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1,199 results for “Alignement”
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>
mtSSU alignment Lecanora markjohnstonii
<p>In order to place the new species <em>Lecanora markjohnstonii </em>within the Lecanoraceae, the <em>mtSSU</em> region was retrieved from our assembled mitogenome and manually added to a six-locus alignment published by Zhao et al. (2015) to infer the phylogeny of Lecanoraceae. The closely-related lichen <em>Lecanora orientoafricana </em>was also added as a possible sister taxon. Only the mtSSU alignment is included in this submission, refer to Zhao et al. (2015) for complete six-locus dataset.</p>
rDNA contig alignment Lecanora markjohnstonii
<p>This rDNA dataset was aligned using MUSCLE-3.7 with default parameters (Edgar 2004) via the CIPRES Science Gateway (Miller, Pfeiffer & Schwartz 2010). This alignment was then manually curated using Mesquite-3.04 (Maddison & Maddison 2015), with ambiguously aligned regions excluded from the alignment. The alignment was made in order to infer a phylogenetic tree of the position of <em>Lecanora markjohnstonii</em>, a new species of lichenized fungus.</p>
SPAligner: alignment of long error-prone reads to assembly graphs
<p>This repository contains benchmarking datasets and scripts for the manuscript "SPAligner: alignment of long error-prone reads to assembly graphs". </p> <p>Graph representation of genome assemblies has been recently used in different applications — from gene finding to haplotype separation. While many of these applications are based on aligning DNA and protein sequences to assembly graphs, existing software tools for finding such alignments have important limitations. We present a novel SPAligner (Saint Petersburg Aligner) tool for aligning long reads to assembly graphs and demonstrate that it generates accurate alignments.</p> <p> </p> <p> </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>
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>
Aligning of water molecules into proton-conducing transmembrane water wires by oxygen atoms of phospholipid ester linkers
<p>A media AVI file that shows how oxygen atoms of ester linkers of the two converging phospholipid molecules form an "oxygen passage" along which water molecules align in a proton-conducting wire. Further details could be found in our article</p>
CWL run of Alignment Workflow (CWLProv 0.6.0 Research Object)
<p>This dataset folder is a <strong>CWLProv Research Object</strong> that captures the <a href="http://commonwl.org/">Common Workflow Language</a> execution provenance, see <a href="https://w3id.org/cwl/prov/0.6.0">CWLProv 0.6.0</a> or use the <a href="https://pypi.org/project/cwlprov/">cwlprov Python tool</a> to explore.</p> <p> </p> <p>The CWL <a href="https://w3id.org/cwl/view/git/923ae325fa3a6d3ee3871539c65afa06ac86ac54/aligner/sbg-alignment-cwl/topmed-alignment.cwl">alignment workflow</a> included in this case study is designed by <a href="https://github.com/DataBiosphere">Data Biosphere</a>. It adapts the alignment pipeline originally developed at Abecasis Lab, The University of Michigan. This <a href="https://github.com/FarahZKhan/topmed-workflows/tree/cwlprov_testing">workflow</a> is part of NIH Data Commons initiative and comprises of four stages.</p> <p>First step, <strong><em>Pre-align</em></strong><em>,</em> accepts a <em>Compressed Alignment Map</em> (CRAM) file (a compressed format for BAM files developed <em>by European Bioinformatics Institute</em> (EBI)) and human genome reference sequence as input and using underlying software utilities of <strong>SAMtools</strong> such as <em>view</em>, <em>sort</em> and <em>fixmate</em> returns a list of fastq files which can be used as input for the next step.</p> <p>The next step <strong><em>Align</em></strong> also accepts the human reference genome as input along with the output files from <em>Pre-align</em> and uses <strong>BWA-mem</strong> to generate aligned reads as BAM files. <strong>SAMBLASTER</strong> is used to mark duplicate reads and <strong>SAMtools</strong> view to convert read files from SAM to BAM format.</p> <p>The BAM files generated after <em>lign</em> are sorted with <strong><em>SAMtool sort</em></strong>'.</p> <p>Finally, these sorted alignment files are merged to produce single sorted BAM file using SAMtools merge in <strong><em>Post-align</em></strong> step.</p> <p> </p> <p><strong>Steps to reproduce</strong></p> <p>This analysis was run using a 16-core Linux cloud instance with 64GB RAM and pre-installed docker.