Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
89
datasets available to search
ShareScore release 0.9.0
Dataset results
89 results for “sequence annotation”
Gene annotation of complete genome sequence of Achromobacter sp. strain E1
Open the record for dataset details and reuse information.
COG and Pfam annotation results of the genome sequences of four novel Endozoicomonas strains associated with the octocoral Litophyton in a long-term aquarium facility
<p>COG and Pfam annotation files from DOE-JGI Microbial Genome Annotation Pipeline (MGAP) version 4(1), for four <em>Endozoicomonas</em> strains associated with the tropical octocoral Litophyton in a long-term aquarium facility. Data correspond to the assemblies of NE35, NE40, NE41, and NE43, available under the BioProject accession numbers <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075803">PRJNA1075803</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075804">PRJNA1075804</a>, <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075805">PRJNA1075805</a> and <a href="https://www.ncbi.nlm.nih.gov/bioproject/1075806">PRJNA1075806</a>, respectively. Results were submitted to the Integrated Microbial Genomes and Microbiomes system v7 (IMG/M) (2) for comparative analysis. The genome annotations can be interactively accessed on IMG/M (https://img.jgi.doe.gov/cgi-bin/m/main.cgi) using the following identifiers: 8036267134 (strain NE35), 8036277142 (strain NE40), 8045494135 (strain NE41); 8036272146 (strain NE43). </p> <p>This dataset is part of the following study:</p> <p>Marques M, da Silva DMG, Santos E, Baylina N, Peixoto R, Kyrpides NC, Woyke T, Whitman WB, Keller-Costa T, Costa R. 2024. Genome sequences of four novel <em>Endozoicomonas </em>strains associated with a tropical octocoral in a long-term aquarium facility. Microbiology Resource Announcements</p> <p> </p> <p>Other reference sources:</p> <p>(1) Huntemann M, Ivanova NN, Mavromatis K, James Tripp H, Paez-Espino D, Palaniappan K, Szeto E, Pillay M, Chen IMA, Pati A, Nielsen T, Markowitz VM, Kyrpides NC. 2015. The standard operating procedure of the DOE-JGI Microbial Genome Annotation Pipeline (MGAP v.4). Stand Genomic Sci 10:1–6.</p> <p>(2) Chen IMA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, Hajek P, Ritter SJ, Webb C, Wu D, Varghese NJ, Reddy TBK, Mukherjee S, Ovchinnikova G, Nolan M, Seshadri R, Roux S, Visel A, Woyke T, Eloe-Fadrosh EA, Kyrpides NC, Ivanova NN. 2023. The IMG/M data management and analysis system v.7: content updates and new features. Nucleic Acids Res 51:D723–D732.</p>
Annotated sequences extracted from bacterial genomes
<p>Three files containing sequences extracted from 1,049,210 bacterial genomes available from GenBank (release 252). Protein coding sequences were annotated with IDTAXA (PMID: 34541527) using taxon-specific KEGG groups (Bacteria_Protein_subset.fas.gz). These annotations were transferred to their corresponding (nucleotide) coding sequences (Bacteria_Nucleotide_subset.fas.gz). Intergenic regions were extracted from each genome and annotated by FindNonCoding (PMID: 34636849) for their overlap with any of 25 common bacterial non-coding RNAs in Rfam (v14). Intergenic regions were required to be at least 100 nucleotides long and contain no ambiguities (Bacteria_Intergenic_subset.fas.gz). Each subset contains only distinct sequences randomly ordered.</p> <p><strong><em>Headers</em></strong></p> <p>Sequence headers contain the assembly accession followed by the annotation and separated by a "|" character. For example:</p> <p><strong>Bacteria_Intergenic_subset.fas.gz</strong></p> <p>>GCA_022121725.1|RF00000<br> ATGTTACCTTCTTGAGTGATACGGGATGAA[...]</p> <p><strong>Bacteria_Protein_subset.fas.gz</strong></p> <p>>GCA_014764685.1|K02049<br> MPRDLIRISGLEKTYADGSVHALSNIDLSIKD[...]</p> <p><strong>Bacteria_Nucleotide_subset.fas.gz</strong></p> <p>>GCA_015948525.1|K02197<br> GTGAACCTGCGACGTAAAAACCGGCTAYG[...]</p> <p><strong><em>Annotations</em></strong></p> <p>Protein and protein coding sequences are labeled with their KEGG group, starting with "K". Intergenic sequences are named by any overlapping Rfam families, starting with a "RF", and separated by commas when multiple are predicted. "RF00000" is a placeholder for the absence of any predicted RF families.</p>
Enhancing Protein Sequence Annotation in Viral Genomics Using Large Language Models and Soft Alignments.
