Information

How to get taxonomic specific ids for kingdom, phylum, class, order, family, genus and species from taxid?

How to get taxonomic specific ids for kingdom, phylum, class, order, family, genus and species from taxid?


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

I have a list of taxids that looks like this:

1204725 2162 1300163 420247

I am looking to get a file with taxonomic ids in order from the taxids above:

kingdom_id phylum_id class_id order_id family_id genus_id species_id

I am using the package "ete3". I use the tool ete-ncbiquery that tells you the lineage from the ids above. (I run it from my linux laptop with the command below)

ete3 ncbiquery --search 1204725 2162 13000163 420247 --info

The result looks like this:

# Taxid Sci.Name Rank Named Lineage Taxid Lineage 2162 Methanobacterium formicicum species root,cellular organisms,Archaea,Euryarchaeota,Methanobacteria,Methanobacteriales,Methanobacteriaceae,Methanobacterium,Methanobacterium formicicum 1,131567,2157,28890,183925,2158,2159,2160,2162 1204725 Methanobacterium formicicum DSM 3637 no rank root,cellular organisms,Archaea,Euryarchaeota,Methanobacteria,Methanobacteriales,Methanobacteriaceae,Methanobacterium,Methanobacterium formicicum,Methanobacterium formicicum DSM 3637 1,131567,2157,28890,183925,2158,2159,2160,2162,1204725 420247 Methanobrevibacter smithii ATCC 35061 no rank root,cellular organisms,Archaea,Euryarchaeota,Methanobacteria,Methanobacteriales,Methanobacteriaceae,Methanobrevibacter,Methanobrevibacter smithii,Methanobrevibacter smithii ATCC 350611,131567,2157,28890,183925,2158,2159,2172,2173,420247

I have no idea which items (IDS) correspond to what I am looking for (if any)


I'll copy/paste my answer from StackOverflow here also.

The following code:

import csv from ete3 import NCBITaxa ncbi = NCBITaxa() def get_desired_ranks(taxid, desired_ranks): lineage = ncbi.get_lineage(taxid) lineage2ranks = ncbi.get_rank(lineage) ranks2lineage = dict((rank, taxid) for (taxid, rank) in lineage2ranks.items()) return {'{}_id'.format(rank): ranks2lineage.get(rank, '') for rank in desired_ranks} def main(taxids, desired_ranks, path): with open(path, 'w') as csvfile: fieldnames = ['{}_id'.format(rank) for rank in desired_ranks] writer = csv.DictWriter(csvfile, delimiter="	", fieldnames=fieldnames) writer.writeheader() for taxid in taxids: writer.writerow(get_desired_ranks(taxid, desired_ranks)) if __name__ == '__main__': taxids = [1204725, 2162, 1300163, 420247] desired_ranks = ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species'] path = 'taxids.csv' main(taxids, desired_ranks, path)

Produces a file that looks like this:

kingdom_id phylum_id class_id order_id family_id genus_id species_id  28890 183925 2158 2159 2160 2162  28890 183925 2158 2159 2160 2162  28890 183925 2158 2159 2160 2162  28890 183925 2158 2159 2172 2173

How to Use Acronyms, Stories, and More to Help Remember the Order of Scientific Classification

Taxonomy is a system of classification used by scientists to separate living things into different categories. Below is a list of the categories in the scientific classification system.

  • Kingdom
  • Phylum
  • Class
  • Order
  • Family
  • Genus
  • Species

The categories begin by being very general, such as Kingdom, which is the broadest of all categories. The system narrows down all the way to Species, which is the most specific category. You will need to memorize these categories as well as how they work. See the following section for tips on memorizing the categories of the system if scientific classification.


How to get taxonomic specific ids for kingdom, phylum, class, order, family, genus and species from taxid? - Biology

Convert accession numbers to taxonomy

taxonomizr provides some simple functions to parse NCBI taxonomy files and accession dumps and efficiently use them to assign taxonomy to accession numbers or taxonomic IDs. This is useful for example to assign taxonomy to BLAST results. This is all done locally after downloading the appropriate files from NCBI using included functions (see below).

  • prepareDatabase : download data from NCBI and prepare SQLite database
  • accessionToTaxa : convert accession numbers to taxonomic IDs
  • getTaxonomy : convert taxonomic IDs to taxonomy

More specialized functions are:

  • getId : convert a biological name to taxonomic ID
  • getRawTaxonomy : find all taxonomic ranks for a taxonomic ID
  • getAccessions : find accessions for a given taxonomic ID
  • makeNewick : generate a Newick formatted tree from taxonomic output

And a simple use case might look like (see below for more details):

This package downloads a few databases from NCBI and stores them in an easily accessible form on the hard drive. This ends up taking a decent amount of space so you'll probably want around 75 Gb of free hard drive space.

The package is on CRAN, so it should install with a simple:

If you want the development version directly from github, use the devtools library and run:

To use the library, load it in R:

Since version 0.5.0, there is a simple function to run all preparations. Note that you'll need a bit of time, download bandwidth and hard drive space before running this command (we're downloading taxonomic assignments for every record in NCBI). To create a SQLite database called accessionTaxa.sql in the current working directory (you may want to store this somewhere more centrally located so it does not need to be duplicated with every project), we can run:

If everything works then that should have prepared a SQLite database ready for use. You can skip the "Manual preparation" steps below.

All files are cached locally and so the preparation is only required once (delete/rename the SQLite database and recall the function to regenerate the database). It is not necessary to manually check for the presence of the database since the function checks to see if SQLite database is present and if so skips downloading/processing. For example, running the command again produces:

Producing accession numbers

NCBI accession numbers are often obtained when doing a BLAST search (usually the second column of output from blastn, blastx, blastp, . ). For example the output might look like:

So to identify a taxon for a given sequence you would blast it against e.g. the NCBI nt database and load the results into R. For NCBI databases, the accession number is often the 4th item in the | (pipe) separated reference field (often the second column in a tab separated result). For example, the CP002582.1 in the gi|326539903|gb|CP002582.1| above.

