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113 results for “mash”
Mash Sketch of RefSeq Bacterial Representative Genomes v217
<p>This was created to get a new mash reference that was current. The script to create this uses ncbi datasets and mash (https://github.com/UPHL-BioNGS/Grandeur/blob/main/bin/new_mash_ref.sh)<br> <br> This was created on April 27, 2023, and is RefSeq v217<br> <br> ```bash</p> <p>#/bin/bash<br> out=mash_db<br> <br> mkdir $out</p> <p>cd $out</p> <p>datasets summary genome taxon bacteria --reference --as-json-lines | \</p> <p>dataformat tsv genome --fields accession,assminfo-refseq-category,organism-name --elide-header | \</p> <p>grep representative | \</p> <p>tee representative_genomes.txt | \</p> <p>cut -f 1 > genome_ids.txt</p> <p>echo "$(date): Downloading genomes for ids"</p> <p>datasets download genome accession --inputfile genome_ids.txt --filename rep-genomes.zip</p> <p>echo "$(date): Decompressing zip file"</p> <p>unzip rep-genomes.zip</p> <p>echo "$(date): Creating file for mash"</p> <p>cat ncbi_dataset/data/*/*.fna | sed 's/ /_/g' | sed 's/,//g' > rep-genomes.fasta</p> <p>echo "$(date): Skeching rep-genomes.fasta"</p> <p>mash sketch -i -p 20 rep-genomes.fasta -o rep-genomes</p> <p>############################################################</p> <p>echo "$(date): File preparation is complete"</p> <p>ls -alh rep-genomes.fasta</p> <p>ls -alh rep-genomes.msh<br> <br> ```</p>
Mash Sketch of RefSeq Bacterial Reference Genomes
<p>The mash reference that can be downloaded from <a href="https://mash.readthedocs.io/en/latest/data.html">the mash documentaion</a> is for RefSeq version 70.</p> <p>I do not inherently have a problem with RefSeq version 70, but RefSeq is well past version 200 now. </p> <p>RefSeq updates four times year, and I needed an easy way to create and distribute a mash sketch file of the representative bacterial/prokaryotic genomes.<br><br>This is intended to be a place to hold the mash sketches from <a href="https://github.com/erinyoung/update_mash_dist">https://github.com/erinyoung/update_mash_dist</a>.<br><br>The mash sketch file from erinyoung/update_mash_dist requires git lfs to be installed when cloning the repository, which is cumbersome for some users.<br><br>The update requency is intended to mirror that of RefSeq (i.e. 4 time a year), but... is likely to be less frequent than that.<br><br>Don't hesitate to <a href="https://github.com/erinyoung/update_mash_dist/issues">submit an issue</a> if this needs to get updated.<br><br>I do have some prior zenodo repositories (https://zenodo.org/records/10519852 , https://zenodo.org/records/7887021 , and https://zenodo.org/records/7348463 ) which hold the same mash sketch reference, but the refseq version is in the title. I'd rather have one repository that gets updated rather than create new repositories each time.<br><br>This is how the mash reference file was created:<br><br></p> <pre><code># Step 1. Download Datasets and Dataformat </code></pre> <pre><code>wget https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/datasets wget https://ftp.ncbi.nlm.nih.gov/pub/datasets/command-line/v2/linux-amd64/dataformat chmod +x datasets dataformat</code></pre> <pre><code> # Step 2. Download Mash <br></code></pre> <pre><code> wget https://github.com/marbl/Mash/releases/download/v2.3/mash-Linux64-v2.3.tar tar -xvf mash-Linux64-v2.3.tar </code></pre> <pre><code><br> # Step 3. Get a list of all the genomes # Note: this also changes how some of the names are represented datasets summary genome taxon bacteria --reference --as-json-lines | \ dataformat tsv genome --fields accession,organism-name --elide-header | \ sed 's/\[//g' | \ sed 's/\]//g' | \ sed 's/["'\'']//g' | \ sed 's/endosymbiont of /endosymbiont_of_/g' > \ ids.txt # Step 4. Download the reference files and