<p>List of 200 most abundant VOG descriptions.</p>
Single cell Iso-Sequencing enables rapid genome annotation for scRNAseq analysis
Open the record for dataset details and reuse information.
Data from: Consistency of VDJ rearrangement and substitution parameters enables accurate B cell receptor sequence annotation
VDJ rearrangement and somatic hypermutation work together to produce antibody-coding B cell receptor (BCR) sequences for a remarkable diversity of antigens. It is now possible to sequence these BCRs in high throughput; analysis of these sequences is bringing new insight into how antibodies develop, in particular for broadly-neutralizing antibodies against HIV and influenza. A fundamental step in such sequence analysis is to annotate each base as coming from a specific one of the V, D, or J genes, or from an N-addition (a.k.a. non-templated insertion). Previous work has used simple parametric distributions to model transitions from state to state in a hidden Markov model (HMM) of VDJ recombination, and assumed that mutations occur via the same process across sites. However, codon frame and other effects have been observed to violate these parametric assumptions for such coding sequences, suggesting that a non-parametric approach to modeling the recombination process could be useful. In our paper, we find that indeed large modern data sets suggest a model using parameter-rich per-allele categorical distributions for HMM transition probabilities and per-allele-per-position mutation probabilities, and that using such a model for inference leads to significantly improved results. We present an accurate and efficient BCR sequence annotation software package using a novel HMM "factorization" strategy. This package, called partis (https://github.com/psathyrella/partis/), is built on a new general-purpose HMM compiler that can perform efficient inference given a simple text description of an HMM.
Genome sequencing and assembly of Phytophthora oleae, isolate VK10A, NCBI sequences annotation.
<p>Annotation of the first draft of genome assembly of Phytophthora oleae, isolate code: VK10A.</p> <p>This isolate has been found in rot of mature olive drupes (Olea europaea var. sylvestris), from a tree located in Sicily: Complesso Speleologico Villasmundo S. Alfio Nature Reserve (NR) (Melilli, Siracusa, Italy); DATUM WGS 84, 37°13'17.2"N 15°06'22.3"E.</p> <p>Supplementary materials related to the manuscript: </p> <p><strong>Genome sequencing and assembly of <em>Phytophthora oleae</em>, isolate VK10A, causative agent of rot of olive drupes.</strong></p> <p><span>The Whole Genome Shotgun (WGS) of <em>P. oleae</em> genome assembly project has been deposited at DDBJ/ENA/GenBank under the accession JBIMZQ000000000 (assembly DJ1425, https://www.ncbi.nlm.nih.gov/datasets/genome/GCA_046127205.1/).</span></p> <p> </p>
Figure 3 from: Abarenkov K, Adams RI, Irinyi L, Agan A, Ambrosio E, Antonelli A, Bahram M, Bengtsson-Palme J, Bok G, Cangren P, Coimbra V, Coleine C, Gustafsson C, He J, Hofmann T, Kristiansson E, Larsson E, Larsson T, Liu Y, Martinsson S, Meyer W, Panova M, Pombubpa N, Ritter C, Ryberg M, Svantesson S, Scharn R, Svensson O, Töpel M, Unterseher M, Visagie C, Wurzbacher C, Taylor AFS, Kõljalg U, Schriml L, Nilsson RH (2016) Annotating public fungal ITS sequences from the built environment according to the MIxS-Built Environment standard – a report from a May 23-24, 2016 workshop (Gothenburg, Sweden). MycoKeys 16: 1-15. https://doi.org/10.3897/mycokeys.16.10000
Figure 3 - Analysis of the MIxS-BE "building occupancy type" (type of building where the underlying sample was taken).
