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19,342 results for “Sequences”
Literature consistency of bioinformatics sequence databases is effective for assessing record quality
<p>Bioinformatics sequence databases such as Genbank or UniProt contain hundreds of millions of records of genomic data. These records are derived from direct submissions from individual laboratories, as well as from bulk submissions from large-scale sequencing centres; their diversity and scale means that they suffer from a range of data quality issues including errors, discrepancies, redundancies, ambiguities, incompleteness and inconsistencies with the published literature. In this work, we seek to investigate and analyze the data quality of sequence databases from the perspective of a curator, who must detect anomalous and suspicious records. Specifically, we emphasize the detection of inconsistent records with respect to the literature. Focusing on GenBank, we propose a set of 24 quality indicators, which are based on treating a record as a query into the published literature, and then use query quality predictors. We then carry out an analysis that shows that the proposed quality indicators and the quality of the records have a mutual relationship, in which one depends on the other. We propose to represent record literature consistency as a vector of these quality indicators. By reducing the dimensionality of this representation for visualization purposes using principal component analysis, we show that records which have been reported as inconsistent with the literature fall roughly in the same area, and therefore share similar characteristics. By manually analyzing records not previously known to be erroneous that fall in the same area than records know to be inconsistent, we show that one record out of four is inconsistent with respect to the literature. This high density of inconsistent record opens the way towards the development of automatic methods for the detection of faulty records. We conclude that literature inconsistency is a meaningful strategy for identifying suspicious records.</p>
Data from: Individual Movement - Sequence Analysis Method (IM-SAM): characterising spatio-temporal patterns of animal trajectories across scales and landscapes
<p>Dataset included in Zenodo supports the analyses performed in "<em>Individual Movement - Sequence Analysis Methods (IM-SAM) characterising spatio-temporal patterns of animal trajectories across scales and landscapes.</em>"</p> <p>The dataset includes one RDS file, that can be easily loaded into R using the readRDS function. The RDS file consists out of a list including two objects per animal:</p> <ul> <li>Object 1 contains a data frame with the real and simulated sequences for an animal. e.g., ls[[1]][[1]] </li> <li>Object 2 contains the home range in raster format of an animal. e.g., ls[[1]][[2]]</li> </ul> <p>The data frames in object 1 contain real habitat use sequences and corresponding simulated habitat use sequences generated in the home range of the specific individual (900 simulated sequences: 6 habitat selection rules x 3 selection coefficients x 50 repetitions). Open and closed habitats are respectively encoded by 0 and 1. The first 96 columns of each row in a data frame represent a 16-day habitat use sequence, with a fixed 4-hour relocation interval (0, 4, 8, 12, 16 and 20h). Column names are named as follows: Day_1_0h, Day_1_4h,..., Day_16_20h. In the next columns we provide the selection coefficients (columns 97-99), the habitat selection rules (or pattern, columns 100-102) and the number of missing values (mvs, columns, 103-104) for each of the real and simulated sequences. Note that simulated sequences have no missing values (i.e. values are always 0.00) and for real sequences there is no selection coefficient or habitat selection rule (i.e. values are always xxx).</p> <p>Rownames of simulated sequences are composed out of the habitat selection rule (c, o, a24, a33, a42 and u), the selection coefficient (5, 10, 50) and the replicate (1 to 50), separated by dashes. For example, the first simulated sequence in the first data frame (ls[[1]][[1]][1,]) is described as a24_10_1. The rownames of real sequences instead are composed out of the individuals' identifier, the biweekly period (1 to 23) and the year. For example, the first real sequence in the first data frame (ls[[1]][[1]][901,]) is described as 1_5_2006.</p> <p><br> </p>
AFLP dataset and sequence alignments from a study of Rhododendron ferrugineum across the whole species range.
