Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
974
datasets available to search
ShareScore release 0.9.0
Dataset results
974 results for “mycobacterium”
Supplementary Material to article "Molecular Diversity of Mycobacterium avium subsp. paratuberculosis in Four Dairy Goat Herds from Thuringia (Germany)"
<p>These data (supplementary material) belong to the publication "Molecular Diversity of <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> in Four Dairy Goat Herds from Thuringia (Germany)". The study determined the diversity of <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> (MAP) isolated from four goat herds affected by paratuberculosis in Thuringia (Germany), as well as the detailed distribution of MAP genotypes among the animals and their environment in one herd (herd 1). A combination of three methods was used to genotype isolates from fecal samples of infected goats, from various intestinal and other tissues of clinically affected goats, and from environmental samples. The six MAP-C genotypes identified could be assigned to five different phylogenetic subgroups. The results suggest individual infection strains within each herd. In herd 1, one predominant strain was found, and two strains occurred sporadically. The identified genotypes were not goat specific.</p>
Supplementary dataset to publication: Complete Genome Sequence of Ovine Mycobacterium avium subsp. paratuberculosis Strain JIII-386 (MAP-S/type III) and Its Comparison to MAP-S/type I, MAP-C, and M. avium Complex Genomes.
<p>This is the modified supplemented material to the publication “Complete genome sequence of ovine Mycobacterium avium subsp. paratuberculosis strain JIII-386 (MAP-S/type III) and its comparison to MAP-S/type I, MAP-C, and M. avium complex genomes”.</p> <p>The complete circular genome of Mycobacterium avium subsp. paratuberculosis (MAP) strain JIII-386 from Germany, closed by Nanopore technology in this study, was presented and compared with the draft genome of JIII-386, previously published in [doi:10.1093/gbe/ew154], the closed genome of the MAP-S/type I strain Telford, the MAP-S/type III draft genome of strain S397, twelve closed MAP-C (type II) strains and eight closed Mycobacterium avium (M. a.) strains of subsp. hominissuis (MAH) and subsp. avium (MAA). Structural comparisons clearly revealed the mosaic nature of MAP genomes, the differences between MAP subtypes I, II and III, and the higher diversity of MAP-S compared to MAP-C genomes. </p> <p>The material provides a wealth of detailed results from these analyses and comparisons. These include a list of identified ncRNA and Riboswitches, as well as additional genes in finished JIII-386, the gene content of identified prophage regions, copy number of identified transposable elements and a list of selected virulence-associated genes in the different MAP-type (I - III) strains. The genomic islands identified and included genes along with their predicted functions were presented for six MAP genomes (belonging to MAP-S/type I and III, and MAP-C), one MAH genome and one MAA genome. One table shows the corresponding genomic islands in the genomes of JIII-386, Telford and three MAP-C genomes. Furthermore, homologous genes of known MAP-S specific Large Sequence Polymorphisms regions (LSP<sup>S</sup> = LSP-S) were recorded in different MAP-S type strains, one MAH and one MAA strain, as well as genes of deletions #1 (LSP<sup>A</sup>-20), #2, and s-delta-1, previously described as MAP-S-specific deletions, their presence or absence in 3 MAP-S, 12 MAP-C, 4 MAH, and 4 MAA strains were listed. Different presence or absence of genes, but also identified frameshifts or disruptions of various virulence-associated genes could lead to the different MAP-type specific phenotypic characteristics. Comprehensive core and pan genome analyses (results listed in six tables) revealed unique genes and genes likely to have been acquired by horizontal gene transfer in different MAP types and subtypes, but also emphasized the highly conserved and close relationship, and the complex evolution of M. a. strains.</p> <p> </p>
The within-host population dynamics of Mycobacterium tuberculosis vary with treatment efficacy.
