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537 results for “genome structure”
Data from: Minor allele frequency thresholds strongly affect population structure inference with genomic datasets
One common method of minimizing errors in large DNA sequence datasets is to drop variable sites with a minor allele frequency below some specified threshold. Though widespread, this procedure has the potential to alter downstream population genetic inferences and has received relatively little rigorous analysis. Here we use simulations and an empirical SNP dataset to demonstrate the impacts of minor allele frequency (MAF) thresholds on inference of population structure. We find that model-based inference of population structure is confounded when singletons are included in the alignment, and that both model-based and multivariate analyses infer less distinct clusters when more stringent MAF cutoffs are applied. We propose that this behavior is caused by the combination of a drop in the total size of the data matrix and by correlations between allele frequencies and mutational age. We recommend a set of best practices for applying MAF filters in studies seeking to describe population structure with genomic data.
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.
Plastid genome structure and phylogenomics of Nymphaeales: conserved gene order and new insights into relationships
<p>The plastid genomes of early-diverging angiosperms were among the first land plant plastomes investigated. Despite their importance to understanding angiosperm evolution, no investigation has so far compared gene content or gene synteny of these plastid genomes with a focus on the Nymphaeales. Here, we report an evaluation and comparison of gene content, gene synteny and inverted repeat length for a set of 15 plastid genomes of early-diverging angiosperms. Seven plastid genomes of the Nymphaeales were newly sequenced for this investigation. We compare gene order and inverted repeat (IR) length across all genomes, review the gene annotations of previously published genomes, generate a multi-gene alignment of 77 plastid-encoded genes and reconstruct the phylogenetic relationships of the taxa under study. Our results show that gene content and synteny are highly conserved across early-diverging angiosperms: All species analyzed display complete gene synteny when accounting for expansions and contractions of the IRs. This conservation was initially obscured by ambiguous and potentially incorrect gene annotations in previously published genomes. We also report the presence of intact open reading frames across all taxa analyzed. The multi-gene phylogeny displays maximum support for the families Cabombaceae and Hydatellaceae, but no support for a clade of all Nymphaeaceae. It further indicates that the genus <em>Victoria</em> is embedded within <em>Nymphaea</em>. Plastid genomes of <em>Trithuria</em> were found to deviate by numerous substitutions and length changes in the IRs. Phylogenetic analyses further indicate that a previously published plastome named <em>Nymphaea mexicana</em> falls into a clade of <em>N. odorata</em> and should be re-evaluated.</p>
Data from: Ribosomal DNA sequence heterogeneity reflects intra-species phylogenies and predicts genome structure in two contrasting yeast species
The ribosomal RNA encapsulates a wealth of evolutionary information, including genetic variation that can be used to discriminate between organisms at a wide range of taxonomic levels. For example, the prokaryotic 16S rDNA sequence is very widely used both in phylogenetic studies and as a marker in metagenomic surveys and the ITS region, frequently used in plant phylogenetics, is now recognised as a fungal DNA barcode. However, this widespread use does not escape criticism, principally due to issues such as difficulties in classification of paralogous versus orthologous rDNA units and intragenomic variation, both of which may be significant barriers to accurate phylogenetic inference. We recently analysed datasets from the Saccharomyces Genome Resequencing Project, characterising rDNA sequence variation within multiple strains of the baker's yeast <i>Saccharomyces cerevisiae</i> and its nearest wild relative <i>Saccharomyces paradoxus</i> in unprecedented detail. Notably, both species possess single locus rDNA systems. Here, we use these new variation datasets to assess whether a more detailed characterisation of the rDNA locus can alleviate the second of these phylogenetic issues, sequence heterogeneity, while controlling for the first. We demonstrate that a strong phylogenetic signal exists within both datasets and illustrate how they can be used, with existing methodology, to estimate intra-species phylogenies of yeast strains consistent with those derived from whole-genome approaches. We also describe the use of partial Single Nucleotide Polymorphisms, a type of sequence variation found only in repetitive genomic regions, in identifying key evolutionary features such as genome hybridisation events and show their consistency with whole-genome Structure analyses. We conclude that our approach can transform rDNA sequence heterogeneity from a problem to a useful source of evolutionary information, enabling the estimation of highly accurate phylogenies of closely related organisms, and discuss how it could be extended to future studies of multi-locus rDNA systems.
