Find research datasets worth reusing

opencc-by-4.0Oct 2025View details →

Dataset for publication: An inter-laboratory study characterizes the impact of bioinformatic approaches on genome-based cluster detection for foodborne bacterial pathogens

This dataset is part of a dry-lab interlaboratory study conducted across Germany, regarding bacterial outbreak detection based on NGS data, with a focus on bioinformatic analysis of four species to identify potential variability caused by different data analysis approaches and human interpretation. Participants were asked to follow their usual in-house protocols while adhering to the general guidelines. A quality assessment (with sample exclusion) was followed by 7-gene Multilocus-Sequence Typing (MLST), core genome Multilocus Sequencing Typing (cgMLST), and SNP calling. The participants were then asked to identify clusters. The study was not intended to resemble a standard proficiency test with a passing/failing grade, but rather to investigate and quantify obvious variability in the results and, where possible, the reasons for it. For this purpose, the datasets included borderline cases in terms of quality.

opencc-by-nc-sa-4.0Aug 2024View details →

BacSPaD: A robust bacterial strains' pathogenicity resource based on integrated and curated genomic metadata

The vast array of omics data in microbiology presents significant opportunities for studying bacterial pathogenesis and creating computational tools for predicting pathogenic potential. However, the field lacks a comprehensive, curated resource that catalogs bacterial strains and their ability to cause human infections. Current methods for identifying pathogenicity determinants often introduce biases and miss critical aspects of bacterial pathogenesis. In response to this gap, we introduce BacSPaD (Bacterial Strains’ Pathogenicity Database), a thoroughly curated database focusing on pathogenicity annotations for a wide range of high-quality, complete bacterial genomes. Our rule-based annotation workflow combines metadata from trusted sources with automated keyword matching, extensive manual curation, and detailed literature review. Our analysis classified 5,502 genomes as pathogenic to humans (HP) and 490 as non-pathogenic to humans (NHP), encompassing 532 species, 193 genera, and 96 families. Statistical analysis demonstrated a significant but moderate correlation between virulence factors and HP classification, highlighting the complexity of bacterial pathogenicity and the need for ongoing research. This resource is poised to enhance our understanding of bacterial pathogenicity mechanisms and aid in the development of predictive models. To improve accessibility and provide key visualization statistics, we developed a user-friendly web interface, accessible at<a href="https://bacspad.altrabio.com/"> </a><a href="https://bacspad.altrabio.com/">https://bacspad.altrabio.com</a>.

opencc-by-sa-4.0Feb 2023View details →

Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic

De novo assembly of ancient metagenomes to reconstruct biosynthetic machinery and access Pleistocene bacterial metabolites. This Zenodo archive belongs to the following publication (DOI: <a href="https://doi.org/10.1126/science.adf5300" target="_blank" rel="noopener">10.1126/science.adf5300</a>): Klapper, M., Hübner, A., Ibrahim A. et al. Natural products from reconstructed bacterial genomes of the Middle and Upper Paleolithic. Science 380, 619–624 (2023). This Zenodo archive contains the GitHub repository <a href="https://github.com/paleobiotechnology/EMN001_Paleofuran" target="_blank" rel="noopener">Paleobiotechnology/EMN001_Paleofuran</a> with all code and and data used for the publication mentioned above, as well as additional figures, huge NMR and GCMS data, which are not stored in the GitHub repository but have been deposited in this long-term Zenodo archive (see directory tree below). All datasets and scripts required for the project analysis can be found in this directory:  ├── GCMS.tar.gz ├── github.tar.gz ├── licenses.tar.gz ├── MS_figures.tar.gz └── NMR.tar.gz

