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19,342 results for “Sequences”
Single-cell RNA sequencing of sclerotome-derived fibroblasts in zebrafish
<p>Despite their importance in tissue maintenance and repair, fibroblast diversity and plasticity remain poorly understood. Using single-cell RNA sequencing, we uncover distinct sclerotome-derived fibroblast populations in zebrafish, including progenitor-like perivascular/interstitial fibroblasts, and specialized fibroblasts such as tenocytes. To determine fibroblast plasticity <em>in vivo</em>, we develop a laser-induced tendon ablation and regeneration model. Lineage tracing reveals that laser-ablated tenocytes are quickly regenerated by preexisting fibroblasts. By combining single-cell clonal analysis and live imaging, we demonstrate that perivascular/interstitial fibroblasts actively migrate to the injury site, where they proliferate and give rise to new tenocytes. By contrast, perivascular fibroblast-derived pericytes or specialized fibroblasts, including tenocytes, exhibit no regenerative plasticity. Interestingly, active Hedgehog (Hh) signaling is required for the proliferation of activated fibroblasts to ensure efficient tenocyte regeneration. Together, our work highlights the functional diversity of fibroblasts and establishes perivascular/interstitial fibroblasts as tenocyte progenitors that promote tendon regeneration in a Hh signaling-dependent manner.</p>
Genomic sequences of 343 Xanthomonas citri pv. citri strains from Florida
<p><i>Xanthomonas citri </i>pv.<i> citri </i>(Xcc) causes the devastating citrus canker disease. Xcc is known to have been introduced into Florida, USA in at least three different events in 1915, 1986 and 1995 with the first two claimed to be eradicated. Here, we investigated the population structure of Xcc to understand the mysteries related to its introduction, spread and eradication, and how Xcc strains have evolved in term of pathogenicity and copper resistance. We sequenced whole genome sequences of 343 Xcc strains collected from Florida groves over 20 years. </p>
mTAGs: taxonomic profiling using degenerate consensus reference sequences of ribosomal RNA gene
<p>mTAGs is a tool for the taxonomic profiling of metagenomes. It detects sequencing reads belonging to the small subunit of the ribosomal RNA (SSU-rRNA) gene and annotates them through the alignment to full-length degenerate consensus SSU-rRNA reference sequences. The tool is capable of processing single-end and pair-end metagenomic reads, takes advantage of the information contained in any region of the SSU-rRNA gene and provides relative abundance profiles at multiple taxonomic ranks (Domain, Phylum, Class, Order, Family, Genus and OTUs defined at a 97% sequence identity cutoff).</p>
RefSeq bacterial protein (amino acid) sequences
<p><strong>Bacteria_Protein.fas.gz</strong></p><p>151,835,459 protein (amino acid) sequences extracted from 44,831 randomly selected bacterial genomes from NCBI's RefSeq (release 220). Sequences are named by their accession number, followed by "|" and their PGAP predicted function ("protein" tag). For example, the first sequence is named:</p><blockquote><p>WP_125174066.1|iron ABC transporter permease</p></blockquote><p>The process of creating the file involved the following steps.<br><strong>Step 1.</strong> Download 318,613 faa and fna files associated with a bacterial assembly in RefSeq. The following query was used:<br><i>esearch -db assembly -query '"Bacteria"[Organism] AND "latest refseq"[properties] AND "refseq has annotation"[properties]' | esummary | xtract -pattern DocumentSummary -element FtpPath_RefSeq</i><br><strong>Step 2.</strong> Verify all protein coding sequences match the expected protein sequence lengths within three codons, otherwise skip the assembly.<br><strong>Step 3.</strong> Remove all redundant protein coding or protein sequences in a genome. Only exact duplicates were removed, but they were removed from both nucleotides and proteins. Hence, a duplicated amino acid sequence would be discarded along with its coding sequence even if the coding sequence was unique. This was done to keep the two sets of sequences consistent.<br><strong>Step 4.</strong> Name sequences by their accession and PGAP predicted function, separated by a "|" character. The PGAP predicted function is generally uniform, although there are subtle difference between some taxon specific predictions. The predicted function is reasonably dependable but certainly not perfect.<br><strong>Step 5.</strong> Discard any sequences without a predicted function ("hypothetical protein"). These were discarded under the assumption that the protein's function would be required for downstream uses of the sequences.<br><strong>Step 6.</strong> Append protein and protein coding (nucleotide) sequences from randomly ordered assemblies to separate gzipped FASTA formatted files until the Zenodo file size limit was met for either file. Hence, there are many exact duplicate sequences in the set, but none for the sequences from each genome.</p><p>The final sets of sequences are intended to provide large sets of matched protein coding (nucleotide) and protein (amino acid) sequences with consistent labels. The FASTA descriptions in both files are identical. Note, the protein coding sequences do not exactly translate into the protein sequences because of slight differences in length (typically inclusion/exclusion of the first or last codon), as well as use of different translation tables depending on the organism.</p><p>See <i>Related works</i> for the companion file of protein coding (nucleotide) sequences (DOI: 10.5281/zenodo.10031801).</p>
RefSeq bacterial protein coding (nucleotide) sequences
