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1,696 results for “DNA sequence”
FIGURE 4 in Actinarctus doryphorus (Tanarctidae) DNA barcodes and phylogenetic reinvestigation of Arthrotardigrada with new A. doryphorus and Echiniscoididae sequences
FIGURE 4. Phylogenetic tree inferred from BI analyses of the combined 18S/28S rDNA dataset. Unavailable 18S sequences were substituted with N's (indicated by "no 18S"). Actinarctus doryphorus is placed together with Tanarctus and Arthrotardigrada is inferred to be paraphyletic. Posterior probabilities (upper value) relating to BI analyses and bootstrap values (lower) from ML analyses are shown at the nodes. (–) indicates bootstrap values <50.
FIGURE 3 in Actinarctus doryphorus (Tanarctidae) DNA barcodes and phylogenetic reinvestigation of Arthrotardigrada with new A. doryphorus and Echiniscoididae sequences
FIGURE 3. Phylogenetic tree inferred from BI analyses of the 18S rDNA dataset. Arthrotardigrada is inferred to be paraphyletic. Posterior probabilities (upper value) relating to BI analyses and bootstrap values (lower) from ML analyses are shown at the nodes. (–) indicates bootstrap values <50.
FIGURE 2 in Actinarctus doryphorus (Tanarctidae) DNA barcodes and phylogenetic reinvestigation of Arthrotardigrada with new A. doryphorus and Echiniscoididae sequences
FIGURE 2. Phylogenetic tree inferred from BI analyses of the 28S rDNA dataset. Actinarctus doryphorus is placed together with Tanarctus sequences within a maximum supported Tanarctidae. Arthrotardigrada is inferred to be paraphyletic. Posterior probabilities (upper value) relating to BI analyses and bootstrap values (lower) from ML analyses are shown at the nodes. (–) indicates bootstrap values <50.
Transposon DNA sequences facilitate the tissue-specific horizontal transfer: te expression supplementary datasets
<p>These datasets contain data on analyses of TE expression stability. Raw RNA seq counts were processed using DESeq2 R package. Low count genes and TEs were removed. Counts across samples were normalized for library sizes and log-transformed using 'regularized log' transformation. Batch normalization was performed on log-transformed data with ComBat function from sva R package. Expression variability (EV) of TEs and genes (probes) was estimated using the previously described method [1, 2].</p> <p>1. Bashkeel, N., Perkins, T.J., Kærn, M. et al. Human gene expression variability and its dependence on methylation and aging. BMC Genomics 20, 941 (2019). https://doi.org/10.1186/s12864-019-6308-7<br> 2. Alemu EY, Carl JW Jr, Corrada Bravo H, Hannenhalli S. Determinants of expression variability. Nucleic Acids Res. 2014;42(6):3503-3514. doi:10.1093/nar/gkt1364</p> <p> </p> <p>PC.zip - the results of TE expression and stability in prostate cancer.</p> <ul> <li>0.PC.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.PC.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.PC.TE.rlogcpm.mad.xls -stability measures according MAD (median absolute deviance) criteria </li> <li>0.PC.TE.rlogcpm.sd.xls - stability measures according SD criteria </li> <li>0.PC_TE_bootstrap.pdf - TE expression stability</li> <li>PC.deseq.logCPM.csv - TE log transformed expression matrix </li> <li>PC.TE_count_table.csv - TE raw count matrix </li> <li>PC_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul> <p> </p> <p>MM.zip - the results of TE expression and stability in multiple myeloma.</p> <ul> <li>0.MM.RlogMAD.pdf - count barplots for TE identified with MAD criteria</li> <li>0.MM.RlogSD.pdf - count barplots for TE identified with SD criteria</li> <li>0.MM_TE_bootstrap.pdf - TE expression stability</li> <li>MM.deseq.logCPM.csv - TE raw count matrix </li> <li>MM.rlog.mad.xlsx- stability measures according MAD (median absolute deviance) criteria</li> <li>MM.rlog.sd.xlsx - stability measures according MAD (median absolute deviance) criteria </li> <li>MM.TE_count_table.csv - TE raw count matrix </li> <li>Myeloma_Deseq2data.Rdata - R data object with deseq objet, raw and normalized counts</li> </ul>
ML DNA sequencing database
<p>The machine learning technique is utilized with the transmission fingerprint database for the MoS<sub>2</sub> nanochannel-assisted identification and classification of DNA nucleotides.</p>
FIGURE 2 in Molecular systematics of Jania species (Corallinales, Rhodophyta) from south-eastern Australia based on cox1 and psbA DNA sequence analyses
FIGURE 2. Bayesian phylogenetic tree of Jania species (Rhodophyta) based on cox1 gene with focus on specimens from south-eastern Australia. Numbers above branches represent posterior probabilities (values <0.5 were omitted). Some branches were shortened to fit the figure. Specimens sequenced in this study marked in bold and have their herbarium code identified. Colored columns indicate species delimitation methods results: ABGD; ASAP P (p-value partition), ASAP W (w rank partition); GMYC S (single-threshold), GMYC M (multiple-threshold); PTP B (Bayesian inference), PTP M (Heuristic or Maximum Likelihood); and SPN 95% and 99% of parsimony probability limit. Black column indicates the consensus across all species delimitation results. Numbers below to each column indicate the total number of species partitions for each method or the consensus. Jania squamata in the PTP B result was divided into three different species.
