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13,117 results for “Maps”
Data from: Identification of multiple QTL hotspots in sockeye salmon (Oncorhynchus nerka) using genotyping-by-sequencing and a dense linkage map
Understanding the genetic architecture of phenotypic traits can provide important information about the mechanisms and genomic regions involved in local adaptation and speciation. Here, we used genotyping-by-sequencing and a combination of previously published and newly generated data to construct sex-specific linkage maps for sockeye salmon (Oncorhynchus nerka). We then used the denser female linkage map to conduct quantitative trait locus (QTL) analysis for 4 phenotypic traits in 3 families. The female linkage map consisted of 6322 loci distributed across 29 linkage groups and was 4082 cM long, and the male map contained 2179 loci found on 28 linkage groups and was 2291 cM long. We found 26 QTL: 6 for thermotolerance, 5 for length, 9 for weight, and 6 for condition factor. QTL were distributed nonrandomly across the genome and were often found in hotspots containing multiple QTL for a variety of phenotypic traits. These hotspots may represent adaptively important regions and are excellent candidates for future research. Comparing our results with studies in other salmonids revealed several regions with overlapping QTL for the same phenotypic trait, indicating these regions may be adaptively important across multiple species. Altogether, our study demonstrates the utility of genomic data for investigating the genetic basis of important phenotypic traits. Additionally, the linkage map created here will enable future research on the genetic basis of phenotypic traits in salmon.
Data from: Mapping of genetic factors that elicit intermale aggressive behavior on mouse chromosome 15: intruder effects and the complex genetic basis
Despite high estimates of the heritability of aggressiveness, the genetic basis for individual differences in aggression remains unclear. Previously, we showed that the wild-derived mouse strain MSM/Ms (MSM) exhibits highly aggressive behaviors, and identified chromosome 15 (Chr 15) as the location of one of the genetic factors behind this escalated aggression by using a panel of consomic strains of MSM in a C57BL/6J (B6) background. To understand the genetic effect of Chr 15 derived from MSM in detail, this study examined the aggressive behavior of a Chr 15 consomic strain towards different types of opponent. Our results showed that both resident and intruder animals had to have the same MSM Chr 15 genotype in order for attack bites to increase and attack latency to be reduced, whereas there was an intruder effect of MSM Chr 15 on tail rattle behavior. To narrow down the region that contains the genetic loci involved in the aggression-eliciting effects on Chr 15, we established a panel of subconsomic strains of MSM Chr 15. Analysis of these strains suggested the existence of multiple genes that enhance and suppress aggressive behavior on Chr 15, and these loci interact in a complex way. Regression analysis successfully identified four genetic loci on Chr 15 that influence attack latency, and one genetic locus that partially elicits aggressive behaviors was narrowed down to a 4.1-Mbp region (from 68.40 Mb to 72.50 Mb) on Chr 15.
Data from: Genome assembly improvement and mapping convergently evolved skeletal traits in sticklebacks with genotyping-by-sequencing
Marine populations of the threespine stickleback (Gasterosteus aculeatus) have repeatedly colonized and rapidly adapted to freshwater habitats, providing a powerful system to map the genetic architecture of evolved traits. Here, we developed and applied a binned genotyping-by-sequencing (GBS) method to build dense genome-wide linkage maps of sticklebacks using two large marine by freshwater F2 crosses of more than 350 fish each. The resulting linkage maps significantly improve the genome assembly by anchoring 78 new scaffolds to chromosomes, reorienting 40 scaffolds, and rearranging scaffolds in 4 locations. In the revised genome assembly, 94.6% of the assembly was anchored to a chromosome. To assess linkage map quality, we mapped quantitative trait loci (QTL) controlling lateral plate number, which mapped as expected to a 200-kb genomic region containing Ectodysplasin, as well as a chromosome 7 QTL overlapping a previously identified modifier QTL. Finally, we mapped eight QTL controlling convergently evolved reductions in gill raker length in the two crosses, which revealed that this classic adaptive trait has a surprisingly modular and nonparallel genetic basis.
