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Supplementary data for: Chromosome-scale genome assemblies of aphids reveal extensively rearranged autosomes and long-term conservation of the X chromosome
<p><strong><em>Myzus persicae </em>clone O v2 frozen release</strong></p> <p>Genome assembly: Myzus_persicae_O_v2.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Myzus_persicae_O_v2.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Myzus_persicae_O_v2.0.scaffolds.braker2.gff3.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Myzus_persicae_O_v2.0_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae de novo</em> repeat library: Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff.out</p> <p>RepeatMasker transposable element annotation using the <em>M. persicae</em> <em>de novo r</em>epeat library (gff format): Myzus_persicae_O_v2.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Acyrthosiphon pisum</em> clone JIC1 v1 frozen release</strong></p> <p>Genome assembly: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.fa.gz</p> <p>BRAKER2 gene models: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff</p> <p>List of gene models containing internal stop codons (removed from the protein and cds fasta files): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.bad_genes.lst</p> <p>BRAKER2 protein sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.fa</p> <p>BRAKER2 coding sequences (longest transcript per gene only): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.braker2.gff.filtered.cds.LTPG.fa</p> <p><em>De novo </em>repeat library (ReapeatModeler merged with repbase insecta): Acyrthosiphon_pisum_JIC1_repeat_lib.repeatmodeler_merged_repbase_insecta.fa</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum</em> <em>de novo</em> repeat library: Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.out</p> <p>RepeatMasker transposable element annotation using the <em>A. pisum de novo</em> repeat library (gff format): Acyrthosiphon_pisum_JIC1_v1.0.scaffolds.repeatmodeler_merged_repbase_insecta.repeatmasker.gff</p> <p><strong><em>Rhodnius prolixus</em> DNA zoo chromosome-scale genome assembly annotation</strong></p> <p><em>R. prolixus </em>chromosome-scale genome assembly was obtained here: <a href="https://www.dnazoo.org/assemblies/Rhodnius_prolixus">https://www.dnazoo.org/assemblies/Rhodnius_prolixus</a>.</p> <p>Genome assembly: Rhodnius_prolixus-3.0.3_HiC.fasta</p> <p>BRAKER2 gene models: Rhodnius_prolixus-3.0.3_HiC.braker2.gff</p> <p>BRAKER2 protein sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): Rhodnius_prolixus-3.0.3_HiC.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: Rhodnius_prolixus-3.0.3_HiC.braker2.gff.cds.fa</p> <p><strong><em>Triatoma rubrofasciata</em> chromosome-scale genome assembly annotation</strong></p> <p><em>T. rubrofasciata </em>chromosome-scale genome assembly was obtained here: <a href="http://dx.doi.org/10.5524/100614">http://dx.doi.org/10.5524/100614</a></p> <p>Genome assembly: zhuichun_assembly.fasta</p> <p>BRAKER2 gene models: zhuichun_assembly.braker2.gff</p> <p>BRAKER2 protein sequences: zhuichun_assembly.braker2.gff.aa.fa</p> <p>BRAKER2 protein sequences (longest transcript per gene only): zhuichun_assembly.braker2.gff.aa.LTPG.fa</p> <p>BRAKER2 coding sequences: zhuichun_assembly.braker2.gff.cds.fa</p> <p><strong>Hemiptera orthogroups and species tree</strong></p> <p>OrthoFinder was used to cluster proteomes of 14 Hemiptera into orthogroups for phylogenomic analysis. All proteomes were reduced to the longest transcript per gene. See here for full details:</p> <p>Species included, taxon IDs and data source:</p> <p>Mcer = Myzus cerasi v1.1 (<a href="https://bipaa.genouest.org/sp/myzus_cerasi/">https://bipaa.genouest.org/sp/myzus_cerasi/</a>)</p> <p>MperO = Myzus persicae clone O v2 (This study)</p> <p>Dnox = Diuraphis noxia Thorpe et. al. gene predictions (<a href="https://bipaa.genouest.org/sp/diuraphis_noxia/">https://bipaa.genouest.org/sp/diuraphis_noxia/</a>)</p> <p>Apis = Acyrthosiphon pisum JIC1 v1 (This study)</p> <p>Pnig = Pentalonia nigronervosa (This study)</p> <p>Rmai = Rhopalosiphum