Dataset and source code for manuscript "Chromosomal gene order defines several structural classes of Staphylococcus epidermidis genomes"
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Structural equation models to interpret genome-wide association studies for morphological and productive traits in soybean [Glycine max (L.)]
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Table 1 in Complete mitochondrial genome of the terrestrial isopod Cubaris murina Brandt, 1833: new family gene order and novel tRNA secondary structures
<p><b>Table 1.</b> Arrangement and annotation of the mitochondrial genome of Cubaris murina.</p><table><tbody><tr><th></th><th></th><th></th><th></th><th></th><th>Length</th><th></th><th></th><th></th><th></th></tr></tbody><tbody><tr><th>Name</th><td>Type</td><td>Start</td><td>Stop</td><td>Strand</td><td>(bp)</td><td>Start</td><td>Stop</td><td>Inter-genic space</td><td>Overlap</td></tr><tr><th>Cox1</th><td>Coding</td><td>1</td><td>1536</td><td>+</td><td>1536</td><td>ATG</td><td>TAA</td><td>1</td><td></td></tr><tr><th>trnL2(tta)</th><td>tRNA</td><td>1538</td><td>1598</td><td>+</td><td>61</td><td></td><td></td><td>48</td><td></td></tr><tr><th>Cox2</th><td>Coding</td><td>1647</td><td>2282</td><td>+</td><td>636</td><td>ATA</td><td>TAG</td><td></td><td>2</td></tr><tr><th>trnK(aaa)</th><td>tRNA</td><td>2281</td><td>2336</td><td>+</td><td>56</td><td></td><td></td><td></td><td>8</td></tr><tr><th>trnD(gac)</th><td>tRNA</td><td>2329</td><td>2403</td><td>+</td><td>75</td><td></td><td></td><td></td><td>17</td></tr><tr><th>atp8</th><td>Coding</td><td>2387</td><td>2533</td><td>+</td><td>147</td><td>ATA</td><td>TAA</td><td></td><td>4</td></tr><tr><th>atp6</th><td>Coding</td><td>2530</td><td>3192</td><td>+</td><td>663</td><td>ATA</td><td>TAA</td><td>2</td><td></td></tr><tr><th>Cox3</th><td>Coding</td><td>3195</td><td>3989</td><td>+</td><td>795</td><td>ATG</td><td>TAG</td><td></td><td>2</td></tr><tr><th>trnR(cga)</th><td>tRNA</td><td>3988</td><td>4055</td><td>+</td><td>68</td><td></td><td></td><td>55</td><td></td></tr><tr><th>nad3</th><td>Coding</td><td>4111</td><td>4407</td><td>+</td><td>297</td><td>ATA</td><td>TAA</td><td></td><td>9</td></tr><tr><th>trnA(gca)</th><td>tRNA</td><td>4399</td><td>4446</td><td>+</td><td>48</td><td></td><td></td><td></td><td>8</td></tr><tr><th>nad1 CR putative</th><td>Coding</td><td>4439 5219</td><td>5218 5360</td><td>−</td><td>780 142</td><td>ATG</td><td>TAG</td><td></td><td>14</td></tr><tr><th>NCR1</th><td>tRNA</td><td>5361</td><td>5429</td><td>+</td><td>69</td><td></td><td></td><td></td><td>17</td></tr><tr><th>rrnS</th><td>rRNA</td><td>5413</td><td>6139</td><td>+</td><td>727</td><td></td><td></td><td>48</td><td></td></tr><tr><th>trnW(tga)</th><td>tRNA</td><td>6188</td><td>6244</td><td>+</td><td>57</td><td></td><td></td><td></td><td>7</td></tr><tr><th>trnS1(aga)</th><td>tRNA</td><td>6238</td><td>6296</td><td>−</td><td>59</td><td></td><td></td><td></td><td></td></tr><tr><th>NCR2</th><td></td><td>6297</td><td>6666</td><td></td><td>370</td><td></td><td></td><td></td><td></td></tr><tr><th>trnL1(cta)</th><td>tRNA</td><td>6667</td><td>6731</td><td>−</td><td>65</td><td></td><td></td><td>29</td><td></td></tr><tr><th>cob</th><td>Coding</td><td>6759</td><td>7907</td><td>−</td><td>1,149</td><td>ATA</td><td>TAG</td><td>38</td><td></td></tr><tr><th>trnT(aca)</th><td>tRNA</td><td>7946</td><td>8017</td><td>−</td><td>72</td><td></td><td></td><td>29</td><td></td></tr><tr><th>nad5</th><td>Coding</td><td>8047</td><td>9648</td><td>+</td><td>1,602</td><td>ATG</td><td>TAG</td><td></td><td>3</td></tr><tr><th>trnF(ttc)</th><td>tRNA</td