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19,342 results for “Sequence”
Data for: Range and niche expansion through multiple interspecific hybridization - a genotyping by sequencing analysis of Cherleria (Caryophyllaceae)
<p><b>Background:</b> <i>Cherleria</i> (Caryophyllaceae) is a circumboreal genus that also occurs in the high mountains of the northern hemisphere. In this study, we focus on a clade that diversified in the European High Mountains, which was identified using nuclear ribosomal (nrDNA) sequence data in a previous study. With the nrDNA data, all but one species was monophyletic, with little sequence variation within most species. Here, we use genotyping by sequencing (GBS) data to determine whether the nrDNA data showed the full picture of the evolution in the genomes of these species.</p> <p><b>Results:</b> The overall relationships found with the GBS data were congruent with those from the nrDNA study. Most of the species were still monophyletic and many of the same subclades were recovered, including a clade of three narrow endemic species from Greece and a clade of largely calcifuge species. The GBS data provided additional resolution within the two species with the best sampling, <i>C. langii</i> and <i>C. laricifolia</i>, with structure that was congruent with geography. In addition, the GBS data showed significant hybridization between several species, including species whose ranges did not currently overlap.</p> <p><b>Conclusions:</b> The hybridization led us to hypothesize that lineages came in contact on the Balkan Peninsula after they diverged, even when those lineages are no longer present on the Balkan Peninsula. Hybridization may also have helped lineages expand their niches to colonize new substrates and different areas. Not only do genome-wide data provide increased phylogenetic resolution of difficult nodes, they also give evidence for a more complex evolutionary history than what can be depicted by a simple, branching phylogeny.</p>
OTU level 16s sequence data for "Algae drive convergent bacterial community assembly when nutrients are scarce"
<p>16s sequence data at the OTU level for the experiments conducted in "Algae drive convergent bacterial community assembly when nutrients are scarce"</p> <p>The file is in fasta format, which can be read by many software packages including biopython, R, and SILVA's alignment, classification and tree service.</p> <p>The sequence ids can be used to find the phylogeny and OTU ID of the sequences from Supplementary dataset 4. </p>
Main Sequence + Compact Object binary candidates from Gaia DR3 astrometric and spectroscopic excess noise
<p>MS+CO systems selected from Gaia DR3 via inferred periods and mass ratios derived from astrometric and spectroscopic errors.</p> <p>The sample is split into a bronze list (significant astrometric and spectroscopic RUWE, mass ratio > 1 and companion mass > 3 Msun). </p> <p>A subset of these is chosen as a silver list (propagating errors on mass ratio and companion mass to deselect systems which are not significantly above the previous criteria)</p> <p>Finally, a gold list is constructed from the subset of the silver list which shows no evidence of being significantly brighter than a single MS star and with no significant excess photometric noise.</p> <p>We include the most relevant Gaia data for the system, as well as our inferred spectroscopic and photometric errors and RUWEs, and the inferred periods and mass ratios. Gaia's DR3 source id, and the ra, dec position are included and thus other Gaia data, or data from other astronomical catalogs, can be found for these systems.</p> <p>The catalog and the underlying methods are explained in more detail in <a href="https://arxiv.org/abs/2206.04392">Andrew et al. 2022</a>.</p>
Direct RNA targeted in situ sequencing for transcriptomic profiling in tissue
<p>You can find here the Direct RNA In Situ Sequencing (HybISS-based) maps generated using the Hight Sensitivity kit from CARTANA AB. They include half a mouse brain coronal section, targeting 50 genes. Genes were targeted in a sequential manner. Both reads, DAPI staining and segmented cells are included. The analysis of the same cells, but using 10X magnification are also provided in an anndata object.</p>
Coronavirus Spike Protein Sequences from NCBI Virus
<p>For the study of taxonomic classification of coronaviruses across all genera (Alpha-, Beta-, Gamma-, and Deltacoronavirus), we can use spike protein sequences downloaded from the NCBI Virus website, \url{https://www.ncbi.nlm.nih.gov/labs/virus/vssi/}. The spike protein sequences used in this study were downloaded on November 21 and 27, 2021 using search terms such as ``spike'', ``S1 protein'', ``S2 protein'', and ``S protein'', in order to download as many spike protein sequences as possible.</p>
Geometric Control on Seismic Rupture and Earthquake Sequence along the Yingxiu-Beichuan Fault with Implications for the 2008 Wenchuan Earthquak