<p>Data used for the publication of a paper entitled: <strong>The within-host population dynamics of <em>Mycobacterium tuberculosis</em> vary with treatment efficacy.</strong></p> <p>The data were derived from:</p> <p>1. the deep sequencing of serial sputum samples from 12 TB patients,</p> <p>2. the deep sequencing of liquid cultures derived from the expansion of individual colonies <em>in vitro</em>,</p> <p>3. <em>In </em><em>silico</em> simulations of DNA sequencing, populations and mutagenesis.</p> <p>The analytical scripts associated with the generation of the data can be found at:</p> <p>https://github.com/swisstph/TBRU_serialTB/</p> <p><strong>Paper Abstract:</strong></p> <p><strong>Background:</strong></p> <p>Combination therapy is one of the most effective tools for limiting the emergence of drug resistance. Despite the widespread adoption of combination therapy across diseases, drug resistance rates continue to rise, leading to failing treatment regimens. The mechanisms underlying treatment failure are well studied, but the processes governing successful combination therapy are poorly understood. We addressed this question by studying the population dynamics of <em>Mycobacterium tuberculosis</em> within tuberculosis patients undergoing treatment with different combinations of antibiotics.</p> <p><strong>Results:</strong></p> <p>By combining very deep whole genome sequencing (~1,000-fold genome-wide coverage) with sequential sputum sampling, we were able to detect transient genetic diversity driven by the apparently continuous turnover of minor alleles, which could serve as the source of drug-resistant bacteria. However, we report that treatment efficacy had a clear impact on the population dynamics: sufficient drug pressure bore a clear signature of purifying selection leading to apparent genetic stability. In contrast, <em>M. tuberculosis</em> populations subject to less drug pressure showed markedly different dynamics, including cases of acquisition of additional drug resistance.</p> <p><strong>Conclusions:</strong></p> <p>Our findings show that for a pathogen like <em>M. tuberculosis</em>, which is well adapted to the human host, purifying selection constrains the evolutionary trajectory to resistance in effectively treated individuals. Nonetheless, we also report a continuous turnover of minor variants, which could give rise to the emergence of drug resistance in cases of drug pressure weakening. Monitoring bacterial population dynamics could therefore provide an informative metric for assessing the efficacy of novel drug combinations.</p>
Time-lapse 3D confocal microscopy videos of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains
<div>The dataset consists of time-lapse, 3D confocal images of mitochondrial dynamics in human alveolar epithelial cells (A549-DsRed) infected with Mycobacterium marinum (Mmar) strains. Images were captured at 60X magnification in an environmental chamber at 35°C for live-cell imaging. Host cell mitochondria were labeled with red fluorescent protein (RFP) and infected with both wildtype (wt) and ESAT-6 operon knockout mutant labeled with green fluorescent protein (GFP) at MOI of 100 for 24 hours at 35°C. Infected cells were identified and analyzed to explore the effect of pathogenic mycobacteria on mitochondrial morphology over time.</div> <div> </div> <div>More details available in this preprint: <a href="https://doi.org/10.48550/arXiv.2411.06035">https://doi.org/10.48550/arXiv.2411.06035</a></div>
Defining the genes required for survival of Mycobacterium bovis in the bovine host offers novel insights into the genetic basis of survival of pathogenic mycobacteria
<p>Supplementary dataset from "<strong><em>Defining the genes required for survival of Mycobacterium bovis in the bovine host offers novel insights into the genetic basis of survival of pathogenic mycobacteria</em></strong>"</p> <p> </p> <p><strong>Supplementary Figure legends</strong></p> <p><strong>Figure S1. Illustration of the transposon insertions around the <em>M. bovis </em>genome. </strong>Sequencing of the input library showed that transposon insertions were evenly distributed around the genome and 27,419 of the permissible 66,931 thymine–adenine dinucleotide (TA) sites contained an insertion representing an insertion density of ~41%. The outer ring are the genomic coordinates, the blue lines represent transposon insertions and the gray boxes indicate regions of that did not have any insertions. Plot made with Circlize (Gu et al, 2014).