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10 results for “kraken2”
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>
Kraken2 Database (Human, SARS-CoV2)
<p>Version 1.0.0:</p> <p>Kraken2 Database built on human GRCH38.p13 and NCBI RefSeq genomes of SARS-CoV2 downloaded on 25.05.2020.</p> <p>Update 2.0.0:</p> <ul> <li>Kraken2 Database built on human GRCH38.p13 all GISAID SARS-CoV-2 Sequences available on 08 Feb 2021 that passed QC by https://gitlab.com/RKIBioinformaticsPipelines/president</li> <li>fixed typo in the file name</li> </ul> <p>Update 3.0.0</p> <ul> <li>please refer for notes regarding database generation to the bundled README.md</li> <li>Kraken2 Database built on human GCF_000001405.39_GRCh38.p13, 10% of GISAID SARS-CoV-2 sequences available on 2022-02-07 that passed QC by https://gitlab.com/RKIBioinformaticsPipelines/president</li> </ul>
Kraken2 Database - Begomavirus
<p>This dataset contains formatted sequences specifically prepared for use with Kraken2 as a custom database. The sequences include begomovirus entries retrieved from the NCBI GenBank on October 21, 2024.</p>
Kraken2 Human database
<p>A kraken2 database built from just the Human library on 29/06/2023. This archive contains just the three files required by kraken2, hash.k2d, opts.k2d, and taxo.k2d.</p> <p>The commands used to download and build this database are:</p> <pre><code>k2 download-taxonomy --db db/ k2 download-library --db db/ --library human k2 build --kmer-len 35 --minimizer-len 31 --minimizer-spaces 7 --threads 8 --db db/</code></pre> <p> </p>
Viral RefSeq databases for Centrifuge, Kraken2 and DIAMOND
<p>Owing to technological advances in ancient DNA, it is now possible to sequence viruses from the past to track down their origin and evolution. However, ancient DNA data is considerably more degraded and contaminated than modern data making the identification of ancient viral genomes particularly challenging. Several methods to characterise the modern microbiome (and, within this, the virome) have been developed; in particular, tools that assign sequenced reads to specific taxa in order to characterise the organisms present in a sample of interest. While these existing tools are routinely used in modern data, their performance when applied to ancient microbiome data to screen for ancient viruses remains unknown. </p> <p>In this work, we conducted an extensive simulation study using public viral sequences to establish which tool is the most suitable to screen ancient samples for human DNA viruses. We compared the performance of four widely used classifiers, namely Centrifuge, Kraken2, DIAMOND and MetaPhlAn2, in correctly assigning sequencing reads to the corresponding viruses. To do so, we simulated reads by adding noise typical of ancient DNA to a set of publicly available human DNA viral sequences and to the human genome. We fragmented the DNA into different lengths, added sequencing error and C to T and G to A deamination substitutions at the read termini. Then we measured the resulting sensitivity and precision for all classifiers. </p> <p>Across most simulations, more than 228 out of the 233 simulated viruses are recovered by Centrifuge, Kraken2 and DIAMOND, in contrast to MetaPhlAn2 which recovers only around one third. Overall, Centrifuge and Kraken2 have the best performance with the highest values of sensitivity and precision. We found that deamination damage has little impact on the performance of the classifiers, less than the sequencing error and the length of the reads. Since Centrifuge can handle short reads (in contrast to DIAMOND and Kraken2 with default settings) and since it achieves the highest sensitivity and precision at the species level across all the simulations performed, it is our recommended tool. Regardless of the tool used, our simulations indicate that, for ancient human studies, users should use strict filters to remove all reads of potential human origin. Finally, we recommend to verify which species are present in the database used, as it might happen that default databases lack sequences for viruses of interest.</p>
Viral RefSeq databases for Centrifuge, Kraken2 and DIAMOND
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Zoo elephant metagenomic Kraken2 reports
<p>Non-invasive biological samples benefit studies that investigate rare, elusive, endangered, and/or dangerous species. Integrating genomic techniques that use non-invasive biological sampling with advances in computational approaches can benefit and inform wildlife conservation and management. We use non-invasive fecal DNA samples to generate low- to medium-coverage genomes (e.g., >90% of the complete nuclear genome at 6 X-fold coverage) and metagenomic sequences, combining widely available and accessible DNA collection cards with commonly used DNA extraction and library-building approaches. DNA preservation cards are easy to transport and can be stored non-refrigerated, avoiding cumbersome and/or costly sample methods. The genomic library construction and shotgun sequencing approach did not require enrichment or targeted DNA amplification. The utility and potential of the generated data generated were demonstrated through genome-scale analysis and metagenomics of zoo and free-ranging African savanna elephants (<em>Loxodonta</em> <em>africana</em>). Fecal samples collected from free-ranging individuals contained an average of 12.41% (5.54–21.65%) endogenous elephant DNA. Clustering of these elephants with others from the same geographic region was demonstrated by principal component analysis of genetic variation using nuclear genome-wide SNPs. Metagenomic analyses generated compositional taxon classifications that included Loxodonta, green plants, fungi, arthropods, bacteria, viruses, and archaea, showcasing the utility of this approach for addressing complementary questions based on host-associated DNA, e.g., pathogen and parasite identification. The molecular and bioinformatic analyses presented here contribute towards the expansion and application of genomic techniques to conservation science and practice.</p>
Kraken2 mini standard database for baargin workflow
<p>This is a minimal nucleotide database for Kraken2 to be used with the baargin workflow: https://github.com/jhayer/baargin</p> <p>It was build as a standard database but with maximum size of 4Gb, in February 2023.</p> <p>The compressed archive is 2.9Gb, and the size of the folder after decompression is 4Gb.</p>
Kraken2 Human Pangenome Reference Consortium database
<p>A kraken2 database built from the genome assemblies used by the Human Pangenome Reference Consortium (https://projects.ensembl.org/hprc/). 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, ktaxonomy.tsv, which contains the taxonomy information of the database (obtained by running https://github.com/jenniferlu717/KrakenTools#make_ktaxonomypy).</p> <p>The genomes for this database were downloaded using the assembly summary text file included in this dataset and genome_updater.sh (v0.6.3; https://github.com/pirovc/genome_updater)</p> <pre><code>genome_updater.sh -m -a -f "genomic.fna.gz" -t 8 -e "hprc_assembly_summary.txt" -o HPRC_genomes/</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 -T 9606 -o HPRC.fna HPRC_genomes/</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 HPRC.fna --db db/ kraken2-build --build --db db/ --threads 16</code></pre>
Zoo elephant metagenomic Kraken2 reports
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