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19,342 results for “sequence”
Figure 1 in Evolutionary systematics of the Indian mouse Mus famulus Bonhote, 1898: molecular (DNA/DNA hybridization and 12S rRNA sequences) and morphological evidence
Figure 1. Phylogenetic trees derived from the DNA/DNA hybridization analysis. A and B: Consensus trees resulting from the bootstrap analysis of delta-Tm (A) and delta-mode (B) 12*12 matrices. BP values are indicated when different from 100%. The lengths of the branches correspond to one tree arbitrarily selected among those of the consensus. C and D: Average consensus trees resulting from the weighted jacknife procedure for delta-Tm (C) and delta-mode (D) 13*13 matrices. The thin lines represent nodes that were not present in maximum and minimum consensus trees or that are not supported for all combinations of single deletion analysis. uUnlabelled taxa. The names in bold indicate the differences that can be observed between the two distance estimators (Tm, Mode).
Figure 4 in Evolutionary systematics of the Indian mouse Mus famulus Bonhote, 1898: molecular (DNA/DNA hybridization and 12S rRNA sequences) and morphological evidence
Figure 4. Fifty per cent majority rule consensus of 52 trees derived from the morphological analysis. Each mostparsimonious tree is 54 steps long, and has a Consistency Index of 0.52, a Retention Index of 0.72, and a Rescaled Consistency Index of 0.37. Values given below the branches represent the percentage of trees containing the specified clades.
Figure 1 in Phylogenetic structure of the Sphaeriinae, a global clade of freshwater bivalve molluscs, inferred from nuclear (ITS-1) and mitochondrial (16S) ribosomal gene sequences
Figure 1. Strict consensus of the four equally most parsimonious trees (L = 526; CI = 0.447; RI = 0.743) obtained from the phylogenetic analysis of sphaeriid mitochondrial 16S rDNA sequences. Two Eupera species, E. cubensis and E. platensis, were designated as outgroups and inferred sequence gaps were considered as missing data. Numbers above the branches represent bootstrap values and numbers below indicate decay index values.
Figure 3. Synthetic tree derived from the 12S in Evolutionary systematics of the Indian mouse Mus famulus Bonhote, 1898: molecular (DNA/DNA hybridization and 12S rRNA sequences) and morphological evidence
Figure 3. Synthetic tree derived from the 12S rRNA datasets with the inclusion of all substitutions (TV + TI). The thin lines indicate nodes that are not robustly supported by all kinds of analysis. The robustness of the different nodes are indicated as follows: [BP(BPweighted analysis)/BSI (Parsimony)]/[BP(NJ)/Reliability Percentage (ML)].
WE sequences
<p>WE representative sequences produced at IHPE (http://ihpe.univ-perp.fr) and published in:</p> <p>"Satellite-like W Elements: repetitive, transcribed, and putative mobile genetic factors with potential roles for biology and evolution of <em>Schistosoma mansoni" </em></p> <p><em>https://doi.org/10.1093/gbe/evab204</em></p>
CNN models and training, validation and test datasets for "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"
<p>Convolutional neural network (CNN) models and their respective training, validation and test datasets used in manuscript:</p> <p>Tuomo Hartonen, Teemu Kivioja and Jussi Taipale, "PlotMI: interpretation of pairwise interactions and positional preferences learned by a deep learning model from sequence data"</p>
TaqMan array sequence set for 52-target respiratory pathogen array
<p>Sequence list for probes and primers used in the respiratory Taqman array card investigated in project, protocol 10.5281/zenodo.5081880</p>
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects with ancient samples
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects with high coverage sequenced ancient samples. The ancient samples are the Altai, Chagyrskaya, and Vindija Neanderthals, the Denisovan, and a high-coverage family of four from the Afanasievo Culture.</p> <p>Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred with <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1 and <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4, as described in <a href="http://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>. The files were compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_high_cov_ancients_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>import json site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>
Main-sequence pulsar: the peculiar subset of hot magnetic stars