</p> <p> </p> <p><strong>Figure S2. Diversity of the output library isolated from lung and thoracic lymph node lesions compared to the input library. </strong>On average, libraries recovered from lung lesions contained 14,456 unique mutants and those recovered from the lymph nodes contained an average of 16,210 unique mutants. Insertion density is represented as a proportion of the TA sites that contained insertions. The numbers on the x-axis refer to the sequencing file from that sample and come from individual animals (Bioproject ID: PRJNA816175, Submission ID: SUB11067380).</p> <p> </p> <p><strong>Figure S3. Volcano plots showing the distribution of log<sub>2</sub> fold-changes and -log<sub>10</sub> of adjusted p-values for representative lung (A) and lymph node (B) samples. </strong>Adjusted p-values (BH-fdr correction) < 0.000001 cluster at the limits of the plot and precision reflects the number of resampling iterations (10,000).</p> <p> </p> <p><strong>Figure S4. Scatterplot of mean log<sub>2</sub> fold change per gene for all lung samples against all thoracic lymph node samples</strong>. Correlation between mean log<sub>2</sub> fold change among genes between the tissues was calculated with Spearman's ranked correlation, = 0.878, p-value < 2.2e-16.</p> <p> </p> <p><strong>Figure S5. Fold-changes caused by transposon insertions in <em>RD1<sup>BCG</sup></em> and <em>RD1<sup>MIC</sup> </em>in the lungs and lymph nodes of infected cattle. </strong>Boxplot for log<sub>2 </sub>fold-changes in genes of the RD1<sup>BCG</sup> region. Samples with adjusted p-values (BH-fdr corrected) <0.05 are indicated with purple points. Gene names highlighted in magenta have fewer than 5 TA sites located in the gene; too few to determine the statistical significance of changes in insertion levels with this method.</p> <p> </p> <p><strong>Supplementary Tables </strong></p> <p><strong>Table S1. Sequencing statistics of the input and output transposon libraries. </strong>The numbers in the column labelled “filename” refers to the sequencing file from that sample and come from individual animals (Bioproject ID: PRJNA816175, Submission ID: SUB11067380).</p> <p> </p> <p><strong>Table S2. Tissues collected and scored for gross pathology. </strong>Tissues from head and neck lymph nodes (from the right and left sub-mandibular lymph nodes, the right and left medial retropharyngeal lymph nodes), thoracic lymph nodes (the right and left bronchial lymph nodes, the cranial tracheobronchial lymph nodes, the cranial and caudal mediastinal lymph nodes) and from lung lesions, were collected and scored.</p> <p> </p> <p><strong>Table S3. Log<sub>2</sub> fold-changes for insertions across the entire genome of <em>M. bovis</em> AF2122/97. </strong>Cells are coloured according to log<sub>2</sub> fold-change. Refer to the text for the gene groups in individual tabs.</p> <p> </p> <p><strong>Table S4. </strong>Custom transposon sequencing primers and adaptors used in sequencing of the transposon libraries.</p> <p> </p>
Supplementary material for journal article "Challenge dose titration in a Mycobacterium bovis infection model in goats"
<p>Supplementary Figure and Table to Journal article. Figure shows daily rectal temperature of each animal after inoculation. Table 1 shows number and volume of pulmonary lesions for each animal as detected by computed tomography imaging. Table 2 gives details about scoring used at clinical examination.</p>
Development of a spectral library for the discovery of altered genomic events in Mycobacterium avium associated with virulence using mass spectrometry-based proteogenomic analysis
<p><em>Mycobacterium avium</em> is one of the prominent disease-causing bacteria in humans. It causes lymphadenitis, chronic and extrapulmonary, and disseminated infections in adults, children, and immunocompromised patients. <em>M. avium</em> has ~4,500 predicted protein-coding regions on an average, which can be helpful in discovering several variants at the proteome level. Many of them are potentially associated with virulence, thus identifying such proteins can be a helpful feature in the development of panel-based theranostics. In line with such a long-term goal, we carried out an in-depth proteomic analysis of <em>M. avium</em> with both data-dependent and data-independent acquisition methods. Further, a set of proteogenomic investigations were carried out using the protein database for <em>Mycobacterium tuberculosis,</em> and a genome six-frame translated database and a variant protein database of <em>M. avium</em>. A search of mass spectrometry data analysis against <em>M. avium</em> protein database resulted in the identification of 2,954 proteins. Further, proteogenomic analyses aided in the identification of 1,301 novel peptide sequences and correction of translation start sites for 15 proteins. At the end, we created a spectral library of <em>M. avium</em> proteins including novel genome search-specific peptides and variant peptides detected in this study. We validated the spectral library by a data-independent acquisition of the <em>M. avium</em> proteome. Thus, we present a <em>M. avium </em>spectral library of 29,033 peptide precursors supported by 0.4 million fragment ions for further use by the biomedical community.