So just as an example, reading in blast results might look something like:

Finding taxonomy for NCBI accession numbers

Now we are ready to convert NCBI accession numbers to taxonomic IDs. For example, to find the taxonomic IDs associated with NCBI accession numbers "LN847353.1" and "AL079352.3":

And to get the taxonomy for those IDs:

You can also get taxonomy for NCBI accession numbers without versions (the .X following the main number e.g. the ".1" in LN847353.1) using the version='base' argument of accessionToTaxa :

Finding taxonomy for taxonomic names

If you'd like to find IDs for taxonomic names then you can do something like:

And again to get the taxonomy for those IDs use getTaxonomy :

You can use the condenseTaxa function to find the agreements among taxonomic hits. For example to condense the taxonomy from the previous section to the lowest taxonomic rank shared by all three taxa:

This function can also be fed a large number of grouped hits, e.g. BLAST hits for high throughput sequencing reads after filtering for the best hits for each read, and output a condensed taxonomy for each grouping:

Find all taxonomic assignments for a given taxa

To get all taxonomic assignments for a given taxa regardless of their particular rank, you can use the getRawTaxonomy function. Note that there are often many intermediate ranks outside the more common taxonomic ranks. The function returns a list since different IDs can have differing numbers of ranks. It is used similarly to getTaxonomy :

Finding accessions for a given taxonomic ID

To find all the accessions for a given taxonomic ID, you can use the getAccessions function. This is a bit of an unusual use case so to preserve space, an index is not created by default in read.accession2taxid . If you are going to use this function, you will want to rebuild the SQLite database with the indexTaxa argument set to true with something like:

Then you can get the accessions for taxa 3702 with a command like (note that the limit argument is used here in order to preserve space):

Convert taxonomy to Newick tree

This is probably only useful in a few specific cases but a convenience function makeNewick to convert taxonomy into a Newick tree is included. The function takes a matrix giving with columns corresponding to taxonomic categories and rows different to taxonomic assignments, e.g. the output from condenseTaxa or getTaxonomy and reduces it to a Newick formatted tree. For example:

  • Fix named vector bug in accessionToTaxa
  • Add makeNewick function
  • Deal with default 60 second timeout for downloads in R
  • Remove nucl_est and nucl_gss from defaults since NCBI folded them into nucl_gb and removed
  • Squash R:devel bug
  • Transitioned from data.table to SQLite
  • Addeded convenience prepareDatabase() function
  • Squashed Windows testing errors

Manual preparation of database (usually not necessary)

Note: Since version 0.5.0, it is usually not necessary to run the following manually, the function prepareDatabase() should do most of this automatically for you (see above).

In order to avoid constant internet access and slow APIs, the first step in using the package is to downloads all necessary files from NCBI. This uses a bit of disk space but makes future access reliable and fast.

Note: It is not necessary to manually check for the presence of these files since the functions automatically check to see if their output is present and if so skip downloading/processing. Delete the local files if you would like to redownload or reprocess them.

First, download the necessary names and nodes files from NCBI:

Download accession to taxa files

Then download accession to taxa id conversion files from NCBI. Note: this is a pretty big download (several gigabytes):

If you would also like to identify protein accession numbers, also download the prot file from NCBI (again this is a big download):

Convert names, nodes and accessions to database

Then process the downloaded names and nodes files into a more easily accessed form:

Next process the downloaded accession files into the same database (this one could take a while):

Now everything should be ready for processing. All files are cached locally and so the preparation is only required once (or whenever you would like to update the data). It is not necessary to manually check for the presence of these files since the functions automatically check to see if their output is present and if so skip downloading/processing. Delete the local files if you would like to redownload or reprocess them.


How to get taxonomic specific ids for kingdom, phylum, class, order, family, genus and species from taxid? - Biology

CCMetagen processes sequence alignments produced with KMA, which implements the ConClave sorting scheme to achieve highly accurate read mappings. The pipeline is fast enough to use the whole NCBI nt collection as reference, facilitating the inclusion of understudied organisms, such as microbial eukaryotes, in metagenome surveys. CCMetagen produces ranked taxonomic results in user-friendly formats that are ready for publication or downstream statistical analyses.

If you this tool, please cite CCMetagen and KMA:

Besides the guidelines below, we also provide a tutorial to reproduce our metagenome clasisfication analyses of the microbiome of wild birds here.

The guidelines below will guide you in using the command-line version of the CCMetagen pipeline.

CCMetagen is also available as a web service at https://cge.cbs.dtu.dk/services/ccmetagen/. Note that we recommend using this command-line version to analyze data exceeding 1.5Gb.

Requirements and Installation

Make sure you have the dependencies below installed and accessible in your $PATH. The guidelines below are for Unix systems.

  • If you do not have it already, download and install Python 3.6 CCMetagen requires the Python modules pandas (>0.23) and ETE3. The easiest way to install these modules is via conda or pip:

sudo apt-get install libz-dev

Note - a new version of KMA - v1.3.0 – has been released, featuring higher speed and precision. We recommend that you update KMA to v.1.3.0

Install CCMetagen via git:

This will download CCMetagen and the tutorial files. You can also just download the python files from this github directory (CCMetagen.py, CCMetagen_merge.py) and the ones in the ccmetagen folder if you rather avoid downloading all other files.

Then add the CCMetagen python scripts to the path, temporarily or permanently. For example: PATH=$PATH<your_folder>/CCMetagen

To update CCMetagen, go to the CCMetagen folder and type: git pull

Or install CCMetagen via pip:

This will automatically install the necessary python packages (pandas and ete3), so you can skip that step if you use pip.

Option 1 Download the indexed (ready-to-go) nt or RefSeq database either here or here. Download the ncbi_nt_kma.zip file (96GB zipped file, 165GB uncompressed) or the RefSeq_bf.zip (90GB zipped file) Unzip the database, e.g.: unzip ncbi_nt_kma . The nt database contains the whole in NCBI nucleotide collection (of of Jan 2018), and therefore is suitable to identify a range of microorganisms, including prokaryotes and eukaryotes. The RefSeq_bf database contains complete reference bacterial and fungal genomes, suitable for better known habitats such as the human gut or when trying to detect well known species.