<p>In my talk, I will describe some peculiar phenomena observed from a subset of hot magnetic stars, also known as 'main-sequnce pulsar's and discuss their usefulness. This class of stars is distinguished from the rest of the population by their ability to produce highly directed coherent radio emission via electron cyclotron maser emission (ECME) in their magnetospheres. The fundamental difference between the two classes is however still vague. I will describe our efforts to understand this difference; this includes a survey being carried out with the Giant Metrewave Radio telescope (GMRT) in order to find more hot magnetic stars that show coherent radio emission, and also wideband observations taken with two telescopes: the GMRT and the Very Large Array (VLA), of the already known coherent radio emitters to find out the bandwidth of the emission process. In this process, we have also come across certain new phenomena like the inversion of circular polarization and that of the sequence of ECME pulse arrival with frequency. I will discuss the implications of these new revelations and their potential to become tools for estimating plasma density close to the stellar surface, as well as constraining the inhomogeneity and asymmetry in the magnetosphere.</p>
Data related to research article: Towards mouse genetic-specific RNA-sequencing read mapping
<p>This dataset contains data related to the research article: "Towards mouse genetic-specific RNA-sequencing read mapping".</p>
Fluorine NMR study of proline-rich sequences using fluoroprolines
<p>Experimental NMR dataset related to the publication in Magnetic Resonance 2021 entitled:</p> <p>"Fluorine NMR study of proline-rich sequences using fluoroprolines"</p> <p>by</p> <p>Davy Sinnaeve, Abir Ben Bouzayene, Emile Ottoy, Gert-Jan Hofman, Eva Erdmann,<br> Bruno Linclau, Ilya Kuprov, José C. Martins, Vladimir Torbeev and Bruno Kieffer</p> <p><strong>Abstract.</strong> Proline homopolymer motifs are found in many proteins; their peculiar conformational and dynamic properties are often directly involved in those proteins' functions. However, the dynamics of proline homopolymers is hard to study by NMR due to lack of amide protons and small chemical shift dispersion. Exploiting the spectroscopic properties of fluorinated prolines opens interesting perspectives to address these issues. Fluorinated prolines are already widely used in protein structure engineering – they introduce conformational and dynamical biases – but their use as <sup>19</sup>F NMR reporters of proline conformation has not yet been explored. In this work, we look at model peptides where Cγ-fluorinated prolines with opposite configurations of the chiral Cγ centre have been introduced at two postions in distinct polyproline segments. By looking at the effects of swapping these (4<em>R</em>)- and (4<em>S</em>)-4-fluoroprolines within the polyproline segments, we were able to separate the intrinsic conformational properties of the polyproline sequence from the conformational alterations instilled by fluorination. We assess the fluoroproline <sup>19</sup>F relaxation properties, and exploit the latter in elucidating binding kinetics to the SH3 domain</p> <p>CONTENT OF THE REDEPOSITORY</p> <p><strong>Peptide assignment </strong></p> <p>To assign MpRS and MpSR peptides we recorded at 298 K the following experiments:</p> <ul> <li>H-<sup>13</sup>C HSQC. Recorded at 700MHz. The corresponding data are in the following datasets: MpRS_HSQC and MpSR_HSQC</li> <li>H-<sup>1</sup>H TOCSY. Recorded at 600MHz. The corresponding data are in the following datasets: MpRS_TOCSY and MpSR_TOCSY</li> <li>H-<sup>13</sup>C HSQC-NOESY. Recorded at 700MHz. The corresponding data are in the following datasets: MpRS_HSQC_NOESY and MpSR_HSQC_NOESY</li> <li>H-<sup>1</sup>H NOESY. Recorded at 600MHz. The corresponding data are in the following datasets: MpRS_NOESY and MpSR_NOESY</li> </ul> <p> </p> <p><strong>Homonuclear scalar coupling constants </strong></p> <p>Homonuclear <sup>3</sup>J<sub>H-H </sub>scalar couplings were measured using SERF experiments on a 700MHz spectrometer at 298 K. The corresponding data can be examined in the followed datasets: MpRS_SERF and MpSR_SERF for both MpRS and MpSR peptides, respectively.</p> <p> </p> <p><strong><sup>1</sup></strong><strong>D <sup>19</sup>F spectra </strong></p> <p> </p> <p><sup>1</sup>D <sup>19</sup>F spectra of MpRS and MpSR peptides were recorded on a 600MHz spectrometer at 298 K, with samples dissolved in D2O. 3mm tubes were used. The data are stored in the following datasets: MpRS_1D_F and MpSR_1D_F for both MpRS and MpSR peptides respectively.</p> <p> </p> <p><strong><sup>19</sup></strong><strong>F Relaxation </strong></p> <p>Longitudinal and transverse relaxation rates of fluorine were measured for both MpRS and MpSR peptides. Standard inversion recovery, spin-echo and CPMG experiments were collected for both MpSR and MpRS peptides on a 600MHz at 298 K. The corresponding data may be examined in these datasets:</p> <p>For MpRS: MpRS_R1, MpRS_R2_spin_echo, MpRS_R2_CPMG</p> <p>For MpSR: MpSR_R1, MpSR_R2_spin_echo, MpSR_R2_CPMG</p> <p> </p> <p><sup>1</sup>HG-<sup>19</sup>F heteronuclear-nOes were measured using nOe buildup experiments recorded on a 600MHz spectrometer at 298 K. The experimental data of both peptides are in the following datasets: MpRS_HGF_NOE and MpSR_HGF_NOE</p> <p> </p> <p><strong>Titration experiments with Vinexin SH3.3 domain</strong></p> <p>Titration experiments were performed on a 600MHz spectrometer at 298K using 3 mm tubes. The respective volumes of peptide and SH3 protein are given in the title file. The 1D <sup>19</sup>F spectra of MpRS and MpSR peptides are stored in titrMpRS_1D_F and titrMpSR_1D_F, respectively. The <sup>1</sup>H-<sup>15</sup>N HSQC spectra are stored in titrMpRS_HSQC and titrMpSR_HSQC datasets.</p> <p>Stock solutions were:</p> <p>SH3: 314 µM</p> <p>MpRS S0: 5.67 mM</p> <p>MpSR S0: 5.1 mM</p> <p>S1 stocks resulted from S0 by a factor 2 dilution.</p>
