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Supplemental_Data_S1 for "Kmer Manifold Approximation and Projection for visualizing DNA sequences"
<p>This dataset includes the results generated by KMAP software applied to the htselexdata dataset. Each folder within the dataset contains outputs from multiple dimensionality reduction techniques, including KMAP, UMAP, t-SNE, and MDS. Additionally, motifs and logos have been derived using both KMAP and MEME methods. This data provides insights into motif patterns and structures, which can be beneficial for further bioinformatics and computational biology analyses.</p>
Verification of library complexity in the HEK-Cas9 sublibraries - sequence data of the generated sublibraries A and B
<p>Sequence data of the generated HEK-Cas9 sublibraries A and B, linked to the manuscript 10.1128/mbio.01925-24: The <em>Bordetella</em> effector protein BteA induces host cell death by disruption of calcium homeostasis by Martin Zmuda, Eliska Sedlackova, Barbora Pravdova, Monika Cizkova, Marketa Dalecka, Ondrej Cerny, Tania Romero Allsop, Tomas Grousl, Ivana Malcova, and Jana Kamanova</p>
Transparent Magritte Test Sequence
<p> <strong>Transparent-Magritte sequence by LISA ULB</strong></p> <p>The test sequence "Transparent Magritte" is provided by Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.</p> <p> </p> <p><strong> License:</strong></p> <p>CC BY-NC-SA</p> <p> </p> <p><strong> Terms of Use:</strong></p> <p>Anykind of publication or report using this sequence should refer to the following references.</p> <p><br> [1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "Transparent Magritte Test Sequence", 2021.</p> <p>@misc{fachada_transparent_2021,<br> title = {Transparent {Magritte} {Test} {Sequence}},<br> author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},<br> month = feb,<br> year = {2021},<br> doi = {10.5281/zenodo.5048275}<br> }</p> <p>[2] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit, "Light Field Rendering for non-Lambertian Objects," presented at the Electronic Imaging, 2021.</p> <p>@inproceedings{fachada_light_2021,<br> title = {Light {Field} {Rendering} for non-{Lambertian} {Objects}},<br> booktitle = {Electronic {Imaging}},<br> author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},<br> year = {2021}<br> }</p> <p><strong> Production</strong></p> <p>Laboratory of Image Synthesis and Analysis, LISA department, EPB, Universite Libre de Bruxelles, Belgium.</p> <p> </p> <p> <strong>Content:</strong></p> <p>This dataset contains a test scene created and rendered with Blender [1] and the addon script [2] extended for Blender 2.8. We provide the Bblender file and the rendered scene.</p> <p>The scene contains a transparent refractive torus rendered in a regular camera array of 21x21 cameras.<br> In addition to the 3D model, two folders are available:<br> - `centered_cameras` resolution of 1000x1000, the cameras are centered on the refractive torus.<br> - `parallel_cameras` resolution of 2000x2000, the cameras are parallel, with a principal point at the center of the image.<br> Each of these folders contains:<br> - a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],<br> - a `parameters.cfg` generated with [2],<br> - a `texture` folder containing the rendered views in png format,<br> - a `depth` folder containing the associated depth maps in exr format.<br> <br> <strong> References and links:</strong></p> <p>[1] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.</p> <p>[2] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,</p> <p>https://github.com/lightfield-analysis/blender-addon</p> <p>https://github.com/dbonattoj/blender-addon</p> <p>[3] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.</p> <p>https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-format</p>
Mirror Magritte Torus Test Sequence