><td>9646</td><td>9707</td><td>+</td><td>62</td><td></td><td></td><td></td><td>15</td></tr><tr><th>trnH(cac)</th><td>tRNA</td><td>9693</td><td>9758</td><td>−</td><td>66</td><td></td><td></td><td></td><td>23</td></tr><tr><th>nad4</th><td>Coding</td><td>9736</td><td>11,082</td><td>−</td><td>1,312</td><td>ATA</td><td>TAA</td><td>13</td><td></td></tr><tr><th>nad4L</th><td>Coding</td><td>11,096</td><td>11,374</td><td>−</td><td>279</td><td>ATA</td><td>TAA</td><td></td><td>13</td></tr><tr><th>trnP(cca)</th><td>tRNA</td><td>11,362</td><td>11,422</td><td>−</td><td>61</td><td></td><td></td><td>25</td><td></td></tr><tr><th>nad6</th><td>Coding</td><td>11,448</td><td>11,903</td><td>+</td><td>456</td><td>ATA</td><td>TAG</td><td></td><td>2</td></tr><tr><th>trnS2(tca)</th><td>tRNA</td><td>11,902</td><td>11,962</td><td>+</td><td>61</td><td></td><td></td><td>17</td><td></td></tr><tr><th>rrnL</th><td>rRNA</td><td>11,980</td><td>12,549</td><td>−</td><td>570</td><td></td><td></td><td></td><td></td></tr><tr><th>NCR3</th><td></td><td>12,550</td><td>12,753</td><td></td><td>204</td><td></td><td></td><td></td><td></td></tr><tr><th>trnE(gaa)</th><td>tRNA</td><td>12,754</td><td>12,812</td><td>−</td><td>59</td><td></td><td></td><td></td><td></td></tr><tr><th>NCR4</th><td></td><td>12,813</td><td>12,950</td><td></td><td>138</td><td></td><td></td><td></td><td></td></tr><tr><th>trnV(gta)</th><td>tRNA</td><td>12,951</td><td>13,019</td><td>−</td><td>69</td><td></td><td></td><td></td><td>5</td></tr><tr><th>trnQ(caa)</th><td>tRNA</td><td>13,015</td><td>13,077</td><td>−</td><td>63</td><td></td><td></td><td></td><td>6</td></tr><tr><th>trnM(atg)</th><td>tRNA</td><td>13,072</td><td>13,141</td><td>+</td><td>70</td><td></td><td></td><td>25</td><td></td></tr><tr><th>nad2</th><td>Coding</td><td>13,167</td><td>14,123</td><td>+</td><td>978</td><td>ATA</td><td>TAG</td><td></td><td>15</td></tr><tr><th>trnC(tgc)</th><td>tRNA</td><td>14,109</td><td>14,158</td><td>−</td><td>50</td><td></td><td></td><td></td><td></td></tr><tr><th>trnY(tac)</th><td>tRNA</td><td>14,159</td><td>14,205</td><td>−</td><td>47</td><td></td><td></td><td>7</td><td></td></tr></tbody></table>
Table 2 in Complete mitochondrial genome of the terrestrial isopod Cubaris murina Brandt, 1833: new family gene order and novel tRNA secondary structures
<p><b>Table 2.</b> Base composition (%) of nucleotide, AT content, and AT- and GC-skew of the mitochondrial genome of <i>Cubaris murina.</i> Values in bold indicate positive AT-skew.</p><table><tbody><tr><th></th><th></th><th></th><th>Base composition (%)</th><th></th><th></th><th></th><th></th></tr></tbody><tbody><tr><th>Total</th><td></td><td>A</td><td>C</td><td>G</td><td>T</td><td>%AT</td><td>AT skew</td><td>GC skew</td></tr><tr><th>14,212 bp</th><td>28.90%</td><td>15.80%</td><td>23.40%</td><td>31.90%</td><td>60.80%</td><td>−0.049</td><td>0.194</td></tr><tr><th></th><td></td><td></td><td>Base composition (%)</td><td></td><td></td><td></td><td></td></tr><tr><th>Gene Strand</th><td>A</td><td>C</td><td>G</td><td>T</td><td>%AT</td><td>AT skew</td><td>GC skew</td></tr><tr><th><i>cox1</i></th><td>(+)</td><td>22.4%</td><td>18.6%</td><td>24.2%</td><td>34.8%</td><td>57.2%</td><td>−0.217</td><td>0.131</td></tr><tr><th><i>cox2</i></th><td>(+)</td><td>20.6%</td><td>21.7%</td><td>27.7%</td><td>30.0%</td><td>50.6%</td><td>−0.186</td><td>0.121</td></tr><tr><th><i>atp8</i></th><td>(+)</td><td>21.1%</td><td>15.0%</td><td>34.7%</td><td>29.3%</td><td>50.4%</td><td>−0.163</td><td>0.396</td></tr><tr><th><i>atp6</i></th><td>(+)</td><td>20.4%</td><td>19.8%</td><td>29.1%</td><td>30.8%</td><td>51.2%</td><td>−0.203</