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19,342 results for “Sequences”
Multiple alignment of DNA-B sequences from CMMGV, EACMCV, EACMV, EACMKV, EACMMV, EACMZV, SACMV (7 "species")
<p>All sequences available in GenBank as of 2019-06-03 were downloaded via the Taxonomy Browser interface. Sequence names were normalized/simplified and orientations of these circular sequences were standardized to begin at the replication origin nick site. Sequences were aligned with MUSCLE and alignments were adjusted with SeAl (A. Rambaut) and AliView (A. Larsson).</p> <p>These results are described in a paper by Crespo-Bellido et al. (2021) https://doi.org/10.1128/JVI.00541-21</p>
Multiple alignment of ACMV and ACMBFV DNA-B sequences
<p>All sequences available in GenBank as of 2019-06-03 were downloaded via the Taxonomy Browser interface. Sequence names were normalized/simplified and orientations of these circular sequences were standardized to begin at the replication origin nick site. Sequences were aligned with MUSCLE and alignments were adjusted with SeAl (A. Rambaut) and AliView (A. Larsson).</p> <p>These results are described in a paper by Crespo-Bellido et al. (2021) https://doi.org/10.1128/JVI.00541-21</p>
Metagenomics of a pustular microbial mat from Shark Bay, Australia: Raw sequences and assembled MAGs
<p>This data accompanies the paper, "<a href="https://www.nature.com/articles/s43705-022-00128-1">Metagenomic, (bio)chemical, and microscopic analyses reveal the potential for the cycling of sulfated EPS in Shark Bay pustular mats</a>" which looks at the cycling of sulfated polysaccharides in peritidal pustular mats from Shark Bay, Australia. The microbial community was sequenced, assembled, and binned. The raw sequencing reads used in this analysis are the following:</p> <ul> <li>SB_forward_paired_copy.fastq.gz </li> <li>SB_reverse_paired_copy.fastq.gz </li> </ul> <p>The resulting metagenome-assembled genomes (MAGs) are presented in the following folder:</p> <ul> <li>MAGs.zip</li> </ul> <p> </p> <ul> </ul> <p> </p>
Virus+ Sequence Masked Mouse Reference Genome (GRCm38)
<p>A version of the mouse genome (<a href="https://www.ncbi.nlm.nih.gov/assembly/327618">GRCm38</a>) masked for all possible viral sequences.</p> <p>See <a href="https://zenodo.org/record/4116107#.X5B7ti9h3UI">Virus+ Masked Human Genome</a> for a masked human reference database.</p> <p>The following commands were used to generate the additional virus sequence masked reference database:</p> <p><strong>1) Download all RefSeq and Neighbor nucleotide records:</strong></p> <p><a href="https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])">https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])</a></p> <p><strong>2) Shred the downloaded viral genomes using shred.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>shred.sh in=refseq_virus_reformated.fasta out=virus_shred.fasta.gz length=85 minlength=75 overlap=30</p> <p><strong>3) Map shredded virus sequence to the GRCm38</strong><strong> genome using bbmap.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmap.sh ref=GRCm38.fa.gz in=virus_shred.fasta.gz outm=map_mouse_all_viruses.sam minid=0.90</p> <p><strong>4) Mask virus sequenced mapped regions from the GRCm38 genome using bbmask.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmask.sh in=GRCm38.fa.gz out=GRCm38_virus_masked.fasta.gz sam=map_mouse_all_viruses.sam</p> <p><strong>5) Remove all N's to further reduce file size using <a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></strong><br> seqkit -is replace -p "n" -r "" GRCm38_virus_masked.fasta.gz > mouse_virus_masked.fasta_Ns_removed.gz</p> <p><strong>Additional References:</strong></p> <ol> <li><a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a></li> <li><a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></li> <li><a href="https://www.ncbi.nlm.nih.gov/genome/viruses/">NCBI Virus Genome RefSeq</a></li> </ol>
Virus+ Sequence Masked Human Reference Genome (hg19)
<p>A version of the human genome (hg19) originally masked for ribosomal, plant, animal, fungal and low-entropy sequences by Brian Bushnell (<a href="https://zenodo.org/record/1208052#.X5BuTy9h3UI">Bushnell Masked Human Genome</a>) additionally masked for all possible viral sequences.</p> <p>The following commands were used to generate the additional virus sequence masked reference database:</p> <p><strong>1) Download all RefSeq and Neighbor nucleotide records:</strong></p> <p><a href="https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])">https://www.ncbi.nlm.nih.gov/nuccore/?term=Viruses[Organism]%20NOT%20cellular%20organisms[ORGN]%20NOT%20wgs[PROP]%20NOT%20gbdiv%20syn[prop]%20AND%20(srcdb_refseq[PROP]%20OR%20nuccore%20genome%20samespecies[Filter])</a></p> <p><strong>2) Shred the downloaded viral genomes using shred.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>shred.sh in=refseq_virus_reformated.fasta out=virus_shred.fasta.gz length=85 minlength=75 overlap=30</p> <p><strong>3) Map shredded virus sequence to the hg19-masked human genome using bbmap.