<p><strong> # Mirror-Magritte-Torus sequence by LISA ULB</strong></p> <p><br> The test sequence "Mirror Magritte Torus" is provided by Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.</p> <p><strong> # License:</strong></p> <p><br> CC BY-NC-SA</p> <p><strong> # Terms of Use:</strong></p> <p><br> Anykind of publication or report using this sequence should refer to the following references.</p> <p>[1] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, Gauthier Lafruit, "Mirror Magritte Torus Test Sequence", 2021.</p> <p><em>@misc{fachada_mirror_2021,<br> title = {Mirror {Magritte} {Torus} {Test} {Sequence}},<br> author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},<br> month = feb,<br> year = {2021},<br> doi = {</em>10.5281/zenodo.5048262<em>}<br> }</em></p> <p>[2] Sarah Fachada, Daniele Bonatto, Mehrdad Teratani, and Gauthier Lafruit, "Light Field Rendering for non-Lambertian Objects," presented at the Electronic Imaging, 2021.</p> <p><em>@inproceedings{fachada_light_2021,<br> title = {Light {Field} {Rendering} for non-{Lambertian} {Objects}},<br> booktitle = {Electronic {Imaging}},<br> author = {Fachada, Sarah and Bonatto, Daniele and Teratani, Mehrdad and Lafruit, Gauthier},<br> year = {2021}<br> }</em></p> <p><strong> # Production:</strong></p> <p><br> Laboratory of Image Synthesis and Analysis, LISA department, EPB, Universite Libre de Bruxelles, Belgium.</p> <p><strong> # Content:</strong></p> <p><br> This dataset contains a test scene created and rendered with Blender [1] and the addon script [2] extended for Blender 2.8. We provide the Blender file and the rendered scene.</p> <p>The scene contains a mirror reflective torus rendered in a regular camera array of 21x21 cameras.</p> <p>In addition to the 3D model, we provide the images in the folder `parallel_cameras` : resolution of 2000x2000, the cameras are parallel, with a principal point at the center of the image.</p> <p>The dataset contains:<br> - a `camera.json` file in OMAF coordinates system (Camera position: X: forwards, Y:left, Z: up, Rotation: yaw, pitch, roll) [3],<br> - a `parameters.cfg` generated with [2],<br> - a `texture` folder containing the rendered views in png format,<br> - a `depth` folder containing the associated depth maps in exr format.<br> <br> <br> <strong> # References and links:</strong><br> <br> [1] Blender Online Community, "Blender - a 3D modelling and rendering package." Blender Institute, Amsterdam: Blender Foundation, 2020.</p> <p>[2] K. Honauer, O. Johannsen, D. Kondermann, and B. Goldluecke, "A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields" in Asian Conference on Computer Vision, 2016,<br> https://github.com/lightfield-analysis/blender-addon<br> https://github.com/dbonattoj/blender-addon</p> <p>[3] B. Kroon, "Reference View Synthesizer (RVS) manual [N18068]," ISO/IEC JTC1/SC29/WG11, Macau SAR, China, p. 19, Oct. 2018.<br> https://mpeg.chiariglione.org/standards/mpeg-i/omnidirectional-media-format</p> <p> </p> <p> </p>
RabbitStamp Test Sequence
<p><strong> RabbitStamp sequence by LISA ULB</strong></p> <p><br> The test sequence "RabbitStamp" is provided by Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Universite Libre de Bruxelles), Belgium.</p> <p><strong> License:</strong></p> <p>CC BY-NC-SA</p> <p>ONLY Available for Academic Usage</p> <p><strong> Terms of Use:</strong></p> <p><br> Anykind of publication or report using this sequence should refer to the following references.</p> <p>[1] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "RabbitStamp Test Sequence", 2021.</p> <p>@misc{fachada_RabbitStamp_2021,<br> title = {{RabbitStamp} {Test} {Sequence}},<br> author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad },<br> month = jul,<br> year = {2021},<br> doi = {10.5281/zenodo.5053770}<br> }</p> <p>[2] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "[DLF] Plenoptic 2.0 Multiview Lenslet Dataset and Preliminary Experiments [m56429]", 2021.</p> <p>@article{fachada_RabbitStamp_2021,<br> title = {[DLF] {Plenoptic} 2.0 {Multiview} {Lenslet} {Dataset} and {Preliminary} {Experiments} [m56429]},<br> author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad },<br> journal = {ISO/IEC JTC1/SC29/WG11},<br> month = apr,<br> year = {2021}<br> }</p> <p>[3] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "[LVC] Update for RabbitStamp: Plenoptic 2.0 Multiview Lenslet Dataset [m57100]", 2021.</p> <p>@article{fachada_RabbitStamp_2021,<br> title = {[LVC] {Update} for {RabbitStamp}: {Plenoptic} 2.0 {Multiview} {Dataset} [m56429]},<br> author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad },<br> journal = {ISO/IEC JTC1/SC29/WG11},<br> month = jul,<br> year = {2021}<br> }</p> <p>[4] Sarah Fachada, Yupeng Xie, Daniele Bonatto, Gauthier Lafruit, Mehrdad Teratani, "[LVC] Exploration Experiments using RabbitStamp Multiview Lenslet Images [m57101]", 2021.