sketch them # Note: Since this is done in Github Actions (GA), I need to keep everything below 30G. # The best way to do this is to download the process each reference file individually, and then combine it to the whole. # This obviously does not need to be followed if not under those same limitations. while read line do id=$(echo $line | awk '{print $1}') ge=$(echo $line | awk '{print $2}') if [ ! -n "$ge" ] ; then ge="unknown" ; fi sp=$(echo $line | awk '{print $3}') if [ ! -n "$sp" ] ; then sp="unknown" ; fi datasets download genome accession $id unzip ncbi_dataset.zip cp ncbi_dataset/data/*/*_genomic.fna ${ge}_${sp}_${id}.fasta if [ ! -f RefSeqSketches_${version}.msh ] then mash sketch ${ge}_${sp}_${id}.fasta -o RefSeqSketches_${version} else mash sketch ${ge}_${sp}_${id}.fasta -o ${ge}_${sp}_${id} mv RefSeqSketches_${version}.msh tmp.msh mash paste RefSeqSketches_${version} tmp.msh ${ge}_${sp}_${id}.msh rm tmp.msh ${ge}_${sp}_${id}.msh fi rm ${ge}_${sp}_${id}.fasta rm -rf ncbi_dataset/ rm ncbi_dataset.zip rm README.md rm md5sum.txt done < ids.txt</code></pre> <pre><br><br>To use</pre> <pre><code># download file wget <insert url for file> mask sketch sample.fasta RefSeqSketches_<version>.msh > mash_results.txt # These results are unsorted, so many find it useful to sort them. sort -gk3 mash_results.txt > sorted_mash_results.txt</code></pre> <p> <br>The should look like the following:</p> <pre><code>2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_pyogenes_GCF_900475035.1.fasta 0.0116661 0 643/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_dysgalactiae_GCF_016128095.1.fasta 0.0782587 0 107/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_canis_GCF_900636575.1.fasta 0.132399 2.34894e-153 32/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_agalactiae_GCF_001552035.1.fasta 0.164662 1.32611e-72 16/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_castoreus_GCF_000425025.1.fasta 0.174408 2.34302e-58 13/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_didelphis_GCF_000380005.1.fasta 0.182269 8.30736e-49 11/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_uberis_GCF_900475595.1.fasta 0.186761 5.62934e-44 10/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_iniae_GCF_000831485.1.fasta 0.191731 3.33152e-39 9/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_ictaluri_GCF_000188015.2.fasta 0.197292 1.75608e-34 8/1000 2024CK-00429-UT-M03999-240412_contigs.fa Streptococcus_phocae_GCF_001302265.1.fasta 0.203604 2.46548e-30 7/1000</code></pre> <pre> </pre>
Mash-based analyses of E. coli genomes reveal 14 distinct phylogroups
<p>To replicate this study the scripts should be ran in the following order. It is expected that you download and unzip the Data.zip file and replace the variable `input_root_folder` in all scripts with the complete file path of the folder where the unzipped Data folder is. </p> <p>Step 1: Run dataset_creation_filtering_and_stat_filtering.py</p> <p>Step 2: Run heatmaps_meoids_newick_pcoa.R (Produces Figure 1 and Figure 3b)</p> <p>Step 3: Run dataset_analysis.py</p> <p>Step 4: Run SRA_heatmap_plots.py (Produces Figure 2c)</p> <p>Step 5: Run Figure_3a.R (Produces Figure 3a)</p> <p>Step 6: Run pangenome_plot_MS.py (Produces Figure 3c)</p> <p> </p> <p> </p>
GTDB r220 Mash Database (UNOFFICIAL MIRROR)