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmap.sh ref=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz in=virus_shred.fasta.gz outm=map_human_all_viruses.sam minid=0.90</p> <p><strong>4) Mask virus sequenced mapped regions from the hg19-masked human genome using bbmask.sh from the <a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a> package</strong></p> <p>bbmask.sh in=hg19_main_mask_ribo_animal_allplant_allfungus.fa.gz out=human_virus_masked.fasta.gz sam=map_human_all_viruses<br> .sam</p> <p><strong>5) Remove all N's to further reduce file size using <a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></strong><br> seqkit -is replace -p "n" -r "" human_virus_masked.fasta.gz > human_virus_masked.fasta_Ns_removed.gz</p> <p><strong>Additional References:</strong></p> <ol> <li><a href="http://seqanswers.com/forums/showthread.php?t=42552">http://seqanswers.com/forums/showthread.php?t=42552</a> for additional information on the original masking of hg19</li> <li><a href="https://jgi.doe.gov/data-and-tools/bbtools/">bbtools</a></li> <li><a href="https://bioinf.shenwei.me/seqkit/">seqkit</a></li> <li><a href="https://www.ncbi.nlm.nih.gov/genome/viruses/">NCBI Virus Genome RefSeq</a></li> </ol>
RNA sequencing dataset for prediction of liver hepatocellular carcinoma using SIMON analysis
<p>The LIHC dataset was used for data mining and for the generation of machine learning model for the detection of liver hepatocellular carcinoma cells (LIHC) using the SIMON platform as described in the "SIMON: open-source knowledge discovery platform" publication (<a href="https://doi.org/10.1101/2020.08.16.252767">https://doi.org/10.1101/2020.08.16.252767</a>). The LIHC dataset was obtained from the <em>GSEABenchmarkeR</em> package ( <a href="https://doi.org/10.1093/bib/bbz158">https://doi.org/10.1093/bib/bbz158</a>) and it contains RNA expression data from 374 liver hepatocellular carcinoma (LIHC) cells and 50 adjacent normal cells.</p>
Transcriptome analysis of the effect of over-expressing H2A.J mutants in proliferating WI38 fibroblasts for the paper entitled: The H2A.J histone variant contributes to Interferon-Stimulated Gene expression in senescence by its weak interaction with H1 and the derepression of repeated DNA sequences
<p>Abstract for overall study:</p> <p>The histone variant H2A.J was previously shown to accumulate in senescent human fibroblasts with persistent DNA damage to promote inflammatory gene expression, but its mechanism of action was unknown. We show that H2A.J accumulation contributes to weakening the association of histone H1 to chromatin and increasing its turnover. Decreased H1 in senescence is correlated with increased expression of some repeated DNA sequences, increased expression of STAT/IRF transcription factors, and transcriptional activation of Interferon-Stimulated Genes (ISGs). The H2A.J-specific Val-11 moderates the transcriptional activity of H2A.J, and H2A.J-specific Ser-123 can be phosphorylated in response to DNA damage with potentiation of its transcriptional activity by the phospho-mimetic S123E mutation. Our work demonstrates the functional importance of H2A.J-specific residues and potential mechanisms for its function in promoting inflammatory gene expression in senescence.</p> <p>Specific description for this dataset:</p> <p>H2A.J differs from canonical H2A only by a valine at position 11 instead of alanine, and the 7 C-terminal amino acids containing a potential minimal phosphorylation site SQ for DNA-damage response kinases. To test the functional importance of these H2A.J-specific sequences, we mutated Val-11 to Ala as is found in all canonical H2A sequences, and we mutated Ser-123 to either Glu to mimic a phospho-serine residue or to Ala to prevent phosphorylation. We also substituted the C-terminus of H2A.J with the C-terminus of H2A. These mutants, WT-H2A.J and canonical H2A-type1 were ectopically expressed in proliferating fibroblasts, and their microarray transcriptomes were compared to that of proliferating and senescent fibroblasts without ectopic histone expression. Genome-wide transcriptome analysis indicated that senescent fibroblasts clustered distinctly from proliferating fibroblasts, and proliferating fibroblasts expressing the H2A.J-V11A and H2A.J-S123E mutants clustered distinctly from fibroblasts expressing the other H2A.J mutants, WT-H2A.J, and H2A. Hallmark gene set enrichment analysis of the transcriptomes of fibroblasts expressing H2A.J-V11A or H2A.J-S123E versus control proliferating fibroblasts indicated that they showed the same highly significant enrichment for the Epithelial-Mesenchyme Transition, TNF-Alpha Signaling Via NF-kB, and Inflammatory Response gene sets. Notable inflammatory genes including IL1A, IL1B, IL6, CXCL8, and CCL2 are contained in these gene sets and are often induced in senescence as part of the senescence-associated secretory phenotype. Heat maps showed that the H2A.J-V11A and H2A.J-S123E mutants were particularly apt at activating the expression of these inflammatory genes in proliferating fibroblasts</p>
Genomes and full-length 16S reference sequences for 27 Alpha- and Gamma-Proteobacterial isolates from Red Sea Acropora corals