Option 2 We have indexed a more recent version of the ncbi nucleotide collection (June 2019) that does not contain environemntal or artificial sequences. The file ncbi_nt_no_env_11jun2019.zip can be found here and contains all ncbi nt entries excluding the descendants of environmental eukaryotes (taxid 61964), environmental prokaryotes (48479), unclassified sequences (12908) and artificial sequences (28384).

Option 3: Build your own reference database (recommended!) Follow the instructions in the KMA website to index the database. It is important that taxids are incorporated in sequence headers for processing with CCMetagen. Sequence headers should look like >1234|sequence_description , where 1234 is the taxid. We provide scripts to rename sequences in the nt database here.

If you want to use the RefSeq database, the format is similar to the one required for Kraken. The Opiniomics blog describes how to download sequences in an adequate format. Note that you still need to build the index with KMA: kma_index -i refseq.fna -o refseq_indexed -NI -Sparse - or kma_index -i refseq.fna -o refseq_indexed -NI -Sparse TG for faster analysis.

If you want to calculate abundance in reads per million (RPM) or in number of reads (fragments), or if you want to calculate the proportion of mapped reads, add the flag -ef (extended features):

$db is the path to the reference database $th is the number of threads $SAMPLE_R1 is the path to the mate1 of a paired-end metagenome/metatranscriptome sample (fastq or fasta) $SAMPLE_R2 is the path to the mate2 of a paired-end metagenome/metatranscriptome sample (fastq or fasta) $SAMPLE is the path to a single-end metagenome/metatranscriptome file (reads or contigs)

Where $sample_out_kma.res is alignment results produced by KMA.

Note that if you are running CCMetagen from the local folder (instead of adding it to your path), you may need to add 'python' before CCMetagen: python CCMetagen.py -i $sample_out_kma.res -o results

Done! This will make an additional quality filter and output a text file with ranked taxonomic classifications and a krona graph file for interactive visualization.

An example of the CCMetagen output can be found here (.csv file) and here (.html file).

In the .csv file, you will find the depth (abundance) of each match.

Depth can be estimated in four ways: by counting the number of nucleotides matching the reference sequence (use flag --depth_unit nc), by applying an additional correction for template length (default in KMA and CCMetagen), by calculating depth in Reads Per Million (RPM, use flag --depth_unit rpm), or by counting the number of fragments (i.e. number of PE reads matching to teh reference sequence, use flag --depth_unit fr). If you want RPM or fragment units, you will need to suply the .mapstats file generated with KMA (which you get when running kma with the flag '-ef').

Balancing sensitivity and specificity

You can adjust the stringency of the taxonomic assignments by adjusting the minimum coverage (--coverage), the minimum abundance (--depth), and the minimum level of sequence similarity (--query_identity). Coverage is the percentage of bases in the reference sequence that is covered by the consensus sequence (your query), it can be over 100% when the consensus sequence is larger than the reference (due to insertions for example). You can also adjust the KMA settings to facilitate the identification of more distant-related taxa (see below)

If you change the default depth unit, we recommend adjusting the minimum abundance (--depth) to remove taxa found in low abundance accordingly. For example, you can use -d 200 (200 nucleotides) when using --depth_unit nc, which is similar to -d 0.2 when using the default '--depth_unit kma' option. If you choose to calculate abundances in RPM, you may want to adjust the minimum abundance according to your sequence depth. For example, to calculate abundances in RPM, and filter out all matches with less than one read per million:

If you would like to know the proportion of reads mapped to each template, run kma with the '-ef' flag. This will generate a file with the '.mapstat' extension. Then provide this file to CCMetagen (-map $sample_out_kma.mapstat) and add the flag '-ef y':

This will filter the .mapstat file, removing the templates that did not pass CCMetagen's quality control, will add the percentage of mapped reads for each template and will output a file with extension 'stats_csv'. It will also output the overall proportion of reads mapped to these templates in the terminal. For more details about the additional columns of this file, please check KMA's manual.

When working with highly complex environemnts for which reference databases are scarce (e.g. many soil and marine metagenomes), it is common to obtain a low proportion of classified reads, especially if the sequencing depth is low. For a more sensitive analysis, besides relaxing the CCMetatgen settings, you can adjust the KMA aligner settings, by for example: removing the -and and the -apm f flags, so that you can get a match even when the reference sequences are not significantly overrepresented or when only one of the PE reads maps to the template. Check the KMA manual for more details. It can also be useful to build a customized reference database with additional genomes of organisms that are closely related to what you expect to find in your samples.

Understanding the ranked taxonomic output of CCMetagen:

The taxonomic classifications reflect the sequence similarity between query and reference sequences, according to default or user-defined similarity thresholds. For example, if a match is 97% similar to the reference sequence, the match will not get a species-level classification. If the match is 85% similar to the reference sequence, then the species, genus and family-level classifications will be 'none'. Note that this is different from identifications tagged as unk_x (unknown taxa). These unknowns indicate taxa where higher-rank classifications have not been defined (according to the NCBI taxonomy database), and it is unrelated to sequence similarity.

For a list of options to customize your analyze, type:

  • To get the abundance of each taxon, and/or summarize results for multiple samples, use CCMetagen_merge:

Where $CCMetagen_out is the folder containing the CCMetagen taxonomic classifications. The results must be in .csv format (default or '--mode text' output of CCMetagen), and these files must end in ".ccm.csv".

The flag '-t' define the taxonomic level to merge the results. The default is species-level.

You can also filter out specific taxa, at any taxonomic level:

Use flag -kr to keep (k) or remove (r) taxa. Use flag -l to set the taxonomic level for the filtering. Use flag -tlist to list the taxa to keep or remove (separated by comma).