<p><strong>This is an UNOFFICIAL host for the GTDB mash sketch based on GTDB r220</strong></p> <p>Intended use of this file is to include in the VEBA database for quicker GTDB-Tk analysis. </p> <p>Created by running the following command using <strong>GTDB-Tk v2.4.0 </strong>on the S1 sample from <a href="../record/7946802">Zenodo:7946802</a>: </p> <pre><code>gtdbtk classify_wf --genome_dir veba_output/binning/prokaryotic/S1/output/genomes/ --out_dir test_output -x fa --cpus 1 --mash_db ./gtdb_r220.msh</code><br><br></pre> <p><strong>Source Files: </strong></p> <p><a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release220/220.0/auxillary_files/gtdbtk_package/full_package/gtdbtk_r220_data.tar.gz">gtdbtk_r220_data.tar.gz</a></p> <pre><a href="https://data.ace.uq.edu.au/public/gtdb/data/releases/release220/220.0/RELEASE_NOTES.txt">RELEASE_NOTES.txt</a><br><br></pre> <blockquote> <pre>Release 220.0: -------------- GTDB release R09-RS220 comprises 596,859 genomes organised into 113,104 species clusters. Additional statistics for this release are available on the GTDB Statistics page. Release notes: -------------- - Average nucleotide identity (ANI) between genomes is now calculated using skani (Shaw et al., Nat Methods, 2023) instead of FastANI (Jain et al, Nat Commun, 2018). skani provides a substantial reduction in computational requirements while producing similar ANI values and more accurate alignment fraction (AF) values. - CheckM v2 information is included on the website and in the metadata files, noting at this stage that these data were not used for the QC step in release 220. - Post-curation cycle, we identified updated spelling for 15 taxon names: p__Calescibacterota (updated name: Calescibacteriota) c__Brachyspirae (updated name: Brachyspiria) c__Leptospirae (updated name: Leptospiria) o__Ammonifexales (updated name: Ammonificales) o__Exiguobacterales (updated name: Exiguobacteriales) o__Hydrogenedentiales (updated name: Hydrogenedentales) o__Phormidesmiales (updated name: Phormidesmidales) f__Arcanobacteraceae (updated name: Arcanibacteraceae) f__Acetonemaceae (updated name: Acetonemataceae) f__Ethanoligenenaceae (updated name: Ethanoligenentaceae) f__Exiguobacteraceae (updated name: Exiguobacteriaceae) f__Geitlerinemaceae (updated name: Geitlerinemataceae) f__Koribacteraceae (updated name: Korobacteraceae) f__Phormidesmiaceae (updated name: Phormidesmidaceae) f__Porisulfidaceae (updated name: Poriferisulfidaceae) Note that the LPSN linkouts point to the correct updated names. We encourage users to use the updated names as these will appear in the next release. - Post-curation cycle, we discovered that two provisionally named families, Nitrincolaceae and Denitrovibrionaceae have been validly named under the ICNP as Balneatricaceae and Geovibrionaceae, respectively. We encourage users to use the validly published names as these will appear in the next release. - We thank Jan Mares for his assistance in curating the class Cyanobacteriia and Brian Kemish for providing IT support to the project.</pre> </blockquote> <p> </p> <p><strong>If you have found this useful, please cite the original publications: </strong></p> <ul> <li>Chaumeil PA, et al. 2022. <a href="https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btac672/6758240?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email">GTDB-Tk v2: memory friendly classification with the Genome Taxonomy Database</a>. <em>Bioinformatics</em>, btac672.</li> <li>Parks, D.H., et al. (2021). <a href="https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkab776/6370255">GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy</a>. <em>Nucleic Acids Research</em>, <strong>50</strong>: D785–D794.</li> </ul>
Mash Sketch of RefSeq Bacterial Representative Genomes v215
<p>Prepared as described in https://github.com/UPHL-BioNGS/DesolationCanyon.</p> <p> </p> <p>This is the representative genomes of refseq 215 with their taxon name.</p>
GTDB r214.1 Mash Database (UNOFFICIAL MIRROR)