<p>Coral-associated bacteria contribute to the biology of their host, but the underlying molecular interactions are largely unknown. To further our functional understanding, we obtained 27 alpha- and gamma-proteobacterial isolates, many of which are Rhodobacteraceae, from three coral species of the genus <em>Acropora </em>and assembled/annotated their genomes as a resource for further functional studies. Our results reveal the immense taxonomic and genetic diversity of common alpha- and gamma-proteobacterial coral-associated bacteria. We hope these data provide a framework to study the function of specific bacteria in the coral holobiont. Isolates are available upon request.</p>
L'Aquila 2009 seismic sequence: integrated dataset of automatic first motion polarities focal mechanisms and RMT with HypoDD high quality relative earthquake locations
<p>This dataset is related to the L'Aquila 2009 seismic sequence that happened in Central Apennines (Italy).</p> <p>It contains:</p> <ul> <li>2782 quality selected focal mechanisms produced with the standard software FPFIT based on automatically determined first motion polarities of automatically detected and analyzed foreshocks and aftershocks recorded from January 2009 to December 2009 (flag <strong>fty</strong> in the header is MP)</li> <li>475 (out of 627) quality selected focal mechanisms produced with the standard software FPFIT also based on automatically determined first motion polarities but for only 3204 M<sub>L</sub> >= 1.9 earthquakes and by using take-off angles calculated within a local 3d tomographic velocity model (<a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011GL047365">Di Stefano et al., 2011</a>) , published and released in <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2011JB008352">Chiaraluce et al., 2011</a> (flag <strong>fty</strong> in the header is JG)</li> <li>165 (out of 181) Regional Moment Tensors determined for earthquakes M<sub>L</sub> >= 3.0 based on broadband waveform inversion of ground velocities and published by <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a> (flag <strong>fty</strong> in the header is HM)</li> <li>The hypocenters of the total 3422 earthquakes reported in the present focal solutions dataset have been taken from the very high quality double difference locations of the about 64000 aftershocks reported in <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> and published, as part of the full dataset, <a href="https://doi.org/10.5281/zenodo.4036248">on Zenodo</a>. </li> </ul> <p>The association between the focal solutions and the HypoDD hypocenters has been performed through the direct use of the HypoDD event identifier where possible (the whole MP dataset) and through spatial and temporal earthquakes coordinates matching in all the other case by using the capability of a MySQL database. </p> <p>Two files are uploaded, one in plain text with blank separator, the second in plain text with ";" separator and .csv extension.</p> <p>Here below the header is explained.</p> <p><strong>OT_Date:</strong> date of the origin time in the format YYYY-MM-DD</p> <p><strong>OT_Time:</strong> time of the origin time in the format HH:mm:ss.dcm</p> <p><strong>lat:</strong> hypocenter latitude expressed in degrees </p> <p><strong>lon:</strong> hypocenter longitude east of Greenwich, expressed in degrees</p> <p><strong>dep:</strong> hypocenter depth expressed in km </p> <p><strong>ML:</strong> local magnitude (pure number) from <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> (see last column notes also)</p> <p> </p> <p><strong>id_dd:</strong> the hypoDD event identifier, allowing to directly connect to the <a href="https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1002/jgrb.50130">Valoroso et al., 2013</a> full dataset</p> <p><strong>IMPORTANT NOTE about st1 and st2 (below): </strong>the focal solutions are presented here based on the convention they where produced or published, so there are two different (but compatible) conventions for the fault plains orientation in the 3d space</p> <p><strong>st1:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 1 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 1 (FPFIT convention)</li> </ul> <p><strong>dip1: </strong>dip of plane 1</p> <p><strong>rk1: </strong>rake of plane 1</p> <p><strong>st2:</strong></p> <ul> <li><strong>for fty=</strong>HM or JG this is the strike of plane 2 (CMT convention)</li> <li><strong>for fty=</strong>MP this is the <strong>strike of the dip direction </strong>of plain 2 (FPFIT convention)</li> </ul> <p><strong>dip2: </strong>dip of plane 2</p> <p><strong>rk2: </strong>rake of plane 2</p> <p><strong>fty:</strong> flag to distinguish the type of solution, CMT=HM or JG, FPFIT=MP</p> <p><strong>MW:</strong> only for HM, this columns reports also MW from <a href="https://pubs.geoscienceworld.org/ssa/bssa/article-abstract/101/3/975/349796/Regional-Moment-Tensors-of-the-2009-L-Aquila">Hermann et al., 2011</a></p>
Whole genome sequencing of Turkish genomes reveals functional private alleles and impact of genetic interactions with Europe, Asia and Africa.
