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402 results for “MtDNA”
Figure 16 in Species boundaries and phylogeographic patterns in new species of Nannoniscus (Janiroidea: Nannoniscidae) from the equatorial Pacific nodule province inferred from mtDNA and morphology
Figure 16. Nannoniscus menoti, (A) holotype female (ZMH K-55354, Na27), (B–E, G) paratype female (ZMH K-55356, Na18), (F, H) paratype female (ZMH K-55352, Na6): (A) habitus, dorsal view; (B–H) PI-VII. Scale bars: A = 200 µm; B–H = 100 µm.
ENDOG_NGS data related to article "Biallelic variants in ENDOG associated with mitochondrial myopathy and multiple mtDNA deletions" submitted to Cells
<p>vcf file of the targeted NGS; csv of the top 50 rare variants from WES prioritized by eVAI software; csv of WES rare variants prioritized by eVAI software using patient's phenotype information.</p>
Figure 8 in Phylogenetics and phylogeography of red deer mtDNA lineages during the last 50 000 years in Eurasia
Figure 8. Potential refugial areas of different mtDNA haplogroups of Cervus elaphus and their post-LGM migration routes hypothesised based on the results of this study. The northern limit of the range of C. elaphus during the LGM is determined based on the study of Niedziałkowska et al. (2021). C. elaphus records are listed in the Supporting Information, Tables S1, S2.
Figure 7 in Phylogenetics and phylogeography of red deer mtDNA lineages during the last 50 000 years in Eurasia
Figure 7. Distribution of mtDNA red deer s.l. haplogroups in Eurasia after the LGM. Upper panel, between 18 and 9 kya; Middle panel, between 9 and 4 kya; Lower panel, between 4 kya and today. The colours of samples correspond to haplogroups as in previous figures. Red deer s.l. records are listed in the Supporting Information, Tables S1, S2.
Figure 6 in Phylogenetics and phylogeography of red deer mtDNA lineages during the last 50 000 years in Eurasia
Figure 6. Distribution of mtDNA red deer s.l. haplogroups in Eurasia before the LGM. Upper panel, in the period older than 41 kya; Lower panel, between 41 and 26 kya. The colours of samples correspond to haplogroups as in previous figures. Red deer s.l. records are listed in the Supporting Information, Tables S1, S2.
Figure 5 in Phylogenetics and phylogeography of red deer mtDNA lineages during the last 50 000 years in Eurasia
Figure 5. Dated phylogeny of the elaphoid deer obtained in BEAST from radiocarbon-dated ancient sequences and randomly chosen modern sequences. Black circles at nodes indicate posterior probabilities above 0.7 and red circles posterior probabilities above 0.9. Rectangles at nodes represent 95% HPD of selected node ages. The graph below the phylogeny shows temperature change according to data from the Vostock ice core (Petit et al., 1999).
Figure 4 in Phylogenetics and phylogeography of red deer mtDNA lineages during the last 50 000 years in Eurasia
Figure 4. Dated phylogeny of the elaphoid and wapitoid deer obtained in BEAST from radiocarbon-dated ancient sequences and randomly chosen modern sequences, displayed in the context of temperature change. Black circles at nodes indicate posterior probabilities above 0.7. and red circles posterior probabilities above 0.9. Purple rectangles at well-supported nodes represent 95% HPD of estimated ages. For better resolution, the deepest node interval is not fully shown. Ancient samples placed outside haplogroups are described by sample name, geographic location and median radiocarbon age. The graph below the phylogeny shows temperature oscillations according to data from the Vostock ice core (Petit et al., 1999).
Figure 3 in Phylogenetics and phylogeography of red deer mtDNA lineages during the last 50 000 years in Eurasia
Figure 3. Median-joining haplotype network calculated with PopART. Black circles represent missing haplotypes. Hatch marks represent mutational steps between haplotypes. Haplotype names and haplogroup colours correspond to those in the Bayesian tree (Fig. 2).
Figure 2 in Phylogenetics and phylogeography of red deer mtDNA lineages during the last 50 000 years in Eurasia
Figure 2. Bayesian phylogeny of Cytb haplotypes (1131 bp) from ancient and modern specimens calculated with MrBayes. Black circles at nodes indicate posterior probabilities above 0.7 and red circles posterior probabilities above 0.9. Names of haplotypes occurring only in modern samples are represented by haplotype names in bold, whereas those occurring in both modern and ancient samples are represented by italicized names, and those found only in ancient samples are in normal font. Names in bigger grey or black font indicate intermediate ancient haplotypes from elaphoid and wapitoid clades, respectively, that do not belong to any of the distinguished haplogroups. Detailed descriptions of haplotypes are presented in the Supporting Information, Tables S1, S2.