</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>
Multiple Sequence Alignment of a diverse dataset with 1788 Mycobacterium tuberculosis isolates
<p><strong>Multiple Sequence Alignment of a diverse dataset with 1788 <em>Mycobacterium tuberculosis</em> isolates used for <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a> benchmarking</strong></p> <p>The dataset comprises whole-genome sequence data published by <a href="https://doi.org/10.1016/S1473-3099(15)00062-6">Walker et al. 2015</a>. For the multiple sequence analysis, we proceeded as follows:</p> <ol> <li>Reads were downloaded from ENA BioProject <a href="https://www.ncbi.nlm.nih.gov/bioproject/PRJNA282721">PRJNA282721</a> (accessed on March 16<sup>th</sup>, 2023) and trimmed using Trimmomatic (<a href="https://pubmed.ncbi.nlm.nih.gov/24695404/">Bolger et al., 2014</a>) with <a href="https://github.com/B-UMMI/INNUca">INNUca</a> default settings;</li> <li>Quality-processed reads were individually mapped against the H37Rv reference genome (Genbank accession: <a href="https://www.ncbi.nlm.nih.gov/nuccore/NC_000962.3/">NC_000962.3</a>) using <a href="https://github.com/tseemann/snippy">Snippy</a> v4.5.1 and SNP-calling was performed on variant sites with the following criteria: a minimum proportion of reads differing from the reference of 70%, a minimum mapping quality of 30 and a minimum coverage for SNP calling of 10;</li> <li>A full alignment was extracted using Snippy’s core module (snippy-core), with masking of SNPs falling within known <em>M. tuberculosis</em> genomic regions with high GC content, repetitive elements and resistance-associated positions (corresponding to ~8% of the genome), as previously described for surveillance purposes (<a href="https://pubmed.ncbi.nlm.nih.gov/30948181/">Macedo et al., 2019</a>);</li> <li><em>M. tuberculosis </em>lineages were determined using tb-profiler v4.4.1 (<a href="https://pubmed.ncbi.nlm.nih.gov/31234910/">Phelan et al., 2019</a>), with samples from the <em>M. tuberculosis</em> complex other than <em>M. tuberculosis</em>, representing a mix of multiple lineages, or with less than 95% of mapped positions in the reference, being excluded;</li> <li>A filtered alignment comprising the maximum number of informative sites (88,562 nucleotide sites with at least one mutation in a given sequence) was extracted from the full alignment using the alignment_processing.py v1.1.0 (default settings) of <a href="https://github.com/insapathogenomics/ReporTree">ReporTree</a>, and then used as input for the benchmarking.</li> </ol> <p>In this repository, we provide two alignment files:</p> <ul> <li>Core_MTB_1787_strs.full.aln: this corresponds to the full multiple sequence alignment comprising 1787 samples and the reference (corresponding to the point 4 of the methodology).</li> <li>MTb_original_align_profile.fasta: this corresponds to the multiple sequence alignment comprising 1787 samples and the reference and only presenting the alignment informative sites (corresponding to the point 5 of the methodology)</li> </ul>
X-ray diffraction images for the H145E mutant of the iron-dependent superoxide dismutase from Mycobacterium tuberculosis.
<p>X-ray diffraction images of the H145E mutant (prefixed h145e) which were collected in October 1995 using a graphite-monochromated copper K-alpha rotating anode source (wavelength 1.5418 Å) with a Marresearch 90 cm image plate detector at a distance of 120 mm from the crystal. The data were collected at room temperature in two passes, each consisting of 100 one degree rotations of the crystal. Each image had an exposure time of 20 minutes. The crystal was rotated in the capillary tube prior to collection of the second pass in order to record the 'blind' region of the diffraction pattern and this set of images is prefixed h145eb. </p>
X-ray diffraction images for the H145Q mutant of the iron-dependent superoxide dismutase from Mycobacterium tuberculosis.