EX1: Filter out bacteria: CCMetagen_merge.py -i $CCMetagen_out -kr r -l Kingdom -tlist Bacteria

EX2: Filter out bacteria and Metazoa: CCMetagen_merge.py -i $CCMetagen_out -kr r -l Kingdom -tlist Bacteria, Metazoa

EX3: Merge results at family-level, and remove Metazoa and Viridiplantae taxa at Kingdom level:

For species-level filtering (where there is a space in taxa names), use quotation marks. Ex 4: Keep only Escherichia coli and Candida albicans:

If you only have one sample, you can also use CMetagen_merge to get one line per taxa.

This file should look like this.

This script will produce a fasta file containing all reads assigned to a taxon of interest. Ex: Generate a fasta file containing all sequences that mapped to the genus Eschericha:

Where $CCMetagen_out is the .csv file generated with CCMetagen and $sample_out_kma.frag is the .frag file generated with KMA. The frag file needs to be decompressed: gunzip *.frag.gz

For species-level filtering (where there is a space in taxon names), use quotation marks. Ex: Generate a fasta file containing all sequences that mapped to E. coli:

Check out our tutorial for an applied example of the CCMetagen pipeline.

  • Error taxid not found. You probably need to update your local ETE3 database, which contains the taxids and lineage information:
  • TypeError: concat() got an unexpected keyword argument 'sort'. If you get this error, please update the python module pandas:

WARNING: no NCBI's taxid found for accession [something], this match will not get taxonomic ranks

This is not an error, this is just a warning indicating that one of your query sequences matchs to a genbank record for which the NCBI taxonomic identifier (taxid) is not known. CCMetagen therefore will not be able to assign taxonomic ranks to this match, but you will still be able to see it in the output file.

KeyError: "['Superkingdom' 'Kingdom' 'Phylum' 'Class' 'Order' 'Family' . ] not in index" Make sure that the output of CCMetagen ends in '.csv'.


How to get taxonomic specific ids for kingdom, phylum, class, order, family, genus and species from taxid? - Biology

