15

As Pierre mentioned, NCBI is a good resource for this kind of transformation. You can still use taxize to perform the conversion: library("taxize") species <- c('Helianthus annuus', 'Mycobacterium bovis', 'Rattus rattus', 'XX', 'Mus musculus') uids <- get_uid(species) # keep only uids which you have in the database uids.found <- as.uid(uids[!is....


14

use the NCBI taxon dump under ftp://ftp.ncbi.nih.gov/pub/taxonomy in taxdmp.zip you'll find all the names for a given NCBI taxon $ grep -w ^9606 names.dmp 9606 | Homo sapiens | | scientific name | 9606 | Homo sapiens Linnaeus, 1758 | | authority | 9606 | human | | genbank common name | 9606 | man | |...


7

No, there are no standardized three-letter abbreviations for species names. Three-letter abbreviations with roman letters is 26^3 combinations, which is only 17,576 possible combinations. As a result there are not enough three-letter abbreviations for all living species. All three-letter abbreviations are either specific to a database or perhaps are shared ...


6

These are not. The closest to "standard" is the 5-character abbreviation by Swissprot. It names 25,886 species and has been used for decades. It is also easy to remember for some common species such as HUMAN, RAT, MOUSE and HORSE. I think databases should stop inventing new species abbreviations.


4

You could take a look at kraken2 It should fit your purpose.


4

Getting back to the original question about using taxize to search specifically in EOL, it looks like the most recent version of the package does not produce this error. The current version of R I'm using (3.4.2) produces the following output: sci2comm( c("Cancer", "Lophopanopeus"), db="eol" ) $Cancer [1] "En art taskekrabbe" "...


3

If you like blast, maybe you can try DIAMOND - the authors claim it's much faster than BLAST.


3

If I understand correctly, your question boils down to: parsing a bunch of numbers in a row from a .csv file gives a different result from parsing a bunch of numbers as rows of a text file. I that right? I suggest that you look at the source of your text file. For example, was the text file created on a Windows host (running bash, or in the command ...


3

The NCBI taxonomy is a quite extensive resource: https://www.ncbi.nlm.nih.gov/taxonomy I would thus expect there is a good chance that it covers all of the taxa that you are interested in. It officially claims that it is not an authoritative source, but it's still pretty darned good.


3

NCBI maintains an SQL dump of their taxonomic database You can see what that contains here For smaller requests (depending on what you need), you might also consider the ENSEMBL REST API - although I don't know of a way to retrieve the complete tree (shoot them an email): https://rest.ensembl.org/taxonomy/classification/53399?content-type=application/json


2

Probably the best currently available online database is RBG Kew's Plants of the World Online. It organises taxonomic, morphological and distribution data from a range of databases. You can search for your taxa of interest and find their associated higher ranks. Alternatively, there is World Flora Online, replacing the now-defunct The Plant List v1.1, ...


2

Are you not allowed to use the web BLAST tool? That's what I use if I need to quickly find out the likely origin of a DNA sequence. There are command-line ways to do the same thing, but I only use them if I need to search hundreds or thousands of sequences at once.


1

maybe you can open the demovir.R file with Rstudio and execute line by line to check where the failure is. - @zorbax Thanks for your suggestion. I eventually got a correct final output already. The key issue was Prodigal would add extra strings after my original contig IDs, so it would cause R not recongnising my viral contigs. Therefore, I added a flag -...


1

It would be worth looking at the Tree of Life web project, it isn't downloadable but its a reference source that can be improved on and updated in your website. ToL contains trees linking everything from bacteria, archaea through to the complete diversity of eukaryotes, but its a bit old with 2008 from my taxonomic knowledge being the last update for some of ...


1

If you want to make an all vs all with blast this can be helpful. You can create a blast database with your sequences and then search against itself. makeblastdb -in 16S_sequences.fasta -dbtype nucl -out my_16S_sequences_db blastn -db my_16S_sequences_db -query 16S_sequences.fasta -outfmt 6 \ -out allvsall_16S.tsv -num_threads "$(nproc)" ...


1

I would start by saying that your statement that no animals/plants are microbes is false. There are indeed microbial algae (plants) and microbial animals. So it is indeed difficult. (I'll briefly note that unless your protocol specifically gets rid of environmental DNA, you will probably have some contaminating non-microbial DNA no matter what.) However, ...


1

I am not NGS expert, I known ML. Essentially you are performing data augmentation which is essential for ML. What I question is whether a replicate of e6 from a population of e7 would really be sufficient for ML because you need sample sizes (replicates in this case) of >100000. The answer is really simple you bootstrap the data (sample with replacement), ...


1

This isn't a Python library, but it's easily invoked from Python code, easy to install, and has a lot of useful features. I just discovered the amazing taxonkit library this week. In particular, the taxonkit name2taxid command seems to be what you're looking for. $ cat NAMES Polistes dominula Sinorhizobium meliloti Gossypium hirsutum $ cat NAMES | taxonkit ...


1

Biopython has an efetch frontend. from Bio import Entrez Entrez.email = "michaelg@example.ac.uk" handle = Entrez.efetch(db="nucleotide", id="KR052012.1", rettype="gb", retmode="text") print(handle.read()) OUT LOCUS KR052012 10714 bp RNA linear VRL 09-MAY-2015 DEFINITION Dengue virus strain Hb33/CHN/2014, complete genome. ...


1

The maximum number of steps (or changes) is the number of taxa with state 1 or 0, whichever is smaller. Conceptually, this is the number of character changes if each taxon evolved its state independently of the other taxa. If for a given character the character state 0 occurred twice and character state 1 occurred five times, 2 would be the maximum number ...


1

src_nat = isolated directly from a natural tissue src_gen = the source was genetically manipulated The files that you use (PDBML/XML) are derived from the primary PDB data format which is PDBx/mmCIF. The names in XML seem to correspond to mmCIF names, so you can read documentation on http://mmcif.wwpdb.org/, and here are direct links: category ...


1

As Pierre suggested, you can get a dump of such names from NCBI. Then, to query it for the common name for a species using its Latin name, you can do: $ awk -F'\t' -vname="Mus musculus" '($7=="scientific name" && $3==name){ a[$1]=$3 } ($1 ...


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