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Greengenes: How to access and download curated 16S rRNA gene sequences and taxonomies

You actually only have pointed to one classifier (the top arrow). The bottom arrow is pointing to the reference database, used to train that feature classifier above. You can learn more about the reference database here:

greengenes database download

The second arrow is not pointing at classifiers of any sort, but rather the marker-gene reference databases that are used to train the classifiers above. These reference sequences can be imported then either used to train your own classifier with qiime feature-classifier fit-classifier-naive-bayes, or used directly for alignment/consensus taxonomy classification with classify-consensus-vsearch.

might I note: training your own classifier could be highly beneficial. I have used the HOMD database in the past for saliva bacterial classification with q2-feature-classifier; it is an oral microbiome-specific database and using it together with q2-feature-classifier will increase likelihood of species-level classification. Worth comparing vs. greengenes or silva.

Yes, you are right. I am going to try to do your two options.Starting with the first one, i would have to download the HOLD database and then? I find the tutorial q2-feature-classifier a little bit confused

For the RDP. 1. Go to the website and click on "Browser":2. Change the options if you want to include shorter sequences or low quality ones and click on "browse"3. Click on "+" left to Bacteria if you want all the bacterial sequences, do the same for other groups if you are interested4. Click on download and then check the options for formatting and then click your option under "Choose an alignment model for download"if you click on "Remove all gaps" the sequences will be unaligned

The naïve Bayesian RDP Classifier [39] is one of several effective algorithms for assigning taxonomy. (For benchmarking and comparison with other methods, see [40,41,42,43].) This type of supervised learning algorithm requires a training set, which is a set of input-output examples to learn a function that can be used to make predictions [44]. In this case, sequences are input and taxonomic assignments are output. Properly formatted versions of the broad 16S rRNA gene databases SILVA, RDP, and Greengenes are available to train the most popular implementations of the naïve Bayesian RDP Classifier. The quality of the training set strongly influences taxonomic assignment and habitat-specific training sets have been developed to increase accuracy of taxonomic assignments [27, 33, 40,41,42, 45]. However, the resolution of available training sets is mostly limited to the genus level. An exception is the manually curated subset of the Greengenes database corresponding to 89 clinically relevant bacterial genera that was used to assign species-level taxonomy of full-length 16S rRNA gene sequences of clinical isolates [46]. Notwithstanding, species-level taxonomy assignment of short-read 16S rRNA gene datasets remains a challenge.

Relationships between the datasets, databases, and training sets in constructing training sets for a specific habitat: the human aerodigestive tract. a Datasets gathered from public repositories or obtained by sequencing of new samples are used to explore the 16S rRNA gene diversity of the habitat of interest. These include both 16S rRNA full-length sequences and region-specific short-read sequences used for method validation or benchmarking. b A curated habitat-specific full-length 16S rRNA gene reference database is assembled and expanded in an iterative way by selecting from those datasets representative sequences for both named and as-yet unnamed or uncultivated species (i.e., HMTs in eHOMD), and placing them in a phylogenetic tree (See Figure 1 in [20]). c Training sets are derived from the taxonomical hierarchy of the habitat-specific database and enhanced by the following steps: compiling multiple 16S rRNA gene sequences to represent the natural sequence variability of each taxon, trimming the training set to match the sequenced region/s, and placing species sharing closely related sequences into a supraspecies taxonomic level. Datasets in gray are the specific examples used for the construction of the eHOMD derived training sets described here. Solid arrows indicate where the sequences described come from and dotted arrows indicate when datasets were used for validation or benchmarking

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To test our hypothesis, we developed and validated short- and long-read training sets for the microbiota of the human aerodigestive tract (mouth, nasal passages, sinuses, throat, and esophagus) using our expanded Human Oral Microbiome Database (eHOMD). This database was originally created and later expanded to serve as a resource for the community of investigators generating datasets to study habitats within the human aerodigestive tract [20, 26, 36]. In addition to 16S rRNA gene reference sequences (eHOMDrefs), it also includes genomic and proteomic data. (It also works well for the lower respiratory tract [20].) The lack of proper taxonomical representation in traditional databases is a challenge in predicting taxonomic assignments [43]. A strength of the eHOMD is that, by placing 16S rRNA gene reference sequences for each human microbial taxon (HMT) on a phylogenetic tree ( =HOMD&show_tree=_), as-yet unnamed or uncultivated species are defined based on sequence identity and added to the phylogeny using a provisional naming scheme that permits taxonomic assignment for cross-study comparison [26]. Also, sequences that are misnamed in other databases are easily identified and given a correct designation in eHOMD. Furthermore, each HMT in eHOMD is represented by one to six highly curated eHOMDrefs to account for intraspecies variability across different strains and dissimilar 16S rRNA genomic copies within individual strains [20]. Another key strength of eHOMD is that it is locally comprehensive often allowing approximately 95% of sequences from V1 to V3 aerodigestive tract datasets to be assigned to the taxonomy [20].

