RaPDTool up-to-date version continues in https://github.com/kjestradag/RaPDTool
1. Generate a taxonomic profile from massive sequencing data (the input file should be a metagenome assembly).
RaPDTool use metagenomic assemblies and call FOCUS profiler to report the organisms/abundance present in the metagenome.
*Warning: Taxonomic profiles are usually inferred from raw reads; assembled-contigs profiling is an "special case" in order to explore what part of the community could be assembled into regular genomic composites. Use at your own risk. For example, we have observed that Focus is not good at detecting contamination in the eukaryote genome and can return false positives in this case.
If the input consist on a metagenome assembly, RaPDTool automatically call Metabat2 to aggregate individual genome bins. The bins are subsequently refined with Binning_refiner to produce a non-redundant set.
In the version 2.0 of this pipeline, the refined set of bins are automatically processed with miComplete, a much faster tool than CheckM for this purpose.
RaPDTool compare each bin against curated taxonomic mash databases like type material genome database from NCBI (NCBI_type_material.msh), the Genome Taxonomy Database (GTDBr202.msh) and a database that we built to enrich the one that comes by default with Focus, using almost entirely, the type material database. these databases are offered as representations or sketches that reduce storage space and computing time.
Metabat2 (https://bitbucket.org/berkeleylab/metabat/src/master/) (version tested 2:2.15)
Binning_refiner (https://github.com/songweizhi/Binning_refiner)
Prodigal (https://github.com/hyattpd/Prodigal)
HMMER (http://hmmer.org/)
miComplete (https://github.com/EricHugo/miComplete)
RaPDTool preconfigured database DB Mash format
Perl Modules
Getopt::Std
Text::SimpleTable::AutoWidth
RaPDTool pipeline it is written in Python and Perl and used several C scripts. For greater convenience/ease of installing rapdtools, we use the Singularity container platform and build an image with the complete environment (scripts, dependencies and databases) needed to run RapdTool.
You just need to have "Singularity" or install it with:
$ conda install -c conda-forge singularity
and download the Singularity image rapdtool
$ rapdtool <input.fasta> [output_dir]
the input file should be a metagenome assembly
optional:
output_dir_name (default: rapdtool_results)
notes:
1- You need to put "rapdtool" in your path, otherwise you must give the whole path so that it can be found.
2- The input fasta must exist in your $HOME, otherwise you need to set the environment variable SINGULARITY_BIND
to bind paths where your sequences are located
ex: export SINGULARITY_BIND="../path/for/the/input/fasta"
Make sure you have all dependencies installed and the DATABASE downloaded. You also need to download and have in your path all the "bin" scripts.
RaPDTool runs natively by calling the script: rapdtool_local.py
$ rapdtool_local.py -i <input.fasta> -d database.msh [-r output_dir]
*no argument for this help
the input file should be a metagenome assembly
optional:
output_dir_name (default: rapdtool_results)
example : ./rapdtool.py -i INPUT.fasta -d DATABASE.msh -r OUTPUT_FOLDER
The output of RaPDTool produces 4 directories and 3 main files:
Contains the log file of the RaPDTool execution (logfmbm.txt).
fmbm is a kind of acronym that includes the main operations of the pipeline (Focus/Metabat/Binning_refiner/Mash).
Summarizes the best/most reliable **Mash** hits to be able to classify at the genus or species level. For the gender level it is considered a cut-off value <= 0.08 and <= 0.05 for species level. Additionally it contains the results of the taxonomic classification with Focus, leaving only the species with a relative abundance greater than 1.
rapdtools_confidence.tbl contains the same data but with a prettier aesthetic
Krona charts through which you can navigate/explore the taxonomic annotation made up to the species level. Krona charts can be viewed with any modern web browser.
Store the FOCUS taxonomic profile inferred from the inputs (metagenome assembly). You should see several files -in tabular format (csv)- reporting relative abundance from Kingdom to Species . FOCUS also ventures to infer Strains, but I would be cautious at that taxonomic level.
Some points to note with this result:
1-We could assume that the short-reads contain a "genomic space" more representative of the community, than that contained in the assembly; the assembly _per se_ supposes a loss of taxonomic information. Assembled contigs profiling only represents an approximation of taxonomic composition at the genomic level, so be cautious with the interpretations.
2-The native FOCUS database plays an important role in the accuracy of the profile. The initial launch of FOCUS considered 2,766 reference genomes to build a kmer frecuencies database ( _k_ = 6; _k_ = 7) . For the implementation of RaPDTool, we have considered 14,551 genomes from the Type Material to give taxonomic certainty to the profiles, while enriching the initial database.