Figure 1 in Phylogenetics and phylogeography of red deer mtDNA lineages during the last 50 000 years in Eurasia
Figure 1. Geographic distribution of ancient and historical red deer s.l. samples analysed in this study. Modern range of C. elaphus was according to Niedziałkowska et al. (2021) and references therein. Modern range of C. canadensis refers to its range in Asia only and was created based on IUCN data and data from Stepanova (2010). The localization of borders of the easternmost range of elaphoids and the westernmost range of wapitiods is based on the results of the present study and published data (Meiri et al., 2018).
FIGURE 7. Phylogenetic trees reconstructed using the mtDNA 16S in Johnius sasakii, a new species of croaker (Perciformes: Sciaenidae) with a key to Johnius from East Malaysia, Borneo
FIGURE 7. Phylogenetic trees reconstructed using the mtDNA 16S genes of five Johnius (Johnius) species. Values above the branches are respective bootstrap values from 5000 replications for the Neighbour-Joining (NJ) and Maximum-Likelihood (ML) analyses. Bar indicates genetic distances of 0.02.
Supplementary Data - Nanopore cDNA With and Without mtDNA
<p>Update (v1.5.8-SUPcaller): added scripts and data files for mt-Nd1 comparisons for different basecaller versions:</p> <ul> <li>LAST_gci_*.csv - gap-compressed identity statistics</li> <li>compStats_*.csv - called base-level statistics</li> <li>process.txt - script example file demonstrating how to generate statistics</li> <li>smealgol.r - R script to generate gap-compressed identities from LAST output</li> <li>est_vs_actual.png - density plots comparing gap-compressed identity with mean estimated accuracy from the caller</li> <li>GCI_albacore_guppy.png - violin plots comparing gap-compressed identity with different callers</li> <li>BaseQ_vs_GCI.png - scatter plots comparing gap-compressed identity with mean estimated accuracy from the caller</li> </ul> <p>Update (v1.5.7-Nd1Reads): added mt-Nd1 read subset from CGDec2017 run:</p> <ul> <li>4T1-WT_reads_CGDec2017_singlePlusMulti.tar</li> </ul> <p>Update (v1.5.6-countAnalysis): added / updated a script to convert transcript mappings to a count table:</p> <ul> <li>count_analysis.r</li> </ul> <p>Update (v1.5.5-Int): added intermediate (post-demultiplexing) demonstration dataset:</p> <ul> <li>SampleDESet_60k_demultiplexed.tar.gz</li> </ul> <p>Also updated count tables and DESeq2 results:</p> <ul> <li>raw_counts_2019-Nov-29.csv</li> <li>DE_orig_VST_GRCm38_CG_GL261_2019-Nov-29.csv.gz</li> <li>DE_shrunk_VST_GRCm38_CG_GL261_2019-Nov-29.csv.gz</li> </ul> <p>Update (v1.5.4-MT): updated demonstration dataset (now 40Mb) to include mitochondrial transcripts, and exclude non-target sequences from chimeric reads:</p> <ul> <li>reads_SampleDESet_sampled_60k.fastq.gz</li> <li>Mm_subset.GRCm38.ensembl_v98.fa</li> </ul> <p>Update (v1.5.3-Ensembl): added/updated Ensembl reference transcripts, including non-coding RNA:</p> <ul> <li>ensembl_mm10_geneFeatureLocations.txt.gz - genome annotation</li> <li>Mus_musculus.GRCm38.ensembl_v98.fa.gz - transcript sequences</li> </ul> <p>Update (v1.5.2-Demo): added 60Mb demonstration dataset for teaching / learning purposes (<em>reads_SampleDESet_sampled_60k.fastq.gz</em>). This dataset can be used for testing demultiplexing, or carrying out differential expression analysis on nanopore reads. There are 15k reads sampled from each run (subset on ~300 genes), plus 7.5k chimeric reads from the Dec17 run, plus 7.5k reads sampled from from CG009/BC08. The gene subset includes ~100 genes differentially-expressed between 4T1WT and 4T1ρ<sup>0</sup> cell lines, plus an additional ~200 genes with minimal differential expression observed between any 4T1 cell