<p>STRUCTURE DATASET</p> <p>The raw data are in the files:</p> <ol> <li>Rfe_KAR_AAT-CAC2_2017-10-18-16-55-16.zip</li> <li>Rfe_KAR_ATC-CAC_2017-10-25-12-39-28.zip</li> <li>Rfe_KAR_ATG-CTG2_2017-10-24-09-40-25.zip</li> </ol> <p>Population data: populations.txt</p> <p>The final structure file: Rfe.str</p> <p>We analysed the obtained results in GeneMapper (Applied Biosystems) with default settings for AFLP analysis. The combinations of selective starters amplified the following numbers of polymorphic markers: AAT-CAC=127 (mean=40,4; SD=8,1), ATC-CAC=119 (mean=35,3; SD=5,4), ATG-CTG=144 (mean=44,4; SD=5,0). The blank samples yielded 0, 6 and 5 markers, respectively, which were removed from the final data matrix. We also removed the markers present in only one individual. Three samples (one from each of the populations L22, K27 and S05) failed to amplify in one of the selective primer reactions and were thus removed from the final matrix. The estimated error rate was 2.87% and the final matrix comprises of 87 individuals and 254 polymorphic loci.</p> <p>SEQUENCE ALIGNMENTS</p> <ol> <li>Rfe_ITS_align.fas</li> <li>Rfe_rpl32-trnL_align.fas </li> <li>Rfe_RPS12-RPL20_align.fas </li> <li>Rfe_trnfM-trnS_align.fas </li> <li>Rfe_trnLF_align.fas </li> </ol>
Aligned DNA sequence matrix for phylogenetic analyses of Scinax in the article "Advertisement calls and DNA sequences reveal a new species of Scinax (Anura: Hylidae) on the Pacific Lowlands of Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of Scinax in the article "Advertisement calls and DNA sequences reveal a new species of Scinax (Anura: Hylidae) on the Pacific Lowlands of Ecuador". The matrix is in NEXUS format.</p> <p>Gene partition as arranged as follows (tRNAs are included as part of larger adjacent genes):</p> <p>12S RNA: 1-1033</p> <p>16S RNA: 1034-2832</p> <p>NADH dehydrogenase subunit 1: 2833-3793</p> <p>Cytochrome Oxidase sub-unit I: 3965-4654</p> <p>Cytochrome B: 4655-5447</p> <p> </p>
Combined high-depth Illumina+PacBio Sequencing of several samples from FDA-ARGOS
<p>The (real) sequencing data is compiled from a concatenation of sequencing runs from Database for Reference Grade Microbial Sequences (FDA-ARGOS). Specifically, the following samples were sequenced with both Illumina and PacBio. The sample accessions are shown below.</p> <pre><code> BioSample Run Platform Organism bases source <chr> <chr> <chr> <chr> <dbl> <chr> 1 SAMN06173354 SRR5409204 ILLUMINA Bacillus anthracis 1778000000 Colorado Serum Co., Anthrax Spore Vaccine 2 SAMN06173354 SRR5409205 PACBIO_SMRT Bacillus anthracis 2420000000 Colorado Serum Co., Anthrax Spore Vaccine 3 SAMN06173356 SRR5448657 ILLUMINA Bacillus circulans 2311000000 swab with brown-gray powder 4 SAMN06173356 SRR5448656 PACBIO_SMRT Bacillus circulans 242000000 swab with brown-gray powder 5 SAMN04875535 SRR4123920 ILLUMINA Elizabethkingia anophelis 1357000000 blood 6 SAMN04875535 SRR4123919 PACBIO_SMRT Elizabethkingia anophelis 2173000000 blood 7 SAMN06173306 SRR5413253 ILLUMINA Escherichia coli O157 3051000000 clinical isolate 8 SAMN06173306 SRR5413252 PACBIO_SMRT Escherichia coli O157 915000000 clinical isolate 9 SAMN06173318 SRR5413272 ILLUMINA Mycobacterium avium subsp. paratuberculosis 1032000000 feces 10 SAMN06173318 SRR5413271 PACBIO_SMRT Mycobacterium avium subsp. paratuberculosis 462000000 feces 11 SAMN07312468 SRR5879398 ILLUMINA Mycobacterium tuberculosis 1054000000 human 12 SAMN07312468 SRR5879396 PACBIO_SMRT Mycobacterium tuberculosis 1854000000 human 13 SAMN04875542 SRR4123931 ILLUMINA Neisseria gonorrhoeae 1053000000 ATCC strain 14 SAMN04875542 SRR4123930 PACBIO_SMRT Neisseria gonorrhoeae 1117000000 ATCC strain </code></pre> <p>Samples were selected with the SRA Run selector. The SraRunTable.txt file was exported containing the metadata for each sample, and fastq-dump from the SRA toolkit was used to write out fastq files for each run, with paired Illumina data being split into separate _1.fastq.gz and _2.fastq.gz files. </p>
Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics
<p>Bridging the Chromosome-Centric and Biology and Disease Human Proteome Projects: Accessible and automated tools for interpreting biological and pathological impact of protein sequence variants detected via proteogenomics</p>
Sequence feature comparison between xenomiRs and mobile miRNA, and xenomiRs and circulating miRNA
<p>Sequence feature comparison between xenomiRs and mobile miRNA, and xenomiRs and circulating miRNA. Bold indicates the mean of that feature in xenomiR is larger.</p>
Analysis results for association study of long-term kidney transplant rejection using whole-exome sequencing
<p>Association study results for long-term kidney transplant rejection. Single-variant association results are provided as Plink output files. Meta-analysis results are provided as METAL output files. FDR results are sorted lists of the top association result from random sample label permutations and are included with the plink and meta-analysis results. SKAT and GSEA results are provided for gene and pathway level analyses, respectively.</p>
Aligned DNA sequence matrix for phylogenetic analyses of the article "A bizarre new species of Lynchius (Amphibia, Anura, Strabomantidae) from the Andes of Ecuador and first report of Lynchius parkeri in Ecuador"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "A bizarre new species of <em>Lynchius</em> (Amphibia, Anura, Strabomantidae) from the Andes of Ecuador and first report of <em>Lynchius parkeri</em> in Ecuador"</p> <p>The matrix is in NEXUS format. Genes are arranged as follows:</p> <p>RAG1: 1-652<br> Tyrosinase: 653-1195<br> 12S RNA: 1196-2242<br> tRNA Val: 2243-2313<br> 16S RNA: 2314-3994<br> tRNA Leu = 3995-4065<br> ND1: 4066-5026<br> tRNA Ile: 5027-5144;</p>
MADFORWATER: WP2: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task 2.3–Agro-industrial wastewater treatment: Subtask 2.3.1. –Treatment of olive mill wastewater (OMWW): Aerobic biological treatment in sequenced batch reactors (SBRs): Subset 1
<p>This dataset contains the data underlying the following publication: Fatma AROUS, Chadlia HAMDI, Souhir KMIHA, Nadia KHAMMASSI, Amani AYARI, Mohamed NEIFAR, Tahar MECHICHI, Atef JAOUANI (2018). Treatment of olive mill wastewater through employing sequencing batch reactor: Performance and microbial diversity assessment. 3 Biotech 2018, 8, 481. https://doi.org/10.1007/s13205-018-1486-6.</p>
Phase contrast microscopy sequence of U373 cells.
<p>The .zip file contains .CSV file for frame</p> <p>each line contains: #cell, #frame, x position, y position</p> <p>Cell cultures in Falcon plastic dishes maintained at 37 C° were observed and recorded automatically every 4 min. The images were acquired under a phase-contrast microscope (Olympus, model IX50, magnification ratio 10:1), with a<br> charge-coupled device video camera (Hitachi Denshi, modelKP-M1E/K-S10) coupled with an acquisition board (32-bits<br> Matrix Vision PC-GRAB-GI frame grabber).</p> <p>The image dimensions are 700 500 pixels (with a pixel resolution of 0.92µm at the magnification ratio abovementioned) discretized on 256 gray levels (8 bits/pixel). The images are compressed using jpeg compression.</p>
Single Nucleotide Polymorphisms (SNPs) identified from the whole genome sequences of hilsa shad (Tenualosa ilisha) of the Bay of Bengal