</p> <ol> <li> <p>Install <strong>gsutils</strong><br> </p> <pre><code class="language-bash">export CLOUD_SDK_REPO="cloud-sdk-$(lsb_release -c -s)" echo "deb http://packages.cloud.google.com/apt $CLOUD_SDK_REPO main" | \ sudo tee -a /etc/apt/sources.list.d/google-cloud-sdk.list curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | \ sudo apt-key add - sudo apt-get update && sudo apt-get install google-cloud-sdk</code></pre> <p> </p> </li> <li> <p>Get the data and make the analysis environment ready:<br> </p> <pre><code class="language-bash">git clone https://github.com/FarahZKhan/topmed-workflows.git cd topmed-workflows git checkout cwlprov_testing cd aligner/sbg-alignment-cwl # this is a custom script download google bucket files from json files and create a local json # it needs gsutil to be installed though git clone https://github.com/DailyDreaming/fetch_gs_frm_json.git # Wait... this should download ~18Gb. python2.7 fetch_gs_frm_json/dl_gsfiles_frm_json.py topmed-alignment.sample.json </code></pre> <p> </p> </li> <li> <p>Run the following commands to create the CWLProv Research Object:</p> <pre><code class="language-bash">time cwltool --no-match-user --provenance alignmnentwf0.6.0 --tmp-outdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp --tmpdir-prefix=/CWLProv_workflow_testing/intermediate_temp/temp topmed-alignment.cwl topmed-alignment.sample.json.new zip -r alignment_0.6.0_linux.zip alignment_0.6.0_linux sha256sum alignment_0.6.0_linux.zip > alignment_0.6.0_linux.zip.sha25</code></pre> <p> </p> </li> </ol>
Towards a rapid sequencing-based molecular surveillance and mosaicism investigation of Toxoplasma gondii (nucleotide alignment dataset)
<p>This dataset includes the nucleotide alignment of eight Toxoplasma gondii genome loci (Sag1 / Chromossome VIII, Gra6 / Chromossome X, PK1 / Chromossome VI, Sag3 / Chromossome XII, L363 / Chromossome VIIb, CB21-4 / Chromossome III, M102 / Chromossome VIIa, Sag2 / Chromossome VIII). Each alignment includes sequences from T. gondii reference strains (retrieved from ToxoDB) as well as sequences from multiple clinical strains (obtained by Sanger / Next-generation sequencing) of the collection of the National Reference Laboratory of Parasitic and Fungal Infections, Department of Infectious Diseases, National Institute of Health Dr. Ricardo Jorge, Portugal. </p>
Aligned DNA sequence matrixes for the study of the divergent times of phytoplasmas
<p>Sequence alignments of 16S rRNA and methionine aminopeptidase (map) are provided in FASTA files “Cao_et_al_16S.fas” and “Cao_et_al_map.fas”, respectively. Detailed information of the data matrixes is as follows:</p> <p> </p> <p>File name: Cao_et_al_16S.fas</p> <p>Number of taxa: 220</p> <p>Number of characters: 1655</p> <p>Gap: -</p> <p> </p> <p>File name: Cao_et_al_map.fas</p> <p>Number of taxa: 83</p> <p>Number of characters: 564</p> <p>Gap: -</p>
Aligned DNA sequence matrixes for the study of the divergent times of phytoplasmas
<p>Sequence alignments of 16S rRNA and methionine aminopeptidase (map) are provided in FASTA files “Cao_et_al_16S.fas” and “Cao_et_al_map.fas”, respectively. Detailed information of the data matrixes is as follows:</p> <p> </p> <p>File name: Cao_et_al_16S.fas</p> <p>Number of taxa: 220</p> <p>Number of characters: 1655</p> <p>Gap: -</p> <p> </p> <p>File name: Cao_et_al_map.fas</p> <p>Number of taxa: 83</p> <p>Number of characters: 564</p> <p>Gap: -</p>
Treatment outcome with orthodontic aligners and fixed appliances: a systematic review with meta-analyses
<p>Dataset for all analyses of the paper in European Journal of Orthodontics (doi: 10.1093/ejo/cjz094)</p>
LIchenothelia_Saxomyces_phylogenomic_alignments
<p>Alignments used in Ametrano et al. (2019) "Genome-scale data resolve ancestral rock-inhabiting lifestyle in Dothideomycetes"; IMAFungus</p>
Data file with manuscript titled 'A Structurally Validated Sequence Alignment of 497 Human Protein Kinase Domains'
<p>The files used in different analysis reported in the manuscript titled - 'A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains' are shared at two locations. Following is a brief description of these files.</p> <p>Location - https://github.com/DunbrackLab/Kinases<br> 1. HMM profile files - HMM files for each of the nine groups computed separately labeled as Groupname.hmm, like AGC.hmm<br> 2. HMM profile file - HMM file computed from the full alignment including all the sequences - Human-PK.hmm<br> 3. Score files - HMM scores of each kinase sequence against all the groupwise HMMs both for iteration1 (HMM-iter1-scores-tables.txt) and iteration2 (HMM-iter1-scores-tables.txt)<br> 4. Jalview session file - Kinase alignment with sequences colored by secondary structure information from PDB file if the structure is known; or predicted secondary structure if the experimental structure is not known. The file could be opened in Jalview - kinases-PDB-SSPred.jvp</p> <p>Location - https://zenodo.org/record/3445533<br> 1. The file contains list of residue pairs aligned in pairwise structural alignments of 272 human protein kinases which were used as a benchmark in the study. The alignments were created by FATCAT and optimized by SE program.</p>