</p> <p>@article{fachada_RabbitStamp_2021,<br> title = {[LVC] {Exploration} {Experiments} {Using} {RabbitStamp} {Multiview} {Lenslet} {Images} [m56429]},<br> author = {Fachada, Sarah and Xie; Yupeng and Bonatto, Daniele and Lafruit, Gauthier and Teratani, Mehrdad},<br> journal = {ISO/IEC JTC1/SC29/WG11},<br> month = jul,<br> year = {2021}<br> }</p> <p> <strong>Production:</strong></p> <p><br> Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Universite Libre de Bruxelles, Belgium.</p> <p><strong> Content:</strong></p> <p><br> This dataset contains a test scene acquired with a raytrix camera [1] array of 7x3 views. For details of the dataset, please refer to the references mentioned above.</p> <p>The dataset contains:<br> - a `depth_7x3_center` depth maps computed with DERS reference software [2] in yuv40016ble format and json configuration files to do so,<br> - a `multiview_7x3_5x5_images` Calibrated subimages computed with RLC [3] in yuv42010ble format, the cameras.json with the camera parameters and view_synthesis.json with the view synthesis experiment.<br> - a `multiview_7x3_lenslets` folder containing the lenslet views in yuv42010ble format, the Raytrix xml calibration file and RLC cfg file for conversion to multiview.<br> <br> <br> <strong> References and links:</strong><br> <br> [1] Raytrix, https://raytrix.de/</p> <p>[2] S. Rogge and D. Bonatto and J. Sancho and R. Salvador and E. Juarez and A. Munteanu and G. Lafruit, "MPEG-I Depth Estimation Reference Software", in 2019 International Conference on 3D Immersion (IC3D), 2019.</p> <p>[3] M. Teratani and T. Fujii, "[MPEG-I Visual] Conversion of Lenslet Data Capture by Single Focussed Plenoptic Camera to Multiview Video using RLC0.3 [N18567]", ISO/IEC JTC1/SC29/WG11, 2019</p>
Generation of transcriptional novelty by transposable element insertions in Arabidopsis, Genome Sequencing and eccDNA Data
<p><strong>Raw Illumina sequencing data from the Manuscript entitled "Generation of transcriptional novelty by transposable element insertions in Arabidopsis"</strong></p> <p><strong>A. Illumina genome sequencing reads of Arabidopsis control and hcLines that contain novel transposable element insertions.</strong></p> <p>To identify the genomic position of the new <em>ONSEN</em> insertions, the extracted DNA of the 11 selected lines (nine lines with new insertions and two control lines) was sent to BGI, Hong-Kong for Illumina paired-end 150 bp sequencing, aiming for a minimum of 20X sequencing coverage. Quality control of the raw reads was done using FastQC (Andrews S. (2010). FastQC: a quality control tool for high throughput sequence data. Available online at: <a href="http://www.bioinformatics.babraham.ac.uk/projects/fastqc">http://www.bioinformatics.babraham.ac.uk/projects/fastqc</a>) and trimming/clipping was done using Trimmomatic with parameters ILLUMINACLIP: TruSeq3:2:30:10 LEADING:20 TRAILING:20 SLIDINGWINDOW:4:20 and MINLEN:36. Quality of the reads was deemed excellent and no further actions were taken.</p> <p>Samples identifications: genome_hcLineX with "_1" indicating the forward and "_2" the reverse reads.</p> <p><strong>B. Illumina eccDNA sequencing of Arabidopsis control and hcLines following stress treatments</strong></p> <p>Extrachromosomal circular DNA was prepared and sequenced as follows: twenty plants from each petri dish were pooled separately and DNA was extracted using the CTAB method (<a href="https://dx.doi.org/10.17504/protocols.io.quidwue">dx.doi.org/10.17504/protocols.io.quidwue</a>). Following the mobilome-seq method described in (Lanciano et al., 2017), for all samples, we digested linear DNA from 2 µg of total DNA for 17 hours at 37<sup>o</sup>C using 10 U of PlasmidSafe (<em>LubioScience cat# E3101K</em>), followed by enzyme denaturation (30 mins at 70<sup>o</sup>C). Digested DNA was precipitated with isopropanol supplemented with 1 µg of GlycoBlue coprecipitant (<em>Fisher Scientific cat# 10391565</em>). Circular DNA was then amplified through rolling circle amplification (RCA) with the Illustra TempliPhi kit (<em>GE Healthcare cat# 25-6400-10</em>), following the manufacturer recommendation and leaving the reaction for 16h at 30<sup>o</sup>C. DNA was once again precipitated with isopropanol and sent for Illumina paired end 150 bp sequencing at BGI, Hong Kong. </p> <p>Samples identification: </p> <p>eccDNA_A.thaliana_ctrl: control reads</p> <p>eccDNA_A.thaliana_HS: heat stressed plants reads</p> <p>eccDNA_A.thaliana_AZ_HS: reads of alpha-amanitin, zebularine and heat-stressed plants</p> <p>"R1" indicates forward and "R2" reverse reads.</p> <p> </p>