lines (i.e. a housekeeping gene set):</p> <ul> <li>BC01, BC02, BC04, BC06 - 4T1 cell line reads from three sequencing runs (005, 005, 004, Dec17)</li> <li>BC03, BC05, BC07 - 4T1ρ<sup>0</sup> (without mtDNA) cell line reads from three sequencing runs (005, 004, Dec17)</li> <li>BC08 - 4T1ρ<sup>0</sup>SC (from subcutaneous tumour) cell line reads from one sequencing run (009)</li> </ul> <p>[note: v1.5.1-Demo has the wrong sequence ID for BC08 reads]</p> <p>Update (v1.5.0-Called): added full 4T1 call set (called using guppy high-accuracy v3.2.2), gene transcript count tables, and differential expression results</p> <p>Update (v1.4.1-Chimeric): added more example called chimeric reads of various [unclassified] categories</p> <p>Update (v1.4.0-Chimeric): added example chimeric reads, one formed [presumably] during sample prep ["conjugation"], another formed in-silico ["double read"]</p> <p>Update (v1.3.0-DESeq2): added count table and metadata file for DESeq2 analysis</p> <p>Update (v1.2.1-10k): added 10k of FASTQ reads from a more recent run (part of the same differential expression project).</p> <p>Supplementary data to go with David Eccles' poster about nanopore cDNA sequencing with and without mitochondrial DNA.</p> <p>Reference mouse mtDNA and mtDNA reads from 4T1 cells (from MinION cDNA sequencing, and from SRA run SRR6747859).</p> <ul> <li>fwd_4T1_BC06.correctedReads.uniqueOnly.fasta.gz -- Canu-corrected cDNA reads from Wildtype 4T1 strain, filtered as transcript-forward orientation</li> <li>rev_4T1_BC06.correctedReads.uniqueOnly.fasta.gz -- Canu-corrected cDNA reads from Wildtype 4T1 strain, filtered as transcript-reverse orientation</li> <li>fwd_4T1_BC07.correctedReads.uniqueOnly.fasta.gz -- Canu-corrected cDNA reads from 4T1ρ0 strain (no mitochondrial DNA), filtered as transcript-forward orientation</li> <li>rev_4T1_BC07.correctedReads.uniqueOnly.fasta.gz -- Canu-corrected cDNA reads from 4T1ρ0 strain (no mitochondrial DNA), filtered as transcript-reverse orientation</li> </ul> <p>Other intermediate data files are also included, see `4way_mapping_process.txt` for more details.</p> <p>The mpileup differential coverage script can be found <a href="https://github.com/gringer/bioinfscripts/blob/master/mpileupDC.pl">here</a>.</p>
Distribution. NW Madagascar, confined to the Lokobe region on Nosy Be. This may or may not be the species that occurred on the island of Nosy Komba as well, but in any case sportive lemurs have not been observed there for several years; in 2007, D. Zinner and coworkers found that some specimens from the mainland had identical mtDNA to that of the type specimen of the Nosy Be Sportive Lemur. in Lepilemuridae
Distribution. NW Madagascar, confined to the Lokobe region on Nosy Be. This may or may not be the species that occurred on the island of Nosy Komba as well, but in any case sportive lemurs have not been observed there for several years; in 2007, D. Zinner and coworkers found that some specimens from the mainland had identical mtDNA to that of the type specimen of the Nosy Be Sportive Lemur.
Lophuromys margarettae was initially de- scribed as a subspecies of L. aquilus and later synonymized under L. flavopunctatus. It was elevated to a distinct species based on analyses of external and skull measurements and mtDNA of specimens from the type locality. Monotypic. Distribution. Highlands of W Uganda and Kenya. in Muridae
Lophuromys margarettae was initially de- scribed as a subspecies of L. aquilus and later synonymized under L. flavopunctatus. It was elevated to a distinct species based on analyses of external and skull measurements and mtDNA of specimens from the type locality. Monotypic. Distribution. Highlands of W Uganda and Kenya.