Figure 2 from: Abarenkov K, Adams RI, Irinyi L, Agan A, Ambrosio E, Antonelli A, Bahram M, Bengtsson-Palme J, Bok G, Cangren P, Coimbra V, Coleine C, Gustafsson C, He J, Hofmann T, Kristiansson E, Larsson E, Larsson T, Liu Y, Martinsson S, Meyer W, Panova M, Pombubpa N, Ritter C, Ryberg M, Svantesson S, Scharn R, Svensson O, Töpel M, Unterseher M, Visagie C, Wurzbacher C, Taylor AFS, Kõljalg U, Schriml L, Nilsson RH (2016) Annotating public fungal ITS sequences from the built environment according to the MIxS-Built Environment standard – a report from a May 23-24, 2016 workshop (Gothenburg, Sweden). MycoKeys 16: 1-15. https://doi.org/10.3897/mycokeys.16.10000
Figure 2 - Krona chart of the taxonomic affiliation of the BMS sequences down to order level. The Krona chart lists all annotated BMS sequences except those classified as Fungi sp. (36.4%) and those of non-fungal origin (0.9%). An interactive version of the Krona chart is provided as Suppl. material 3. The figure includes pre-existing data plus the data added during the workshop, such that these charts indicate the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2016. Sequences that were not annotated with a single built environment-related term in the INSDC were not included in this effort, and are not represented in these charts.
Figure 1 from: Abarenkov K, Adams RI, Irinyi L, Agan A, Ambrosio E, Antonelli A, Bahram M, Bengtsson-Palme J, Bok G, Cangren P, Coimbra V, Coleine C, Gustafsson C, He J, Hofmann T, Kristiansson E, Larsson E, Larsson T, Liu Y, Martinsson S, Meyer W, Panova M, Pombubpa N, Ritter C, Ryberg M, Svantesson S, Scharn R, Svensson O, Töpel M, Unterseher M, Visagie C, Wurzbacher C, Taylor AFS, Kõljalg U, Schriml L, Nilsson RH (2016) Annotating public fungal ITS sequences from the built environment according to the MIxS-Built Environment standard – a report from a May 23-24, 2016 workshop (Gothenburg, Sweden). MycoKeys 16: 1-15. https://doi.org/10.3897/mycokeys.16.10000
Figure 1 - Analysis of the BMS sequences for country of collection. Country centroids marked with bubbles of different size on the global map indicate the number of BMS sequences originating from these countries (54 distinct countries, sequence count ranging from 1 to 2,914). For an additional 2.9% of the sequences, country information could not be restored during the workshop. The figure includes pre-existing data plus the data added during the workshop, such that these charts indicate the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2016. Sequences that were not annotated with a single built environment-related term in the INSDC were not included in this effort, and are not represented in these charts.
Supplementary material 4 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
The interactive Krona chart associated with Figure 2 :
Supplementary material 2 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
The MIxS-BE annotations implemented for the built environment sequences during the workshop :
Figure 3 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
Figure 3 Analysis of the MIxS-BE "building occupancy type" (type of building where the underlying sample was taken). The figure is based on Abarenkov et al. (2016) plus the data added during the workshop, such that it indicates the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2017.
Figure 2 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
Figure 2 Krona chart of the taxonomic affiliation of the built environment sequences down to order level. The Krona chart lists all annotated built environment sequences except those classified as Fungi sp. (32%) and those of non-fungal origin (1%). An interactive version of the Krona chart is provided as Supplementary material 4. The figure is based on Abarenkov et al. (2016) plus the data added during the workshop, such that it indicates the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2017.