Data from: Stochastic character mapping of state-dependent diversification reveals the tempo of evolutionary decline in self-compatible Onagraceae lineages
A major goal of evolutionary biology is to identify key evolutionary transitions that correspond with shifts in speciation and extinction rates. Stochastic character mapping has become the primary method used to infer the timing, nature, and number of character state transitions along the branches of a phylogeny. The method is widely employed for standard substitution models of character evolution. However, current approaches cannot be used for models that specifically test the association of character state transitions with shifts in diversification rates such as state-dependent speciation and extinction (SSE) models. Here we introduce a new stochastic character mapping algorithm that overcomes these limitations, and apply it to study mating system evolution over a time-calibrated phylogeny of the plant family Onagraceae. Utilizing a hidden state SSE model we tested the association of the loss of self-incompatibility with shifts in diversification rates. Confirming long standing theory, we found that self-compatible lineages have higher extinction rates and lower net diversification rates compared to self-incompatible lineages. Furthermore, these results provide empirical evidence for the "senescing" diversification rates predicted in highly selfing lineages: our mapped character histories show that the loss of self-incompatibility is followed by a short-term spike in speciation rates, which declines after a time lag of several million years resulting in negative net diversification. Lineages that have long been self-compatible such as Fuchsia and Clarkia are in a previously unrecognized and ongoing evolutionary decline. Our results demonstrate that stochastic character mapping of SSE models is a powerful tool for examining the timing and nature of both character state transitions and shifts in diversification rates over the phylogeny.
Data from: Neural structure mapping in human probabilistic reward learning
Humans can learn abstract concepts that describe invariances over relational patterns in data. One such concept, known as magnitude, allows stimuli to be compactly represented on a single dimension (i.e. on a mental line). Here, we measured representations of magnitude in humans by recording neural signals whilst they viewed symbolic numbers. During a subsequent reward-guided learning task, the neural patterns elicited by novel complex visual images reflected their payout probability in a way that suggested they were encoded onto the same mental number line, with 'bad' bandits sharing neural representation with 'small' numbers and 'good' bandits with 'large' numbers. Using neural network simulations, we provide a mechanistic model that explains our findings and shows how structural alignment can promote transfer learning. Our findings suggest that in humans, learning about reward probability is accompanied by structural alignment of value representations with neural codes for the abstract concept of magnitude.
Data from: Fine-Scale Mapping of Recombination Rate in Drosophila Refines its Correlation to Diversity and Divergence
Regional rates of recombination often correlate with levels of nucleotide diversity, and either selective or neutral hypotheses can explain this relationship. Regional recombination rates also correlate with nucleotide differences between human and chimpanzee, consistent with models where recombination is mutagenic; however, a lack of correlation is observed in the Drosophila melanogaster group, consistent with models invoking natural selection. Here, we revisit the relationship among recombination, diversity, and interspecies difference by generating empirical estimates of these parameters in Drosophila pseudoobscura. To measure recombination rate, we genotyped 1,294 backcross hybrids at 50 markers across the largest assembled linkage group in this species. Genome-wide diversity was estimated by sequencing a second isolate of D. pseudoobscura at shallow coverage. Alignment to the sequenced genome of the closely related species, Drosophila persimilis, provided nucleotide site orthology. Our findings demonstrate that scale is critical in determining correlates to recombination rate: fine-scale cross-over rate estimates are far stronger predictors of both diversity and interspecies difference than broad-scale estimates. The correlation of fine-scale recombination rate to diversity and interspecies difference appears to be genome-wide, evidenced by examination of an X-linked region in greater detail. Because we observe a strong correlation of cross-over rate with interspecies difference, even after correcting for segregating ancestral variation, we suggest that both mutagenic and selective forces generate these correlations, the latter in regions of low crossing over. We propose that it is not cross-overs per se that are mutagenic, but rather repair of DNA double-strand break precursors via crossing over and gene conversion.
Data from: A citation-based map of concepts in invasion biology
Invasion biology has been quickly expanding in the last decades, so that it is now metaphorically flooded with publications, concepts and hypotheses. Among experts, there is no clear consensus about the relationships between invasion concepts, and almost no one seems to have a good overview of the literature anymore. Similar observations can be made for other research fields. Science needs new navigation tools, so that researchers within and outside of a research field as well as science journalists, students, teachers, practitioners, policy-makers and others interested in the field can more easily understand its key ideas. Such navigation tools could, for example, be maps of the major concepts and hypotheses of a research field. Applying a bibliometric method, we created such maps for invasion biology. We analysed research papers of the last two decades citing at least two of 35 common invasion hypotheses. Co-citation analysis yields four distinct clusters of hypotheses. These clusters can describe the main directions in invasion biology and explain basic driving forces behind biological invasions. The method we outline here for invasion biology can be easily applied for other research fields.