maidis v0.1 (<a href="http://gigadb.org/dataset/100572">http://gigadb.org/dataset/100572</a>)</p> <p>Rpad = Rhopalosiphum padi v1.0 (<a href="https://bipaa.genouest.org/sp/rhopalosiphum_padi/">https://bipaa.genouest.org/sp/rhopalosiphum_padi/</a>)</p> <p>Agly = Aphis glycines biotype 4 v2.1 (<a href="https://zenodo.org/record/3453468#.XnpL5JOgLRY">https://zenodo.org/record/3453468#.XnpL5JOgLRY</a>)</p> <p>BtabMEAM1 = Bemissia tabacci MEAM1 v1.2 (<a href="http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi">http://www.whiteflygenomics.org/cgi-bin/bta/index.cgi</a>)</p> <p>Trub = Triatoma rubrofasciata (This study)</p> <p>Rpro = Rhodnius prolixus (This study)</p> <p>Ofas = Oncopeltus fasciatus OGS v1.0 (<a href="https://i5k.nal.usda.gov/Oncopeltus_fasciatus">https://i5k.nal.usda.gov/Oncopeltus_fasciatus</a>)</p> <p>Sfuc = Sogatella furcifera v1 (<a href="http://dx.doi.org/10.5524/100255">http://dx.doi.org/10.5524/100255</a>)</p> <p>Nlug = Nilaparvata lugens (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0521-0#Sec42</a>)</p> <p>Files:</p> <p>Proteomes included in the analysis: proteomes.tar.gz</p> <p>Orthogroups: Orthogroups.txt</p> <p>Gene counts per orthogroup, per species: Orthogroups.GeneCount.csv</p> <p>Single copy conserved orthogroups used for species tree: SingleCopyOrthogroups.txt</p> <p>Species tree alignment: SpeciesTreeAlignment.fa</p> <p>r8s configuration file (includes time calibrations and OrthoFinder ML species tree with branch lengths): species_tree_rooted.r8s.nex</p> <p>r8s time calibrated species tree: r8s_tree.nwk</p>
Chromosome-scale, haplotype-resolved genome assembly of Suaeda glauca
<p><em>Suaeda glauca</em>is an annual herb of Suaeda and an important saline-alkali plant resource, which is widespread on beaches and saline lands around the world. It is also a good candidate for food, feed, and drug development. There has been no publication of the <em>Suaeda glauca</em>genome assembly, limiting the evolutionary study of Amaranthaceae and the bioavailability of <em>Suaeda glauca</em>.</p> <p>Using PacBio HiFi and Hi-C sequencing data, we successfully generated chromosome-scale, haplotype-resolved assemblies of the <em>Suaeda glauca</em>genome. The size of the final primary assembly was 622.95 Mb, and the contig N50 was 19.42 Mb, which was successfully anchored to 9 chromosomes, accounting for 96.79% of the total assembly size. The repeat content and genome size of <em>Suaeda glauca</em>are much higher than those of the same genus <em>Suaeda aralocaspica</em>, presumably due to a recent burst of LTR insertions. Using HiFi reads, we assembled the complete circular chloroplast genome of <em>Suaeda glauca</em>. Through gene family and phylogenetic tree analysis, it was shown that <em>Suaeda glauca</em>and <em>Suaeda aralocaspica</em>differentiated at ~26.36 million years ago (MYA), and Amaranthaceae species began to differentiate at ~52.00 MYA.</p>
A Chromosome-Scale Genome of the Korean Cultivar Sesamum indicum var. Goenbaek
<p>Supporting data set of the Korean sesame cultivar, Sesamum indicum var. Goenbaek genome. For any inquiry, please contact Keunpyo Lee Ph.D. at kplee@korea.kr</p>
An allozyme polymorphism is associated with a large chromosomal inversion in the marine snail Littorina fabalis
<p>This Zenodo archive contains the dataset analysed in the paper "An allozyme polymorphism is associated with a large chromosomal inversion in the marine snail Littorina fabalis" published in Evolutionary Application in 2022:</p> <ul> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/FAB_LG3_maf1_SNP_Hexcess_depth10.vcf">FAB_LG3_maf1_SNP_Hexcess_depth10.vcf </a>: vcf for LG3 unpruned for LD containing 295 individuals genotyped at 58,246 filtered SNPs</li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/FAB_LG3_maf1_SNP_Hexcess_depth10_thin.vcf">FAB_LG3_maf1_SNP_Hexcess_depth10_thin.vcf </a>: vcf for LG3 pruned for LD containing 295 individuals genotyped at 9,905 filtered SNPs</li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/FAB_AK_maf1_SNP_Hexcess_depth10.vcf">FAB_AK_maf1_SNP_Hexcess_depth10.vcf</a> : vcf for contig265 containing the arginine kinase gene: 295 individuals genotyped at 70 filtered SNPs</li> </ul> <p>The archive also include some of the R script used to performed the analyses of the manuscrit:</p> <ul> <li> </li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/Population_genetic_Ark_analyses.R">Population_genetic_Ark_analyses.R </a>: Script to perform PCA +phenotypic cline + FST + Hobs + FIS</li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/Suspension_bridge_fit.R">Suspension_bridge_fit.R </a>: Script to perform the suspension bridge fit used to found evidence of gene flux inside the inversion.