<p>BACKGROUND:</p> <p>Turkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32×-48×).</p> <p>RESULTS:</p> <p>We show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency.</p> <p>CONCLUSION:</p> <p>This study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey.</p>
Supplementary Material for "Sequence Comparison in Historical Linguistics"
<p>This is the official version of the supplementary material which was used as the basis for the study on "Sequence Comparison in Historical Linguistics" (List, Düsseldorf, Düsseldorf University Press).</p>
A Bayesian Approach to Detect Pedestrian Destination-Sequences from WiFi Signatures: Data (Transp. Res. Part C, 2014)
<p>This dataset contains and describes the data used in</p> <p>Danalet, A., Farooq, B., & Bierlaire, M. (2014). A Bayesian approach to detect pedestrian destination-sequences from WiFi signatures. <em>Transportation Research Part C: Emerging Technologies</em>, <strong>44</strong>, 146-170. doi:10.1016/j.trc.2014.03.015</p> <p>Specifically it contains WiFi traces, pedestrian Semantically-Enriched Routing Graph (SERG), and Potential Attractivity measure (PAM).</p>
SNP and indel discovery and genotyping in next-generation sequencing data
<p>Code, logs and data for discovery and genotyping of SNPs and indels, in the the D.melanogaster genome, using GATK HaplotypeCaller. Code is in the zipped folder named code.zip. Run logs for this code as in the zipped folder named logs.zip. The unfiltered vcf genotypes file is named lhm_rg_HC_2015-09-15.vcf.gz. The filtered vcf genotypes file is named f1.lhm_rg_HC_raw.vcf.gz. The vcf submitted to NCBI dbSNP (filtered, and with indels >50bp and variants with null alternate alleles both removed) is named dbSNP.lhm_rg_HC_raw.vcf.gz. The folder local_reference.zip contains the reference assembly files against which genotypes were called against, and includes the code used to format the data prior to use. Also included is genotypes data from the two in-house reference line samples sequenced (BDGP6+ISO1 mito/dm6, Bloomington <em>Drosophila</em> Stock Center no. 2057)</p> <p>Samples are 220 Sussex-LH<sub>M</sub> hemiclones, and 2 RG. The first run did not include chromosome 4 and the mitochondrial genome, so these were genotyped separately, and then added to the rest of the results.</p> <p>The link for the NCBI dbSNP record is currently https://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewBatch.cgi?sbid=1062461and the submitter handle is MORROW_EBE_SUSSEX.</p> <p>At the time of writting, the NCBI D.melanogaster build is still being updated, and therefore ss identifiers, but not rs identifers are available.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p>
Structural variant discovery and genotyping in next-generation sequencing data
<p>Code, logs, data, and summaries for detection and genotyping of genomic structural variants in the D.melanogaster Sussex LHM hemiclones (and one in-house reference line individual), using Genomestrip/2.0</p> <p>The unfiltered CNV pipleline results are lhm_gs.cnvs.raw.vcf.gz</p> <p>Filtered CNV results (including removal of bad samples) are filtered.goodS.lhm_gs.cnvs.raw.vcf.gz</p> <p>The file uploaded to NCBI dbVAR (which comprises of the filtered CNVs and indels >50bp from the HaplotypeCaller method) is lhm_sx16.dbVAR.vcf.gz</p> <p>The NCBI dbVAR accession number is nstd134. Code, logs and summary data are in the zipped archives, named accordingly. The archive reference_data.zip contains additional input files required for Genomestrip, including a shell script for making some of them. The file gstrip_lhm_RG_bams.list is also an input for Genomestrip, indicating bam file names and paths.</p> <p>The pre-print manuscript for this data is available on biorxiv: "Whole genome resequencing of a laboratory-adapted Drosophila melanogaster population sample" http://biorxiv.org/content/early/2016/10/17/081554 doi: http://dx.doi.org/10.1101/081554</p> <p> </p>
Widespread Polycistronic Transcripts in Fungi Revealed by Single-Molecule mRNA Sequencing