Data from: Many-to-many mapping of phenotype to performance: an extension of the F-matrix for studying functional complexity
Performance capacity influences ecology, behavior and fitness, and is determined by the underlying phenotype. The phenotype-performance relationship can influence the evolutionary trajectory of an organism. Several types of phenotype-performance relationships have been described, including one-to-one relationships between a single phenotypic trait and performance measure, trade-offs and facilitations between a phenotypic trait and multiple performance measures, and redundancies between multiple phenotypic traits and a single performance measure. The F-matrix is an intraspecific matrix of measures of statistical association between phenotype and performance that is used to quantify these relationships. We extend the F-matrix in two ways. First, we use the F-matrix to describe how the different phenotype-performance relationships occur simultaneously and interact in functional systems, a phenomenon we call many-to-many mapping. Second, we develop methods to compare F-matrices among species and compare phenotype-performance relationships at microevolutionary and macroevolutionary levels. We demonstrate the expanded F-matrix approach with a dataset of eight phrynosomatine lizard species, including six phenotypic traits and two measures of locomotor performance. Our results suggest that all types of relationships occur in this system and that phenotypic traits involved in trade-offs are more functionally constrained and tend evolve slower interspecifically than those involved in facilitations or one-to-one relationships.
Data from: Spatial detection of outlier loci with Moran eigenvector maps (MEM)
The spatial signature of microevolutionary processes structuring genetic variation may play an important role in the detection of loci under selection. However, the spatial location of samples has not yet been used to quantify this. Here, we present a new two-step method of spatial outlier detection at the individual and deme levels using the power spectrum of Moran eigenvector maps (MEM). The MEM power spectrum quantifies how the variation in a variable, such as the frequency of an allele at a SNP locus, is distributed across a range of spatial scales defined by MEM spatial eigenvectors. The first step (Moran spectral outlier detection: MSOD) uses genetic and spatial information to identify outlier loci by their unusual power spectrum. The second step uses Moran spectral randomization (MSR) to test the association between outlier loci and environmental predictors, accounting for spatial autocorrelation. Using simulated data from two published papers, we tested this two-step method in different scenarios of landscape configuration, selection strength, dispersal capacity and sampling design. Under scenarios that included spatial structure, MSOD alone was sufficient to detect outlier loci at the individual and deme levels without the need for incorporating environmental predictors. Follow-up with MSR generally reduced (already low) false-positive rates, though in some cases led to a reduction in power. The results were surprisingly robust to differences in sample size and sampling design. Our method represents a new tool for detecting potential loci under selection with individual-based and population-based sampling by leveraging spatial information that has hitherto been neglected.
Data from: Molecular mapping and candidate gene analysis for numerous spines on the fruit of cucumber
Number of spines on the fruit is an important quality trait in cucumber. The inheritance and identification of molecular markers for fruit spine density gene can provide a basis for breeding and lay the foundation for gene cloning. Cucumber inbred lines NCG-122 with numerous spines and NCG-121 with few spines were used for genetic analysis and gene mapping in this study. Genetic analysis showed that the numerous spines trait in NCG-122 was qualitative, and a single recessive nuclear gene (ns) controlled this trait. The few spines trait was dominant over the numerous spines trait. In the preliminary genetic mapping of the ns gene, 8 SSR markers were found to be linked to ns, which mapped to chromosome 2 (Chr.2) of cucumber. The closest flanking markers SSR22338 and SSR11596 were linked to the ns gene, with genetic distances of 10.2 and 1.7cM, respectively. One-hundred and thirty pairs of new SSR primers and 28 pairs of Indel primers were developed based on sequence information in the preliminary mapping region of ns. Fifteen SSR markers and 2 Indel markers were identified to be linked to the ns gene after analysis on the F2 mapping population using the new molecular markers. The 2 closest flanking markers, SSRns-127 and SSR04219, were 0.7 and 2.4 cM from ns, respectively. The physical distance between SSRns-127 and SSR04219 was 266.1kb, containing 27 predicted genes. Csa2G285390 was speculated as the probable candidate gene for numerous spines. The accuracy of the closest linked marker to the ns gene, SSRns-127, for MAS breeding was 95.0%.
