crest4 is a python package for automatically assigning taxonomic names to DNA sequences obtained from environmental sequencing.
More specifically, the acronym CREST stands for "Classification Resources for Environmental Sequence Tags" and is a collection of software and databases for taxonomic classification of environmental marker genes obtained from community sequencing studies. Such studies are also known as "meta-genomics", "meta-transcriptomics", "meta-barcoding", "taxonomic profiling" or "phylogenetic profiling".
Simply put, given the following fragment of an rRNA 16S sequence from an uncultured microbe:
TGGGGAATTTTCCGCAATGGGCGAAAGCCTGACGGAGCAATACCGCGTGAGGGAGGAAGGCCTTAGGGTT
GTAAACCTCTTTTCTCTGGGAAGAAGATCTGACGGTACCAGAGGAATAAGCCTCGGCTAACTCCGTGCCA
GCAGCCGCGGTAAGACGGAGGAGGCAAGCGTTATCCGGAATTATTGGGCGTAAAGCGTCCGTAGGCGGTT
AATTAAGTCTGTTGTTAAAGCCCACAGCTCAACTGTGGATCGGCAATGGAAACTGGTTGACTAGAGTGTG
GTAGGGGTAGAGGGAATTCCCGGTGTAGCGGTGAAATGCGTAGATATCG
crest4 will be able to tell you that this gene is likely originating from the following taxonomy genus:
Bacteria; Terrabacteria; Cyanobacteria; Cyanobacteriia; Phormidesmiales; Nodosilineaceae; Nodosilinea
To produce this result, the input sequence is compared against a built-in reference database of marker genes (such as the SSU rRNA), using the BLAST or VSEARCH algorithms. All high similarity hits are recorded and filtered for both a minimum score threshold, and a minimum identify threshold. Next, for every surviving hit, the exact position in the phylogenetic tree of life is recorded. Finally, the full name of the lowest common ancestor (given this collection of nodes in the tree) is determined and reported as a confident taxonomic classification. Simply put, if for instance all hits for a given sequence only agree at the order level, the assignment stops at the order level.
This strategy contrasts with the other tools that instead use a naive bayesian classifier for taxonomic assignment. Often referred to as the Wang method and used for example in the RDP software, it consists of the following steps: calculate the probability that the query sequence would be part of any given reference taxonomy sequence based on the decomposed kmer content and pick the taxonomy with the highest probability while considering a confidence limit computed by a bootstrapping algorithm.
If you use CREST in your research, please cite this publication:
CREST - Classification Resources for Environmental Sequence Tags, PLoS ONE, 7:e49334
Lanzén A, Jørgensen SL, Huson D, Gorfer M, Grindhaug SH, Jonassen I, Øvreås L, Urich T (2012)
Since crest4 is written in python it is compatible with all operating systems: Linux, macOS and Windows. The only prerequisite is python version 3.8 or above which is often installed by default. Simply choose one of the two following methods to install, depending on which package manager you prefer to use.
$ conda install -c bioconda -c conda-forge -c xapple crest4
Or to create a custom environment named crest which you activate later:
$ conda create -n crest -c bioconda -c conda-forge -c xapple crest4
$ pip3 install crest4
Once the installation completes you are ready to use the crest4 executable command from the shell. Please note that the reference databases are downloaded automatically during first run, so this might take some time depending on your internet connection.
In order to search the reference databases, you will also need either BLAST or VSEARCH installed. These can be installed automatically with the conda package manager.
$ conda install blast
$ conda install vsearch
If you don't use conda, you can obtain them with these commands on Linux, provided you have admin rights:
$ sudo apt update
$ sudo apt install ncbi-blast+
$ sudo apt install vsearch
Or these commands on macOS that work without sudo access:
$ brew install blast
$ brew install vsearch
If you wish to install crest4 from the repository source code you can follow these instructions instead.
- If you do not have
condaon your system you can refer to this section. - If you do not have
pip3on your system you can refer to this section. - If you do not have
python3on your system or have an outdated version, you can refer to this other section. - If you can't run the
crest4command after a successful installation, make sure that the python bin directory is in your path. This is usually$HOME/.local/bin/for Ubuntu. - If none of the above has enabled you to install
crest4, please open an issue on the bug tracker and we will get back to you shortly.