<p><strong>Bacteria_Nucleotide.fas.gz</strong></p><p>151,835,459 protein coding (nucleotide) sequences extracted from 44,831 randomly selected bacterial genomes from NCBI's RefSeq (release 220). Sequences are named by their accession number, followed by "|" and their PGAP predicted function ("protein" tag). For example, the first sequence is named:</p><blockquote><p>WP_125174066.1|iron ABC transporter permease</p></blockquote><p>The process of creating the file involved the following steps.<br><strong>Step 1.</strong> Download 318,613 faa and fna files associated with a bacterial assembly in RefSeq. The following query was used:<br><i>esearch -db assembly -query '"Bacteria"[Organism] AND "latest refseq"[properties] AND "refseq has annotation"[properties]' | esummary | xtract -pattern DocumentSummary -element FtpPath_RefSeq</i><br><strong>Step 2.</strong> Verify all protein coding sequences match the expected protein sequence lengths within three codons, otherwise skip the assembly.<br><strong>Step 3.</strong> Remove all redundant protein coding or protein sequences in a genome. Only exact duplicates were removed, but they were removed from both nucleotides and proteins. Hence, a duplicated amino acid sequence would be discarded along with its coding sequence even if the coding sequence was unique. This was done to keep the two sets of sequences consistent.<br><strong>Step 4.</strong> Name sequences by their accession and PGAP predicted function, separated by a "|" character. The PGAP predicted function is generally uniform, although there are subtle difference between some taxon specific predictions. The predicted function is reasonably dependable but certainly not perfect.<br><strong>Step 5.</strong> Discard any sequences without a predicted function ("hypothetical protein"). These were discarded under the assumption that the protein's function would be required for downstream uses of the sequences.<br><strong>Step 6.</strong> Append protein and protein coding (nucleotide) sequences from randomly ordered assemblies to separate gzipped FASTA formatted files until the Zenodo file size limit was met for either file. Hence, there are many exact duplicate sequences in the set, but none for the sequences from each genome.</p><p>The final sets of sequences are intended to provide large sets of matched protein coding (nucleotide) and protein (amino acid) sequences with consistent labels. The FASTA descriptions in both files are identical. Note, the protein coding sequences do not exactly translate into the protein sequences because of slight differences in length (typically inclusion/exclusion of the first or last codon), as well as use of different translation tables depending on the organism.</p><p>See <i>Related works</i> for the companion file of protein (amino acid) sequences (DOI: 10.5281/zenodo.10030000).</p>
ASMC: sequence and result files
<p>This repository contains the data specified in the application note entitled "ASMC: investigating the amino acid diversity of enzyme active sites". It includes:</p> <p>1. *.fa.gz - The starting sequence sets in FASTA format.</p> <p>2. *_removed_sequences.txt.gz - The list of sequences discarded due to important gaps.</p> <p>3. *_groups_X_min_Y.tsv.gz - The ASMC results in TSV format. The columns must be read as follows: "Protein ID | Active Site sequences | ASMC group". The letters X and Y correspond to the DBSCAN parameters defined with the 'auto' option (eps and min_samples, respectively).</p>
Message-Based Psychotherapy and Digital Treatment Sequences for Depression
ClinicalTrials.gov study NCT04513080. IPD Sharing: YES. Countries: 1. Publications: 2.
The Effectiveness of Brushing and Flossing Sequence on Control of Plaque and Gingival Inflammation
ClinicalTrials.gov study NCT03989427. IPD Sharing: YES. Countries: 1. Publications: 5.
Comparing bacterial microbiome composition of Xylocopa species across populations using PacBio 16S rRNA gene sequencing
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Kinetic sequencing (k-Seq) as a massively parallel assay for ribozyme kinetics: utility and critical parameters
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Supplementary data for: Comparison of optical flow derivation techniques for retrieving tropospheric winds from satellite image sequences
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Immune repertoire sequencing reveals differences in treatment response to camrelizumab plus platinum-based chemotherapy in advanced ESCC
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Data from: Improved genome assembly of the whiteleg shrimp Penaeus (Litopenaeus) vannamei using long- and short-read sequences from public databases
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Data for: High-throughput profiling of sequence recognition by tyrosine kinases and SH2 domains using bacterial peptide display
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Green turtle ddRAD raw sequencing data
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Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
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Data for: Range and niche expansion through multiple interspecific hybridization - a genotyping by sequencing analysis of Cherleria (Caryophyllaceae)
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Data from: Using transcriptome sequencing and pooled exome capture to study local adaptation in the giga-genome of Pinus cembra
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Whole genome sequence and annotation of Penstemon davidsonii
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Hi-reComb: Constructing recombination maps from bulk gamete Hi-C sequencing
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.