Data from: Analysis of genome-wide structure, diversity and fine mapping of Mendelian traits in traditional and village chickens
Extensive phenotypic variation is a common feature among village chickens found throughout much of the developing world, and in traditional chicken breeds that have been artificially selected for traits such as plumage variety. We present here an assessment of traditional and village chicken populations, for fine mapping of Mendelian traits using genome-wide single-nucleotide polymorphism (SNP) genotyping while providing information on their genetic structure and diversity. Bayesian clustering analysis reveals two main genetic backgrounds in traditional breeds, Kenyan, Ethiopian and Chilean village chickens. Analysis of linkage disequilibrium (LD) reveals useful LD (r2greater than or equal to0.3) in both traditional and village chickens at pairwise marker distances of ~10 Kb; while haplotype block analysis indicates a median block size of 11–12 Kb. Association mapping yielded refined mapping intervals for duplex comb (Gga 2:38.55–38.89 Mb) and rose comb (Gga 7:18.41–22.09 Mb) phenotypes in traditional breeds. Combined mapping information from traditional breeds and Chilean village chicken allows the oocyan phenotype to be fine mapped to two small regions (Gga 1:67.25–67.28 Mb, Gga 1:67.28–67.32 Mb) totalling ~75 Kb. Mapping the unmapped earlobe pigmentation phenotype supports previous findings that the trait is sex-linked and polygenic. A critical assessment of the number of SNPs required to map simple traits indicate that between 90 and 110K SNPs are required for full genome-wide analysis of haplotype block structure/ancestry, and for association mapping in both traditional and village chickens. Our results demonstrate the importance and uniqueness of phenotypic diversity and genetic structure of traditional chicken breeds for fine-scale mapping of Mendelian traits in the species, with village chicken populations providing further opportunities to enhance mapping resolutions.
Data Management in Data Lakes : Systematic mapping
<p>The bubble charts depicts the result of the systematic mapping.</p>
Data from: Construction of ultra-dense linkage maps with Lep-MAP2: stickleback F2 recombinant crosses as an example
High-density linkage maps are important tools for genome biology and evolutionary genetics by quantifying the extent of recombination, linkage disequilibrium and chromosomal rearrangements across chromosomes, sexes and populations. They provide one of the best ways to validate and refine de novo genome assemblies, with the power to identify errors in assemblies increasing with marker density. However, assembly of high-density linkage maps is still challenging due to software limitations. We describe Lep-MAP2, a software for ultra-dense genome-wide linkage map construction. Lep-MAP2 can handle various family structures and can account for achiasmatic meiosis to gain linkage map accuracy. Simulations show that Lep-MAP2 outperforms other available mapping software both in computational efficiency and accuracy. When applied to two large F2-generation recombinant crosses between two nine-spined stickleback (Pungitius pungitius) populations, it produced two high-density (~6 markers/cM) linkage maps containing 18 691 and 20 054 SNPs. The two maps showed a high degree of synteny, but female maps were 1.5 to 2 times longer than male maps in all linkage groups, suggesting genome-wide recombination suppression in males. Comparison with the genome sequence of the three-spined stickleback (Gasterosteus aculeatus) revealed a high degree of interspecific synteny with a low frequency (<5%) of interchromosomal re-arrangements. However, a fairly large (ca. 10Mb) translocation from autosome to sex chromosome was detected in both maps. These results illustrate the utility and novel features of Lep-MAP2 in assembling high-density linkage maps, and their usefulness in revealing evolutionarily interesting properties of genomes, such as strong genome-wide sex-bias in recombination rates.