<p>The data file contains 792,939 isolated SNPs identified by discoSnp++ v2.3.x (Uricaru et al., 2015) from the whole genome sequence of T. ilisha of the Bay of Bengal. The central sequence of length 2k-1 is seen in upper case, while the flanking sequences are seen in lower case. SNP_higher/lower: one of the two alleles. id: id of the SNP (each SNP has a unique id).</p> <p>FOR SNPs:</p> <p>P_i:pos_Alt1/Alt2: Information about a ith SNP (If more than a unique SNP is found, the following format is used: P_1:pos_Alt1/Alt2,P_2:pos_Alt1/Alt2,...</p> <p>pos: position of the SNP with respect to the starting position of the bubble, i.e. the starting of the upper case sequence.</p> <p>Alt1: One of the two alleles</p> <p>Alt2: the other</p> <p>FOR INDELs:</p> <p>P_1:pos_size_repeatSize</p> <p>pos: predicted position of the indel with respect to the starting position of the bubble, i.e. the starting of the upper case sequence.</p> <p>size: predicted size of the indel</p> <p>repeatSize: Size of the longest sequence both prefix of the indel and prefix of the sequence located just after the insertion.</p> <p>high/low: sequence complexity. If the sequence if of low complexity (e.g. ATATATATATATATAT) this variable would be low</p> <p>nb_pol: number of polymorphism.</p> <p>left_unitig_length: size of the full left extension.</p> <p>right_unitig_length: size of the right extension.</p> <p>left_contig_length: size of the full left extension.</p> <p>right_contig_length: size of the right extension.</p> <p>C1: number of reads mapping the central upper case sequence from the first read set.</p> <p>C2: number of reads mapping the central upper case sequence from the second read set.</p> <p>Q1 [if reads were given in fastq]: average phred quality of the central nucleotide from the mapped reads from the first read set.</p> <p>Q2 [if reads were given in fastq]: average phred quality of the central nucleotide from the mapped reads from the second read set.</p> <p>G1: Genotype of the variant in the first read set.</p> <p>G2: Genotype of the variant in the second read set.</p> <p>rank: ranks the predictions according to their read coverage in each condition favoring SNPs that are discriminant between conditions.</p>
Amino acid sequences of the proteins predicted from the whole genome of hilsa shad (Tenualosa ilisha) of the Bay of Bengal
<p>Gene prediction was performed by AUGUSTUS (Stanke et al., 2006) from the whole genome sequence of <em>T. ilisha</em> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/400122">PRJNA400122</a>). The data contain amino acid sequences of 37,450 predicted protein coding genes.</p>
Species abundance information improves sequence taxonomy classification accuracy - Qiita data
<p><a href="https://qiime2.org">QIIME 2</a> Artifacts containing microbiome samples, organised by EMPO 3 classification.</p> <p>Used to test <a href="https://library.qiime2.org/plugins/q2-clawback/">q2-clawback</a>.</p> <p>Downloaded from <a href="https://qiita.ucsd.edu">Qiita</a>. Draws from the following Qiita study ids:</p> <p>11113[1], 11444, 1716, 10369[2], 990[3], 2080, 1713, 894, 1289, 1883, 1673, 1288, 10353, 2192[4], 10323, 678, 1773, 662, 1799, 864, 1481, 1024[5], 1064, 2182, 10934, 1674, 1795[6], 10273, 10283[7], 10422[8], 804, 10308, 1056[9], 2382[5], 1240, 889, 1041, 1717, 1222, 11149, 11669, 807[10], 10245, 1711, 1721, 910, 1001, 895, 550[11], 1747[12], 713[13], 755, 861, 958[14], 11161[15], 11154[16], 945, 723, 1715, 1714, 10798.</p> <p>References<br> 1. Schulfer, A. F. et al. Nat Microbiol 3, 234–242 (2017).<br> 2. Ruhe, J. et al. Front Plant Sci 7 (2016).<br> 3. O'Brien, S. L. et al. Environ Microbiol 18, 2039–2051 (2016).<br> 4. Lax, S. et al. Science 345, 1048–1052 (2014).<br> 5. Zarraonaindia, I. et al. mBio 6 (2015).<br> 6. Navas-Molina, J. A. et al. in Methods Enzymol 371–444 (2013).<br> 7. Fang, X. et al. Front Microbiol 9 (2018).<br> 8. Tripathi, A. et al. mSystems 3 (2018).<br> 9. Delsuc, F. et al. Mol Ecol 23, 1301–1317 (2013).<br> 10. Gibbons, S. M. et al. PLoS ONE 9, e97435 (2014).<br> 11. Caporaso, J. G. et al. Genome Biol 12, R50 (2011).<br> 12. Hyde, E. R. et al. mSystems 1 (2016).<br> 13. Brazelton, W. J., Nelson, B. & Schrenk, M. O. Front Microbiol 2 (2012).<br> 14. Vitaglione, P. et al. Am J Clin Nutr 101, 251–261 (2014).<br> 15. Spirito, C. M., Marzilli, A. M. & Angenent, L. T. Environ Sci Technol 52, 13438–13447 (2018).<br> 16. Pham, V. T. H. et al. Sci Rep 7 (2017).</p>