opencc-by-4.0Mar 2023View details →

Species-colored Themisto v3 index with 640k bacterial genomes

This is a Themisto v3 [1] index containing the 639,981 high-quality genomes from 661k bacterial genomes dataset of Blackwell et al. [2]. The index contains all distinct 31-mers of the dataset (both strands). There are 71 billion distinct 31-mers in the data (35.5 billion reverse complement pairs). Each k-mer is annotated with the set of species identifiers that contain that 31-mer. The species identifiers are called colors. There are 2340 distinct colors in the dataset, so the color identifiers range from 0 to 2339. To pseudoalign reads.fastq against the index using 16 threads, install <a href="https://github.com/algbio/themisto">Themisto v3</a>, and use the following command: <pre><code class="language-bash">themisto pseudoalign -q reads.fastq -i themisto_640k/index -t 16 --temp-dir .</code></pre> This will output one line of space-separated integers per read in the input. The first integer on a line is the zero-based rank of the read in the fastq file, and the rest of the integers are the identifiers of colors that are compatible with the read. The file color_names.csv lists the species name and the taxid for each color. The pseudoalignment counts should not be directly used as abundance estimates because they only describe which reads are compatible with which species, and a single read may be compatible with many. We recommend using mSWEEP to estimate abundances based on the pseudoalignment data: https://github.com/PROBIC/mSWEEP. -- The index was constructed with Themisto v3.0.0 using the following command line parameters: <pre><code class="language-bash">themisto build -i input_file_list.txt --file-colors --reverse-complements -o 640k_bacteria -m 512000 -t 48 -k 31 --temp-dir temp --verbose -d 20</code></pre> The file source_accessions.txt lists the accession numbers of assemblies included in the database. [1] Alanko, J. N., Vuohtoniemi, J., Maklin, T., & Puglisi, S. J. (2023). Themisto: a scalable colored k-mer index for sensitive pseudoalignment against hundreds of thousands of bacterial genomes. bioRxiv, 2023-02. [2] Blackwell, G. A., Hunt, M., Malone, K. M., Lima, L., Horesh, G., Alako, B. T., ... & Iqbal, Z. (2021). Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences. PLoS biology, 19(11), e3001421.

opengpl-2.0Feb 2021View details →

R script for identification and localisation of prophage within bacterial genomes using outward-oriented paired-end reads.

This R script shows an analysis example of using outwards-oriented paired-end reads (OPRs), identified using the OPR finder function in the mVIRs package, to identify p22 in S. Tm LT2 as described in the publication "High throughput sequencing provides exact genomic locations of inducible prophages and accurate phage-to-host ratios in gut microbial strains" by Zünd et al. Microbiome (2021)

opencc-by-4.0May 2017View details →

Bacterial training dataset for Galaxy training network tutorials on Genome assembly

This training dataset is from an imaginary Staphylococcus aureus bacterium with a miniature genome. There is a reference genome in various formats as well as some fastq reads of a closely related but also imaginary mutant strain. It is a useful dataset for demonstrating: <ul> <li>de novo genome assembly</li> <li>read mapping and variant calling</li> <li>genome annotation</li> </ul> The files included are: <ul> <li>wildtype.fna: the reference genome sequence of the wildtype strain in fasta format (a header line, then the nucleotide sequence of the genome.)</li> <li>wildtype.gff: the reference genome sequence of the wildtype strain in general feature format (a list of features - one feature per line, then the nucleotide sequence of the genome.)</li> <li>wildtype.gbk: the reference genome sequence in genbank format.</li> <li>mutant_R1.fastq and mutant_R2.fastq: Fastq sequence reads of a closely related mutant strain. <ul> <li>The reads are paired-end.</li> <li>Each read is 150 bases long.</li> <li>The number of bases sequenced is equivalent to 19x the genome sequence of the wildtype strain. (Read coverage 19x - rather low!).</li> </ul> </li> </ul>

opencc-by-4.0Jun 2024View details →

Dataset for "Bacterial genome annotation" and "AMR gene detection" workflows

This dataset is associated with the workflows "Bacterial genome annotation" and "AMR gene detection in an assembled bacterial genome".