<p>A 65000 years seismic sequence is numerical simulated using TriBIE on the unplanar fault plane with variation normal stress. The code is now available in an open-source Git-hub project, <a href="https://github.com/daisy20170101/TriBIE/tree/normal_stress_variation">https://github.com/daisy20170101/TriBIE/tree/normal_stress_variation</a>.</p> <p>The modeling will output the bianary format files of normal stress, fault slip velocity, shear stress, slip during the interseismic loading and coseismic rupture stage, respectively. Since it is impossible to output the data at every time step, especially for the large-scale fault model. Thus, during the interseismic loading, we set a constant time interval to output data and the t-inter-***.dat file will record every time, when the data is outputted. During coseimic rupture, t-cos-**.dat file records time of outputing data. So, the size of t-inter-**.dat and t-cos-**.dat file is the number of outputting steps.The fault plane is discretized into 3,1440 elements and the simulation is carried out by parallel computing on 6 servers with 120 CPUs . Each CPU will dispose data of 262 elements. </p> <p> </p> <p> </p> <p> </p> <p> </p>
Training data for 'Functional annotation of protein sequences' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial for functional annotation of protein sequences.</p>
Data files: Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures
<p>Scripts, preprocessed count matrices, single-cell data objects, and generated data (tables and .rds files) from the scRNA-seq analyses performed in <strong>“Single-cell RNA sequencing of Plasmodium vivax sporozoites reveals stage- and species-specific transcriptomic signatures".</strong></p> <p> </p>
BeerDEcoded Nanopore Sequencing Run of Chimay Now. 2019
<p>The BeerDEcoded workshops are a project of the Street Science Community. https://streetscience.community/projects/beerdecoded/<br> We are extracting DNA from different Beers and sequence them using the Nanopore sequencing technique. The data contains the reads extracted from the beer of the brand Chimay and was performed on 26.11.2019. <br> The Sequencing was performed on a MinION using a Flow Cell and the MinKNOW software. The Software collects sequencing data in real-time and has a base-calling integrated to convert the fast5 files into fastq files.</p>
Data and analysis script to support "Multi-site analysis of sequence in leaf-out and flowering reveals evidence of local adaptation"
<p>Data files and R script used to download and analyze plant leaf-out and flowering observations maintained by the USA National Phenology Network to evaluate the consistency of leaf-out and flowering among species pairs over multiple years.</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>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 0.25% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 0.25% contaminant spike-in.</p>
Squeegee: de novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes, simulation dataset 1% spike-in contaminant sequences
<p>Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic "bread crumbs” across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision.</p> <p> </p> <p>Simulation Dataset 1% contaminant spike-in.</p>
rbcL reference sequences of marine and freshwater diatoms
<p>This database has been modified from the R-syst::Diatom rbcL database, version 9, accessed on 3.18.2021. (see Rimet et al.2016 for details)</p> <p>Sequences of mostly <em>Pseudo-nitzschia</em> from the Gulf of Trieste and some others missing from the database were added.</p> <p>The names of the sequences in the .fasta file are formatted in a way that they can be used directly in dada2 for taxonomic classification. </p> <p>This database represents a comprehensive marine diatom sequence archive. </p>
Structure conditioned hallucinated CDR sequences
<p><em><strong>Datasets accompanying publication https://doi.org/10.1101/2022.06.06.494991</strong></em> (Published in Frontiers in Immunology). Please refer to the final version of the manuscript on Frontiers.</p> <p><strong>RAbD dataset (Figure 2 in preprint/publication)</strong>: Structure conditioned hallucinated sequences for all 6 CDR loops of 60 antibodies (see publication above for more details) with wildtype seeding (runs_wtseed) and without wildtype seeding (runs_noseed). Full sequences are under runs_<>/<benchamark_name>/<cdr>/results/sequences.fasta.</p> <p><strong>DeepAb Testset (SI Table 1, SI Figure 4 in preprint/publication)</strong>: Structure conditioned hallucinated sequences for all 6 CDR loops of 20 antibodies selected from the DeepAb test set (see publication above for more details) with wildtype seeding (runs_wtseed) and without wildtype seeding (runs_noseed). Full sequences are under runs_<>/<benchamark_name>/<cdr>/results/sequences.fasta.</p> <p><strong>Trastuzumab hallucination in various modes described in the manuscript.</strong></p> <p>1. <strong>Unrestricted hallucination</strong>: In <strong>"unrestricted.tar"</strong>. Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>2. <strong>Motif-restricted hallucination (positions 95, 100A on heavy chain)</strong>: In "<strong>res2pos_95and100A.tar"</strong>. Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>3. <strong>Motif-restricted hallucination (positions 99, 100A on heavy chain)</strong>: In "<strong>res2pos_99and100A.tar</strong>". Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>4. <strong>Motif-restricted hallucination (positions 95, 99, 100, 100A on heavy chain)</strong>: In "<strong>res4pos.tar</strong>". Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>5. <strong>Motif-restricted (positions 99, 100A on heavy chain) and sequence-restricted (restricted to wildtype sequence) hallucination</strong>: In "<strong>res2pos_95and100A_and_sequence.tar</strong>". Contains hallucination results in the folder "results". Forward folded structures and metrics in "forward_folding", results of virtual screening in "virtual_binding", results of filtering for both folding and virtual screening in "results_filtered_output", and the results from comparison of multiple folding methods (DeepAb and IgFold) in "results_folding_methods".</p> <p>For reproducing data, refer to code and methods in the preprint/published version.</p>
BeerDEcoded Nanopore Sequencing Run of Chimay Now. 2019 Flongle
<p>The BeerDEcoded workshops are a project of the Street Science Community. https://streetscience.community/projects/beerdecoded/<br> We are extracting DNA from different Beers and sequence them using the Nanopore sequencing technique. The data contains the reads extracted from the beer of the brand Chimay and was performed on 26.11.2019. <br> The Sequencing was performed on a MinION using a flongle Flow Cell and the MinKNOW software. The Software collects sequencing data in real-time and has a base-calling integrated to convert the fast5 files into fastq files.</p>
DeepSelectNet: Deep Neural Network Based Selective Sequencing for Oxford Nanopore Sequencing
<p>Curated dataset for the manuscript named "DeepSelectNet: Deep Neural Network Based Selective Sequencing for Oxford Nanopore Sequencing".</p> <p>Five publicly available datasets sequenced on ONT MinION/GridION were used for the experiments (see below for original sources). These datasets contained raw signal data in single-FAST5 format (one file per each read), which were converted to BLOW5 format using slow5tools to enable convenient and efficient file manipulation. Then, 40,000 reads containing at least 4500 signal samples were extracted from each dataset. From each dataset, 20,000 reads are for training (<species>/train-<species>.blow5) and the rest for testing (<species>/test-<species>.blow5). Basecalled reads for the dataset used for testing are also available (test-<species>.fastq). Guppy version 6.1.3 under dna_r9.4.1_450bps_hac mode was used. The reference genomes are also given (<species>/<species>-ref.fasta)</p> <p>Original datasets are from the following sources:<br> SARS-CoV-2: https://community.artic.network/t/links-to-raw-fast5-fastq-data-for-artic-protocol/17<br> Zymo Metagenome: https://github.com/LomanLab/mockcommunity<br> Chlamydomonas: https://sra-download.ncbi.nlm.nih.gov/traces/era20/ERZ/003237/ERR3237140/Chlamydomonas_0.tar.gz<br> Saccharomyces cerevisiae: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA510813</p>
Sei whole-genome sequence class annotations
<p>Sei sequence class whole-genome annotations are available in the following files:</p> <ul> <li> <p>sorted.hg38.tiling.bed.ipca_randomized_300.labels.merged.bed - The sorted, merged sequence class assignments from Louvain community clustering of the 30 million sequences, uniformly tiling the whole human genome. The fourth column is the sequence class number, with any sequence classes numbering 40-61 excluded from our analyses in the publication. Sequence classes 0-39 can be mapped to the following labels: <a href="https://github.com/FunctionLab/sei-framework/blob/main/model/seqclass.names">https://github.com/FunctionLab/sei-framework/blob/main/model/seqclass.names</a></p> </li> <li> <p>sorted.hg19.tiling.bed.ipca_randomized_300.labels.merged.bed - lifted over version of the hg38 BED file.</p> </li> </ul>
Data from : Genomic Sequence of Klebsiella pneumoniae IIEMP-3, a Vitamin B12-Producing Strain from Indonesian Tempeh
<p>Klebsiella pneumoniae strain IIEMP-3, isolated from Indonesian tempeh, is a vitamin B<sub>12</sub>-producing strain that exhibited a different genetic profile from pathogenic isolates. Here we report the draft genome sequence of strain IIEMP-3, which may provide insights on the nature of fermentation, nutrition, and immunological function of Indonesian tempeh.</p>
GWAS summary statistics and code for "Sequence variants affecting voice pitch in humans"
<p>Contents: GWAS summary statistics for voice pitch (median F0 in reading) and code for acoustic analysis</p> <p>Please refer to the corresponding publication:</p> <p>Gisladottir et al. Sequence variants affecting voice pitch in humans. <em>Science Advances</em></p> <p>The GWAS summary statistics is also available at: https://www.decode.com/summarydata/</p> <p>The code for acoustic analysis is also available at: https://github.com/cadia-lvl/deCODE</p> <p> </p> <p> </p> <p> </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.