<p><strong>This is an UNOFFICIAL host for the GTDB mash sketch based on GTDB r214.1</strong></p> <p>Intended use of this file is to include in the VEBA database for quicker GTDB-Tk analysis. </p> <p>Created by running the following command using <strong>GTDB-Tk v2.3.0 </strong>on the S1 sample from <a href="https://zenodo.org/record/7946802">Zenodo:7946802</a>: </p> <pre><code>gtdbtk classify_wf --genome_dir veba_output/binning/prokaryotic/S1/output/genomes/ --out_dir test_output -x fa --cpus 1 --mash_db ./gtdb_r214.msh</code></pre> <p> </p> <p><strong>Source Files: </strong></p> <p><a href="https://data.gtdb.ecogenomic.org/releases/release214/214.1/auxillary_files/gtdbtk_r214_data.tar.gz">gtdbtk_r214_data.tar.gz</a></p> <p> </p> <p><strong><a href="https://data.gtdb.ecogenomic.org/releases/release214/214.1/RELEASE_NOTES.txt">RELEASE_NOTES.txt</a></strong></p> <blockquote> <pre>Release Notes: Release 214.1: ------------- Correction regarding the classification of the genome "GB_GCA_902406375.1" in 214.1 release. We have identified an error in the taxonomy assignment for this particular genome. The genome GB_GCA_902406375.1 was previously classified as Collinsella sp905215505 in some files . We have reevaluated the taxonomy and determined that the correct classification should be Collinsella sp002232035. We have rectified this error and made the necessary updates to the following files within the package: - bac120_taxonomy_r214.tsv - sp_clusters_r214.tsv - ssu_all_r214.tar.gz Notes: ------ - We thank Jan MareÅ¡ for his help in curating the Cyanobacteria - Phylum names have been updated following the valid publication of 42 names in IJSEM (https://pubmed.ncbi.nlm.nih.gov/34694987/), including Bacillota and Pseudomonadota - Fixed issue with SSU files where sequences started 2 bp after correct start and stopped 1 bp after correct end of sequence. Thanks to CX for bringing this issue to our attention: https://forum.gtdb.ecogenomic.org/t/16s-23s-and-ssu-all-r207/307/2 - SSU files now provide sequences in their 5' to 3' orientation - Changed QC criterion for number of contigs from 1000 to 2000 in order to better align the GTDB criteria with RefSeq (https://www.ncbi.nlm.nih.gov/assembly/help/anomnotrefseq/) - Changed QC criterion to use ar53 instead of ar122 marker set. The impact of this change was evaluated on the 353,569 genomes (~6,100 archaeal) considered for GTDB R207: -- only 1 additional genome passed QC -- only 21 additional genomes failed QC which included the following species representatives: -- s__Methanoregula sp002497485 -- s__Methanobrevibacter_A sp017634055 -- s__Methanosphaera sp003266165 -- s__MGIIa-L1 sp002688825 -- s__MGIIb-N2 sp002503665 -- s__MGIIa-L2 sp002692685 -- s__MGIIb-O3 sp002730445 -- s__DTDI01 sp011334935 -- s__Methanosphaera sp017652595 -- s__Nitrosopelagicus sp902606945 -- s__Methanolinea sp002501965</pre> </blockquote> <p><strong>If you have found this useful, please cite the original publications: </strong></p> <ul> <li>Chaumeil PA, et al. 2022. <a href="https://academic.oup.com/bioinformatics/advance-article-abstract/doi/10.1093/bioinformatics/btac672/6758240?utm_source=advanceaccess&utm_campaign=bioinformatics&utm_medium=email">GTDB-Tk v2: memory friendly classification with the Genome Taxonomy Database</a>. <em>Bioinformatics</em>, btac672.</li> <li>Parks, D.H., et al. (2021). <a href="https://academic.oup.com/nar/advance-article/doi/10.1093/nar/gkab776/6370255">GTDB: an ongoing census of bacterial and archaeal diversity through a phylogenetically consistent, rank normalized and complete genome-based taxonomy</a>. <em>Nucleic Acids Research</em>, <strong>50</strong>: D785–D794.</li> </ul>
Effect of Endoscopic Sleeve Gastroplasty in Patients With Obesity and MASH: A Randomized Controlled Trial
ClinicalTrials.gov study NCT06138821. IPD Sharing: YES. Countries: 1. Publications: 28.
Resmiterom Efficacy & Safety in Patients With MASH
ClinicalTrials.gov study NCT07249788. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Global Research Initiative for Patients Screening on MASH
ClinicalTrials.gov study NCT05651724. IPD Sharing: UNDECIDED. Countries: 10. Publications: 1.