To download the databases that are used in the classification algorithm, crest4 needs somewhere to write to on the filesystem. This will default to your home directory at: ~/.crest4/. If you wish to change this, simply set the environment variable $CREST4_DIR to another writable directory path prior to execution.
Bellow are some examples to illustrate the various ways there are to use this package.
crest4 -f sequences.fasta
Simply specifying a FASTA file with the sequences to classify is sufficient, and crest4 will choose default values for all the parameters automatically. The results produced will be placed in a subdirectory inside the same directory as the FASTA file.
To change the output directory, specify the following option:
crest4 -f sequences.fasta -o ~/data/results/crest_test/
To parallelize the sequence similarity search with 32 threads use this option:
crest4 -f sequences.fasta -t 32
Silvamod138pr2 is the default reference database. To use another database, e.g., midori, the -d option must be specified followed by the database name:
crest4 -f sequences.fasta -d midori248
The full list of options is as follows:
Required arguments:
--fasta PATH, -f PATH
The path to a single FASTA file as a string.
These are the sequences that will be taxonomically
classified.
Optional arguments:
--search_algo ALGORITHM, -a ALGORITHM
The algorithm used for the sequence similarity search
that will be run to match the sequences against the
database chosen. Either `blast` or `vsearch`. No
other values are currently supported. By default,
`blast`.
--num_threads NUM, -t NUM
The number of processors to use for the sequence
similarity search. By default, parallelism is turned
off and this value is 1. If you pass the value `True`
we will run as many processes as there are CPUs but
no more than 32.
--search_db DATABASE, -d DATABASE
The database used for the sequence similarity search.
Either `midori253darn`, `silvamod138pr2`, 'mitofish' or
`silvamod128`.
By default, `silvamod138pr2`. Optionally, the user can
provide a custom database by specifying the full path
to a directory containing all required files under
`search_db`. See the README for more information.
--output_dir DIR, -o DIR
The directory into which all the classification
results will be written to. This defaults to a
directory with the same name as the original FASTA
file and a `.crest4` suffix appended.
--search_hits PATH, -s PATH
The path where the search results will be stored.
This defaults to the output directory. However,
if the search operation has already been completed
beforehand, specify the path here to skip the
sequence similarity search step and go directly to
the taxonomy step. If a hits file exists in the output
directory and this option is not specified, it is
deleted and regenerated.
--min_score MINIMUM, -m MINIMUM
The minimum bit-score for a search hit to be considered
when using BLAST as the search algorithm. All hits below
this score are ignored. When using VSEARCH, this value
instead indicates the minimum identity between two
sequences for the hit to be considered.
The default is `155` for BLAST and `0.75` for VSEARCH.
--score_drop SCORE_DROP, -c SCORE_DROP
Determines the range of hits to retain and the range
to discard based on a drop in percentage from the score
of the best hit. Any hit below the following value:
"(100 - score_drop)/100 * best_hit_score" is ignored.
By default `2.0`.
--min_smlrty MIN_SMLRTY, -i MIN_SMLRTY
Determines if the minimum similarity filter is turned
on or off. Pass any value like `False` to turn it off.
The minimum similarity filter prevents classification
to higher ranks when a minimum rank-identity is not met.
The default is `True`.
--otu_table OTU_TABLE, -u OTU_TABLE
Optionally, one can specify the path to an existing OTU
table in CSV or TSV format when running `crest4`.
The sequence names in the OTU table must be rows and
have to match the names in the FASTA file. The column,
on the other hand, provide your samples names.
When this option is used, then two extra output files
are generated. Firstly, a table summarizing the
assignment counts per taxa. Secondly, a table
propagating the sequence counts upwards
in a cumulative fashion.
Other arguments:
--version, -v Show program's version number and exit.
--help, -h Show this help message and exit.
--pytest Run the test suite and exit.
If you want to integrate crest4 directly into your python pipeline, you may do so by accessing the convenient Classify object as follows:
# Import #
from crest4 import Classify
# Create a new instance #
get_tax = Classify('~/data/sequences.fasta', num_threads=16)
# Run the similarity search and classification #
get_tax()
# Print the results #
for name, query in get_tax.queries_by_id.items():
print(name, query.taxonomy)
The specific arguments accepted are the same as the command line version as specified in the internal API documentation.
