain ratings started significantly improving in 2000; others improved after 1990 (p<0.001). Research designs with higher ROB were nonrandomized intervention and time series designs compared to RCT and prospective cohort designs respectively (p<0.001). Conclusions: Opportunities for improvement in food and nutrition research are in the Selection, Performance, and Detection ROB domains.
A targeted metabolomics-based assay using human induced pluripotent stem cell-derived cardiomyocytes identifies structural and functional cardiotoxicity potential
<p>Implementing screening assays that identify functional and structural cardiotoxicity earlier in the drug development pipeline has the potential to improve safety and the cost and time required to bring new drugs to market. In this study, a metabolic biomarker-based assay was developed that predicts the cardiotoxicity potential of a drug based on changes in the metabolism and viability of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM). Assay development and testing was conducted in two phases: (1) biomarker identification and (2) targeted assay development. In the first phase, metabolomic data from hiPSC-CM spent media following exposure to 66 drugs was used to identify biomarkers that identified both functional and structural cardiotoxicants. Four metabolites that represent different metabolic pathways (arachidonic acid, lactic acid, 2'-deoxycytidine, and thymidine) were identified as indicators of cardiotoxicity. In phase two, a targeted, exposure-based biomarker assay was developed that measured these metabolites and hiPSC-CM viability across an eight-point concentration curve. Metabolite-specific predictive thresholds for identifying the cardiotoxicity potential of a drug were established and optimized for balanced accuracy or sensitivity. When predictive thresholds were optimized for balanced accuracy, the assay predicted the cardiotoxicity potential of 81 drugs with 86% balanced accuracy, 83% sensitivity, and 90% specificity. Alternatively, optimizing the thresholds for sensitivity yields a balanced accuracy of 85%, 90% sensitivity, and 79% specificity. This new hiPSC-CM-based assay provides a paradigm that can identify structural and functional cardiotoxic drugs that could be used in conjunction with other endpoints to provide a more comprehensive evaluation of a drug's cardiotoxicity potential.</p>
Replication Data for: OPTICAL COHERENCE TOMOGRAPHY IDENTIFIES LOWER LABIAL SALIVARY GLAND SURFACE DENSITY IN CYSTIC FIBROSIS
<p>Swept-source optical coherence tomography images of the mucosa of the lower lip acquired in 18 cystic fibrosis patients (CF.zip, includes an XLS file with additional data) and 18 healthy volunteers (HS.zip). Within the volumetric datasets consisting of sets of images the labial salivary glands can be identified.</p> <p>Authors: Nowak JK, Grulkowski I, Karnowski K, Wojtkowski M, Walkowiak J.<br /> When using the data, please always refer to the original paper (currently under review in PLOS ONE; title as specified above).</p> <p>The methodology used to obtain the volumetric datasets is described in:<br /> Grulkowski I, Nowak JK, Karnowski K, Zebryk P, Puszczewicz M, Walkowiak J, Wojtkowski M. Quantitative assessment of oral mucosa and labial minor salivary glands in patients with Sjögren’s syndrome using swept source OCT. Biomedical Optics Express, Vol. 5, Issue 1, pp. 259-274 (2014). DOI: http://dx.doi.org/10.1364/BOE.5.000259</p>
What Makes Sports Fans Interactive? Identifying Factors Affecting Chat Interactions in Online Sports Viewing
<p> </p> <p> </p> <p><strong>1. Chat message data</strong></p> <p>- kbo2011_naverbasevall_comment.sql: chat messages in Naver Sports that users posted in 2011<br /> - kbo2012_naverbasevall_comment.sql: chat messages in Naver Sports that users posted in 2012</p> <p>Each MySQL table consists of the following six columns.<br /> 1. comment_id: Integer variable to identify each comment<br /> 2. gid: Integer variable to identify each game<br /> 3. uid: Integer variable to identify each user<br /> 4. category_id: A team name that a user selected when a chat message is posted<br /> 5. content: Chat message text<br /> 6. post_time_relative: Elapsed seconds since the game starts</p> <p> </p> <p><strong>2. KBO game information</strong></p> <p>- game_basic.sql: KBO game information in 2011/2012<br /> <br /> (This data was collected from here, http://sports.news.naver.com/schedule/index.nhn?uCategory=&category=kbo&year=2010&month=03)</p> <p>This MySQL table consists of the following 12 columns.<br /> 1. gid: Integer variable to identify each game<br /> 2. game: Game ID used in Naver Sports<br /> 3. year: year of the game play<br /> 4. month: month of the game play<br /> 5. date: date of the game play<br /> 6. time: time of the game play<br /> 7. day: day of the game play<br /> 8. team_away: name of the away team<br /> 9 team_home: name of the home team<br /> 10. score_away: score by the away team<br /> 11. score_home: score by the home team<br /> 12. location: location of the game play</p> <p> </p> <p><strong>3. KBO game play-by-play data </strong></p> <p>- game_playbyplay.sql: playbyplay data in 2011/2012</p> <p>(This data was collected from Naver Sports; EX, http://sports.news.naver.com/gameCenter/gameRecord.nhn?category=kbo&gameId=20100327HHSK0)</p> <p>This MySQL table consists of the following 21 columns.