<p>Genes in prokaryotic genomes are often arranged into clusters and co-transcribed into poly- cistronic RNAs. Isolated examples of polycistronic RNAs were also reported in some higher eukaryotes but their presence was generally considered rare. Here we developed a long- read sequencing strategy to identify polycistronic transcripts in several mushroom forming fungal species including Plicaturopsis crispa, Phanerochaete chrysosporium, Trametes ver- sicolor, and Gloeophyllum trabeum. We found genome-wide prevalence of polycistronic transcription in these Agaricomycetes, involving up to 8% of the transcribed genes. Unlike polycistronic mRNAs in prokaryotes, these co-transcribed genes are also independently transcribed. We show that polycistronic transcription may interfere with expression of the downstream tandem gene. Further comparative genomic analysis indicates that polycis- tronic transcription is conserved among a wide range of mushroom forming fungi. In sum- mary, our study revealed, for the first time, the genome prevalence of polycistronic transcription in a phylogenetic range of higher fungi. Furthermore, we systematically show that our long-read sequencing approach and combined bioinformatics pipeline is a generic powerful tool for precise characterization of complex transcriptomes that enables identifica- tion of mRNA isoforms not recovered via short-read assembly.</p>
Flow diagram for analysis of high-throughput sequencing data
<p>Tex code and resulting pdf image, summarising the data processing pipeline of high-throughput sequencing data (fastq format files), through mapping the data to a reference genome, and then discovery and genotyping of sequence variants. The latter stage uses both 'GATK Haplotype Caller' for smaller variants, such as single-nucleotide polymorphisms and insertion-deletion polymorphisms, and Genomestrip for variants such as deletions and duplications greater than 1000 nucelotide bases in length. Note that the flow diagram is intended to represent what steps were take in the study, and does not necessarily represent the current optimum methods.</p> <p>The manuscript for which this image is a part of can be found open-access at F1000 Research "Whole genome resequencing of a laboratory-adapted <em>Drosophila melanogaster </em>population sample" https://f1000research.com/articles/5-2644/v1 doi: 10.12688/f1000research.9912.1</p> <p> </p>
IBP-database and environmental IBP sequences for functional analysis of microalgae
<p>Database for analysis of ice binding protein (IBP) sequences (Uhlig et al. (2015)):</p> <p>(1) DUF3494_seqs_Uniprot.fasta: full length sequences with DUF3494 domain used for the calculation of the backbone tree in the phylogenetic placement</p> <p>(2) env_IBPs.fasta: potential IBP sequences from one Arctic and five Antarctic sea ice metatranscriptomes (Sanger or 454)</p> <p>(3) DUF3494_substree_fig2a_UniprotIDs.txt: UniProtIDs for subtree in Fig 2a</p> <p>(4) DUF3494_confirmed_IBPactivity_UniprotIDs.txt: UniProtIDs for sequences with confirmed IBP function of the protein</p> <p>If using this dataset please cite the following publication: Uhlig, C., Kilpert, F., Frickenhaus, S., Kegel, J.U., Krell, A., Mock, T., Valentin, K., Beszteri, B., (2015) The significance of antifreeze proteins for eukaryotic microbial communities of Arctic and Antarctic sea ice, The ISME Journal, 9, 2537–2540, doi:10.1038/ismej.2015.43</p>
Dataset for Fisher et al. (2023). Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.
<p>This dataset contains the video clips used to produce the results presented in:</p><p>Fisher, M., French, G., Gorpincenko, A., Holah, H., Clayton, L., Skirrow, R. and Mackiewicz, M., 2023. Motion stereo at sea: Dense 3D reconstruction from image sequences monitoring conveyor systems on board fishing vessels. IET Image Processing, 17(2), pp.349-361.</p>
Datasets of sequences, alignments and structural models generated for the structural prediction of complexes mediated by intrinsically disordered regions.