</li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/Cline_function.R">Cline_function.R </a>: function used to fit the allelic frequency variation (cline) along the transect</li> </ul> <p>The raw sequences are available in NCBI.</p> <p>Abstract of the study: Understanding the genetic targets of natural selection is one of the most challenging goalsof population genetics. Some of the earliest candidate genes were identified from associations between allozyme allele frequencies and environmental variation. One such example is the clinal polymorphism in the arginine kinase (<em>Ak</em>) gene in the marine snail <em>Littorina fabalis</em>. While other enzyme loci do not show differences in allozyme frequencies among populations, the <em>Ak</em> alleles are near differential fixation across repeated wave exposure gradients in Europe. Here, we use this case to illustrate how a new sequencing toolbox can be employed to characterize the genomic architecture associated with historical candidate genes. We found that the <em>Ak</em> alleles differ by 9 non-synonymous substitutions, which perfectly explain the different migration patterns of the allozymes during electrophoresis. Moreover, by exploring the genomic context of the <em>Ak</em> gene, we found that the three main <em>Ak</em> alleles are located on different arrangements of a putative chromosomal inversion that reaches near fixation at the opposing ends of two transects covering a wave exposure gradient. This shows <em>Ak</em> is part of a large (3/4 of the chromosome) genomic block of differentiation, in which <em>Ak</em> is unlikely to be the only target of divergent selection. Nevertheless, the non-synonymous substitutions among <em>Ak</em> alleles and the complete association of one allele with one inversion arrangement suggest that the <em>Ak</em> gene is a strong candidate to contribute to the adaptive significance of the inversion.</p> <p> </p> <p> </p>
Data from: Chromosome-scale assembly with a phased sex-determining region resolves features of early Z and W chromosome differentiation in a wild octoploid strawberry
<p>Abstract: When sex chromosomes stop recombining, they start to accumulate differences. The sex-limited chromosome (Y or W) especially is expected to degenerate via the loss of nucleotide sequence and the accumulation of repetitive sequences. However, how early signs of degeneration can be detected in a new sex chromosome is still unclear. The sex determining region (SDR) of the octoploid strawberries is young, small, and dynamic. Using PacBio HiFi reads, we obtained a chromosome scale assembly of a female (ZW) <em>Fragaria chiloensis</em> plant carrying the youngest and largest of the known SDR on the W in strawberries. We fully characterized the previously incomplete SDR, confirming its gene content, genomic location and evolutionary history. Resolution of gaps in the previous characterization of the SDR added 10 kbp of sequence including a non-canonical LTR-retrotransposon; whereas the Z sequence revealed a <em>Harbinger</em> transposable element adjoining the SDR insertion site. Limited genetic differentiation of the sex chromosomes coupled with structural variation may indicate an early stage of W degeneration. The sex chromosomes have a similar percentage of repeats but differ in their repeat distribution. Differences in the pattern of repeats (transposable element polymorphism) apparently precede sex chromosome differentiation, thus potentially contributing to recombination cessation as opposed to being a consequence of it.</p> <p>Repository content: data (sequence alignments, phylogenetic trees, genome assembly, and vcf files) and scripts associated with the manuscript "Chromosome-scale assembly with a phased sex-determining region resolves features of early Z and W chromosome differentiation in a wild octoploid strawberry"</p>