FIGURE 3 in Molecular systematics of Jania species (Corallinales, Rhodophyta) from south-eastern Australia based on cox1 and psbA DNA sequence analyses
FIGURE 3. Maximum likelihood phylogeny of Jania species based on psbA gene focusing on specimens from south-eastern Australia. Numbers above branches represent non-parametric bootstrap support (values <70 omitted). Species names are followed by GenBank accession number and specimen locality (for sequences downloaded from GenBank), or species name, herbarium codes for all specimens sharing that particular haplotype, and specimen locality (marked in bold for sequences produced in this study). Scale bar = substitutions per site.
FIGURE 1 in Molecular systematics of Jania species (Corallinales, Rhodophyta) from south-eastern Australia based on cox1 and psbA DNA sequence analyses
FIGURE 1. Maximum likelihood phylogenetic tree of Jania species (Rhodophyta) based on cox1 gene with focus on specimens from south-eastern Australia. Numbers above branches represent non-parametric bootstrap support (values <70 omitted). Tree tip names are composed by species name, GenBank accession number and specimen locality (for sequences downloaded from GenBank), or species name, herbarium code for all sequenced specimens presented by that sequence, and specimen locality (for sequences produced in this study, all marked in bold). Scale bar = substitutions per site.
FIGURE 4 in Molecular systematics of Jania species (Corallinales, Rhodophyta) from south-eastern Australia based on cox1 and psbA DNA sequence analyses
FIGURE 4. Bayesian phylogenetic tree of Jania species based on psbA gene with focus on specimens from south-eastern Australia. Numbers above branches represent posterior probabilities (values <0.5 omitted). Specimens sequenced in this study are marked in bold and have their herbarium code identified. Colored columns indicate species delimitation results: ABGD; ASAP P (p-value partition), ASAP W (w rank partition); GMYC S (single-threshold), GMYC M (multiple-threshold); PTP B (Bayesian inference), PTP M (Heuristic or Maximum Likelihood); and SPN 95% and 99% of parsimony probability limit. Black column indicates the consensus across all species delimitation results. Numbers below each column indicate the total number of species partitions for each method, including the consensus.
FIGURE 5 in Molecular systematics of Jania species (Corallinales, Rhodophyta) from south-eastern Australia based on cox1 and psbA DNA sequence analyses
FIGURE 5. Maximum likelihood phylogenetic tree of Corallina species based on psbA DNA sequences. Numbers above branches represent non-parametric bootstrap support. Values <50 were omitted. Species names are followed by GenBank accession number. South-eastern Australian sequences produced in this study are marked in bold. Bossiella, Calliarthron and Arthrocardia were used as outgroups.