<p>This repository contains input and ouput files used and generated for the scanning of intrinsically disordered region and the prediction of their binding sites to receptor proteins using the <a href="https://github.com/i2bc/SCAN_IDR">SCAN_IDR</a> pipeline with AlphaFold2-Multimer.</p><p>It contains two archives: </p><ol><li><a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> dedicated to the analysis of a dataset of 42 protein complexes non redundant with the dataset used for AlphaFold2 training,</li><li><a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> dedicated to the analysis of 923 complexes from the ELM database.</li></ol><p>These data can be used to rerun specific sections of the pipeline and scripts provided in: <a href="https://github.com/i2bc/SCAN_IDR">https://github.com/i2bc/SCAN_IDR</a></p><h4><strong>Dataset of 42 non redundant complexes</strong></h4><p>The first archive <a href="https://zenodo.org/api/records/10068949/draft/files/scanidr_data_repository_corr6J08.tar/content"><i><strong>scanidr_data_repository_corr6J08.tar</strong></i></a> contains 3 compressed directories and a README file detailing their contents :</p><ul><li>the initial raw sequence and alignment data for every chain -> DIRECTORY <strong>fasta_msa/</strong></li><li>the input and output data of every Alphafold run for every complex -> DIRECTORY <strong>af2_runs/</strong></li><li>the native reference structures -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p>The protein-peptide complex cases have been assigned a distinct index number, from 1 to 42, consistent across the several directories of the archive. Their corresponding directories are labelled as <i><index>_<pdbcode></i>.</p><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.2</i></p><h4><strong>Dataset of 923 complexes selected from the ELM database</strong></h4><p>The second archive <a href="https://zenodo.org/api/records/10068949/draft/files/923_elm_cases_repository.tar.gz/content"><i><strong>923_elm_cases_repository.tar.gz</strong></i></a> contains input and ouput files used and generated for the analysis of 923 Eukaryotic Linear Motifs (ELM) database entries.</p><p>Each ELM entry is indexed with specific integer id and is composed of a receptor and a ligand protein. </p><p>The archive contains a Table associating ELM indexes with the ELM entry information, 5 directories and a README file detailing their contents:</p><ul><li>the table describing ELM entries -> FILE <strong>Table_923ELM_uid_delimitations_info_for_archive.txt</strong></li><li>the initial raw sequence and multiple sequence alignment (MSA) data for every chain -> DIRECTORY <strong>fasta_msa/</strong></li><li>the concatenated MSA model for every ELM complex and protocol used -> DIRECTORY <strong>af2_elm_coali_inputs/</strong></li><li>the best model of every AF2 protocol for every complex according to the AF2 -> DIRECTORY <strong>af2_elm_models/</strong></li><li>the best model cut in the ligand part to select only the ELM motifs as used for the evaluation of the models -> DIRECTORY <strong>elm_cut_models/</strong></li><li>the reference structures used for the evaluation of the models -> DIRECTORY <strong>ref_capri_curated/</strong></li></ul><p><i>The models in this archive were generated using AlphaFold2-Multimer v2.3</i></p>
Tracking Down Chimeric Assemblies In The TrackIt DNA Ladder Using Nanopore Sequencing
<h2>Dataset Description</h2><p>These files represent two different LSK114 sequencing runs on a TrackIt 1kb Plus DNA Ladder sample, and associated data analysis.</p><ul><li>July 20 2023 Flongle Run (191 Mb; 465k reads)<ul><li>pod5_files_2023-Jul-20_DAE_DNA_Ladder.tar.gz<br>- raw POD5 format files</li><li>called_2023-Jul-20_DAE_DNA_Ladder_duplex.bam<br>- duplex called reads, called using dorado v4.0 with the 2023-09-22 bacterial methylation model</li><li>sequence_QC_2023-Jul-20_DAE_DNA_Ladder.pdf<br>- sequence length / quality QC plots</li><li>LAST_2023-Jul-20_DAE_DNA_Ladder_reads_vs_reference.tar.gz<br>- Alignment summary statistics from LAST mapping of reads to their associated reference</li><li>lengths_summary_2023-Jul-20_DAE_DNA_Ladder.txt<br>- Length / QC summary statistics</li></ul></li><li>October 12 2023 P2 Solo Run (1.95 Gb, 1.11M reads)<ul><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_fail.tar.gz<br>- raw