2 contributors

Users who have contributed to this file

# '
# ' Classifies sequences against reference training dataset.
# '
# ' assignTaxonomy implements the RDP Naive Bayesian Classifier algorithm described in
# ' Wang et al. Applied and Environmental Microbiology 2007, with kmer size 8 and 100 bootstrap
# ' replicates. Properly formatted reference files for several popular taxonomic databases
# ' are available url
# '
# ' @param seqs (Required). A character vector of the sequences to be assigned, or an object
# ' coercible by code>.
# '
# ' @param refFasta (Required). The path to the reference fasta file, or an
# ' R connection Can be compressed.
# ' This reference fasta file should be formatted so that the id lines correspond to the
# ' taxonomy (or classification) of the associated sequence, and each taxonomic level is
# ' separated by a semicolon. Eg.
# '
# ' >KingomPhylumClassOrderFamilyGenus
# ' ACGAATGTGAAGTAA.
# '
# ' @param minBoot (Optional). Default 50.
# ' The minimum bootstrap confidence for assigning a taxonomic level.
# '
# ' @param tryRC (Optional). Default FALSE.
# ' If TRUE, the reverse-complement of each sequences will be used for classification if it is a better match to the reference
# ' sequences than the forward sequence.
# '
# ' @param outputBootstraps (Optional). Default FALSE.
# ' If TRUE, bootstrap values will be retained in an integer matrix. A named list containing the assigned taxonomies (named "taxa")
# ' and the bootstrap values (named "boot") will be returned. Minimum bootstrap confidence filtering still takes place,
# ' to see full taxonomy set minBoot=0
# '
# ' @param taxLevels (Optional). Default is c("Kingdom", "Phylum", "Class", "Order", "Family", "Genus", "Species").
# ' The taxonomic levels being assigned. Truncates if deeper levels not present in
# ' training fasta.
# '
# ' @param multithread (Optional). Default is FALSE.
# ' If TRUE, multithreading is enabled and the number of available threads is automatically determined.
# ' If an integer is provided, the number of threads to use is set by passing the argument on to
# ' code>.
# '
# ' @param verbose (Optional). Default FALSE.
# ' If TRUE, print status to standard output.
# '
# ' @return A character matrix of assigned taxonomies exceeding the minBoot level of
# ' bootstrapping confidence. Rows correspond to the provided sequences, columns to the
# ' taxonomic levels. NA indicates that the sequence was not consistently classified at
# ' that level at the minBoot threshhold.
# '
# ' If outputBootstraps is TRUE, a named list containing the assigned taxonomies (named "taxa")
# ' and the bootstrap values (named "boot") will be returned.
# '
# ' @export
# '
# ' @importFrom ShortRead readFasta
# ' @importFrom ShortRead sread
# ' @importFrom ShortRead id
# '
# ' @examples
# ' seqs <- getSequences(system.file("extdata", "example_seqs.fa", package="dada2"))
# ' training_fasta <- system.file("extdata", "example_train_set.fa.gz", package="dada2")
# ' taxa <- assignTaxonomy(seqs, training_fasta)
# ' taxa80 <- assignTaxonomy(seqs, training_fasta, minBoot=80, multithread=2)
# '
assignTaxonomy <- function ( seqs , refFasta , minBoot = 50 , tryRC = FALSE , outputBootstraps = FALSE ,
taxLevels = c( " Kingdom " , " Phylum " , " Class " , " Order " , " Family " , " Genus " , " Species " ),
multithread = FALSE , verbose = FALSE ) <
MIN_REF_LEN <- 20 # Enforced minimum length of reference seqs. Must be bigger than the kmer-size used (8).
MIN_TAX_LEN <- 50 # Minimum length of input sequences to get a taxonomic assignment
# Get character vector of sequences
seqs <- getSequences( seqs )
if (min(nchar( seqs )) < MIN_TAX_LEN ) <
warning( " Some sequences were shorter than " , MIN_TAX_LEN , " nts and will not receive a taxonomic classification. " )
>
# Read in the reference fasta
refsr <- readFasta( refFasta )
lens <- width(sread( refsr ))
if (any( lens < MIN_REF_LEN )) <
refsr <- refsr [ lens > = MIN_REF_LEN ]
warning(paste0( " Some reference sequences were too short (< " , MIN_REF_LEN , " nts) and were excluded. " ))
>
refs <- as.character(sread( refsr ))
tax <- as.character(id( refsr ))
tax <- sapply( tax , function ( x ) gsub( " ^ s+| s+$ " , " " , x )) # Remove leading/trailing whitespace
# Sniff and parse UNITE fasta format
UNITE <- FALSE
if (all(grepl( " FU |re[pf]s " , tax [ 1 : 10 ]))) <
UNITE <- TRUE
cat( " UNITE fungal taxonomic reference detected. " )
tax <- sapply(strsplit( tax , " | " ), `[` , 5 )
tax <- gsub( " [pcofg]__unidentified " , " _DADA2_UNSPECIFIED " , tax )
tax <- gsub( " s__( w+)_ " , " s__ " , tax )
tax <- gsub( " s__sp$ " , " _DADA2_UNSPECIFIED " , tax )
>
# Crude format check
if ( ! grepl( " " , tax [[ 1 ]])) <
if (length(unlist(strsplit( tax [[ 1 ]], " s " ))) == 3 ) <
stop( " Incorrect reference file format for assignTaxonomy (this looks like a file formatted for assignSpecies). " )
> else <
stop( " Incorrect reference file format for assignTaxonomy. " )
>
>
# Parse the taxonomies from the id string
tax.depth <- sapply(strsplit( tax , " " ), length )
td <- max( tax.depth )
for ( i in seq(length( tax ))) <
if ( tax.depth [[ i ]] < td ) <
for ( j in seq( td - tax.depth [[ i ]])) <
tax [[ i ]] <- paste0( tax [[ i ]], " _DADA2_UNSPECIFIED " )
>
>
>
# Create the integer maps from reference to type ("genus") and for each tax level
genus.unq <- unique( tax )
ref.to.genus <- match( tax , genus.unq )
tax.mat <- matrix (unlist(strsplit( genus.unq , " " )), ncol = td , byrow = TRUE )
tax.df <- as.data.frame( tax.mat )
for ( i in seq(ncol( tax.df ))) <
tax.df [, i ] <- factor ( tax.df [, i ])
tax.df [, i ] <- as.integer( tax.df [, i ])
>
tax.mat.int <- as.matrix( tax.df )
# ## Assign
# Parse multithreading argument
if (is.logical( multithread )) <
if ( multithread == TRUE )
else
> else if (is.numeric( multithread )) <
RcppParallel :: setThreadOptions( numThreads = multithread )
> else <
warning( " Invalid multithread parameter. Running as a single thread. " )
RcppParallel :: setThreadOptions( numThreads = 1 )
>
# Run C assignemnt code
assignment <- C_assign_taxonomy2( seqs , rc( seqs ), refs , ref.to.genus , tax.mat.int , tryRC , verbose )
# Parse results and return tax consistent with minBoot
bestHit <- genus.unq [ assignment $ tax ]
boots <- assignment $ boot
taxes <- strsplit( bestHit , " " )
taxes <- lapply(seq_along( taxes ), function ( i ) taxes [[ i ]][ boots [ i ,] > = minBoot ])
# Convert to character matrix
tax.out <- matrix ( NA_character_ , nrow = length( seqs ), ncol = td )
for ( i in seq(length( seqs ))) <
if (length( taxes [[ i ]]) > 0 ) <
tax.out [ i , 1 : length( taxes [[ i ]])] <- taxes [[ i ]]
>
>
rownames( tax.out ) <- seqs
colnames( tax.out ) <- taxLevels [ 1 : ncol( tax.out )]
tax.out [ tax.out == " _DADA2_UNSPECIFIED " ] <- NA_character_
if ( outputBootstraps ) <
# Convert boots to integer matrix
boots.out <- matrix ( boots , nrow = length( seqs ), ncol = td )
rownames( boots.out ) <- seqs
colnames( boots.out ) <- taxLevels [ 1 : ncol( boots.out )]
list ( tax = tax.out , boot = boots.out )
> else <
tax.out
>
>
# Helper function for assignSpecies
mapHits <- function ( x , refs , keep , sep = " / " ) <
hits <- refs [ x ]
hits [grepl( " Escherichia " , hits , fixed = TRUE ) | grepl( " Shigella " , hits , fixed = TRUE )] <- " Escherichia/Shigella "
if (length(unique( hits )) < = keep ) <
rval <- do.call( paste , c(as.list(sort(unique( hits ))), sep = sep ))
> else
if (length( rval ) == 0 ) rval <- NA_character_
rval
>
# Match curated genus names to binomial genus names
# Handles Clostridium groups and split genera names
matchGenera <- function ( gen.tax , gen.binom , split.glyph = " / " ) <
if (is.na( gen.tax ) || is.na( gen.binom ))
if (( gen.tax == gen.binom ) ||
grepl(paste0( " ^ " , gen.binom , " [ _ " , split.glyph , " ] " ), gen.tax ) ||
grepl(paste0( split.glyph , gen.binom , " $ " ), gen.tax )) <