opencc-by-4.0Oct 2023View details →

skDER Representative Genomes for Select Bacterial Taxa

Genomes belonging to a single genus or order were gathered using a loose search of taxonomic classifications in GTDB R214. By loose we required the string 'g__{GENUSNAME}' to be found in taxonomic info column by GTDB, thus allowing gathering of associated genera (which GTDB suggests are different, but literature/domain experts have yet to rename).Genomes belonging to a taxa were dereplicated using skDER (v1.0.7) in "greedy" clustering mode with default values for parameters (99% ANI cutoff, 90% AF cutoff).Overview of Files:- The 'Genome_Dereplication_Overview.tsv' contains details of all the genomes considered as potential representatives for each taxonomic group and their GTDB R214 taxonomic classifications.- 18 _Clustering_Information.txt files which contains the relationship information of non-representative genomes to their nearest representative genome. Generated using the `-n` argument in skder v.1.0.7.  - 18 tar.gz compressed directories are provided. Each compressed directory features representative genomes in FASTA format determined for a particular taxon using skDER with greedy clustering and default cutoffs. Genome assemblies are renamed to feature both the GTDB taxonomic classification and the GCA identifier.         - Acinetobacter - 1,643 rep genomes (17.8% of 9,221 total genomes considered)         - Bacillales - 3,150 rep genomes (35.9% of 8,766 total genomes considered)         - Corynebacterium - 726 rep genomes (43.0% of 1,688 total genomes considered)         - Cutibacterium - 27 rep genomes (5.4% of 502 total genomes considered)         - Enterobacter - 878 rep genomes (19.9% of 4,408 total genomes considered)         - Enterococcus - 937 rep genomes (14.6% of 6,426 total genomes considered)         - Escherichia - 2,436 rep genomes (7.1% of 34,358 total genomes considered)         - Klebsiella - 1,022 rep genomes (5.6% of 18,145 total genomes considered)         - Lactobacillus - 541 rep genomes (30.9% of 1,747 total genomes considered)         - Listeria - 353 rep genomes (6.9% of 5,062 total genomes considered)         - Micromonospora - 211 rep genomes (73.3% of 288 total genomes considered)         - Mycobacterium - 744 rep genomes (6.9% of 10,657 total genomes considered)         - Neisseria - 414 rep genomes (12.8% of 3,235 total genomes considered)         - Pseudomonas - 2,666 rep genomes (18.9% of 14,066 total genomes considered)         - Salmonella - 308 rep genomes (2.2% of 14,109 total genomes considered)         - Staphylococcus - 496 rep genomes (2.5% of 19,627 total genomes considered)         - Streptococcus - 2,452 rep genomes (13.3% of 18,492 total genomes considered)         - Streptomyces - 1,555 rep genomes (57.7% of 2,697 total genomes considered)

opencc-by-4.0Oct 2021View details →

75 Coral Endolith Bacterial Genomes (MAGs) from Red Sea corals Goniastrea edwardsi and Porites lutea

The skeleton of reef-building corals harbors diverse microbial communities that can supply energy to the coral host tissues and might have an integral role in holobiont nutrient cycling. However, we are lacking functional insight of the endolithic microbiome at large. In particular, the link between endolithic microbiome diversity and encoded genomic potential to supply energy to the coral host during coral bleaching, in the absence of their Symbiodiniaceae photosymbionts, remains poorly understood. Here we assembled endolith bacterial MAGs from DNA of coral skeletons from Goniastrea edwardsi and Porites lutea, two common reef‐building corals from the central Red Sea.

opencc-by-4.0Mar 2022View details →

One to host them all: genomics of the diverse bacterial endosymbionts of the spider Oedothorax gibbosus