VCFs of Chromosome 17 of Case 5 from the EGAD00001008392 Dataset
<p>This synthetic data is a subset of the VCFs (chr17) from case 5 from the Rare Disease Synthetic Dataset (<a href="https://ega-archive.org/datasets/EGAD00001008392">EGAD00001008392</a>) dataset and the Human genomic and phenotypic synthetic data for the study of rare diseases (<a href="https://ega-archive.org/studies/EGAS00001005702">EGAS00001005702</a>) study. For more info go to <a href="https://ega-archive.org/">https://ega-archive.org/</a>.</p> <p>All this data was created with the support of the RD-Connect GPAP (<a href="https://platform.rd-connect.eu/">https://platform.rd-connect.eu/</a>), EC H2020 project EJP-RD (grant # 825575), EC H2020 project B1MG (grant # 951724) and Generalitat de Catalunya VEIS project (grant # 001-P-001647).</p>
Data of: Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts
<p>These files are results obtained in<br><span><span><span><span>Imputation-free reconstructions of three-dimensional chromosome architectures in human diploid single-cells using allele-specified contacts</span></span></span></span><br>by Yoshito Hirata, Arisa H. Oda, Chie Motono, Masanori Shiro & Kunihiro Ohta.</p> <p>There are 33 files for the corresponding each reconstruction of three-dimensional chromosomone structures<br>for each cell.<br>There are 3D structures for 15 GM cells and 18 PBMC cells, which are obtained from the single cell Hi-C data of Tan et al. Science (2018).</p> <p>For each file, there are 6 columns:<br>The first column corresponds to the allele (0: maternal, 1: paternal)<br>The second column corresponds to the chromosome (1-22: chromosome's number, 23: X, 24: Y)<br>The third column corrsponds to the base point.<br>The fourth column, the fifth column and the sixth column correspond to x-, y-, and z-axes of our reconstruction.</p>
Evaluating homophily of human PPI with respect to chromosomes
<p>Homophily/heterophily evaluation, expressed in terms of z-score values, is related to the human Protein-Protein Interaction Network (PPI), obtained from the STRING v11.5 database (<a href="https://string-db.org/">https://string-db.org</a>) setting standard threshold on edge score (T=700). Each protein occurring in the PPI was assigned to a class corresponding to the chromosome the related gene belongs to.</p> <p>A total of 23 classes (<em>chr1</em>, <em>chr2</em>, ..., <em>chr22</em>, <em>chrX</em>) were considered (excluding the class corresponding to chromosome Y because of the small number of genes occurring in the network).</p> <p>The homophily/heterophily nature of the network, with respect to chromosome classes, was evaluated through HONTO tool (<a href="https://github.com/cumbof/honto">https://github.com/cumbof/honto</a>).</p> <p>In other words, the tendency of proteins to preferentially interact with proteins whose genes are physically located on the same chromosome (homophily) or on different chromosomes (heterophily) was investigated and evaluated in terms of z-scores.</p> <p>Values related to intra (along the diagonal) and inter chromosomal interactions (other than the diagonal) are also reported as a heatmap.</p> <p>As one can observe, values occurring in the diagonal are clearly higher than values out of the diagonal, leading to assess a homophilic nature of the network, confirming the link between shared chromosome and interaction in the PPI.</p>
Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq
<p>Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds.</p>
Cytoplasmic components of the machinery mediating telomere-led rapid chromosome movements in mouse meiosis