Species abundance information improves sequence taxonomy classification accuracy - HMP and NCBI data
<p>Data used to test <a href="https://library.qiime2.org/plugins/q2-clawback/">q2-clawback</a> using taxonomic weights derived from shotgun sequencing experiments.</p> <p>Includes reference sequences and taxonomies derived from the NCBI RefSeq database[1] and paired amplicon and shotgun sequencing results downloaded from the Human Microbiome Project[2].</p> <p>References<br> 1. O’Leary, N. A. et al. en. Nucleic Acids Res. 44, D733–45 (2016).<br> 2. Huttenhower, C. et al. Nature 486, 207 (2012).</p>
Aligned DNA sequence matrix for phylogenetic analyses in the article "Systematics of Huicundomantis, a new subgenus of Pristimantis (Anura, Strabomantidae) with extraordinary cryptic diversity and eleven new species"
<p>Aligned DNA sequence matrix for phylogenetic analyses of the article "Systematics of Huicundomantis, a new subgenus of Pristimantis (Anura, Strabomantidae) with extraordinary cryptic diversity and eleven new species"</p> <p>The matrix is in NEXUS format. Genes are arranged as follows:</p> <p>16S RNA, tRNA-Leu = 1–1408<br> ND1 codon position 1 = 1409–2369\3;<br> ND1 codon position 2 = 1410–2367\3;<br> ND1 codon position 3 = 1411–2368\3;<br> tRNA-Ile, tRNA-Gln, rRNA-Met = 2370–2544; <br> RAG1 codon position 3 = 2545–3199\3;<br> RAG1 codon position 1 = 2546–3197\3<br> RAG1 codon position 2 = 2547–3198\3;</p>
Dataset: EF1-α sequence alignement of Lecanostica acicola
<p>Sequence alignement of elongation factor 1-α (EF1-α) gene. The sequence dataset was aligned using the MUSCLE algorithm in MEGA 6.0 (Tamura <em>et al.</em>, 2013).</p>
MAPNET Population Genomics Workshop Content: Whole genome sequencing of Bactericera cockerelli
<p>Materials for the Workshop "Getting Started with Low Coverage Population Genomics" , Otago University, 22 March 2019. Alignment files for whole-genome sequence of N=143 Tomato and Potato Psyllids (<em>Bactericera cockerelli)</em> from 4 New Zealand , 1 Californian and 1 Honduran populations. Data files represent 250 kbp of one scaffold of a draft assembly.</p> <p>This data was generated by the Plant and Food Research in research programme funded by the Ministry of Business, Innovation and Employment (MBIE) . </p> <p> </p> <p><a href="https://github.com/MapNetNZ/Pop-Genomics-Workshop2019">Wokshop GitHub Repo</a></p> <p> </p> <p> </p>
Data for 'Comparative Analysis of Single-Cell RNA Sequencing Methods'
<p>Raw sequencing data to "Comparative Analysis of Single-Cell RNA Sequencing Methods". </p> <p>https://www.ncbi.nlm.nih.gov/pubmed/28212749</p> <p> </p> <p>In addition to the GEO submission https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE75790, you can find here raw bam files for UMI-methods tagged with cell barcode and UMI sequences.</p> <p>MD5 checksum: f10825509952fffd9c4dc0c1dcb9eb8e</p>
Fig. 4. Maximum parsimony consensus tree within Paromoionchis gen. nov., performed with ITS2 DNA sequences from 80 in A new genus and three new species of mangrove slugs from the Indo-West Pacific (Mollusca: Gastropoda: Euthyneura: Onchidiidae)
Fig. 4. Maximum parsimony consensus tree within Paromoionchis gen. nov., performed with ITS2 DNA sequences from 80 individuals (including 7 outgroups). Numbers by the branches are the bootstrap values (only numbers> 50% are indicated). Numbers for each individual correspond to unique identifiers for DNA extraction. All sequences for specimens of Paromoionchis gen. nov. are new. Information on specimens can be found in the lists of material examined and in Table 1. The letter A corresponds to a clade referred to in the text. The color used for each (mitochondrial) unit is the same as that used in Figs 1–3 and 5–6.
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.