return ( TRUE )
> else <
return ( FALSE )
>
>
# '
# ' Taxonomic assignment to the species level by exact matching.
# '
# ' code uses exact matching against a reference fasta to identify the
# ' genus-species binomial classification of the input sequences.
# '
# ' @param seqs (Required). A character vector of the sequences to be assigned, or an object
# ' coercible by code>.
# '
# ' @param refFasta (Required). The path to the reference fasta file, or an
# ' R connection. Can be compressed.
# ' This reference fasta file should be formatted so that the id lines correspond to the
# ' genus-species of the associated sequence:
# '
# ' >SeqID genus species
# ' ACGAATGTGAAGTAA.
# '
# ' @param allowMultiple (Optional). Default FALSE.
# ' Defines the behavior when multiple exact matches against different species are returned.
# ' By default only unambiguous identifications are return. If TRUE, a concatenated string
# ' of all exactly matched species is returned. If an integer is provided, multiple
# ' identifications up to that many are returned as a concatenated string.
# '
# ' @param tryRC (Optional). Default FALSE.
# ' If TRUE, the reverse-complement of each sequences will also be tested for exact matching
# ' to the reference sequences.
# '
# ' @param n (Optional). Default code<2000>.
# ' The number of sequences to perform assignment on at one time.
# ' This controls the peak memory requirement so that large numbers of sequences are supported.
# '
# ' @param verbose (Optional). Default FALSE.
# ' If TRUE, print status to standard output.
# '
# ' @return A two-column character matrix. Rows correspond to the provided sequences,
# ' columns to the genus and species taxonomic levels. NA indicates that the sequence
# ' was not classified at that level.
# '
# ' @export
# '
# ' @importFrom Biostrings vcountPDict
# ' @importFrom Biostrings PDict
# ' @importFrom ShortRead readFasta
# ' @importFrom ShortRead sread
# ' @importFrom ShortRead reverseComplement
# ' @importFrom ShortRead id
# ' @importFrom methods as
# '
# ' @examples
# ' seqs <- getSequences(system.file("extdata", "example_seqs.fa", package="dada2"))
# ' species_fasta <- system.file("extdata", "example_species_assignment.fa.gz", package="dada2")
# ' spec <- assignSpecies(seqs, species_fasta)
# '
assignSpecies <- function ( seqs , refFasta , allowMultiple = FALSE , tryRC = FALSE , n = 2000 , verbose = FALSE ) <
# Define number of multiple species to return
if (is.logical( allowMultiple )) <
if ( allowMultiple ) keep <- Inf
else keep <- 1
> else <
keep <- as.integer( allowMultiple )
>
# Get character vector of sequences
seqs <- getSequences( seqs )
# Read in the reference fasta
refsr <- readFasta( refFasta )
ids <- as(id( refsr ), " character " )
# Crude format check
if ( ! length(unlist(strsplit( ids [[ 1 ]], " s " ))) > = 3 ) <
if (length(unlist(gregexpr( " " , ids [[ 1 ]]))) > = 3 ) <
stop( " Incorrect reference file format for assignSpecies (this looks like a file formatted for assignTaxonomy). " )
> else <
stop( " Incorrect reference file format for assignSpecies. " )
>
>
genus <- sapply(strsplit( ids , " s " ), `[` , 2 )
species <- sapply(strsplit( ids , " s " ), `[` , 3 )
# Identify the exact hits
hits <- vector( " list " , length( seqs ))
lens <- nchar( seqs )
for ( len in unique( lens )) < # Requires all same length sequences
i.len <- which( lens == len ) n.len <- length( i.len )
j.lo <- 1 j.hi <- min( n , n.len )
while ( j.lo < = n.len ) <
i.loop <- i.len [ j.lo : j.hi ]
seqdict <- PDict( seqs [ i.loop ])
vhit <- (vcountPDict( seqdict , sread( refsr )) > 0 )
if ( tryRC ) vhit <- vhit | (vcountPDict( seqdict , reverseComplement(sread( refsr ))) > 0 )
hits [ i.loop ] <- lapply(seq(nrow( vhit )), function ( x ) vhit [ x ,])
j.lo <- j.lo + n j.hi <- min( j.hi + n , n.len )
rm( seqdict )
gc()
>
>
# Get genus species return strings
rval <- cbind(unlist(sapply( hits , mapHits , refs = genus , keep = 1 )),
unlist(sapply( hits , mapHits , refs = species , keep = keep )))
colnames( rval ) <- c( " Genus " , " Species " )
rownames( rval ) <- seqs
gc()
if ( verbose ) cat(sum( ! is.na( rval [, " Species " ])), " out of " , length( seqs ), " were assigned to the species level. " )
rval
>
# '
# ' Add species-level annotation to a taxonomic table.
# '
# ' code wraps the code> function to assign genus-species
# ' binomials to the input sequences by exact matching against a reference fasta. Those binomials
# ' are then merged with the input taxonomic table with species annotations appended as an
# ' additional column to the input table.
# ' Only species identifications where the genera in the input table and the binomial
# ' classification are consistent are included in the return table.
# '
# ' @param taxtab (Required). A taxonomic table, the output of code>.
# '
# ' @param refFasta (Required). The path to the reference fasta file, or an
# ' R connection. Can be compressed.
# ' This reference fasta file should be formatted so that the id lines correspond to the
# ' genus-species binomial of the associated sequence:
# '
# ' >SeqID genus species
# ' ACGAATGTGAAGTAA.
# '
# ' @param allowMultiple (Optional). Default FALSE.
# ' Defines the behavior when multiple exact matches against different species are returned.
# ' By default only unambiguous identifications are return. If TRUE, a concatenated string
# ' of all exactly matched species is returned. If an integer is provided, multiple
# ' identifications up to that many are returned as a concatenated string.
# '
# ' @param tryRC (Optional). Default FALSE.
# ' If TRUE, the reverse-complement of each sequences will be used for classification if it is a better match to the reference
# ' sequences than the forward sequence.
# '
# ' @param n (Optional). Default code<1e5>.
# ' The number of records (reads) to read in and filter at any one time.
# ' This controls the peak memory requirement so that very large fastq files are supported.
# ' See code> for details.
# '
# ' @param verbose (Optional). Default FALSE.
# ' If TRUE, print status to standard output.
# '
# ' @return A character matrix one column larger than input. Rows correspond to
# ' sequences, and columns to the taxonomic levels. NA indicates that the sequence
# ' was not classified at that level.
# '
# ' @seealso
# ' code>, code>
# '
# ' @export
# '
# ' @examples
# '
# ' seqs <- getSequences(system.file("extdata", "example_seqs.fa", package="dada2"))
# ' training_fasta <- system.file("extdata", "example_train_set.fa.gz", package="dada2")
# ' taxa <- assignTaxonomy(seqs, training_fasta)
# ' species_fasta <- system.file("extdata", "example_species_assignment.fa.gz", package="dada2")
# ' taxa.spec <- addSpecies(taxa, species_fasta)
# ' taxa.spec.multi <- addSpecies(taxa, species_fasta, allowMultiple=TRUE)
# '
addSpecies <- function ( taxtab , refFasta , allowMultiple = FALSE , tryRC = FALSE , n = 2000 , verbose = FALSE ) <
seqs <- rownames( taxtab )
binom <- assignSpecies( seqs , refFasta = refFasta , allowMultiple = allowMultiple , tryRC = tryRC , n = n , verbose = verbose )
# Merge tables
if ( " Genus " %in% colnames( taxtab )) gcol <- which(colnames( taxtab ) == " Genus " )
else gcol <- ncol( taxtab )
# Match genera
gen.match <- mapply( matchGenera , taxtab [, gcol ], binom [, 1 ])
taxtab <- cbind( taxtab , binom [, 2 ])
colnames( taxtab )[ncol( taxtab )] <- " Species "
taxtab [ ! gen.match , " Species " ] <- NA_character_
if ( verbose ) cat( " Of which " , sum( ! is.na( taxtab [, " Species " ])), " had genera consistent with the input table. " )
taxtab
>
# ' This function creates the dada2 assignTaxonomy training fasta for the RDP trainset .fa file
# ' The RDP trainset data was downloaded from: https://sourceforge.net/projects/rdp-classifier/files/RDP_Classifier_TrainingData/
# '
# ' ## RDP Trainset 18
# ' path <- "