Minocycline for Aneurysmal Subarachnoid Hemorrhage (MASH)
ClinicalTrials.gov study NCT04876638. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Determinging the effect of mashing regime, protease and carbohydrase additions on viscosity development during gelatinisation of faba bean starch as well as on wort run off rate
<p>(i) and (ii) Preliminary experiments have found that when faba bean accounts for a high proportion of the beer recipe the efficiency of the mashing step is negatively impacted with slow solid removal and reduced yields. The addition of commercial enzymes is an established practice to increase levels of fermentable sugars, nitrogen availability and wort run off rate therefore enzymatic additions have been trialled on faba bean wort.<br> (iii) 2019<br> (iv) Vicia faba<br> (v) N/A<br> (vi) A rheometer method has been developed to recreate two typical mashing regimes. Samples are milled to produce a fine grist. A fixed amount of the grist is then used to form a slurry with a calculated weight of water with enzyme additions and run on a pre-set rheometer programme. The programme consists of an initial rapid stir followed by continuous stirring as one of two potential mashing temperature regimes are followed. The slurry is then filtered and the volume of filtrate recorded over time.<br> (vii) An ANOVA confirmed that all three enzymes were necessary for a significant reduction in the wort flow rate. <br> (viii) Complete</p>
[DATA_SCIENCE] Interviews: The Medical and Environmental Data Mash-up Infrastructure (MEDMI)
<p>This is a set of interview transcripts executed by Niccolò Tempini between March and October 2016, as part of the ERC project "The Epistemology of Data-Intensive Science", and in the context of a case study of MEDMI. Please read the "Notes on transcript editing" document for further information.</p> <p>Three papers or chapters that specifically make use of these interviews have been published as of 2020:</p> <ul> <li>Tempini, N., Leonelli, S., 2018. Concealment and discovery: The role of information security in biomedical data re-use. Soc Stud Sci 48, 663–690. <a href="https://doi.org/10.1177/0306312718804875">https://doi.org/10.1177/0306312718804875</a></li> <li>Leonelli, S., Tempini, N., 2018. Where health and environment meet: the use of invariant parameters in big data analysis. Synthese 1–20. <a href="https://doi.org/10.1007/s11229-018-1844-2">https://doi.org/10.1007/s11229-018-1844-2</a></li> <li>Tempini, N. 2020. The Reuse of Digital Computer Data: Transformation, Recombination and Generation of <em>Data Mixes</em> in Big Data Science. In: Leonelli S., Tempini N. (eds) Data Journeys in the Sciences. Springer, Cham. <a href="https://doi.org/10.1007/978-3-030-37177-7_13">https://doi.org/10.1007/978-3-030-37177-7_13</a></li> </ul> <p>The transcripts document MEDMI researchers' experience of infrastructure development and data curation and re-use practices. Researchers have consented to have these transcripts made available as Open Data. Other interviewees did not give consent, so those transcripts are held securely by the research team in Exeter.<br> You also find the information sheet provided to interviewees, which gives you the context for this project. Further information and related publications can be found at www.datastudies.eu.</p>
Mojito Study (Mashed Or Just Integral Pill of TicagrelOr ? )
ClinicalTrials.gov study NCT01992523. IPD Sharing: Not stated. Countries: 2. Publications: 0.
A Phase 3 Study Evaluating the Safety and Efficacy of Denifanstat in Patients With MASH and F2/F3 Fibrosis
ClinicalTrials.gov study NCT06594523. IPD Sharing: NO. Countries: 0. Publications: 0.
A Precision Medicine Approach Using Gene Silencing to Treat a Chronic Liver Disease Called Metabolic Dysfunction-Associated Steatohepatitis (MASH) in Adult Participants at Increased Genetic Risk for T
ClinicalTrials.gov study NCT05519475. IPD Sharing: YES. Countries: 4. Publications: 0.
Resolvin D1–mediated cellular crosstalk protects against MASH [scRNA-seq]
GEO Series GSE263770. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
Antigen-driven CD8+ T cell clonal expansion is a prominent feature of MASH in humans and mice [human]
GEO Series GSE270488. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing; Other.
Duodenal-derived organoids from MASH patients exhibit altered digestive homeostasis.
GEO Series GSE268518. Homo sapiens. 39 samples. Type: Expression profiling by high throughput sequencing.
LXRalpha Controls Metabolism Associated Steatotic Hepatitis (MASH)
GEO Series GSE267011. Mus musculus. 36 samples. Type: Expression profiling by high throughput sequencing.
DIO-MASH mice analysed for hepatic gene expression after treatment with semaglutide
GEO Series GSE294629. Mus musculus. 77 samples. Type: Expression profiling by high throughput sequencing.
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