The new k = 6; k = 7 kmer archives for Update FOCUS Database will be available here
Contains several relevant subdirectories and files:
binmetabat > Store Metabat2 binning results. The genome composites aggregated from the initial metagenomic assembly
outbinningref > Binning_refiner results. All bins obtained with Metabat2 are "refined" with Binning_refiner to produce a set of probable MAGs
outmicomplete > Hugoson et al, 2020 published a paper with a fairly "generous" alternative to estimate quality of assembled microbial genomes (https://doi.org/10.1093/bioinformatics/btz664). Although the gold standard is still CheckM, miComplete is more resource friendly and offers a weighted calculation.
The result of miComplete is a table with the quality assessment of the refined bins as shown in the image:
outmash > Full Mash dist comparison for each bin produced, against the input database. Remember that these databases contain a set of genomes curated as Mash representations or sketches. This indicates that bin1 is compared against the ~65,336 records in the database (that's extremely fast with Mash), and the result is a table with 5 columns representing the following:
| Query_genome | Match_in_database | Genomic_Distance | p_value | Shared_Hashes |
|---|---|---|---|---|
| Bin1.fna | GCA_Reference.fna | 0.0327655 | 0 | 471/1000 |
The genomic distance in the third column refers to the Mash distance, also defined as mutational distance. You will find more information on the interpretation of these tables in: https://doi.org/10.1186/s13059-016-0997-x. A practical interpretation of this comparison suggests that if two genomic contexts share < 0.05 distance, they are likely to be genomically coherents, and that has implications for the prokaryotic species concept.
This also means that those contexts with smaller genomic distances will potentially be the closest phylogenetic neighbors to your query; very useful if you want to explore the phylogenetic hypothesis.
Other subdirectories contain the log files of each task
Contain the ten closest hits from the Mash paired comparison for each genome. This simplifies the interpretation of the results by limiting the Mash comparison to the ten closest neighbors to the query, which can be useful in phylogenetics and taxonomy. The user can take this list as the basis for a finer comparison by estimating the Overall genome relatedness index (OGRI) like ANI...
As you can see, they are conveniently sorted from smallest to largest, so that it is easy to establish or rule out probable genomic coherence; and use the elements of the reference in subsequent more refined analyzes.
For example, in the previous image the bin **meta-assembly_bin_1.fna** shares a genomic distance of ~0.062 with the assembly GCF_002165255.2, that belongs to the species Acinetobacter sp. WCHA45 (proteobacteria); and ~0.07 with the assembly GCA_000430225.1 that belongs to the species _Acinetobacter_junii_. Other hits in this comparison also match elements of the _Acinetobacter_ genus. It is not difficult to hypothesize that the bin **meta-assembly_bin_1.fna** is related with the clade _Acinetobacter_ (probably at the genus level, although nothing can be said about the species yet). So, presumably **meta-assembly_bin_1.fna** can be clasified as _Acinetobacter_sp.
Potential tests could be the estimation of the Average Nucleotide Identity against these close hits and reconstructing a phylogenomic tree in order to place the query in a finer taxonomic context.
Sánchez-Reyes, A.; Fernández-López, M.G. Sketched reference databases for genome-based taxonomy and comparative genomics. https://doi.org/10.1590/1519-6984.256673
Mash: fast genome and metagenome distance estimation using MinHash. Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, Phillippy AM. Genome Biol. 2016 Jun 20;17(1):132. doi: 10.1186/s13059-016-0997-x.
Silva, G. G. Z., D. A. Cuevas, B. E. Dutilh, and R. A. Edwards, 2014: FOCUS: an alignment-free model to identify organisms in metagenomes using non-negative least squares. PeerJ, 2, e425, doi:10.7717/peerj.425.
Song WZ, Thomas T (2017) Binning_refiner: Improving genome bins through the combination of different binning programs. Bioinformatics, 33(12), 1873-1875.
Kang, D. D., Li, F., Kirton, E., Thomas, A., Egan, R., An, H., & Wang, Z. (2019). MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ, 7, e7359. https://doi.org/10.7717/peerj.7359.
Eric Hugoson, Wai Tin Lam, Lionel Guy, miComplete: weighted quality evaluation of assembled microbial genomes, Bioinformatics, Volume 36, Issue 3, 1 February 2020, Pages 936–937, https://doi.org/10.1093/bioinformatics/btz664
This work was developed in the group of Dr. Ayixon Sánchez-Reyes
"Researchers for Mexico" Program-(CONACYT)-Institute of Biotechnology-National Autonomous University of Mexico
**Contact personal: ayixon@gmail.com **Contact institutional: ayixon.sanchez@mail.ibt.unam.mx
Teammates: M.C. Karel Johan Estrada Guerra (developer); Dra. Luz Bretón Deval; Dr. Maikel G. Fernández-López
We thank Ing. Roberto Peredo for his help in the development of this tool
This work was funded in part by the project CF 2019 265222 (Fondo Institucional para el Desarrollo Científico, Tecnológico y de Innovación FORDECYT-PRONACES CONACYT- México)