You can’t perform that action at this time.

You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session.


Requirements and Installation

Make sure you have the dependencies below installed and accessible in your $PATH. The guidelines below are for Unix systems.

  • If you do not have it already, download and install Python 3.6 CCMetagen requires the Python modules pandas (>0.23) and ETE3. The easiest way to install these modules is via conda or pip:

sudo apt-get install libz-dev

Note - a new version of KMA - v1.3.0 – has been released, featuring higher speed and precision. We recommend that you update KMA to v.1.3.0

    is required for graphs. To install Krona it in the local folder:
  • Then download CCMetagen and add it to your path. You have two options:

Install CCMetagen via git:

This will download CCMetagen and the tutorial files. You can also just download the python files from this github directory (CCMetagen.py, CCMetagen_merge.py) and the ones in the ccmetagen folder if you rather avoid downloading all other files.

Then add the CCMetagen python scripts to the path, temporarily or permanently. For example: PATH=$PATH<your_folder>/CCMetagen

To update CCMetagen, go to the CCMetagen folder and type: git pull

Or install CCMetagen via pip:

This will automatically install the necessary python packages (pandas and ete3), so you can skip that step if you use pip.


Taxonomy Rules – Naming And Grouping Animals Scientifically

I was quite surprised to hear when many of our teachers and tutors gave feedback regarding the rules of taxonomic nomenclature (i.e. how to write the names of animal goups) apparently, a significant few have difficulties with those norms. I recall having written a text going through the essential principles and the framework for how you write these formal names for my old website, which I shut down in favour of this blog. So, I thought I could post that text here as well, hoping it can clarify some confusion.

The taxonomic system for the hierarchical (ranked) classification of living organisms (and initially also of rocks, but that failed) is very simple. Organisms are assigned into different groups based on their characteristics, and these groups are hierarchical. The figure below shows the seven main types of groups. Kingdom is higher than phylum, while class is lower , and so on.

For some, but not all groups there are subdivisions of these group types, such as subclass (subdivision within a class), infraorder (subdivision within a suborder i.e. infra- is below sub-), and grouping of groups e.g. superorder (group of orders).

Since the system is hierarchical, organisms belonging to the same class also belong to the same phylum and kingdom. For instance, all animals belonging to the class Reptilia (reptiles, then) also belong to the phylum Chordata (animals with a notochord, or backbone) and the kingdom Animalia (animals).

Now, the order Primates (primates), although belonging to the class Mammalia instead of Reptilia, also belongs to the phylum Chordata and kingdom Animalia. This might complicate things, but it is simply due to that the two classes Reptilia and Mammalia both belong to the same phylum and (therefore) kingdom. Notice that the name of the class is written with a capital first letter when you refer to the actual group. If you instead write carnivorans (belonging to the mammalian order Carnivora not equal to carnivore , which refers to a feeding strategy not a taxonomic group), you are really referring to the members of the group, and you do not use capital letters. This rule is useful for distinguishing between, for example, Primates and primates.

While on the subject of formal rules, the genus and species are special. First, both are always written in italics. Always. Second, the genus name is written with capital first letter, but the species never has a capital. Third, you may refer to the genus alone, e.g. Tyrannosaurus, but never ever write only the species name. Never. This is because there may be several different species with the same name, (for instance, they may be named after the same discoverer) but they never belong to the same genus (if they do, they are simply not allowed to have the same species name). In this way, we get an endless variety of specific names for an endless variety of species. Finally, you may shorten the genus name to only the first letter (capital) followed by a dot and the species name (if you do not include the species name, you may not shorten the genus name it would be silly to write something like T. had remarkably short arms ). For example, we take the genus Tyrannosaurus (species name is excluded, since I refer to the genus), which has one species: Tyrannosaurus rex although some researchers argue that Tarbosaurus bataar really belongs to Tyrannosaurus in that case, we would also have Tyrannosaurus bataar (the species name is the same, but is assigned to a different genus). Notice that I should not shorten the genus name here, since it may be unclear what I mean by T. bataar.