</p> <p>1. play_no: Integer variable to identify each play<br /> 2. gid: Integer variable to identify each game<br /> 3. inning: inning of the play<br /> 4. pitcher: current pitcher name<br /> 5. batter: current batter name<br /> 6. batter_no: current batter order<br /> 7. pitch: the pitch result<br /> 8. strike: current number of strikes<br /> 9. ball: current number of balls<br /> 10. out_cnt: current number of outs<br /> 11. score_home: current score of the home team<br /> 12. score_away: current score of the away team<br /> 13. 1st_base: player name on the 1st base <br /> 14. 2nd_base: player name on the 2nd base<br /> 15. 3rd_base: player name on the 3rd base<br /> 16. runner_state: overall state of the three bases (ex: 1 --> A player is on 1st base, but 2nd and 3rd bases are empty, 13 --> Two players are on 1st and 3rd bases respectively, but 2nd base is empty)<br /> 17. runner_cnt: the number of players on bases<br /> 18. result_r: r by this play<br /> 19. result_rbi: rbi by this play<br /> 20. result_score_home: total home score after this play<br /> 21. result_score_away: total away score after this play</p> <p> </p> <p><strong>4. Winning rate data </strong></p> <p>- game_winrate.sql: winning rates of the home/away teams for an inning</p> <p>(This data is calculated based on the playbyplay data. For the details, please refer to our paper--currently under review)</p> <p>This MySQL table consists of the following 21 columns.</p> <p>1. play_no: Integer variable to identify each play<br /> 2. gid: Integer variable to identify each game<br /> 3. inning: inning of the play<br /> 4. away_win: probability that the away team will win this game<br /> 5. draw: probability that the game will finish a tie<br /> 6. home_win: probability that the home team will win this game</p> <p> </p> <p>If you have any question about the dataset, please contact Minsam Ko (msko@kaist.ac.kr).</p>
Supplemental Data for "Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening"
<p>Data file accompanying: Haque IS, Lazarin GA, Kang HP, Evans EA, Goldberg JD, Wapner RJ. Modeled Fetal Risk of Genetic Diseases Identified by Expanded Carrier Screening. <em>JAMA. </em>2016;316(7):734-742. doi:10.1001/jama.2016.11139</p> <p>(SRC = self-reported racial/ethnic category; TG = targeted genotyping; NGS = next-generation sequencing)</p> <p>Data file includes:</p> <ul> <li><strong>Couple Data: </strong>Number of self-identified reproductive couples, separated by tandem or sequential screening status and by (mother SRC, father SRC)</li> <li><strong>Disease Severity</strong>: List of all diseases tested with severity rating as used in the manuscript.</li> <li><strong>Allele Data</strong>: Listing of all alleles considered pathogenic in manuscript's data analysis, with number of observations and number of tested chromosomes in each SRC.</li> <li><strong>Chromosome Frequencies</strong>: for each disease in each SRC: <ul> <li>Effective total chromosome count (effective sample size after integrating TG and NGS-only alleles).</li> <li>Beta posterior a,b: parameters a, b for the best-fit beta distribution approximating the probability that a random chromosome in this SRC carries a pathogenic allele (integrating both TG and NGS alleles).</li> <li># Chromosomes total/positive for TG alleles</li> <li># Chromosomes total/positive for NGS alleles</li> <li># Chromosomes total/positive for individuals tested by TG</li> <li># Chromosomes total/positive for individuals tested by NGS</li> </ul> </li> <li><strong>Disease Risks</strong>: for each disease in each pairing of SRCs <ul> <li>Father/Mother computed carrier frequency: probability that a random individual from father/mother's SRC is a carrier for the given disease</li> <li>Computed risk of affected conceptus (mean, 2.5, 97.5 percentiles): mean and CI of the posterior distribution over the probability that a random conceptus arising from the racial/ethnic pairing indicated would be homozygous or compound heterozygous for pathogenic alleles for the indicated disease.</li> <li>Computed carrier couple frequency: probability that a random couple from the given SRCs would be a carrier couple for the indicated disease (ie, that both members of the couple would be carriers for the indicated disease)</li> <li>Total couples: number of tandemly-tested couples of the indicated SRC pairing who both had the "routine carrier testing" indication for testing and were both tested for the given disease</li> <li>Number of carrier couple: from the set of "Total couples", the number of couples in which both members were carriers for the indicated disease</li> <li>Number of carrier couples expected: based on computed carrier couple frequency and number of tested couples, the expected number of carrier couples under the model described in sections 4.3.2 and 4.4 of the supplement.</li> <li>P-value: probability that the number of observed carrier couples or a more extreme count would have occurred by chance, given the posterior distribution over carrier couple counts (see section 4.4 of the supplement). One-tailed p-value.</li> </ul> </li> </ul>
FIGURE 4 in Identifying species of Bythinella (Caenogastropoda: Rissooidea): A plea for an integrative approach
FIGURE 4. SEM images of morphological and anatomical details of B. opaca. A. protoconch; B. structure of protoconch; C. radula; D, E. apical view of penis (extended in D) with tubular accessory gland; F. tips of penis and tubular accessory gland with extruded papilla (arrow). A, B, D. Sand Grafenstein; C. Farchtnersee; E. Niedermarkt; F. Granitztal St. Paul. p, penis; tg, tubular accessory gland; t, tentacle. Scale bars = 300 µm in A, 200 µm in D, E, 50 µm in F, 20 µm in B, C.