The hsp65 metabarcoding DNA sequence database for taxonomic allocations using the Mothur (Version 1.0.0)
<ul> <li>The <em>hsp65</em> gene codes for an Heat Shock Protein (Telenti et al., 1993) and is widespread in the Actinobacteria phylum. It is well suited for the species allocation of the Nocardia genus (Rodriguez-Nava et al., 2006).</li> <li>The <em>hsp65</em> database, named ACTIhsp65, was designed to apply the <em>hsp65</em>-metabarcoding analytical scheme published in Vautrin et al. (2021). It includes the full <em>hsp65</em> identifiers, GenBank accession numbers, complete taxonomic records (domain down to strain code) of about 401 nucleotide-long <em>hsp65</em> sequences of 1066 unique taxa belonging to 198 genera.</li> <li>Nucleotide sequences of <em>hsp65</em> (range: 165-565 nucleotides) were either retrieved from public repositories (GenBank) or made available by Veronica Rodriguez-Nava.Vautrin et al. (2021) described the PCR and high throughput Illumina Miseq DNA sequencing procedures used to produce <em>hsp65</em> sequences.</li> <li>ACTIhsp65 V1.0.0 (June 2018 release) is made available under the Creative Commons Attribution 4.0 International Licence. It can be used for the taxonomic allocations of <em>hsp65 </em>sequences down to the species.</li> </ul>
Optimal sequence similarity thresholds for clustering of molecular operational taxonomic units in DNA metabarcoding studies
<p><span>Clustering approaches are pivotal to handle the many sequence variants obtained in DNA metabarcoding datasets, therefore they have become a key step of metabarcoding analysis pipelines. Clustering often relies on a sequence similarity threshold to gather sequences in Molecular Operational Taxonomic Units (MOTUs), each of which ideally representing a homogeneous taxonomic entity, e.g. a species or a genus. However, the choice of the clustering threshold is rarely justified, and its impact on MOTU over-splitting or over-merging even less tested. Here, we evaluated clustering threshold values for several metabarcoding markers under different criteria: limitation of MOTU over-merging, limitation of MOTU over-splitting, and trade-off between over-merging and over-splitting. We extracted sequences from a public database for nine markers, ranging from generalist markers targeting Bacteria or Eukaryota, to more specific markers targeting a class or a subclass (e.g. Insecta, Oligochaeta). Based on the distributions of pairwise sequence similarities within species and within genera, and on the rates of over-splitting and over-merging across different clustering thresholds, we were able to propose threshold values minimizing the risk of over-splitting, that of over-merging, or offering a trade-off between the two risks. For generalist markers, high similarity thresholds (0.96-0.99) are generally appropriate, while more specific markers require lower values (0.85-0.96). These results do not support the use of a fixed clustering threshold. Instead, we advocate a careful examination of the most appropriate threshold based on the research objectives, the potential costs of over-splitting and over-merging, and the features of the studied markers.</span></p>
Results from the revision of MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data
<p>afr47041.zip, lat36378.zip, and eur115620.zip contain All of Us Summary Statistics used in the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Summary statistics for three cohorts are included: Afr47k, Lat36k, and Eur116k. These cohorts have not been downsampled to have equal levels of missingness.</p> <p>pips.tsv contains fine-mapped variants with PIP > 0.01 via MultiSuSiE from the revised version of "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data". Subcohorts with the _unmatched suffix have not been downsampled to have equal levels of phenotyped missingness across ancestries. </p> <p>MultiSuSiE-main.zip contains the MultiSuSiE software packages (corresponds to the Github repo on 10/16/2025).</p> <p>Please cite:</p> <p>Rossen, Jordan, et al. "MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data." <em>medRxiv</em> (2024): 2024-05.</p> <p> </p>
Pooled DNA sequencing to identify SNPs associated with a major QTL for bacterial wilt resistance in Italian ryegrass (Lolium multiflorum Lam.)