Names can be discarded or invalidated, usually by showing that two very similar species actually are the same, in which case the name given first is the one that remains valid. Rejected names are written within quotation marks, and never italicised. A classical example is that "Brontosaurus exelsus" and Apatosaurus ajax were shown to be the same species (and therefore also belonged to one and the same genus) Apatosaurus, being the first to have been described and named, was kept (both genus and species name).

Another notable convention is that families tend to end with -idae, superfamilies with -oidea and subfamilies with -inae (their members would then be -ids, -oids, and -ines, respectively). For example, we have the Hadrosauroidea (superfamily), Hadrosauridae (family) and Hadrosaurinae (subfamily).

Nowadays, the taxonomic system has been overshadowed by phylogenetic systematics, or cladistics. Cladistics is favoured because it systematically investigates evolutionary relationships, rather than just putting the organisms into different groups cladistics tries to work out how they evolved, and how closely related different organisms are. Taxonomy, on the other hand, merely groups similar-looking organisms together in order to make some sense of the overwhelming chaos of life we have out there.


Role in biology

Many DUFs are highly conserved, indicating an important role in biology. However, many such DUFs are not essential, hence their biological role often remains unknown. For instance, DUF143 is present in most bacteria and eukaryotic genomes. Ζ] However, when it was deleted in Escherichia coli no obvious phenotype was detected. Later it was shown that the proteins that contain DUF143, are ribosomal silencing factors that block the assembly of the two ribosomal subunits. Ζ] While this function is not essential, it helps the cells to adapt to low nutrient conditions by shutting down protein biosynthesis. As a result, these proteins and the DUF only become relevant when the cells starve. Ζ] It is thus believed that many DUFs (or proteins of unknown function, PUFs) are only required under certain conditions.


View options

We make a range of alignments for each Pfam-A family. You can see a description of each above . You can view these alignments in various ways but please note that some types of alignment are never generated while others may not be available for all families, most commonly because the alignments are too large to handle.

Seed
(8)
Full
(551)
Representative proteomes UniProt
(857)
RP15
(22)
RP35
(129)
RP55
(391)
RP75
(574)
Jalview View View View View View View View
HTML View View
PP/heatmap 1 View

1 Cannot generate PP/Heatmap alignments for seeds no PP data available

Key: available, not generated, &mdash not available.


Major Animalia phylums

Phylum Porifera

  • Sponges
  • Very primitive, considered barely animals.
  • Don’t have true organs or nerve or muscle cells

Phylum Annelida

  • Segmented Worms (earthworms, leeches)
  • Segmented Worms
  • Earthworms, leeches, and other segmented worms live in water or damp soil
  • Leeches were once used to suck out people’s “excess” blood and reduce harmful high blood pressure.
  • Leeches are uses today to produce anti-blood-clotting medicines, to suck blood from bruises, and to stimulate blood circulation in severed limbs that have been surgically reattached.
  • Each segment is separated from its neighbors by a membrane and has its own excretory system and branches of the main nerves and blood vessels that run the length of the animal.
  • Both segmented and unsegmented worms have definite anterior and posterior ends.
  • Food travels through the digestive system in one direction from anterior to posterior.
  • A cluster of nerve cells at the anterior end serves as a simple brain.
  • Reproduction occurs by splitting or by mutual fertilization

Mollusks (Mollusca)

  • Includes snails, clams, slugs, squid, and their relatives.
  • Mollusks have soft bodies with 3 parts
  • A mass that contains most of the organs
  • A muscular “foot” that is used in movement
  • A thick flap called a mantle, which covers the body and in most species produces a heavy shell of calcium compounds.
  • Mollusks pump water through gills
  • This is how food is also ingested for clams and oysters. Squid and octopuses use the pump for jet propulsion through the water in search of prey.

Arthropods (Arthropoda)

  • The largest animal phylum, and have jointed external skeletons.
  • 1 million species, crabs, shrimp, spiders, scorpions and insects make up this phylum
  • Arthropods molt, have heads with many sensory organs.
  • Simple and complex eyes that detect only light intensity and form images
  • Antennae that smell chemical substances in the environment, arthropods also respond to water vapor, like biting mosquitoes.
  • They reproduce sexually, where sperm is released inside the female’s body, not in water.
  • Larvae of many species develop into very different adults, a process called metamorphosis.
  • Arthropods development of resistance to insecticides demonstrates how quickly they adapt to a changing environment.
  • Short generations and many offspring increase the chance that random mutations will produce a few resistant individuals

Echinoderms (Echinodermata)

  • Sea stars and sea urchins.
  • Reproduce sexually. Sperm and eggs are released in water, where they meet and join.
  • Movement by seawater into and out of a system of internal tubes.

Chordates (Chordata)

  • Vertebrates-fish, amphibians, reptiles, birds, and mammals.
  • Four characteristics
  • Stiff dorsal rod helps to organize the embryo’s development.
  • The central nervous system (brain and spinal cord) is tubular
  • Their sides have slits just behind the head. These pharyngeal slits (pharynx means “throat”) becomes gill slits of adult fish. In air-breathing chordates, they develop into various organs such as internal parts of the ears
  • They have a tail in humans it’s the tailbone, or coccyx, which curls internally.

Help Us Fix his Smile with Your Old Essays, It Takes Seconds!

-We are looking for previous essays, labs and assignments that you aced!

Related Posts

The oldest pig on the farm, Old Major, gathered all the farm animals into the&hellip

Organisms in Archaea live in extreme conditions, such as: Very hot (hot springs, volcanoes, sea&hellip

Most directly one would say that Animal Farm is an allegory of Stalinism, growing out&hellip

Mycology Myco- = fungus-ology= study of General Characteristics of Fungi: EukaryoticDecomposers – the best recyclers&hellip

of Protists: Eukaryotic Most unicellular, some multicellular Some have a cell wall, some do not&hellip

Author: William Anderson (Schoolworkhelper Editorial Team)

Tutor and Freelance Writer. Science Teacher and Lover of Essays. Article last reviewed: 2020 | St. Rosemary Institution © 2010-2021 | Creative Commons 4.0


Watch the video: Classification (May 2022).