<p>Telomere-led rapid chromosome movements (RPMs) are a prominent characteristic of chromosome dynamics during meiosis. Although of crucial importance in maintaining germ cell integrity, the extranuclear portion of the machinery supporting RPMs in mammals is poorly understood. Using an unbiased proteomic approach to identify motor proteins associated to microtubules in mouse meiotic cells, complemented with co-immunoprecipitation confirmation, we uncovered kinesins as candidate of the machinery mediating RPMs in mouse spermatocytes. Further biochemical, microscopy, and functional analysis shows that KIF5B and KIF2B interact with KASH5 and act as motor proteins mediating the LINC complex-microtubule interactions. Our results show that member of the kinesin family of molecular motors act as novel critical modules of the machinery promoting complex dynamic chromosomes in mammals.</p>
Supplementary material 2 from: Steinberg E, Nieves M, Mudry M (2014) Multiple sex chromosome systems in howler monkeys (Platyrrhini, Alouatta). Comparative Cytogenetics 8(1): 43-69. https://doi.org/10.3897/compcytogen.v8i1.6716
Supplementary Figure S. (doi: 10.3897/CompCytogen.v8i1.6716.app2) File format: Microsoft Word file (doc).:
Figure 5. A chromosome structure in case we have 2 visible states and 3 invisible states-Neuroevolution Mechanism for Hidden Markov Model
<p>Generating a population of size n of HMMs at random can be performed with some<br> restrictions:<br> - The weights representing the input layer in the chromosome should be always negligible as<br> initial values.<br> - The weights which are involved in summation of 1.0 in the hidden layer part of the<br> chromosome should be exactly 1.0.<br> Let us assume the following case<br> Visible states are 2 and invisible states (observations) are 3, , then we shall have a<br> chromosome as shown in Figure 5.</p>
Figure 6. Two point crossover of HMM chromosomes.-Neuroevolution Mechanism for Hidden Markov Model
<p>Two point Crossover<br> For the two point crossover we get two parent HMMs and choose at random two cutting points for<br> the weights that have a sum of 1.0 and swap the contents between the crossing points. This is<br> illustrated in the example shown in Figure 6.</p>
Figure 4. A chromosome structure for HMM shown in Figure 2.-Neuroevolution Mechanism for Hidden Markov Model
<p>The chromosome which represents the HMM can be extracted from its corresponding neural<br> network. The general structure of the chromosome is divided into two sections, input layer and<br> hidden layer. Each section contains many slots, and each slot represents a weight from one node in<br> that layer to a node in the next layer (from input to hidden and from hidden to output). The number<br> of slots in the input layer is the same number of input nodes in the neural network. In the hidden<br> layer, number of slots is equal to nodes in the output layer multiplied by the nodes in the hidden<br> layer.</p>
Figure 6. One chromosome from the population and the five chromosomes existing in the evaluation partition.-Genetic Algorithms Principles Towards Hidden Markov Model
<p>For example comparing the<br> chromosome given in Figure 6 with the first chromosome in the evaluation partition, the<br> difference between the relation Med-Med and Med-High as a pair is 0.0 and the difference<br> between the relation High-High and High-Med as a pair is 0.1. Similarly the difference between<br> the relation Med-Cold and Med-Hot as a pair is 0.1 and the difference between the relation<br> High-Cold and High-Hot as a pair is 0.2. We sum all these differences to get the value of<br> compare(i,j), the sum value is 0+0.1+0.1+0.2 = 0.4. Using the same approach we compute the<br> compare function with the other four chromosomes and we get values 0.4, 0.5,0.4 and 0.6. Now<br> we sum the five values 0.4 + 0.4 + 0.5+ 0.4 +0.6 = 2.3. The fitness value is then 1/ 2.3 = 0.434.<br> The highest is the fitness value, the better is the performance of the chromosome.</p>
Figure 7. One point crossover of HMM chromosomes.-Neuroevolution Mechanism for Hidden Markov Model
This crossover is performed in the input layer part only. We choose a crossing cut point in the input layer part of the chromosome, and exchange everything before it. This is illustrated in Figure 7.
