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19,342 results for “Sequence”
Figure 80. Calculated pairwise distances between sequences using the Kimura 2 in Three new species of Cautethia Grote (Lepidoptera: Sphingidae) from the Lucayan Archipelago and keys to West Indies species
Figure 80. Calculated pairwise distances between sequences using the Kimura 2-parameter model.
Nanopore sequencing data analysis using Microsoft Azure cloud computing service
<p>Genetic information provides insights into the exome, genome, epigenetics and structural organisation of the organism. Given the enormous amount of genetic information, scientists are able to perform mammoth tasks to improve the standard of health care such as determining genetic influences on outcome of allogeneic transplantation. Cloud-based computing has increasingly become a key choice for many scientists, engineers and institutions as it offers on-demand network access and users can conveniently rent rather than buy all required computing resources. With the positive advancements of cloud computing and nanopore sequencing data output, we were motivated to develop an automated and scalable analysis pipeline utilizing cloud infrastructure in Microsoft Azure to accelerate HLA genotyping service and improve the efficiency of the workflow at lower cost. In this study, we describe (i) the selection process for suitable virtual machine sizes for computing resources to balance between the best performance versus cost-effectiveness; (ii) the building of Docker containers to include all tools in the cloud computational environment; (iii) the comparison of HLA genotype concordance between the in-house manual method and the automated cloud-based pipeline to assess data accuracy. In conclusion, the Microsoft Azure cloud-based data analysis pipeline was shown to meet all the key imperatives for performance, cost, usability, simplicity and accuracy. Importantly, the pipeline allows for the ongoing maintenance and testing of version changes before implementation. This pipeline is suitable for data analysis from MinION sequencing platforms and could be adopted for other data analysis application processes.</p>
VADER TyrT Illumina sequencing data
<p>FASTQ files used in data analysis for "Virus-assisted directed evolution of enhanced suppressor tRNAs in mammalian cells."</p>
VADER PylT-PyOtR Illumina sequencing data
<p>FASTQ files used in data analysis for "PyOtR: a novel Pyrrolysyl tRNA evolved for enhanced unnatural amino acid incorporation."</p>
VADER TyrT-MARIO Illumina sequencing data
<p>FASTQ files used in data analysis for "Evolution of MARIO, an improved Tyrosyl tRNA for more efficient genetic code expansion."</p>
Supplementary material 1 from: Scacchetti P, Pansonato-Alves J, Utsunomia R, Oliveira C, Foresti F (2011) Karyotypic diversity in four species of the genus Gymnotus Linnaeus, 1758 (Teleostei, Gymnotiformes, Gymnotidae): physical mapping of ribosomal genes and telomeric sequences. Comparative Cytogenetics 5(3): 223-235. https://doi.org/10.3897/compcytogen.v5i3.1375
Nexus file of aligned COI and COII nucleotide sequences.
