go to your Desktop directory and create a folder called: silva_db. Attempts to save taxonomic and sequence information of a taxmap object in the SILVA FASTA format. For fungal taxonomy, the General Fasta release files from the UNITE ITS database can be used as is. By specifying that we want to guess the species origin of sequences, we can get (as accurate as SILVA lets us be) which species each sequence in our set come from. Write an imitation of the SILVA FASTA database. The SILVA database preparation pipeline now employs an updated version (1.2.10) ... FASTA files containing up to 1000 sequences (≤6000 nt each) can be uploaded, and the sequences will be aligned using the same reference alignments that are employed to prepare the SILVA databases. Training files can be defined by users for other taxonomies. Downloading the SILVA database.
change into the directory silva_db. The format is the same as the id_to_taxonomy_map used by the BLAST taxonomy assigner, defined here.You must provide this file as well as a fasta file of reference sequences where the identifiers correspond to the ids in the id_to_taxonomy_map.. Now that we have created this file, we will change to our normal user account on the biolinx machine. A comparison of the number of sequences hosted by the SILVA, greengenes, and RDP II projects revealed that the SILVA SSU Ref database contains roughly the same amount of bacterial and archaeal sequences as greengenes ( 12) [SILVA: 165 928, greengenes: 165 759 (July 2007)] Furthermore, SILVA contains 2423 more nearly full length sequences for Bacteria than RDP II (163 505, release 9.52) ( 11). If the taxmap object was created using parse_silva_fasta, then it should be able to replicate the format exactly with the default settings. formatdb -i SSURef.fasta -t "SSURef Metaxa DB" -o T -p F; With that done, we can now run Metaxa using this database instead of the classification database that comes with the program. To follow along, download the silva_nr_v132_train_set.fa.gz file, and place it in the directory with the fastq files. SILVA provides comprehensive, quality checked and regularly updated datasets of aligned small (16S/18S, SSU) and large subunit (23S/28S, LSU) ribosomal RNA (rRNA) sequences for all three domains of life (Bacteria, Archaea and Eukaryota). Defining alternate training files¶. taxa <- assignTaxonomy(seqtab.nochim, "~/tax/silva_nr_v132_train_set.fa.gz", multithread=TRUE) # create new parameter file for pick_open_reference_otus.py and add setting lines (adapt correct database path) > otu_SILVA_settings.txt echo "pick_otus:enable_rev_strand_match True" >> otu_SILVA_settings.txt Open a terminal . Now it is time to download the SILVA database.