Profile Comparer Extended: phylogeny of LPMO families using profile hidden Markov model alignments
<p>searchable pdf phylogenetic tree (Fig S1) and sequence data (Table S1) belonging to the paper "Profile Comparer Extended: phylogeny of LPMO families using profile hidden Markov model alignments"</p>
Human sequence alignment data set used for analysis of SPDI algorithm and tools
<p>Collection of alignment segments produced on October 30, 2019. The ADS currently consists of over 2,680,000 pairwise alignment segments generated from over 350,000 distinct input sequences. </p> <ul> <li> <p>Old assembly to current Genome Reference Consortium (GRC) <a href="http://f1000.com/work/citation?ids=111899&pre=&suf=&sa=0">(Church et al., 2011)</a> primary assemblies (e.g. GRCh36(hg18) or GRCh37(hg19) with GRCh38(hg38))</p> </li> </ul> <ul> <li> <p>Patches, alternative loci, or pseudoautosomal regions (PAR) to GRC primary assembly</p> </li> <li> <p>RefSeq <a href="http://f1000.com/work/citation?ids=2599029&pre=&suf=&sa=0">(O’Leary et al., 2016)</a> and select GenBank <a href="http://f1000.com/work/citation?ids=6183037&pre=&suf=&sa=0">(Benson et al., 2018)</a> transcripts to selected RefSeq genomic regions, also known as RefSeqGene (NG), a member of the Locus Reference Genome (LRG) collaboration <a href="http://f1000.com/work/citation?ids=3225699&pre=&suf=&sa=0">(Dalgleish et al., 2010)</a>.</p> </li> <li> <p>Current RefSeq transcripts (NM/NR/XM/XR) and RefSeq genomic regions (NG) to the latest Assembly</p> </li> <li> <p>Previous versions of NG and RefSeq transcripts (NM/NR) to GRC primary assembly</p> </li> </ul>
Alignment methods for nanotomography with deep sub-pixel accuracy
<p>This repository contains codes for alignment of projections for tomography and laminography. Additionally we include artificial, i.e. simulated, and experimental examples.</p> <p><strong>Here you will find:</strong></p> <p>1) <em>cSAXS_matlab_tomo_shared.zip</em> - Codes and routines, as introduced in [1], for deep sub-pixel alignment and reconstruction of tomography and laminography datasets.</p> <p>2) <em>example_data.mat</em> - artificial tomography dataset that serves as an example for the alignment codes.</p> <p><br> <strong>Citation and acknowledgements</strong></p> <p>If you use the codes from this repository here is where to find more information and the expected citation.</p> <p>For use or further development of the tomography and laminography alignment and reconstruction codes please cite:<br> [1] M. Odstrcil, M. Holler, J. Holler, M. Guizar-Sicairos,"Alignment methods for nanotomography with deep sub-pixel accuracy", Opt. Express, (2019).</p> <p><br> <strong>Code requirements:</strong></p> <p>Reconstruction scripts were tested for Matlab2018a with parallel toolkit, CUDA 9.0. NVIDIA, RHEL 7.6<br> GPU is required for the tomographic reconstruction.</p> <p><br> <strong>Instalation and reconstruction:</strong></p> <p> 1) Download example datasets and code from https://doi.org/10.5281/zenodo.3539550 or a measured ptychotomography dataset from https://doi.org/10.5281/zenodo.3539513<br> 2) Make sure that the downloaded datasets can be loaded by matlab<br> 3) Choose one of the provided datasets in the "run_simple_example.m" template<br> 4) Run script "run_simple_example.m". The final results will be stored in folder defined in par.output_folder</p> <p><br> <strong>Example dataset</strong> contains following two variables:</p> <p><em>stack_object</em> - complex valued unaligned projection with dimensions [Npix_vertical , Npix_horizontal, number_of_angles]</p> <p> <em>theta</em> - vector of corresponding projection angles in degrees</p> <p> </p> <p>This code and subroutines are part of a continuous development. There is no liability on PSI or cSAXS. License for the codes can be found in the individual scripts.</p>
Assemblies and alignments from a study on the phylotranscriptomic relationships of Oophila, a genus of green algae associated to amphibian egg masses
<p>Dataset for:</p> <p>Vences M, Sachs M, Irisarri I, Bartels F, Eriksson PF, Künzel S, Kurabayashi A, Laugen AT, Vegso ZT, Bishop CD, Kerney R, Arndt H. Phylotranscriptomic relationships of the Oophila clade of green algae associated to amphibian egg masses. Mol Phylogenet Evol. 2024 Aug 6:108165. doi: 10.1016/j.ympev.2024.108165. </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.