Aligned and curated mtDNA sequences from: Ancient DNA reveals interstadials as a driver of common vole population dynamics during the last glacial period
<p><strong><span>Aim: </span></strong><span>Many species experienced population turnover and local extinction during the Late Pleistocene. In the case of megafauna, it remains challenging to disentangle climate change and the activities of Palaeolithic hunter-gatherers as the main cause. In contrast, the impact of humans on rodent populations </span><span>is likely to be negligible. This study investigated which climatic and/or environmental factors affect the population dynamics of the common vole. </span><span>This temperate rodent is widespread across Europe and was one of the most abundant small mammal species throughout the Late Pleistocene.</span></p> <p><span><strong>Location:</strong> </span><span>Europe</span></p> <p><strong><span>Taxon: </span></strong><span>Common vole (<em>Microtus arvalis</em>)</span></p> <p><strong><span>Methods: </span></strong><span>We generated a dataset comprised of a 4.2-kb-long fragment of mitochondrial DNA (mtDNA) from 148 ancient and 51 modern specimens sampled from multiple localities across Europe and covering the last 60 thousand years (ka). We used Bayesian inference to reconstruct their phylogenetic relationships and to estimate the age of the specimens that were not directly dated.</span></p> <p><span><strong>Results:</strong> </span><span>We estimated the time to the most recent common ancestor of all last glacial and extant common vole lineages to be 90 ka ago and the divergence of the main mtDNA lineages present in extant populations to between 55 and 40 ka ago, which is earlier than previous estimates. </span><span>We detected several lineage turnovers in Europe during the period of high climate variability at the end of Marine Isotope Stage 3 (MIS 3; 57–29 ka ago) in addition to those found previously around the Pleistocene/Holocene transition.</span><span> </span><span>In contrast, data from the Western Carpathians suggest continuity throughout the Last Glacial Maximum (LGM), even at high latitudes.</span></p> <p><strong><span>Main conclusions: </span></strong><span>The main factor affecting the common vole populations during the last glacial period was the decrease in open habitat during the interstadials, whereas </span><span>climate </span><span>deterioration </span><span>during</span><span> the LGM had little impact on population dynamics. This suggests that the rapid environmental change rather than other factors was the major force shaping the histories of the Late Pleistocene faunas.</span></p>
Fig. 3 in ddRAD Sequencing Sheds Light on Low Interspecific and High Intraspecific mtDNA Divergences in Two Groups of Caddisflies
Fig. 3. Maximum likelihood trees and population structuring of Apatania (A) and Limnephilus (B) based on ddRAD SNP data.ML trees were inferred from RAxML analysis with 1,000 bootstrap replicates. The bootstrap values are indicated near the branches. Results of population STRUCTURE analyses with posterior probability plots of individual assignments to the inferred genetic clusters for K = 6 and 7 for Apatania and K = 4 for Limnephilus.
Fig. 2 in ddRAD Sequencing Sheds Light on Low Interspecific and High Intraspecific mtDNA Divergences in Two Groups of Caddisflies
Fig. 2. Maximum likelihood trees based on mitochondrial COI sequences of Apatania (A) and Limnephilus (B). The bootstrap values are indicated near the branches (only shown for nodes supported with more than 50% of BS).
Supplementary material 1 from: Potapov G, Kondakov A, Kolosova Y, Tomilova A, Filippov B, Gofarov M, Bolotov I (2018) Widespread continental mtDNA lineages prevail in the bumblebee fauna of Iceland. ZooKeys 774: 141-153. https://doi.org/10.3897/zookeys.774.26466
Table S1 : Explanation note: List of additional COI sequences of Bombus species obtained from GenBank or BOLD, including taxon, accession number, specimen code, locality information and data source.
FIGURE 10 in Molecular phylogenetics of snailfishes (Cottoidei: Liparidae) based on MtDNA and RADseq genomic analyses, with comments on selected morphological characters
FIGURE 10. Phylogeny of the liparid clade Aenigmoliparia from the majority rule (50%) consensus tree from the Bayesian inference of a 490 bp alignment of 270 cytochrome c oxidase subunit one gene (COI) sequences. Nodal values represent Bayesian posterior probabilities and bootstrap values from the maximum likelihood analysis (above and below branches, respectively). Species names are followed by a catalog number or BOLD "Sequence ID" number when represented by a sequence from a single specimen in our dataset. N indicates number of sequences, when multiple sequences support a branch tip. Boldface species names indicate species placed in different positions in COI and RADseq trees. Only unique sequences were subjected to the analyses (Appendix Table 1); other identical sequences surveyed are listed in Appendix Table 2.
FIGURE 7 in Molecular phylogenetics of snailfishes (Cottoidei: Liparidae) based on MtDNA and RADseq genomic analyses, with comments on selected morphological characters
FIGURE 7. Majority-rule (50%) consensus phylogenetic tree of Shen et al. (2017, after fig. S6), derived from a Bayesian inference of a 440 bp alignment of cytochrome c oxidase subunit 1 gene (COI) sequences for 84 samples of 83 liparid species. Bayesian posterior probabilities are above branches. Tree is rooted with species of the Cyclopteridae. Corrected identifications based on our study are in parentheses.
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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.