Figure 1 from: Nilsson RH, Taylor AFS, Adams RI, Baschien C, Bengtsson-Palme J, Cangren P, Coleine C, Daniel H-M, Glassman SI, Hirooka Y, Irinyi L, Iršėnaitė R, Martin-Sanchez PM, Meyer W, Oh S-Y, Sampaio JP, Seifert KA, Sklenář F, Stubbe D, Suh S-O, Summerbell R, Svantesson S, Unterseher M, Visagie CM, Weiss M, Woudenberg JHC, Wurzbacher C, den Wyngaert SV, Yilmaz N, Yurkov A, Kõljalg U, Abarenkov K (2018) Taxonomic annotation of public fungal ITS sequences from the built environment – a report from an April 10–11, 2017 workshop (Aberdeen, UK). MycoKeys 28: 65-82. https://doi.org/10.3897/mycokeys.28.20887
Figure 1 Analysis of the built environment sequences for country of collection. Country centroids based on the geographical centres of contiguous country land masses are marked with bubbles of different size on the global map to indicate the number of built environment sequences originating from these countries as stated explicitly in the underlying INSDC records or as restored during the present effort and in Abarenkov et al. (2016) (57 distinct countries, sequence count ranging from 1 to 3,091). The figure is based on Abarenkov et al. (2016) plus the data added during the workshop, such that it indicates the scientific state of ITS-based Sanger-derived sequencing of the built mycobiome as of spring 2017.
Fig. 3 in Description of Sarcocystis scandentiborneensis sp. nov. from treeshrews (Tupaia minor, T. tana) in northern Borneo with annotations on the utility of COI and 18S rDNA sequences for species delineation
Fig. 3. Phylogenetic tree of nuclear 18S rDNA sequences of the Sarcocystidae, including the new Sarcocystis sp. from treeshrews (black symbols). Taxa of the Eimeriidae served as outgroup. Bayesian Inference was used for phylogeny reconstruction, whereby the general time-reversible substitution model (GTR + G + I) combined with an assumed among-site variation ('covariotide' model) was applied. Values for posterior probability are indicated behind nodes. Note that the new species is part of a monophyletic subclade previously tagged S1 (Wassermann et al., 2017), which includes taxa known to prefer snakes as definitive and rodents as intermediate hosts. Sequences E357-13 and E364-13 are available at GenBank under MN733816 and MN733817, respectively.
Metadata supporting the AFDB90v4 annotated sequence similarity network
<p>Driven by the development and upscaling of fast genome sequencing and assembly pipelines, the number of protein-coding sequences deposited in public protein sequence databases is increasing exponentially. Recently, the dramatic success of deep learning-based approaches applied to protein structure prediction has done the same for protein structures. We are now entering a new era in protein sequence and structure annotation, with hundreds of millions of predicted protein structures made available through the AlphaFold database. These models cover most of the catalogued natural proteins, including those difficult to annotate for function or putative biological role based on standard, homology-based approaches. In this work, we quantified how much of such "dark matter" of the natural protein universe was structurally illuminated by AlphaFold2 at a high predicted accuracy and modelled this diversity as an interactive sequence similarity network that can be navigated at <a href="https://uniprot3d.org/atlas/AFDB90v4">https://uniprot3d.org/atlas/AFDB90v4</a>. The dataset deposited here corresponds to the metadata generated, and that makes the base of the similarity network constructed and its interpretation. These files are either generated or processed using the code available at <a href="https://github.com/ProteinUniverseAtlas/AFDB90v4">https://github.com/ProteinUniverseAtlas/AFDB90v4</a>.</p> <p>This repository further contains the detailed, individual sequence similarity networks (in CLANS format) generated for the 3 example protein (super)families described in the text.</p> <p>The full content of this repository includes:</p> <ol> <li> <p><strong>AFDBv4_pLDDT_diggestion_UniRef50_2023-02-01.csv</strong>: table listing all uniref50 clusters in UniProt, including information on structural representatives from AFDB. Each column provides different annotations, including functional brightness, median and best pLDDT, brightness and structural representatives, etc.