xPore: Identification of differential RNA modifications from nanopore direct RNA sequencing - SGNEx data
<p>xPore is a Python package for identification and quantification of differential RNA modifications from direct RNA sequencing.</p> <p>The detailed usage is documented at <a href="https://xpore.readthedocs.io/en/latest/">https://xpore.readthedocs.io/en/latest</a>, while all scripts and source code are available at <a href="https://github.com/GoekeLab/xpore">https://github.com/GoekeLab/xpore</a>.</p> <p>All the preprocessed datasets used in the paper are provided here. </p> <p>Please cite our paper below when using these data.<br> Ploy N. Pratanwanich et al. "Detection of differential RNA modifications from direct RNA sequencing of human cell lines." bioRxiv (2020).</p>
Quantifying the Tissue-Specific Regulatory Information within Enhancer DNA Sequences
<p>Tables of mouse embryonic differential enhancers and promoters.</p> <p>The tables list the genomic coordinates of differential regulatory elements, the tissues in which they are active (labels column), a differential score, and the DNA sequence of the element. Tissues are numbered from 0 to 7, which correspond to heart, kidney, liver, limb, lung, forebrain, midbrain, and hindbrain, respectively. The tool <a href="https://github.com/pbenner/gonetics/tree/master/tools/segmentationDifferential">segmentationDifferential</a> from the <em>gonetics library</em> was used to compute differential elements. The length of all regions was set to 1000 base pairs around the center. Specifics of the experimental data can be found in the referenced publication.</p>
A unified genealogy of modern and ancient genomes: Unified, inferred tree sequences of 1000 Genomes, Human Genome Diversity, and Simons Genome Diversity Projects
<p>Unified, inferred tree sequences built from the 1000 Genomes phase 3, Human Genome Diversity, and Simons Genome Diversity Projects. Each tree sequence is the arm of an autosome (the short arm of acrocentric chromosomes are not included). Tree sequences were inferred using <a href="https://tsinfer.readthedocs.io/">tsinfer</a> version 0.2.1, dated using <a href="https://tsdate.readthedocs.io/en/latest/">tsdate</a> version 0.1.4 and compressed using <a href="https://tszip.readthedocs.io/en/stable/">tszip</a>. All data is in GRCh38.</p> <p>The full data pipeline used to generate these tree sequences and associated metadata is available on <a href="https://github.com/awohns/unified_genealogy_paper">GitHub</a>. A description can be found in the Supplementary Material of <a href="https://www.biorxiv.org/content/10.1101/2021.02.16.431497v2">Wohns et al. (2021)</a>.</p> <p>Tree sequences can be decompressed as follows:</p> <pre><code>$ tsunzip hgdp_tgp_sgdp_chr1_p.dated.trees.tsz</code></pre> <p>Once decompressed, trees files can be loaded and processed in Python using <a href="https://tskit.readthedocs.io/">tskit</a>. </p> <pre><code>import tskit ts = tskit.load("hgdp_tgp_sgdp_chr1_p.dated.trees") # ts is an instance of tskit.TreeSequence print("The short arm of chromosome 1 contains {} trees".format(ts.num_trees))</code></pre> <p>Metadata associated with nodes contain the mean and variance of tsdate's posterior distribution on node time. To access these values, we can use:</p> <pre><code>import json node = ts.node(10000) metadata_dict = json.loads(node.metadata) print("The mean of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["mn"])) print("The variance of the posterior distribution on the age of node 10000 is {} generations".format(metadata_dict["vr"]))</code></pre> <p>Age estimates for each variant site can be derived from the mean of the age estimates of the upper and lower bounding nodes of the oldest mutation associated with a site. tsdate includes <a href="https://tsdate.readthedocs.io/en/latest/python-api.html?highlight=sites_time_from_ts#tsdate.sites_time_from_ts">a function to find the age estimates of all sites in the tree sequence</a>:</p> <pre><code>import tsdate site_times = tsdate.sites_time_from_ts(ts, node_selection='arithmetic')</code></pre> <p>This returns a numpy array which has a length equal to the number of sites.