td><td>0.190</td></tr><tr><th><i>cox3</i></th><td>(+)</td><td>17.2%</td><td>23.6%</td><td>28.6%</td><td>30.6%</td><td>47.8%</td><td>−0.280</td><td>0.096</td></tr><tr><th><i>nad3</i></th><td>(+)</td><td>20.2%</td><td>16.2%</td><td>32.3%</td><td>31.3%</td><td>51.5%</td><td>−0.216</td><td>0.332</td></tr><tr><th><i>nad1</i></th><td>(−)</td><td>31.7%</td><td>22.2%</td><td>27.6%</td><td>18.6%</td><td>50.3%</td><td><b>0.260</b></td><td>0.108</td></tr><tr><th><i>NCR1</i></th><td></td><td>28.9%</td><td>27.5%</td><td>19.7%</td><td>23.9%</td><td>52.8%</td><td>0.095</td><td>−0.165</td></tr><tr><th><i>NCR2</i></th><td></td><td>27.0%</td><td>22.2%</td><td>18.1%</td><td>32.7%</td><td>59.7%</td><td>−0.095</td><td>−0.102</td></tr><tr><th><i>cob</i></th><td>(−)</td><td>36.0%</td><td>12.0%</td><td>23.5%</td><td>28.5%</td><td>64.5%</td><td><b>0.116</b></td><td>0.324</td></tr><tr><th><i>nad5</i></th><td>(+)</td><td>28.5%</td><td>10.5%</td><td>21.7%</td><td>39.3%</td><td>67.8%</td><td>−0.159</td><td>0.348</td></tr><tr><th><i>nad4</i></th><td>(−)</td><td>38.4%</td><td>11.9%</td><td>22.8%</td><td>26.9%</td><td>65.3%</td><td><b>0.176</b></td><td>0.314</td></tr><tr><th><i>nad4L</i></th><td>(−)</td><td>41.9%</td><td>12.2%</td><td>17.6%</td><td>28.3%</td><td>70.2%</td><td><b>0.194</b></td><td>0.181</td></tr><tr><th><i>nad6</i></th><td>(+)</td><td>25.7%</td><td>10.5%</td><td>17.8%</td><td>46.1%</td><td>71.8%</td><td>−0.284</td><td>0.258</td></tr><tr><th><i>NCR3</i></th><td></td><td>36.8%</td><td>9.8%</td><td>19.1%</td><td>34.3%</td><td>71.1%</td><td>0.035</td><td>0.322</td></tr><tr><th><i>NCR4</i></th><td></td><td>35.5%</td><td>13.0%</td><td>15.2%</td><td>36.2%</td><td>71.7%</td><td>−0.010</td><td>0.078</td></tr><tr><th><i>nad2</i></th><td>(+)</td><td>28.9%</td><td>12.4%</td><td>22.6%</td><td>36.1%</td><td>65.0%</td><td>−0.111</td><td>0.291</td></tr></tbody></table>
Data from: Mechanical incompatibility caused by modifications of multiple male genital structures using genomic introgression in Drosophila
Mechanical incompatibility of male and female genitalia is common in animals with internal fertilization. However, our knowledge regarding the precise mechanisms is limited. One key question regards the susceptibility of the match between male and female genitalia to morphological modification. To address this issue, we generated six different second-chromosome introgression lines possessing partially Drosophila mauritiana-like genital morphology in multiple structures in D. simulans background. Three of the six introgression males showed elevated mobility at some stages during copulation with D. simulans females; this was assumed to be an indication of genital mismatch. Notably, one of the introgression males with D. mauritiana-like enlarged anal plates showed occasional leakage of adhesive ejaculate on the body surface when mated with pure D. simulans females, suggesting apparent structural incompatibility in genital coupling. These observations suggested that both sexual and natural selection shape the anal plate morphology, highlighting the role of this structure as an important component of mechanical isolation. Partial replacement (introgression) by a sibling species genome can induce perturbations in genital coupling mechanics, suggesting that genital compatibility can be susceptible to subtle genomic changes at the early stages of divergence in these species.
FIGURE 15 in Solution NMR Structure of CalU16 from Micromonospora echinospora, Northeast Structural Genomics Consortium (NESG) Target MiR12
FIGURE 15. Original plate in the description of Nototrema fissipes by Boulenger (1888).