Bacterial endosymbionts of the groups Wolbachia, Cardinium and Rickettsiaceae are well-known for their diverse effects on their arthropod hosts, ranging from mutualistic relationships to reproductive phenotypes. Here, we analyzed a unique system in which the dwarf spider Oedothorax gibbosus is co-infected with up to five different endosymbionts affiliated with Wolbachia, ‘Candidatus Tisiphia’ (formerly Torix group Rickettsia), Cardinium, and Rhabdochlamydia. Using short-read genome sequencing data, we show that the endosymbionts are heterogeneously distributed among O. gibbosus populations and are frequently found co-infecting spider individuals. To study this intricate host-endosymbiont system on a genome resolved level, we used long-read sequencing to reconstruct closed genomes of the Wolbachia, ‘Ca. Tisiphia’ and Cardinium endosymbionts. We provide insights in the ecology and evolution of the endosymbionts and shed light on the interactions with their spider host. We detected high quantities of transposable elements in all endosymbiont genomes and provide evidence that ancestors of the Cardinium, ‘Ca. Tisiphia’ and Wolbachia endosymbionts have co-infected the same hosts in the past. Our findings contribute to broadening our knowledge about endosymbionts infecting one of the largest animal phyla on earth, and show the usefulness of transposable elements as an evolutionary “contact-tracing” tool.

opencc-by-4.0Aug 2024View details →

Genome Sizes of Bacterial Species Detected in Cell-Free DNA of Patients with Acute Leukemia and Sepsis, Including Those Undergoing Bone Marrow Transplantation

Next Generation Sequencing (NGS) analysis of Cell-Free DNA provides valuable insights into a spectrum of pathogenic species (particularly bacterial) in blood. Patients with Sepsis often face problems like delays in treatment regimens (combination or cocktail of antibiotics) due to the long turnaround time (TAT) of classical and standard blood culture procedures. NGS gives results with lower TAT along with high-depth coverage. The use of NGS may be a possible solution to deciding treatment regimens for patients without losing precious time and more accurately possibly saving lives. Our curated dataset is of bacterial species or strains detected along with their genome size in 107 AML patients diagnosed with Sepsis clinically. Cell-free DNA profiles of patients were built and sequencing was done in Illumina (NovaSeq and NextSeq). Bioinformatic analysis was performed using two classification algorithms namely kraken2 and kaiju. For kraken2  based classification reference bacterial index developed by Carlo Ferravante et al (Zenodo 2020)  (link: https://zenodo.org/records/4055180) was used, while for kaiju-based classification reference database named "nr_euk" dated "2023-05-10" (link: https://bioinformatics-centre.github.io/kaiju/downloads.html) was used. Genome size annotation is important in metagenomics since for the use of depth of coverage (abundance), genome size is required. In metagenomic classification algorithms like kraken/kraken2 and kaiju output computes reads assigned only and not abundance. In kaiju, the problem is more complicated since the reference database does not have a fasta file but only an index file from which alignment is done.  To address the above challenges to compute "depth of coverage" or simply abundance, we build a Genome size annotator tool (https://github.com/patkarlab/Genome-Size-Annotation) which provides genome size for each species detected given its taxid is available. In this tool, the NCBI Datasets tool, NCBI Genome API check tool, and Data Mining from AI search engines like perplexity.ai are used.  We have curated two datasets Kraken2 dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kraken_genome_annotation" Kaiju dataset named "FINAL METAGENOMIC DATA MASTERSHEET - kaiju_genome_annotation" *Please note that for kraken2 curated dataset, we used data mining from the AI search engine perplexity.ai while for kaiju we did not use perplexity, ai, and any species whose genome size was not found was labeled "NA"

opencc-by-4.0Nov 2022View details →

Catalog of metagenome-assembled bacterial genomes from Antarctic endolithic communities