POD5 format files (all failed reads)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_000-059.tar.gz<br>- raw POD5 format files (passed reads, bundle #000-059)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_060-119.tar.gz<br>- raw POD5 format files (passed reads, bundle #060-119)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_120-179.tar.gz<br>- raw POD5 format files (passed reads, bundle #120-179)</li><li>pod5_files_2023-Oct-12_DNA-Ladder-1kbplus_pass_180-222.tar.gz<br>- raw POD5 format files (passed reads, bundle #180-222)</li><li>called_2023-Oct-12_DNA-Ladder-1kbplus_duplex.bam<br>- duplex called reads [October 12, 2023], called using dorado v4.0 with the 2023-09-22 bacterial methylation model</li><li>sequence_QC_2023-Oct-12_DNA-Ladder-1kbplus.pdf<br>- sequence length / quality QC plots</li><li>LAST_2023-Oct-12_DNA-Ladder-1kbplus_reads_vs_reference.tar.gz<br>- Alignment summary statistics from LAST mapping of reads to their associated reference</li><li>lengths_summary_2023-Oct-12_DNA-Ladder-1kbplus.txt<br>- Length / QC summary statistics</li><li>ladder_seqs.fa<br>- assembled DNA ladder sequences, based on simplex reads</li></ul></li></ul><h3>Methods</h3><h3>Sample preparation</h3><p>Preparation of DNA for sequencing was carried out following the ONT Ligation Sequencing DNA V14 (SQK-LSK114) protocol, with modifications to exclude DNA repair, and keeping the sample in the same 1.5ml tube to reduce sample loss.</p><h4>Tris-buffered Saline (TBS) buffer preparation</h4><ol><li>1M stock of NaCl was made by adding 2.922g of NaCl into a 50 ml Falcon tube, then made up to 50 ml with MilliPore water</li><li>A 50 mM TBS stock was created by adding 750 μl 1M NaCl solution to a 15 ml Falcon tube, then made up to 15 ml using Qiagen Elution Buffer (EB, i.e. 10 mM Tris-HCl at pH 8.0)</li><li>The pH was confirmed to be 7.9-8.1 using a pH indicator strip (e.g. MColorpHast 6.5 - 10.0; MER1095430001)</li></ol><h4>End prep</h4><ol><li>1 μg DNA ladder (i.e. 10 μl of 0.1 μg / μl DNA ladder) was transferred into a 1.5ml Eppendorf DNA LoBind tube</li><li>The volume was topped up to 43.5 μl with TBS (i.e. 33.5 μl TBS)</li><li>3.5 μl Ultra II End-prep Reaction Buffer and 3 μl Ultra II End-prep Enzyme Mix was added</li><li>After mixing by gentle pipetting, the mixture was incubated at RT for 5 minutes, then 65 \degrees for 5 minutes</li></ol><h4>Bead cleanup</h4><ol><li>The mixture was combined with 60 μl Ampure XP beads, and incubated on a rotator mixer at RT for 5 minutes</li><li>The tube was transferred to a magnetic rack [https://www.printables.com/model/532085-open-walled-magnetic-rack]</li><li>After the supernatant became clear and colourless, supernatant was pipetted off</li><li>The magnetic beads were washed twice with 150 μl of an 80% ethanol solution</li><li>The sample was dried briefly for 30s, then eluted in 60 μl TBS</li></ol><h4>Adapter ligation and final bead cleanup</h4><ol><li>To the sample tube was added 25 μl ONT Ligation buffer (LNB), 5μl NEBNext Quick T4 DNA Ligase (reduced from the protocol-suggested 10μl because that was all that was left in the tube), and 5μl ONT Ligation Adapter (LA)</li><li>The tube was mixed by gentle pipetting, spun down for 1-3s on a mini centrifuge, then incubated for 10 minutes at RT</li><li>The mixture was combined with 40 μl Ampure XP beads (100μl Ampure XP beads were used for the Flongle sample), and incubated on a rotator mixer at RT for 5 minutes</li><li>The tube was transferred to a magnetic rack [https://www.printables.com/model/532085-open-walled-magnetic-rack]</li><li>After the supernatant became clear and colourless, supernatant was pipetted off</li><li>The magnetic beads were washed twice with 250 μl of ONT Long Fragment Buffer (LFB) for the P2 Solo run, and 250μl ONT Short Fragment Buffer <br>(SFB) for the Flongle run</li><li>The sample was dried briefly for 30s, then eluted for 10 minutes at 37 \degrees in 15 μl ONT Elution buffer (EB)</li></ol><h4>Addition of sequencing library buffers</h4><ol><li>A flow cell was prepared by flushing with ONT Flow Cell Flush (FCF) mixed with ONT Flow Cell Tether (FCT). For the P2 Solo, I used 500 μl of a 1170μl FCF solution that had 30μl FCT added to it; for the Flongle I used 60μl of a 