Raw DNA sequence data of an individual known as "whitequark" (part 1)
<p>Whole genome sequenced on NovaSeq 6000, paired-end 2x150bp with 350bp insert. 30-40× coverage.</p> <p>This dataset can be used by anyone, with attribution.</p>
Raw DNA sequence data of an indivdual known as "whitequark" (part 2)
<p>Whole genome sequenced on NovaSeq 6000, paired-end 2x150bp with 350bp insert. 30-40× coverage.</p> <p>This dataset can be used by anyone, with attribution.</p>
Cross-correlations and symmetries in genetic sequences
<p>Data sets and supporting material used in the manuscript</p> <p>"The common origin of symmetry and structure in genetic sequences" by<br> G. Cristadoro , M. Degli Esposti , E. G. Altmann<br> https://arxiv.org/abs/1710.02348</p> <p>Output of calculations can be seen in the file:<br> Notebook.html</p> <p>In order to repeat calculations or explore different choices:</p> <ol> <li>Download all files to the same directory</li> <li>Uncompress the "data.tar.gz" file (e.g., using tar -xzvf data.tar.gz)</li> <li>Open the Notebook using Python 3.0 in Jupyter (www.jupyter.org)</li> </ol> <p> </p>
New methods for the genotyping of Legionella pneumophila - Establishment, validation and implementation of a DNA-based microarray and a core genome multilocus sequence typing
<p>This data presented here are part a doctoral thesis with the focus on new genotyping methods for the human pathogen <em>Legionella pneumophila</em>. The data are partially published in articles. </p> <p>The thesis can be downloaded: update of the URL is coming soon</p>
Database of 16S sequences from SILVA (r114), filtered, curated and annotated to be used easily by programs of taxonomic assignments
<p>The database used for the taxonomic assignment of reads generally comes from the SILVA database (http://www.arb-silva.de/). The logic behind this database is to use the information from the best one to the worst one. This is why the curated database was splitted in two parts : the [C] sequences for Complete sequences in terms of taxonomy, and the [I] and [E] sequences, for Incomplete and Environmental sequences.</p> <p>Each sequence included into the database must have a specific format summarizing all needed information (example below):<br> >[I]AACY020336309;Archaea(superkingdom);Euryarchaeota(phylum);Thermoplasmata(class);Thermoplasmatales(order);Marine_Group_II(no_rank);;marine_metagenome</p> <p>This sequence is an incomplete one ([I]), with a specific accession number from NCBI or SILVA, or another database (AACY020336309). Then, all taxonomic data is separated using ';' characters, for each considered level (superkingdom, phylum, <br> class, order, family, and genus). The species name is the last one and separated by two ';' characters from the rest of the descriptive line. Finally, the descriptive line must not contain specific characters like spaces. If one or several levels are unknown, this is indicated by 'no_rank'.</p> <p>Another example here for [C] sequences:<br> >[C]AAAK03000010;Bacteria(superkingdom);Firmicutes(phylum);Bacilli(class);Lactobacillales(order);Enterococcaceae(family);Enterococcus(genus);;Enterococcus_faecium_DO<br> This sequence is a complete one ([C]), with a specific accession number from NCBI or SILVA, or another database (AACY020187844). Then, all taxonomic data is separated using ';' characters, for each considered level (superkingdom, phylum, <br> class, order, family, and genus). The species is the last one and separated by two ';' characters from the rest of the descriptive line. Complete sequences must have six levels of information (superkingdom, phylum, class, order, family, and genus). If it is not the case, the sequence will be considered as Incomplete ([I]) (between three and five levels), or Environmental ([E]) (with only the superkingdom and the phylum levels).</p> <p>Another example here for [E] sequences:<br> >[E]U59968;Archaea(superkingdom);Thaumarchaeota(phylum);Soil_Crenarchaeotic_Group(SCG)(no_rank);;uncultured_crenarchaeote<br> This sequence is a environmental one ([E]), with a specific accession number from NCBI or SILVA, or another database (U59968). Then, all taxonomic data is separated using ';' characters, for each considered level (superkingdom, phylum, class, order, family, and genus). The species is the last one and separated by two ';' characters from the rest of the descriptive line. Complete sequences <br> must have six levels of information (superkingdom, phylum, class, order, family, and genus). If it is not the case, the sequence will be considered as Incomplete ([I]) (between three and five levels), or Environmental ([E]) (with only the superkingdom and the phylum levels).</p> <p>More details on the steps defined to clean and define this new database can be available on demand (sebastien.terrat@inra.fr).</p>