Purification of High Molecular Weight DNA for Long-Read Sequencing Using a High-Salt Gel Electroelution Trap
<p><strong>Figure 3. Yield and purity of HMW DNA obtained from difficult samples using the method proposed in this study</strong>.</p> <p>(A) The proposed method extracts more HMW DNA from a complex soil sample than a commercial column purification kit. Shown is a negative image of an ethidium bromide-stained agarose gel. Lane 1: 1/10 aliquot of ~90 ng of HMW DNA isolated using the E.Z.N.A. soil DNA extraction kit from a soil sample containing ~1,5 μg of total DNA (HMW DNA yield around 6%). Lane 2: ~100 ng of CTAB-extracted DNA from the same soil sample. ~10 μg of this crude DNA preparation was used as input for HMW DNA purification using the proposed method. Lane 3: 1/100 aliquot of ~3 μg of HMW DNA isolated using the proposed method from ~10 μg of the CTAB-extracted DNA (HMW DNA yield around 30%).</p> <p>(B) The proposed method yields high-purity HMW DNA from a complex plant sample, as determined by agarose gel electrophoresis. Lane 1: molecular weight marker (GeneRuler DNA ladder, Thermo Fisher Scientific). Lane 2: crude nucleic acid preparation extracted with SDS/Proteinase K from <em>Zingeria trichopoda</em> leaves, which served as an input for HMW DNA purification using the proposed method. Lane 3: purified HMW DNA. Note the absence of low-molecular-weight nucleic acids and heavy covalent complexes in the purified sample.</p> <p>(C) Same as (B) except that crude, CTAB-extracted DNA from a complex soil sample was used as an input for HMW DNA purification. Note the absence of a continuous smear of fragmented DNA as well as heavy covalent complexes in the purified sample (lane 3). Molecular weight marker sizes are indicated in base pairs to the left of each panel.</p>
Repeatome turnover meets stable chromosomes: repetitive DNA sequences mark speciation and gene pool boundaries in sugar beet and wild beets
<p>The present repository provides zipped archives containing the results of the RepeatExplorer2 runs of individual as well as comparative repeat analyses in beet genomes.</p> <p> </p> <p>Sugar beet (<em>Beta vulgaris</em> subsp. <em>vulgaris</em>) and its crop wild relatives share a base chromosome number of nine and similar chromosome morphologies. Yet, interspecific breeding is impeded by chromosome and sequence divergence that is still not fully understood. Since repetitive DNA sequences represent the fastest evolving parts of the genome, they likely impact genomic variability and contribute to the separation of beet gene pools. Hence, we investigated if innovations and losses in the repeatome can be linked to chromosomal differentiation and speciation.</p> <p>We traced genome- and chromosome-wide evolution across sugar beet and twelve wild beets comprising all sections of the beet genera <em>Beta </em>and <em>Patellifolia</em>. For this, we combined data from short and long read sequencing, flow cytometry, and cytogenetics to build a comprehensive data framework for our beet panel that spans the complete scale from DNA sequence to chromosome up to the genome. Genome sizes and repeat profiles reflect the separation of the beet species into three gene pools. These gene pools harbor repeats with contrasting evolutionary patterns: We identified section- and species-specific repeat emergences and losses, e.g. of the retrotransposons causal for genome expansions in the section <em>Corollinae</em>/<em>Nanae</em>. Since most genomic variability was found in the satellite DNAs, we focused on tracing the 19 beetSat families across the three beet sections/genera. These taxa harbor evidence for contrasting strategies in repeat evolution, leading to contrasting satellite DNA profiles and fundamentally different centromere architectures, ranging from chromosomal uniformity in <em>Beta</em> and <em>Patellifolia</em> species to the formation of patchwork chromosomes in <em>Corollinae/Nanae</em> species. </p> <p>We show that repetitive DNA sequences are causal for genome size expansion and contraction across the beet genera, providing insights into the genomic underpinnings of beet speciation. Satellite DNAs in particular vary considerably among beet taxa, leading to the evolution of distinct chromosomal setups. These differences likely contribute to the barriers in beet breeding between the three gene pools. Thus, with their isokaryotypic chromosome sets, beet genomes present an ideal system for studying the link between repeats, genome variability, and chromosomal differentiation/evolution and provide a theoretical basis for understanding barriers in crop breeding.</p>
DNA Sequencing in Clinical Practice, Mayo Clinic Health Tapestry Study
ClinicalTrials.gov study NCT05212428. IPD Sharing: Not stated. Countries: 1. Publications: 3.
DNA Sequencing of MDR TB in Eastern Siberia
ClinicalTrials.gov study NCT02508610. IPD Sharing: Not stated. Countries: 1. Publications: 11.
Prospective Screening and Differentiating Common Cancers Using Peripheral Blood Cell-Free DNA Sequencing
ClinicalTrials.gov study NCT06036563. IPD Sharing: NO. Countries: 1. Publications: 5.
Data from: High-throughput sequencing of nematode communities from total soil DNA extractions
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Data from: Congruent species delimitation of two controversial gold-thread nanmu tree species based on morphological and restriction site-associated DNA sequencing data
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Data from: A cost-efficient and simple protocol to enrich prey DNA from extractions of predatory arthropods for large-scale gut content analysis by Illumina sequencing
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Data from: Sequencing historical specimens: successful preparation of small specimens with low amounts of degraded DNA
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Blast output from: Lost in dead wood? Environmental DNA sequencing from dead wood shows little signs of saproxylic beetles
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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.