117μl FCF solution that had 3 μl FCT added to it</li><li>1 μl of the eluted library was quantified on a Quantus Fluorometer, and approximately 50 fmol (assuming 1kb average length) was transferred to a new 1.5μl tube</li><li>For the P2 Solo run, the volume was topped up to 32 μl TBS; for the Flongle run, the volume was topped up to 12 μl TBS</li><li>To the sample tube was added ONT Sequencing Buffer (SB; P2 Solo - 100μl; Flongle - 30μl) and ONT Library Beads (LIB; P2 Solo - 68μl; Flongle - 20μl)</li><li>The flow cell was re-flushed with additional FCF/FCT mixture (500 μl for the P2 Solo; 30 μl for the Flongle)</li><li>The sequencing library was then added to the flow cell (200 μl for the P2 Solo; 30 μl for the Flongle)</li><li>The prepared flow cell was left for 10 minutes to allow the library to settle before starting sequencing</li></ol><h4>DNA Sequencing and basecalling</h4><ol><li>Sequencing was carried out using MinKNOW v23.04.6, sequencing in fast mode at 400 bases per second with a 20bp minimum sequence length and <br>5 kHz sampling rate, with reads output as POD5 files</li><li>The Flongle flow cell was run for a full standard run length (24h), whereas the PromethION flow cell was run for 1.5 hours (after which the <br>counts of 15kb reads exceeded 200)</li><li>Sequenced reads were recalled in standard (simplex) mode using Dorado v0.4.0 and the 2023-09-22 bacterial methylation model [res_dna_r10.4.1_e8.2_400bps_sup@2023-09-22_bacterial-methylation]</li></ol><h3>Bioinformatics Analysis of Ladder Sequences </h3><h4>Sequence assembly</h4><p>Assembly process for bands that are 3k in length and greater (done on LFB-depleted P2 Solo sequences): </p><ol><li>Filter >q20 reads for a 100bp region around the target length (e.g. 4950-5050bp for the 5k band) [High quality reads were not sufficient for the 15kb band; all reads were needed]</li><li>Chop the reads up with a 1000bp overlap (e.g. 3000bp for the 5k band). This works around a Canu expectation that any read overlaps should be less than X% of the read.</li><li>Assemble the reads with Canu v2.2 [#REF], treating them as "pacbio" reads (for correction and homopolymer compression), with the GenomeSize parameter set to the expected band length (e.g. GenomeSize=5000).</li><li>Extract the first reported assembled contig.</li><li>Map the contig to the nanopore adapter sequences, and trim to exclude any matching sequence.</li></ol><p>[Canu has a default genome size and read length cutoff of 1kb, and performs poorly on sequences shorter than this] <br><br>Assembly process for bands under 3k in length (done on LFB-depleted P2 Solo sequences):</p><ol><li>Filter >q20 reads for a 100bp region around the target length (e.g. 4950-5050bp for the 5k band) [High quality reads were not in sufficient abundance for the 100bp band; all reads were needed]</li><li>Assemble using a<a href="https://gitlab.com/gringer/bioinfscripts/-/blob/master/fastx-kassembler.pl"> kmer-based de-bruijn assembler</a>, trimming off low-count kmers</li><li>Extract the first reported trimmed assembled chain</li><li>Map the assembled chain to the nanopore adapter sequences, and trim to exclude any matching sequence</li><li>Use web BLASTn [#REF] to help trim any additional trailing non-matching sequence</li></ol><h4>Mapping</h4><ol><li>Use a <a href="https://gitlab.com/gringer/bioinfscripts/-/blob/master/fastx-kmapper.pl">kmer-based lightweight mapper</a> to map reads to assembled bands</li><li>Created LAST mismatch matrix using `last-train` on the 5k reads together, using the full assembled ladder sequences as a reference:<br>last-train -Q 1 ladder_seqs.fa 5k_reads.fq.gz</li><li>Mapped all reads to the assembled ladder sequences (only the reference corresponding to the most likely band source), retaining (for each read) the mapping that had the longest combined proportion of read and reference sequence mapped:<br>lastal -p bacterial.mat -P 10 ladder_seqs.fa reads_2023-Oct-12_DNA-Ladder-1kbplus_called_all.fq.gz | \ <br> ~/scripts/maf2csv.pl | \ <br> awk -F ',' '{print $0","($8/100 * $13/100)}' | \ <br> sort -t ',' -k 16rg,16 | sort -t ',' -k 1,1 -u | sort -t ',' -k 1r,1 | \ <br> perl -pe 's/,[^,]*$/\n/' > LAST_reads_vs_ladder_longestMatch.csv.gz</li></ol>
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.