The dataset consists of 2 rar archives and 2 files ( tab-separated values ). Here is a brief summary of their contents: <ul> <li>MAGs_taxonomy: GTDB classification for each MAG.</li> <li>MAGs_genome_info: genome size, completeness, contamination, length, N50.</li> <li>MAGs - candidate species: high quality (HQ) and medium quality (MQ) bacterial metagenome assembled genomes.</li> <li>MAGs_Annotation: EggNOG annotation files. For each MAG, the following files are included: <ul> <li>eggnog.emapper.annotations: the final EggNOG annotation;</li> <li>eggnog.emapper.hmm_hits: list of significant hits to eggNOG Orthologous Groups</li> <li>eggnog.emapper.seed_orthologs: best match of each query within the best Orthologous Group (OG) reported in the eggnog.emapper.hmm_hits file.</li> </ul> </li> </ul>

opencc-by-4.0May 2023View details →

Mash Sketch of RefSeq Bacterial Representative Genomes v217

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) This was created on April 27, 2023, and is RefSeq v217 ```bash #/bin/bash out=mash_db mkdir $out cd $out datasets summary genome taxon bacteria --reference --as-json-lines | \ dataformat tsv genome --fields accession,assminfo-refseq-category,organism-name --elide-header | \ grep representative | \ tee representative_genomes.txt | \ cut -f 1 > genome_ids.txt echo "$(date): Downloading genomes for ids" datasets download genome accession --inputfile genome_ids.txt --filename rep-genomes.zip echo "$(date): Decompressing zip file" unzip rep-genomes.zip echo "$(date): Creating file for mash" cat ncbi_dataset/data/*/*.fna | sed 's/ /_/g' | sed 's/,//g' > rep-genomes.fasta echo "$(date): Skeching rep-genomes.fasta" mash sketch -i -p 20 rep-genomes.fasta -o rep-genomes ############################################################ echo "$(date): File preparation is complete" ls -alh rep-genomes.fasta ls -alh rep-genomes.msh ```

opencc-by-4.0Apr 2020View details →

Systematic shotgun of bacterial genomes [fastq.gz]

Simulated shotgun from complete bacterial genomes. Genome accession is reported in the read name. This is a subsample (188,783,954 x 2 reads) of the whole dataset (1,887,895,180 x 2 reads). FASTQ format.

opencc-by-4.0Feb 2020View details →

Antarctic endolithic bacterial metagenome-assembled genomes

Bacterial assembled genomes and annotation data from the Antarctic cryptoendolithic communities collected during the XXXI (2015-16) Italian Antarctic Expedition. The dataset consists of 4 zip archives and 3 files (comma-separated values). Here is a brief summary of their contents: <ul> <li>MAGs: high quality (HQ) and medium quality (MQ) bacterial metagenome assembled genomes.</li> <li>MAGs_metadata: completeness, contamination, length, N50, GTDB classification for each MAG.</li> <li>MAGs_HQ_CDS:  translated coding sequences for each high quality MAG.</li> <li>MAGs_HQ_Annotation: EggNOG annotation files. For each high quality MAG, the following files are included: <ul> <li>eggnog.emapper.annotations: the final EggNOG annotation;</li> <li>eggnog.emapper.hmm_hits: list of significant hits to eggNOG Orthologous Groups</li> <li>eggnog.emapper.seed_orthologs: best match of each query within the best Orthologous Group (OG) reported in the eggnog.emapper.hmm_hits file.</li> </ul> </li> <li>Jiangella_Antarctica: Candidatus Jiangella antarctica representative genome (UniValnordMG_2_bin.36.fa) and the extracted ribosomal RNA genes (rRNA.fasta).</li> <li>Order_MSA: protein multiple sequence alignments using the 120 GTDB bacterial marker genes. These alignments were used to estimate divergence times on orders containing at least 4 CBS, for a total of 19 orders.</li> <li>Samples_accession: table that relates to the NCBI deposition of the shotgun metagenomes, the following info are included: <ul> <li>NCBI Sequence Read Archive (SRA)</li> <li>BioProject accession numbers</li> <li>JGI Integrated Microbial Genomes & Microbiomes site IDs</li> <li>N50 values</li> <li>Metadata</li> </ul> </li> <li>Samples_metadata: geographic coordinates, temperature, relative humidity and sampling date are reported.</li> </ul>

dryad36/100

Data from: Entangled fates of holobiont genomes during invasion: nested bacterial and host diversities in Caulerpa taxifolia