Training data for 'Mapping-by-sequencing' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that demonstrates mapping-by-sequencing analysis and represent a subsample of the data used in Sun & Schneeberger, 2015 (DOI:10.1007/978-1-4939-2444-8_19).</p>
Figure 6. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The image sequences of sideward walking and turning are shown in Figure 6 respectively.</p>
MNBC: a multithreaded Minimizer-based Naïve Bayes Classifier for improved metagenomic sequence classification
<p>These files provide supplementary data underlying the article <a title="https://doi.org/10.1093/bioinformatics/btae601" href="https://doi.org/10.1093/bioinformatics/btae601" target="_blank" rel="noopener noreferrer nofollow">doi.org/10.1093/bioinformatics/btae601</a>:</p> <ul> <li>CAMI2_reference_database.tar.gz_1 to CAMI2_reference_database.tar.gz_6: Merge them into a single file using the <em>cat</em> command. The folder produced by decompressing this file is the reference database for CAMI2 (RefSeq sequence filenames are in the file CAMI2_reference_database_16864_genomes_list.txt at <a href="https://dx.doi.org/10.5281/zenodo.10568965">https://dx.doi.org/10.5281/zenodo.10568965</a>).</li> </ul> <p> </p>
MNBC: a multithreaded Minimizer-based Naïve Bayes Classifier for improved metagenomic sequence classification
<p>These files provide supplementary data underlying the article <a title="https://doi.org/10.1093/bioinformatics/btae601" href="https://doi.org/10.1093/bioinformatics/btae601" target="_blank" rel="noopener noreferrer nofollow">doi.org/10.1093/bioinformatics/btae601</a> (see Figure 1 of the article):</p> <ul> <li>37345_filtered_training_and_test_genomes_sequence_files.tar.gz_1 to 37345_filtered_training_and_test_genomes_sequence_files.tar.gz_6: Merge them into a single file using the <em>cat</em> command. The folder produced by decompressing this file contains the filtered RefSeq sequence files of all 37345 training and test genomes, which were used to build the uniform reference database and generate the test reads (The filenames are in the file 37345_filtered_training_and_test_genomes_list.txt at <a href="https://dx.doi.org/10.5281/zenodo.10568965">https://dx.doi.org/10.5281/zenodo.10568965</a>).</li> </ul>
Temperature modulates dominance of a superinfecting Arctic virus in its unicellular algal host - Nanopore sequencing reads
<p>Nanopore sequencing reads of two Micromonas polaris viruses, MpoV-45T and MpoV-46T using R9 chemistry.</p>
Protein sequences of LbNoxA, LbNoxB and LbNoxR in L. bicolor and other different species
<p>Ectomycorrhizal symbiosis, which involves mutually beneficial interactions between soil fungi and tree roots, is essential for promoting tree growth. To establish this symbiotic relationship, fungal symbionts must initiate and sustain mutualistic interactions with host plants while avoiding host defence responses. In recent years, the ectomycorrhizal association between the ectomycorrhizal basidiomycete<em> L. bicolor </em>and its host<em> Populus tremula × alba</em>,<em> </em>has been used to elucidate the molecular mechanisms that drive the development of ectomycorrhizal symbiosis. The gene repertoire of<em> L. bicolor</em> contained three <em>Nox</em> genes: <em>LbNoxA</em> (JGI ID Lacbi2|399780), <em>LbNoxB</em> (JGI ID Lacbi2|173015), and <em>LbNoxR</em> (JGI ID Lacbi2|383559). LbNoxA, protein sequence (JGI database + |gene numbers): Clapu1|8615, Sclsc1|12632, Morcra1|822478, Ophsi1|5059, PhlFC14_2|1813626, Pleery1|1506038, Armbor1|1922489, Trave1|68345, Dicsqu463_1|970488, Polbr1|1451927, Ganluc1|486672, Musmu1|NP_031833.3, Lacbi2|399780, LacbiH82_1|443856, Lacbi81306_1|757325, Lacbi55996_1|1021277, LacbiH53_1|522216, LacbiN203_1|820777, LacbiS238N_1|410357, LacbiS238O_1|87145, LacbiCham3_1|66106, LacbiH70_1|500846, Lacbi59489_1|1094392, LacbiH82×H70_1|455396, Lacbi9312_2|1966069, LacbiDR170_1|936588, LacbiD101_1|770957, Lacam2|675282. LbNoxB, protein