To test that the installation was successful you can launch the test suite by executing:
crest4 --pytest
It is possible to run the sequence similarity search yourself without passing through the crest4 executable. This is useful for instance if you want to run BLAST on a dedicated server for increased speed and only want to perform the taxonomic assignment on your local computer.
In such a case you just need to copy the hits file that was generated back to your local computer and specify its location with the following parameter:
crest4 sequences.fasta --hits_file=~/results/seq_search.hits
To create the hits file on a different server you should call the blastn executable with the following options:
blastn -query sequences.fasta -db ~/.crest4/silvamod138pr2/silvamod138pr2.fasta -num_alignments 100 -outfmt "7 qseqid sseqid bitscore length nident" -out seq_search.hits
We also recommend that you use -num_threads to enable multi-threading and speed up the alignments.
The equivalent VSEARCH command is the following:
vsearch --usearch_global sequences.fasta -db ~/.crest4/silvamod138pr2/silvamod138pr2.udb -blast6out seq_search.hits -threads 32 -id 0.75 -maxaccepts 100
The silvamod138pr2 database was derived by manual curation of the SILVA NR SSU Ref v.138 for Bacteria, Archaea, Metazoa and Fungi. For other eukaryotes (protists), the PR2 v4.13 database was used. The SILVA database used was last release in August 2020 and PR2 database in March 2021.
The silvamod128 database was derived by manual curation of the SILVA NR SSU Ref v.128. It supports SSU sequences from bacteria and archaea (16S) as well as eukaryotes (18S), with a high level of manual curation and defined environmental clades. This database was last released in September 2016.
The classification is carried out based on a subset of the best matching alignments using the Lowest Common Ancestor strategy. Briefly, the subset includes sequences that score within x% of the "bit-score" of the best alignment, provided the best score is above a minimum value. Default values are 155 for the minimum bit-score and 2% for the score drop threshold. Based on cross-validation testing using the non-redundant silvamod128 database, this results in relatively few false positives for most datasets. However, the score drop range can be turned up to about 10%, to increase accuracy with short reads and for datasets with many novel sequences.
In addition to the lowest common ancestor classification, a minimum similarity filter is used, based on a set of taxon-specific requirements, by default depending on their taxonomic rank. By default, a sequence must be aligned with at least 99% nucleotide similarity to the best reference sequence in order to be classified to the species rank. For the genus, family, order, class and phylum ranks the respective default cut-offs are 97%, 95%, 90%, 85% and 80%. These cutoffs can be changed manually by editing the .names file of the respective reference database. This filter ensures that classification is made to the taxon of the lowest allowed rank, effectively re-assigning sequences to parent taxa until allowed.
When using amplicon sequences, we strongly recommend preparing the sequences by performing a noise reduction step as well as applying chimera removal. This can be achieved with various third party software such as: VSEARCH, UPARSE, DADA2, SWARM, etc.
For amplicon sequencing experiments with many replicates or similar samples (>~10), the unique noise-reduced sequences may be further clustered using a similarity threshold (often 97% although larger thresholds are probably preferable) into operational taxonomic units (OTUs), prior to classification.
It is possible to construct a custom reference database for use with crest4. The scripts necessary to do this along with some documentation is available in this other git repository:
https://github.com/xapple/crest4_utils
The repository for crest4 comes along with five different GitHub actions for CI/CD which are:
- Pytest master branch -
- Test PyPI release on Ubuntu -
- Test PyPI release on macOS -
- Test conda release on Ubuntu -
- Test conda release on macOS -
Only the first action is set to be run automatically on each commit to the master branch. The four other actions can be manually launched and will run the pytest suite for both python 3.8 and python 3.9 on different operating systems.
-
Instructions for distributing and uploading
crest4on PyPI so that it can be installed bypipcan be found here. The current uploaded version is listed here. -
Instructions for distributing and uploading
crest4on anaconda so that it can be installed bycondacan be found here. The current uploaded version is listed here.
Two scripts that automate these processes can be found in the following repository:
https://github.com/xapple/bumphub
The location of the database files that crest4 will download upon first run can easily be updated by editing this file:
Once that file is updated, all downloads will now point to the new URLs, without even needing to redistribute a new version of crest4. This is possible as the JSON file is checked before initiating any new download.
The internal documentation of the crest4 python package is available at:
http://xapple.github.io/crest4/crest4
This documentation is simply generated from the source code with this command:
$ pdoc --output-dir docs crest4