Successful prevention and mitigation of biological invasions requires retracing the initial steps of introduction, as well as understanding key elements enhancing the adaptability of invasive species. We studied the genetic diversity of the green alga Caulerpa taxifolia and its associated bacterial communities in several areas around the world. The striking congruence of α and ß diversity of the algal genome and endophytic communities reveals a tight association, supporting the holobiont concept as best describing the unit of spreading and invasion. Both genomic compartments support the hypotheses of a unique accidental introduction in the Mediterranean and of multiple invasion events in Southern Australia. In addition to helping with tracing the origin of invasion, bacterial communities exhibit metabolic functions that can potentially enhance adaptability and competitiveness of the consortium they form with their host. We thus hypothesize that low genetic diversities of both host and symbiont communities may contribute to the recent regression in the Mediterranean, in contrast with the persistence of highly diverse assemblages in southern Australia. This study supports the importance of scaling up from the host to the holobiont for a comprehensive understanding of invasions.

opencc-zeroDec 2016View details →

dryad36/100

Integrated genome-wide investigations of the housefly, a global vector of diseases reveal unique dispersal patterns and bacterial communities across farms

Background:Houseflies (Musca domesticaL.) live in intimate association with numerous microorganisms and is a vector of human pathogens. In temperate areas, houseflies willoverwinter in environments constructed by humans and recolonize surrounding areas in early summer. However, the dispersal patterns and associated bacteria across season and location are unclear.We used genotyping-by-sequencing (GBS) for the simultaneous identification and genotyping of thousands of Single Nucleotide Polymorphisms (SNPs) to establish dispersal patterns of houseflies across farms. Secondly, we used16S rRNA gene amplicon sequencing to establish the variation and association between bacterial communities and the housefly across farms. Results: Using GBS we identified 18,000 SNPs across 400 individualssampled within and between 11 dairy farms in Denmark. There was evidence for sub-structuring of Danish housefly populations and with genetic structure that differed across season and sex. Further, there was a strong isolation by distance (IBD) effect, but with large variation suggesting that other hidden geographic barriers are important. Large individual variations were observed in the community structure of the microbiome and it was found to be dependent on location, sex, and collection time. Furthermore, the relative prevalence of putative pathogens was highly dependent on location and collection time. Conclusion:We were able to identify SNPs for the determination of the spatiotemporal housefly genetic structure, and to establish the variation and association between bacterial communities and the housefly across farms using novel next‐generation sequencing (NGS)techniques. These results are important for disease prevention given the fine-scale population structure and IBD for the housefly, and that individual houseflies carry location specific bacteria including putative pathogens.

opencc-zeroFeb 2020View details →

opencc-by-4.0Oct 2024View details →

Mash Sketch of RefSeq Bacterial Reference Genomes

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. I do not inherently have a problem with RefSeq version 70, but RefSeq is well past version 200 now.  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. 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>. 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. 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. Don't hesitate to <a href="https://github.com/erinyoung/update_mash_dist/issues">submit an issue</a> if this needs to get updated. 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. This is how the mash reference file was created: <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 </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> # 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> 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>        The should look like the following: <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>

opencc-by-4.0Nov 2024View details →

Zostera marina leaf associated bacterial metagenome assembled genomes

Metagenome assembled genomes (MAGs) associated with: A genomic resource for exploring bacterial-viral dynamics in seagrass ecosystems Analysis, code, intermediate and supporting files are archived here: <a href="https://doi.org/10.5281/zenodo.14226514">10.5281/zenodo.14226514</a> Viral sequences from this work are archived here: <a href="https://doi.org/10.5281/zenodo.14226038">10.5281/zenodo.14226038</a> This archive contains: (i) Fifty-six fasta files representing the MAGs described in the above titled work with > 80% completion and < 10% contamination based on CheckM2 metrics (ii) Metadata file describing the MAGs (i.e., subset of Table S3 from the above work)