<p>We used pooled DNA sequencing to characterize a major QTL for bacterial wilt resistance of Italian ryegrass and to develop inexpensive sequence-based markers to efficiently target resistance alleles for marker-assisted recurrent selection. From the mapping population segregating for the QTL, DNA of 44 of the most resistant and 44 of the most susceptible F<sub>1</sub> individuals were pooled and sequenced using the Illumina HiSeq2000 platform. Allele frequencies of 18 x 10<sup>6</sup> single nucleotide polymorphisms (SNP) were determined in the resistant and susceptible pool. A total of 271 SNPs on 140 scaffold sequences of the reference parental genome showed significantly different allele frequencies in both pools. We converted 44 selected SNPs to KASP markers, genetically mapped these proximal to the major QTL and thus validated their association with bacterial wilt resistance.</p>
Sequence data for 'Machine-driven parameter-space exploration of biochemical reactions'
<p>The development of complex, multi-step <em>omics</em> methods in molecular biology is a laborious, costly, iterative and often intuition-bound process where an optimum is sought in a parameter space through step-by-step optimisations. The the difficulty of miniaturising assays and the cost of the experiments limit the dynamic range and the number of parameters that can be explored. However, because of non-linearities of the response of biochemical systems to their reagent concentrations, a broad dynamic range is necessary. Here we demonstrate the use of a high-performance nanoliter handling platform (Labcyte Echo 525) and computer generation of liquid transfer programs to explore in quadruplicates more than 600 combination of 4 parameters of a biochemical reaction, which lead us to uncover non-linear responses, parameter interactions and novel mechanical insights. With the increased availability of « <em>cloud biology</em>» computer-driven laboratory platforms, our results participate in changing methods development for biotechnology towards reproducible, computer-aided exhaustive characterisation of biochemical systems.</p> <p>This dataset contains the raw sequencing data produced with an Illumina MiSeq instrument for this project. FASTQ files and sample sheets are found in the usual location (Data/Intensities/BaseCalls). The "Thumbnail_Images" and "L001" directories were deleted to save space.</p> <p>Run IDs: 171227_M00528_0321_000000000-B4GLP, 180403_M00528_0348_000000000-B4GP8, 180517_M00528_0364_000000000-BRGK6, 180123_M00528_0325_000000000-B4PCK, 180411_M00528_0351_000000000-BN3BL, 180606_M00528_0367_000000000-BN3FG, 180326_M00528_0346_000000000-B4GJR, 180501_M00528_0359_000000000-B4PJY, 180607_M00528_0368_000000000-BN9KM</p> <p> </p>
CONCATENATING sample files to prepare for multiple sequence alignment in Galaxy
<p>These are a few sample files to practice the correct way to concatenate files with the reference strain at the top, in order to continue with the next step, which is doing a multiple sequence alignment.</p>
Genome sequencing and assembly of Lathyrus sativus
<p>The dataset contains the whole-genome assembly and protein sequences of <em>Lathyrus sativus</em> cultivar Pusa-24.</p>
Zaprojektowane sekwencje super powtórzeń nebuliny (SR1, SR22)_Designed nebulin super repeat sequences (SR1, SR22)
<p>Zestaw danych zawiera pliki z sekwencją nukleotydową i aminokwasową ludzkiej nebuliny oraz zaprojektowane sekwencje super powtórzeń nebuliny SR1 i SR22 wraz z tagiem, służące wklonowaniu do wektora ekspresyjnego.</p> <p>The dataset includes human nebulin nucleotide and amino acid sequence files and designed SR1 and SR22 nebulin super repeat sequences with a tag for cloning into an expression vector.</p>
Nanopore sequencing of plasmid cleavage fragments produced with type III CRISPR-associated nucleases NucC, Can1 and Can2
<p>Included datasets were generated in the study "<strong>Sequence-specific capture and concentration of viral RNA </strong><strong>by type III CRISPR system enhances diagnostic"</strong> by Nemudraia et al., 2022</p> <p> </p> <p>For questions contact: Artem Nemudryi (artem.nemudryi@gmail.com) or Blake Wiedenheft (bwiedenheft.com)</p>
ScienceDex guides
Understand access before you commit
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.