</p> <p>Accessing variant sites in the tree sequence provides the position and id of variants:</p> <pre><code>site = ts.site(1000) site_metadata = json.loads(site.metadata) print("The position of site 1000 is {} and its ID is {}.".format(site.position, site_metadata["ID"]))</code></pre> <p>Metadata associated with individuals and populations was derived from the original sources (<a href="http://ftp.1000genomes.ebi.ac.uk/vol1/ftp/technical/working/20130606_sample_info/20130606_g1k.ped">TGP</a>, <a>HGDP</a>, and <a href="https://sharehost.hms.harvard.edu/genetics/reich_lab/sgdp/SGDP_metadata.279public.21signedLetter.samples.txt">SGDP</a>) and converted to JSON form. For example, to access individual metadata we can use:</p> <pre><code>ind = ts.individual(0) metadata_dict = json.loads(ind.metadata)</code></pre> <p>The metadata_dict variable will now contain all the metadata for the individual with ID 0 as a dictionary. Metadata associated with populations can be found in a similar way. Population IDs are associated with individuals via their constituent nodes. For example,</p> <pre><code>pop_metadata = [json.loads(pop.metadata) for pop in ts.populations()] ind_node = ts.node(ind.nodes[0]) ind_pop_metadata = pop_metadata[ind_node.population]</code></pre> <p>After this, the ind_pop_metadata variable will contain the population level metadata for individual ID 0.</p>
All plasmid sequences
<p>Here are the full sequences of 33 plasmids used in the following BioRxiv manuscript: https://doi.org/10.1101/2021.09.15.460454</p> <p> </p>
Field data for: Enterovirus sequence data obtained from primate samples in Central Africa suggest a high prevalence of enteroviruses with possible zoonotic potential
<p>Enteroviruses infect humans and animals, can cause disease, and some may be transmitted across species barriers. We collected different types of samples from various species of Central African wildlife, including data on sampling location and tested the samples for the presence of Enterovirus RNA using a family level PCR. Specimen collection was approved by an Institutional Animal Care and Use Committee (IACUC) of the University of California Davis, and the Governments of Cameroon and the Democratic Republic of the Congo. Enterovirus RNA was detected in samples from 17 primates and 2 rodents. Some sequences were very similar while others were dissimilar to known species, highlighting the unexplored enterovirus diversity in wildlife.</p> <p>The samples and filed data were collected by field ecologists as part of the USAID funded PREDICT project (https://ohi.vetmed.ucdavis.edu/programs-projects/predict-project) and screened for enterovirus RNA using consensus PCR. Maps were generated using basic maps from Paintmaps (http://www.paintmaps.com), a free tool for educational and academic use. The dataset contains the metadata on enterovirus screening among wildlife in Cameroon and the Democratic Republic of the Congo from 2003-2014 as part of the USAID funded PREDICT project. Please refer to the article for more information on methods and references.</p>
Data from the article "Plastome sequencing of South American Podocarpus species reveals low rearrangement rates despite ancient Gondwanan disjunctions"
<p>Input data, intermediate and final analysis output files associated to the manuscript "Plastome sequencing of South American <em>Podocarpus </em>species reveals low rearrangement rates despite ancient Gondwanan disjunctions"</p> <p>We sequenced the plastomes of four South American species of <em>Podocarpus</em> from Patagonia, southern Yungas, and Brazilian subtropical forests: <em>P. nubigenus, P. parlatorei, P. salignus </em>and <em>P. selowii</em>. We compared their plastomes to those published from Brazil, Africa, New Zealand, and Southeast Asia, along with representatives from other genera within Podocarpaceae as outgroups. The four newly sequenced plastomes ranged in size between 133,791 bp and 133,991 bp. Gene content and order among chloroplasts from South American, African and Asian <em>Podocarpus</em> were conserved and different from the plastome of <em>P. totara</em>, from New Zealand. Most genes showed substitution patterns consistent with a conservative selective regime. Phylogenies inferred from either complete sequences or protein coding regions were mostly congruent with previous studies, but showed earlier branching of <em>P. salignus</em>, <em>P. totara</em> and <em>P. sellowii</em>.</p>