FIGURE 3 in Identifying species of Bythinella (Caenogastropoda: Rissooidea): A plea for an integrative approach
FIGURE 3. Shells of Carinthian populations. A–G. B. opaca; H. B. angelitae, Tscheppaschlucht. A. Bärental; B. Farchtnersee; C. Granitztal St. Paul; D. Sand Grafenstein; E. Oberdrauburger Bach; F. Niedermarkt; G. Podkraj.
FIGURE 1 in Identifying species of Bythinella (Caenogastropoda: Rissooidea): A plea for an integrative approach
FIGURE 1. ArcGis (ESRI Inc.) generated map showing localities in the southern Austrian province of Carinthia and in Slovenia. Squares, B. angelitae; asterisk, B. robiciana; circles, B. opaca; triangle, Bythinella sp. (species assignments are a result of the current study). Localities are abbreviated by their first four letters (see Table 1).
FIGURE 2. Shells. A. B in Identifying species of Bythinella (Caenogastropoda: Rissooidea): A plea for an integrative approach
FIGURE 2. Shells. A. B. angelitae, holotype (and lectotype of Paludinella opaca Frauenfeld, 1857); B, C. B. angelitae, paratypes (and paralectotypes of P. opaca Frauenfeld, 1857); D. B. robiciana, topotype; E. B. opaca, (topotype of Paludina schmidtii Küster, 1852). Scale bar = 2 mm.
FIGURE 7 in Identifying species of Bythinella (Caenogastropoda: Rissooidea): A plea for an integrative approach
FIGURE 7. Phylogramme of most likely tree resulting from Bayesian analysis with posterior probabilities/majority rule consensus indices/bootstrap support values, the latter two from the maximum parsimony analysis. Outgroup pruned from tree. Populations are identified by their first four letters (see Table 1).
FIGURE 6 in Identifying species of Bythinella (Caenogastropoda: Rissooidea): A plea for an integrative approach
FIGURE 6. Distal genitalia of three females of B. angelitae from the Tscheppaschlucht. ag, albumen gland; bc, bursa copulatrix; bd, bursal duct; cg, capsule gland; go, genital opening; od, oviduct; rs, receptaculum seminis. Scale bar = 400 µm.
FIGURE 6. Karyotypes with 2n in A new species of the genus Alsodes (Anura: Neobatrachia) from the Nothofagus forest, Coastal Range, Southern Chile, identified by its karyotype
FIGURE 6. Karyotypes with 2n = 26 (A) Alsodes valdiviensis with comparative purposes. Reformed from that of (B) Alsodes norae (2n = 30), and (C) A. barrioi (2n = 34) by means of hypothetic mechanisms of chromosome evolution in some Alsodes species. In B and C small numbers beside some chromosomes indicate the original chromosome position procedence.
FIGURE 1 in A new species of the genus Alsodes (Anura: Neobatrachia) from the Nothofagus forest, Coastal Range, Southern Chile, identified by its karyotype
FIGURE 1. Map of Central-South of Chile showing the type locality of Alsodes norae (frame), and the fragmented distribution of the other Alsodes species from the Coastal Range.
FIGURE 5. C in A new species of the genus Alsodes (Anura: Neobatrachia) from the Nothofagus forest, Coastal Range, Southern Chile, identified by its karyotype
FIGURE 5. C-bands ideograms of the five Alsodes species from the Coastal Range of Chile (A) A. valdiviensis, (B) A. norae, (C) A. vanzolinii, (D) A. barrioi, (E) A. nodosus.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.