Figure 3. The crossover operation between two HMM chromosomes Figure-Genetic Algorithms Principles Towards Hidden Markov Model
<p>Crossover<br> In this genetic operator, we choose two chromosomes at random and apply crossover between<br> them. Figure 3 shows the proposed crossover. We choose a crossing cut site at random. It is to be<br> noted that the crossing cut site should be even number. We should have two crossing cut sites. If<br> we make crossing cut site odd number, the resultant child will not have a correct value of<br> probability. The incorrect crossover is shown in Figure 4.</p>
FIGURE 1 in Chromosome Numbers of Some Cultivated Acanthaceae with Notes on Chromosomal Evolution in the Family
FIGURE 1 (upper right). Chromosomes of Acanthaceae in pollen mother cells. A. Dyschoriste thunbergiiflora, metaphase II (only half of cell shown), n = 15. B. Brillantaisia owariensis, telophase I, n = 16 (with one lagging chromosome toward "upper" pole). C. Brillantaisia owariensis, metaphase I, n = 16. D. Ruellia elegans, diakenesis (showing nucleolus, n), n = 17. E. Crossandra infundibuliformis, metaphase I, n = 19. F. Ruellia dipteracanthus, metaphase I, n = 17. G. Justicia scheidweileri, metaphase I, n = 14. Chromosomes shown in outline only are touching or overlapping other chromosomes. Scale applies to all figures. See Table 1 for voucher information.
Chromosomal inversions from an initial ecotypic divergence drive a gradual repeated radiation of Galápagos beetles
<p>Island faunas exhibit some of the most iconic examples where similar forms repeatedly evolve within different islands. Yet, whether these deterministic evolutionary trajectories within islands are driven by an initial, singular divergence and the subsequent exchange of individuals and adaptive genetic variation between islands remains unclear. Here, we address this issue using a gradual, repeated evolution of low-dispersive highland ecotypes from a dispersive lowland ecotype of <em>Calosoma</em> beetles along the island progression of the Galápagos. We show that repeated highland adaptation involved selection on multiple shared alleles within extensive chromosomal inversions that originated from an initial adaptation event on the oldest island. These highland inversions first spread through dispersal of highland individuals. Subsequent admixture with the widely distributed lowland ecotype resulted in polymorphic dispersive populations from which the highland populations evolved on the youngest islands. Our findings emphasize the significance of an ancient divergence in driving repeated evolution and highlight how a mixed contribution of inter-island colonization and within-island evolution can shape parallel species communities on islands.</p>
FIGURE 3 in Chromosome Numbers of Some Cultivated Acanthaceae with Notes on Chromosomal Evolution in the Family
FIGURE 3. Flowers of some species for which chromosome numbers are reported here. A. Ruellia costaricensis. B. Graptophyllum pictum. C. Ruellia elegans. D. Brillantaisia owariensis. E. Justicia fulvicoma. F. Ruellia dipteracanthus. G. Ruellia makoyana. H. Thunbergia grandiflora (white-flowered form). I. Megaskepasma erythrochlamys. J. Odontonema tubaeforme. K. Thunbergia mysorensis. L. Strobilanthes hamiltoniana. M. Justicia scheidweileri. N. Peristrophe speciosa. Photos by the author.
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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.