Microearthquake sequence recorded close to the Montello thrust system (Southeastern Alps)
<p>Seismic catalogues (QuakeML format) of a microearthquake sequence occurred on August 2021 in the Montello thrust area and recorded by the Collalto seismic network (<a href="https://doi.org/10.7914/SN/EV">10.7914/SN/EV</a>); the seismic catalogues are provided, separately, for different earthquake location codes (h71=Hypo71; hel=Hypoellipse; hypoDD=HypoDD; migraloc=Migraloc). DATA_tgz_SAC.tar contains earthquake waveforms (SAC format) with P and S arrival times used for the locations. Other details in Peruzza et al. (2022, doi: 10.3389/feart.2022.1044296). </p>
THOR RNA-sequencing results
<p>Summarized results of RNA-sequencing performed within THOR (targeting smooth muscle cells in atherosclerosis). THOR is a collaborative project of Aarhus University and Novo Nordisk A/S as a part of the Open Discovery Innovation Network (ODIN) initiative.</p> <p>See detailed data description in the file "DESCRIPTION.md".</p>
16S rRNA sequencing gene datasets for CRC data
<p>Used datasets: </p> <table> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>Dataset</th> <th>16S rRNA Region</th> <th>Control (n)</th> <th>Adenoma (n)</th> <th>CRC (n)</th> <th>Available metadata</th> </tr> </thead> <tbody> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4823848/">Baxter</a></td> <td>V4</td> <td>171</td> <td>198</td> <td>120</td> <td>Gender, age, weight, height, BMI, country, race</td> </tr> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4221363/">Zackular</a></td> <td>V4</td> <td>30</td> <td>30</td> <td>30</td> <td>Gender, age, weight, height, BMI, country, race, FOBT, medication</td> </tr> <tr> <td><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4299606/">Zeller</a></td> <td>V4</td> <td>50</td> <td>38</td> <td>41</td> <td>Gender, age, BMI, country, FOBT</td> </tr> <tr> <td><strong>TOTAL</strong></td> <td>V4</td> <td>251</td> <td>266</td> <td>191</td> <td><em>All of the above</em></td> </tr> </tbody> </table> </th> </tr> </thead> <tbody> <tr> <td> </td> </tr> </tbody> </table> <p>Data processing & sharing</p> <p>All datasets were processed using <a href="https://docs.qiime2.org/2021.11/">qiime2</a> pipeline with <a href="https://benjjneb.github.io/dada2/">DADA2</a> for Sequence quality control and feature table construction and <a href="https://www.arb-silva.de/">SILVA</a> database for taxonomic assignment, and then a <em>phyloseq </em>object was constructed.</p> <ul> <li>Abundance table at genus level is in file <em>genus.csv</em> (Sample counts with NO filtering).</li> <li>Clean metadata is in <em>metadata.csv</em> file (Countries: CA - Canada. USA - United States of America. FRA - France.)</li> <li>Phyloseq object is in file <em>physeq.RDS</em> (Saved as an RDS object in R)</li> </ul> <p>More information is <a href="https://hackmd.io/nbsLqCLlSNSRFc5RBX9c5Q?view">here</a>. </p> <p> </p>
Mycenean Linear B sequences - series D
<p>We present a dataset for Symbol-level language modelling of linear B scripts using a Bidirectional Recurrent Neural Network. <br> The RNN is trained to predict the missing symbol in a given Mycenaean sequence.</p> <p>It includes Series "D" sequences 2565 sequences without missing symbols extracted from: <br> J. Chadwick, L. Godart, J. T. Killen, J. P. Olivier, A. Sacconi, and I. A. Sakellarakis. Corpus of Mycenaean Inscriptions from Knossos: Volumes 1-4, Cambridge University Press, 1987.<br> <br> The first 513 sequences result from the processing of series D tablets,<br> The next 725 sequences result from the augmentation techniques described in the paper<br> The remaining 1327 sequences are duplicates to minimize bias.</p> <p>The dataset was used in the following publication: <br> K. PAPAVASSILEIOU, Kosmopoulos, D. I., G. Owens, “A generative model for the Mycenaean Linear B script and its application in infilling text from ancient tablets”, ACM Journal on Computing and Cultural Heritage, https://doi.org/10.1145/3593431.</p> <p>If you use this dataset please cite this publication.</p>