Within these limitations, we note several advantages. First, when coupled with a training set built with our method, the k-mer-based naïve Bayesian approach accommodates the natural variability of 16S rRNA gene sequences that exists within many bacterial species enabling high rates of accurate taxonomic assignment. In contrast, this natural variability limits the utility of any exact match algorithm to assigning species-level taxonomy for only those sequences already existing in a training set (Table 2). Second, despite all of the known limitations of a single-gene taxonomic indicator, the huge number of 16S rRNA gene sequences from diverse ecosystems available in public repositories supports the utility of the 16S rRNA gene for taxonomic assignment. In contrast, the utility of WGS metagenomic sequencing, which holds the promise of strain-level taxonomic assignment, remains limited by the quality and comprehensiveness of the genomic database used for closed-reference assignment. For example, at least one cultivar genome of each species is needed for more accurate species-level assignment. This remains problematic for habitats with many as-yet uncultivated species. Also, accurate strain-level assignment is dependent on the presence of cultivar genomes, and/or single-amplified genomes, of multiple strains of each species in the reference database. Further, a reference database should be free of chimeric metagenome-assembled genomes (MAGs) that combine genomic sequences that are unique to different strains of a species into one genome.

ASVs and CL sequences were assigned species-level taxonomy with the RDP16 (rdp_species_assignment_16.fa.gz) and SILVA132 (silva_species_assignment_v132.fa.gz) training set files downloaded from using the dada2::assignSpecies() function in R with allowMultiple=TRUE [10].

Hi Rebecca, The workflows use the PICRUSt v1 script to install its databases. The workflows have the option to run either PICRUSt 1 or 2. If you install PICRUSt v1 it will have the script that the workflows are looking for and it should resolve the error you are seeing.

Hello,I have the same problem. Missing file after insatling conda biobackery environment.Cant have biobackery workflow and picrust v1 in a same environment (python version).I cant install from source picrust v1 on my cluster. Is there alternative solution? I want to try biockary 16s workflow (dada2 and usearch).

Kraken is a taxonomic sequence classifier that assigns taxonomic labels to DNA sequences. Kraken examines the k-mers within a query sequence and uses the information within those k-mers to query a database. That database maps k-mers to the lowest common ancestor (LCA) of all genomes known to contain a given k-mer.

The first version of Kraken used a large indexed and sorted list of k-mer/LCA pairs as its database. While fast, the large memory requirements posed some problems for users, and so Kraken 2 was created to provide a solution to those problems.

Disk space: Construction of a Kraken 2 standard database requires approximately 100 GB of disk space. A test on 01 Jan 2018 of the default installation showed 42 GB of disk space was used to store the genomic library files, 26 GB was used to store the taxonomy information from NCBI, and 29 GB was used to store the Kraken 2 compact hash table.

Memory: To run efficiently, Kraken 2 requires enough free memory to hold the database (primarily the hash table) in RAM. While this can be accomplished with a ramdisk, Kraken 2 will by default load the database into process-local RAM; the --memory-mapping switch to kraken2 will avoid doing so. The default database size is 29 GB (as of Jan. 2018), and you will need slightly more than that in RAM if you want to build the default database.

Dependencies: Kraken 2 currently makes extensive use of Linux utilities such as sed, find, and wget. Many scripts are written using the Bash shell, and the main scripts are written using Perl. Core programs needed to build the database and run the classifier are written in C++11, and need to be compiled using a somewhat recent version of g++ that will support C++11. Multithreading is handled using OpenMP. Downloads of NCBI data are performed by wget and rsync. Most Linux systems will have all of the above listed programs and development libraries available either by default or via package download.

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