VCF of structural variant calls of Nanopore data aligned to dm6 reference genome
<p>Heterozygous chromosome inversions suppress meiotic crossover (CO) formation within an inversion, potentially because they lead to gross chromosome rearrangements that produce inviable gametes. Interestingly, COs are also severely reduced in regions nearby but outside of inversion breakpoints even though COs in these regions do not result in rearrangements. Our mechanistic understanding of why COs are suppressed outside of inversion breakpoints is limited by a lack of data on the frequency of noncrossover gene conversions (NCOGCs) in these regions. To address this critical gap, we mapped the location and frequency of rare CO and NCOGC events that occurred outside of the <em>dl</em>-<em>49</em> <em>chrX</em> inversion in <em>D</em>. <em>melanogaster</em>. We created full-sibling wildtype and inversion stocks and recovered COs and NCOGCs in the syntenic regions of both stocks, allowing us to directly compare rates and distributions of recombination events. We show that COs are completely suppressed within 500 kb of inversion breakpoints, are severely reduced within 2 Mb of an inversion breakpoint, and increase above wildtype levels 2–4 Mb from the breakpoint. We find that NCOGCs occur evenly throughout the chromosome and, importantly, occur at wild-type levels near inversion breakpoints. We propose a model in which COs are suppressed by inversion breakpoints in a distance-dependent manner through mechanisms that influence DNA double-strand break repair outcome but not double-strand break location or frequency. We suggest that subtle changes in the synaptonemal complex and chromosome pairing might lead to unstable interhomolog interactions during recombination that permits NCOGC formation but not CO formation.</p>
Data from: Ribosomal DNA sequence heterogeneity reflects intra-species phylogenies and predicts genome structure in two contrasting yeast species
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Data from: The relationship of recombination rate, genome structure, and patterns of molecular evolution across angiosperms
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Data from: Global lack of flyway structure in a cosmopolitan bird revealed by a genome wide survey of single nucleotide polymorphisms
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Data from: Leishmania naiffi and Leishmania guyanensis reference genomes highlight genome structure and gene evolution in the Viannia subgenus
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Data from: A tale of two genomes: contrasting patterns of phylogeographic structure in a widely distributed bat
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Data from: Genus-wide screening reveals four distinct types of structural plastid genome organization in Pelargonium (Geraniaceae)
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Data from: Analysis of genome-wide structure, diversity and fine mapping of Mendelian traits in traditional and village chickens
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Data from: Application of genomic estimation methods of inbreeding and population structure in an Arabian horse herd
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Data from: Spatiotemporally explicit demographic modelling supports a joint effect of historical barriers to dispersal and contemporary landscape composition on structuring genomic variation in a red-listed grasshopper
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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.