<p>X-ray diffraction images collected from one crystal at room temperature using a rotating anode copper source (wavelength 1.5418 Å) and a 30 cm Marresearch image plate detector. The crystal-to-detector distance was 150 mm and a 90 mm image plate scan radius was used. Each of the 60 images had an exposure time of 20 minutes and corresponds to a 3 degree phi-rotation of the crystal. Diffraction extends to about 3.3 Å resolution. </p>
User Guide – Dashboard on Zoonotic tuberculosis: Mycobacterium
<p>User Guide – Dashboard on Zoonotic tuberculosis focusing on Mycobacterium bovis and M. caprae</p>
Dataset from Remote analysis of Sputum Smears for Mycobacterium Tuberculosis Quantification using Digital Crowdsourcing
<p>Worldwide, TB is one of the top 10 causes of death and the leading cause from a single infectious agent. Although the development and roll out of Xpert MTB/RIF has recently become a major breakthrough in the field of TB diagnosis, smear microscopy remains the most widely used method for TB diagnosis, especially in low- and middle-income countries.</p> <p>This is a minimal dataset to reproduce our research that tests the feasibility of a crowdsourced approach to tuberculosis image analysis. In particular, we investigated whether anonymous volunteers with no prior experience would be able to count acid-fast bacilli in digitized images of sputum smears by playing an online game. Following this approach 1790 people identified the acid-fast bacilli present in 60 digitized images, the best overall performance was obtained with a specific number of combined analysis from different players and the performance was evaluated with the F1 score, sensitivity and positive predictive value, reaching values of 0.933, 0.968 and 0.91, respectively.</p> <p>The dataset includes 24 digitized images of sputum smears and the corresponding gameplays clicks. </p>
Sequence Similarity Network (SSN) and Genome Neighbourhood Network (GNN) for Mycobacterium Cytochrome P450 enzymes
<p>This dataset was generated in the context of the Horizon 2020 MSCA IF action deCrYPtion (Grant 839116). The aim of this project is to use comparative genomics in order to propose and then test the function of uncharacterised Cytochrome P450 enzymes that are present among Mycobacterium species.</p> <p>More information about this project can be found at: https://cordis.europa.eu/project/id/839116.</p> <p>This dataset contains:</p> <p>- The FASTA sequences files obtained from the UniProt database, for members of the PF00067 protein family (CYP).</p> <p>- A set of reference FASTA sequences, matching the supplementary material from the following publication: Parvez, M. <em>et al.</em> (2016) ‘Molecular evolutionary dynamics of cytochrome P450 monooxygenases across kingdoms: Special focus on mycobacterial P450s’, <em>Scientific Reports</em>, 6(1), p. 33099. doi:<a href="https://doi.org/10.1038/srep33099">10.1038/srep33099</a>.</p> <p>- A combined FASTA files of both previously described, that was used for the generation of SSNs</p> <p>- A PNG image produced from the analysis of the Sequence Similarity Networks generated at AST78 (corresponding to 40% identity, defining CYP families)</p> <p>- A PNG image produced from the analysis of the Sequence Similarity Networks generated at AST141 (corresponding to 55% identity, defining CYP subfamilies)</p> <p>- A Cytoscape session for the Sequence Similarity Networks from the combined FASTA file generated using the Enzyme Function Initiative web tools (https://efi.igb.illinois.edu), at AST78</p> <p>- A Cytoscape session containing Sequence Similarity Networks and Genome Neighborhood Network from the combined FASTA file generated using the Enzyme Function Initiative web tools (https://efi.igb.illinois.edu), at AST141</p>
Flexible nitrogen utilisation by the metabolic generalist pathogen Mycobacterium tuberculosis