Data from: Identification and mapping of linear antibody epitopes in human serum albumin using high-density peptide arrays
We have recently developed a high-density photolithographic, peptide array technology with a theoretical upper limit of 2 million different peptides per array of 2 cm2. Here, we have used this to perform complete and exhaustive analyses of linear B cell epitopes of a medium sized protein target using human serum albumin (HSA) as an example. All possible overlapping 15-mers from HSA were synthesized and probed with a commercially available polyclonal rabbit anti-HSA antibody preparation. To allow for identification of even the weakest epitopes and at the same time perform a detailed characterization of key residues involved in antibody binding, the array also included complete single substitution scans (i.e. including each of the 20 common amino acids) at each position of each 15-mer peptide. As specificity controls, all possible 15-mer peptides from bovine serum albumin (BSA) and from rabbit serum albumin (RSA) were included as well. The resulting layout contained more than 200.000 peptide fields and could be synthesized in a single array on a microscope slide. More than 20 linear epitope candidates were identified and characterized at high resolution i.e. identifying which amino acids in which positions were needed, or not needed, for antibody interaction. As expected, moderate cross-reaction with some peptides in BSA was identified whereas no cross-reaction was observed with peptides from RSA. We conclude that high-density peptide microarrays are a very powerful methodology to identify and characterize linear antibody epitopes, and should advance detailed description of individual specificities at the single antibody level as well as serologic analysis at the proteome-wide level.
Data from: Genome-wide association mapping of quantitative traits in a breeding population of sugarcane
Background: Molecular markers associated with relevant agronomic traits could significantly reduce the time and cost involved in developing new sugarcane varieties. Previous sugarcane genome-wide association analyses (GWAS) have found few molecular markers associated with relevant traits at plant-cane stage. The aim of this study was to establish an appropriate GWAS to find molecular markers associated with yield related traits consistent across harvesting seasons in a breeding population. Sugarcane clones were genotyped with DArT (Diversity Array Technology) and TRAP (Target Region Amplified Polymorphism) markers, and evaluated for cane yield (CY) and sugar content (SC) at two locations during three successive crop cycles. GWAS mapping was applied within a novel mixed-model framework accounting for population structure with Principal Component Analysis scores as random component. Results: A total of 43 markers significantly associated with CY in plant-cane, 42 in first ratoon, and 41 in second ratoon were detected. Out of these markers, 20 were associated with CY in 2 years. Additionally, 38 significant associations for SC were detected in plant-cane, 34 in first ratoon, and 47 in second ratoon. For SC, one marker-trait association was found significant for the 3 years of the study, while twelve markers presented association for 2 years. In the multi-QTL model several markers with large allelic substitution effect were found. Sequences of four DArT markers showed high similitude and e-value with coding sequences of Sorghum bicolor, confirming the high gene microlinearity between sorghum and sugarcane. Conclusions: In contrast with other sugarcane GWAS studies reported earlier, the novel methodology to analyze multi-QTLs through successive crop cycles used in the present study allowed us to find several markers associated with relevant traits. Combining existing phenotypic trial data and genotypic DArT and TRAP marker characterizations within a GWAS approach including population structure as random covariates may prove to be highly successful. Moreover, sequences of DArT marker associated with the traits of interest were aligned in chromosomal regions where sorghum QTLs has previously been reported. This approach could be a valuable tool to assist the improvement of sugarcane and better supply sugarcane demand that has been projected for the upcoming decades.
Systematic exploration of unsupervised methods for mapping behavior
<p>Raw data and raw movies accompanying initial publication</p>
FIGURE 3. Distribution map for E in Description of a new species of Euptychia Hübner, 1818 (Lepidoptera: Nymphalidae: Satyrinae) from the western Andes
FIGURE 3. Distribution map for E. favonius.
FIGURES 29–31. Discozantaena distribution maps. —29. D. galea. —30. D. karroo. —31. D in A revision of the South African endemic humicolous beetle genus Discozantaena Perkins and BalfourBrowne (Coleoptera: Hydraenidae)
FIGURES 29–31. Discozantaena distribution maps. —29. D. galea. —30. D. karroo. —31. D. ovata.
MAP 50 in Review of Glyphidocera Walsingham of Costa Rica (Lepidoptera: Gelechioidea: Glyphidoceridae)
MAP 50. Distribution of Glyphidocera scuticae () and G. c o t i s ().
MAP 36 in Review of Glyphidocera Walsingham of Costa Rica (Lepidoptera: Gelechioidea: Glyphidoceridae)
MAP 36. Distribution of Glyphidocera placentae () and G. for m a e ().
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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.