sequence: Clapu1|2069, Sclsc1|4716, Morcra1|717130, Ophsi1|1953, PhlFC14_2|1930601, Pleery1|1513566, Armbor1|1788315, Trave1|154726, Dicsqu463_1|1002687, Polbr1|1407584, Ganluc1|380884, Musmu1|NP_031833.3, Lacbi2|173015, LacbiH82_1|402315, Lacbi81306_1|388494, Lacbi55996_1|1012552, LacbiN203_1|692258, LacbiS238N_1|466368, LacbiCham3_1|119176, LacbiS238O_1|142990, LacbiH70_1|481432, Lacbi59489_1|1017543, LacbiH82×H70_1|424508, Lacbi9312_2|2376025, LacbiDR170_1|961381, LacbiD101_1|744494, Lacam2|673882, LacbiH53_1|399281. LbNoxR, protein sequence: Clapu1|5169, Sclsc1|101, Morcra1|909770, Ophsi1|2065, PhlFC14_2|1902619, Pleery1|1379754, Armbor1|1887170, Trave1|31426, Dicsqu463_1|961540, Polbr1|1449735, Ganluc1|555878, Musmu1|NP_035007.1, Lacbi2|383559, LacbiH82_1|404736, Lacbi81306_1|796955, Lacbi55996_1|396143, LacbiH53_1|580999, LacbiN203_1|781452, LacbiS238N_1|524632, LacbiS238O_1|142040, LacbiCham3_1|116334, LacbiH70_1|479311, Lacbi59489_1|951497, LacbiDR170_1|1040569, Lacbi9312_2|2114762, Lacam2|671404, LacbiD101_1|660911, LacbiH82×H70_1|470284.</p>
Data for paper: Transfer learning for cross-context prediction of protein expression from 5'UTR sequence
<p>This depsit contains data for the paper entitled: "<strong>Transfer learning for cross-context prediction of protein expression from 5'UTR sequence</strong>".</p> <p>The <strong>rebeca.zip</strong> file contains a snapshot of the rebeca package which can be used to train, fine tune and test the CONV-LSTM model used in this study.</p> <p>The <strong>datasets.zip</strong> file contains the compiled sequence to expression datasets from across all Flow-seq expressions considered in this study. </p> <p>The <strong>analysis.zip</strong> file contains all data files and jupyter notebooks necessary to reproduce our analysis. Each Flow-seq study has a dedicated folder (e.g., `fepB') with two sub-folders: 1. The `data\_split' folder, which contains the steps necessary to split the Flow-seq data for our ML experiments (a `readme.txt' file describes the input and output files and a jupyter notebook is available to reproduce the data split); 2. The `data\_analysis' folder, which contains a jupyter notebook and the necessary input files to reproduce the analysis of our experiments.</p>
Exome sequence analysis identifies rare coding variants associated with a machine learning-based marker for coronary artery disease.
<p>*.sh and *.R are codes to test rare coding variants for association with ISCAD.</p> <p>Petrazzini_etal_2024_*_level_meta_analysis.txt.gz are summary statistics of variant- and gene-level associations of rare coding variants in the exome sequences of 604,914 individuals with an in-silico score for coronary artery disease (ISCAD).</p> <p>Chromosomal positions are mapped to the GRCh38 (hg38) human genome reference.</p> <p>Directions of effect correspond to associations in the UK Biobank, the All of Us Research Program, the BioMe Biobank sample 1 and the BioMe Biobank sample 2, in that order.</p>
Genotypes of Aedes aegypti mosquitoes derived from SNP chip and low-coverage whole genome sequencing for platform cross-validation
<p>The mosquito <em>Aedes aegypti </em>is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people world-wide. Population genetics studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse <em>Ae. aegypti </em>populations to facilitate genomic studies on this species. Here we evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shot-gun sequencing approach using mosquitoes from the species' native (Africa) and invasive range (outside Africa). These analyses indicate that the results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low coverage whole genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole genome sequencing for population genetic studies of <em>Ae. aegypti</em> that do not rely on full allelic frequency spectra. Whole genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches. </p>
ScienceDex guides
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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.