<p>Data on uptake and metabolism of all 20 amino acids by Mycobacterium tuberculosis H37Rv (20 amino acids uptake.zip), shown in Figure 2.</p> <p>Data on kinetics of labelling on 15N-Asp (15N Asp.zip), shown in Figure 4g and h.</p> <p>Data on kinetics of labelling on 15N2-Asn (15N Asn.zip), shown in Figure 4g and h.</p> <p>Data on kinetics of labelling on 15N-Glu (15N Glu.zip), shown in Figure 4g and h.</p> <p>Data on kinetics of labelling on 15N2-Gln (15N Gln.zip), shown in Figure 4g and h.</p> <p>Data on kinetics of labelling on 15N-NH4+ (15N NH4+.zip), shown in Figure 4g and h.</p> <p>Data on position specific labelling with Asn and Gln (PIL.zip), shown in Figure 4b to f.</p> <p>Data on co-metabolism of Glu/Gln and Asp/Asn (co-metabolism.zip), shown in Figure 5.</p>
Mycobacterium tuberculosis database
<p>This database contains the <em>M. tuberculosis</em> H37Rv reference genome (RefSeq accession GCF_000195955.2), plus 17 high-quality <em>M. tuberculosis</em> references from lineages 1-6 (from https://doi.org/10.1186/s13059-021-02474-0 - see mtb_gramtools_lineages.csv for the list of accessions).</p>
Mycobacterium representative kraken2 database
<p>A kraken2 database built from the a representative <em>Mycobacterium</em> set of genomes. This archive contains the three files required by kraken2, hash.k2d, opts.k2d, and taxo.k2d, along with inspect.txt, which is obtained by running kraken2-inspect on the database.</p> <p>The genomes for this database were downloaded wiuth genome_updater.sh (v0.6.3; https://github.com/pirovc/genome_updater) with one RefSeq genome from each species in the Mycobacteriaceae family, plus one RefSeq genome from each species in the following genera: <em>Klebsiella, Escherichia, Salmonella, Enterobacter, Streptococcus, Staphylococcus, Pseudomonas, Xanthomonas</em>, and <em>Bifidobacterium</em>.</p> <pre><code>genome_updater.sh -A "species:1" -m -a -M "gtdb" -f "genomic.fna.gz" -g "bacteria" -d "refseq" -T "f__Mycobacteriaceae,g__Klebsiella,g__Escherichia,g__Enterobacter,g__Salmonella,g__Streptococcus,g__Staphylococcus,g__Pseudomonas,g__Xanthomonas,g__Bifidobacterium" -o GTDB_Mycobacterium/ </code></pre> <p>The python script prepare_kraken_fasta.py was then used to prepare the assemblies for use in kraken with the following command</p> <pre><code>python prepare_kraken_fasta.py -r -x GCF_932530395.1,GCF_017190695.1,GCF_020735285.1,GCA_014701265.1,GCF_000013925.1,GCF_016756075.1,GCF_010727125.1,GCF_001307545.1 -o Mycobacterium.fna -s assembly_summary.txt GTDB_Mycobacterium/</code></pre> <p>The database was then built with kraken2 using the following commands</p> <pre><code>kraken2-build --download-taxonomy --db db/ kraken2-build --add-to-library Mycobacterium.fna --db db/ kraken2-build --build --db db/ --threads 16</code></pre>
Mycobacterium genus database
<p>The database is built as a <em>Mycobacterium</em>-specific one. For this database, we used genome_updater.sh (v0.6.3; https://github.com/pirovc/genome_updater)</p> <pre><code>genome_updater.sh -d "refseq" -g "bacteria" -T "g__Mycobacterium" -f "genomic.fna.gz" -M "gtdb" -A 1 -m</code></pre> <p>In addition, we added 17 high-quality <em>M. tuberculosis</em> genomes to this collection (from https://doi.org/10.1186/s13059-021-02474-0 - see mtb_gramtools_lineages.csv for the list of accessions).</p> <p>The mtb.ids file lists the sequence identifiers for those sequences in the database which are <em>M. tuberculosis</em>.</p>
Data from: Pleomorphic effects of three small-molecule inhibitors on transcription elongation by <em>Mycobacterium tuberculosis</em> RNA polymerase
Open the record for dataset details and reuse information.
Whole genome sequence data of Mycobacterium tuberculosis and Mycolicibacterium smegmatis mutants of the riboflavin biosynthetic pathway- Part 1
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.