</p> </li> <li> <p><strong>AFDBv4_DUF_dark_diggestion_UniRef50_2023-02-06.csv</strong>: table listing all uniref50 clusters in UniProt and whether they include proteins mapped to known domains of unknown function (DUF).</p> </li> <li> <p><strong>AFDBv4_90.fasta</strong>: fasta file with the sequences of all UniRef50 clusters selected, and used for the all-against-all mmseqs searches that make the base of the network.</p> </li> <li> <p><strong>AFDB90v4_data.csv</strong>: the subset of file (1) that corresponds to the AFDB90v4 dataset, including columns such as functional brightness, median and best pLDDT, brightness and structural representatives, etc.</p> </li> <li> <p><strong>AFDB90v4_data_with_graph_labels.csv</strong>: table listing each individual uniref50 cluster included in the AFDB90v4 dataset, together with their mapping to communities, and connected components.</p> </li> <li> <p><strong>AFDB90v4_cc_data.csv</strong>: table of uniref50 clusters in connected components, including their annotations, and the columns in file (5).</p> </li> <li> <p><strong>AFDB90v4_cc_data_uniprot_community_taxonomy_map.csv</strong>: mapping of each uniprotAC entry to their corresponding component, community and taxonomy.</p> </li> <li> <p><strong>AFDB90v4_subgraphs_summary.csv</strong>: table summarising the properties of individual connected components, including the average brightness, the number of members, the number of unique protein sequences, the median length, and the number of communities.</p> </li> <li> <p><strong>communities_summary.csv</strong>: table summarising the properties of individual communities, including average brightness, the number of members, the number of unique protein sequences, the median length, the most common superkingdom represented, the average structure outlier score, etc.</p> </li> <li> <p><strong>communities_edge_list-coordinates.csv</strong>: the coordinates of each community in the graphical representation. Singleton communities or singleton UniRef50 clusters are not included.</p> </li> <li> <p><strong>communities_edge_list_no_duplicates.csv</strong>: list of edges making the graph.</p> </li> <li> <p><strong>node_class.json</strong>: map between each Uniref50 cluster and its corresponding component and community.</p> </li> <li> <p><strong>subgraphs.tar.gz</strong>: tar file containing gml files for each individual connected component.</p> </li> <li> <p><strong>AFDB90v4_outlier_scores.tsv</strong>: table containing the outlier scores for each community representative.</p> </li> <li> <p><strong>AFDB90v4_dark_galaxies_summary.csv</strong>: table containing the summary of all dark connected components, including average brightness, median length, representatives, number of communities, etc.</p> </li> <li> <p><strong>AFDB90v4_uniprot_naming_assessment_counts.csv</strong>: table listing the per-component semantic diversity scores, as well as the major source of the titles of the proteins included and their count.</p> </li> <li> <p><strong>uniprot_naming_assessment.tar.gz</strong>: tar file containing the per community assessment of predicted protein names in UniProt as of February 2023.</p> </li> <li> <p><strong>CLANS_files.tar.gz</strong>: stores the 3 sequence similarity networks, in CLANS format, constructed for the analysis of the sequence diversity and sequence similarities of the proteins in components, 27, 159 and 3314. These CLANS files make the base of panel A in all figures 3 and 4 and extended data figure 5.</p> </li> </ol> <p> </p>
Data from: De novo sequencing, assembly, and annotation of four threespine stickleback genomes based on microfluidic partitioned DNA libraries
Open the record for dataset details and reuse information.
Data from: Consistency of VDJ rearrangement and substitution parameters enables accurate B cell receptor sequence annotation
Open the record for dataset details and reuse information.
Data from: Optimisation of next generation sequencing transcriptome annotation for species lacking sequenced genomes
Open the record for dataset details and reuse information.
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.