Vitamin_B12_related_genes_and_FASTA_sequences
<p>These two files are related to a submitted research paper on the study of a 7-year metagenomic time series carried monthly in a northwestern Mediterranean coastal site (SOLA, Banyuls-sur-Mer, FRANCE) :</p> <p><em>Seasonal succession of different vitamin B12 biosynthesis pathways and producers in coastal marine microbial communities. </em></p> <p>This study focuses on prokaryotes involved in vitamin B12 metabolism (biosynthesis, transport, remodeling) and on the metabolic pathways involved in these processes.</p> <p>The dataset in <strong>.xlsx</strong> format represents the abundance table of all the genes associated with 82 KEGGs involved in vitamin B12 metabolism, and the file in <strong>.fasta</strong> format corresponds to the FASTA sequences associated with these genes.</p>
IDMT-SMT-Chord-Sequences Dataset
<p>The IDMT-SMT-CHORD-SEQUENCES dataset contains 15,000 4-32 seconds long chord progressions synthesized from MIDI data with 45 different instruments. The files are organized as 5,000 triplets with an anchor, similar and dissimilar chord progression. Metadata like tempo, meter, duration in bars, instrument, and chord progression is provided in JSON format. Also, an overview about all metadata along with the dataset split is stored in a CSV file.</p>
Whole-genome capture and sequencing of Mycobacterium tuberculosis directly from clinical samples - Design of RNA oligonucleotide baits for Agilent Technologies' SureSelect target enrichment
<p>This dataset comprises the sequence of <strong>44 278 RNA oligonucleotide "baits" (120 bp each) </strong>designed to perform <strong>whole-genome capture and sequencing of <em>Mycobacterium tuberculosis</em> directly from clinical samples</strong> (DNA) using Agilent Technologies’ SureSelect target enrichment system following the Illumina paired-end multiplexed sequencing library protocol. </p> <p>RNA oligonucleotide “baits” were designed to span the ∼4.5 Mb of the <em>M. tuberculosis</em> genome. In brief, the reference genome sequence of the MTBC H37Rv strain (Genbank #AL123456) was <em>in silico</em> fragmented into 120 bp sequences twice, to ensure an overlap of 60 bp between sequences. Due to their rich GC content, which could interfere with DNA capture, all MTBC genes of the PE, PPE and PE-PGRS family were also independently fragmented into 120 bp sequences, in order to increase capture sensitivity. All resulting sequences were BLASTn searched against the Human Genomic + Transcript database to excluded homologous sequences to the human genome. Overall, a total of 42,278 RNA probes were generated and this custom bait library was then uploaded to the SureDesign software (https://earray.chem.agilent.com/suredesign) and synthesized by Agilent Technologies. During synthesis, the 2198 sequences complementary to the PE, PPE and PE-PGRS family were unbalanced 8:1 to potentiate capture.</p> <p>More details can be found in the following publication:</p> <p>- Macedo, R., Isidro, J., Ferreira, R., Pinto, M., Borges, V., Duarte, S., Vieira, L., & Gomes, J. P. (2023). Molecular Capture of <em>Mycobacterium tuberculosis</em> Genomes Directly from Clinical Samples: A Potential Backup Approach for Epidemiological and Drug Susceptibility Inferences. <em>International journal of molecular sciences</em>, <em>24</em>(3), 2912. https://doi.org/10.3390/ijms24032912</p>
Sequencing data and code - Prince et al 2023
<p>Raw sequencing data, code and intermediate analysis files from "Antiviral activity of molnupiravir precursor NHC against SARS-CoV-2 Variants of Concern (VOCs) and implications for the therapeutic window and resistance" (Prince et al, 2023).</p> <p>Please see sample_metadata.xlsx for all metadata relating to the files contained in this repository.</p> <p>Code for data visualisation can be found in: mut_sub_all_pts_serial-pass.R. Specific paths to data will have to be changes to refer to where you have downloaded the data in this repository. Metadata for use with the R script is serial-pass-nimagen-metadata-forR.csv.</p>
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.