Overview
Databases and versions
Installation
Quick start
Main output files
Known issues
References
License / Copyright
**[Jan 2026] New release, iPHoP v1.4.2 . This release adds an option to specify a number of threads for the WIsH step, independently from the overall number of threads used in the rest of iPHoP stesp. This is to help cases in which a high number of threads leads to iPHoP getting stalled at the WIsH step. **
**[Jul 2025] New release, iPHoP v1.4.1 . This release fixes a few minor bugs, but most importantly is associated with an updated database "iPHoP_db_Jun25_rw", based on GTDB r226 and an extended collection of MAGs.
The "latest" releases on conda and Docker should now point to 1.4.1, and the "latest" (i.e. default) database is now "iPHoP_db_Jun25_rw".
Many thanks to everyone who reported issues that are now fixed (issue with unusual character in sample names, "add_to_db" getting stuck, etc).
Please report any issues to https://bitbucket.org/srouxjgi/iphop/issues **
iPHoP stands for integrated Phage Host Prediction. It is an automated command-line pipeline for predicting host genus of novel bacteriophages and archaeoviruses based on their genome sequences.
The pipeline can be broken down into 6 main steps:
A: Step 1: Running individual host prediction tools
- Phage-based tool: * RaFAH (https://doi.org/10.1016/j.patter.2021.100274): yield a prediction of host genus with associated score, stored for later (see Step 5)
- Host-based tools: * blastn to host genomes. All hits with ≥ 80% identity and ≥ 500bp are considered. Hits covering ≥ 50% of the "host" contig length are ignored as these often derive from contigs (nearly) entirely viral, and can easily be contaminant in genomes or MAGs and thus not reliable for host prediction * blastn to CRISPR spacer database. All hits with up to 4 mistmaches are considered. * WIsH (https://doi.org/10.1093/bioinformatics/btx383) : host association based on k-mer composition similarity between virus and host genome * VHM - s2* similarity (https://doi.org/10.1093/nar/gkw1002 / https://doi.org/10.1093/nargab/lqaa044): host association based on k-mer composition similarity between virus and host genome * PHP (https://doi.org/10.1186/s12915-020-00938-6): host association based on k-mer composition similarity between virus and host genome
B: Step 2: Collect all scores and all distances between hits for host-based tools * Distance between two potential hosts, i.e., two hits for a given tool and a given query virus, is based on the GTDB trees (https://doi.org/10.1093/nar/gkab776)
C: Steps 3 and 4: Compile an organized list of hits for each virus - tool - candidate host combination * For each hit, the top other hits from the same virus with the same tool are compiled and organized according to the distance between the base hit host and the other hit host (see step 2) * These series of hits are used as input for automated classifiers to derive a score for the given virus - candidate host pair * This enables the evaluation of every potential host (every hit) when considering the context of the top hits obtained for this virus
D: Step 5: Derive 3 scores for host-based tools for each virus - candidate host combination * The top scores based only on blast or crispr matches are retained, as these methods can be reliable enough by themselves for host prediction * A third score is obtained by considering all scores from all individual classifiers (see step 4), i.e. taking into account all 5 host-based methods
E: Step 6: Calculate a composite score for each virus - candidate host genus combination integrating host-based and phage-based signals * The 3 host-based scores (see step 5) are then considered alongside the phage-based score (RaFAH - https://doi.org/10.1016/j.patter.2021.100274) to obtain a single score for all pairs of virus - candidate host genus.
The current version of iPHoP is v1.4.1. This version of iPHoP introduced a new compressed database, that enabled updating the host database to GTDB r226 as well as 2025 IMG and MGNify public bins. This version also introduces a few changes that should make it easier to add large MAG collection to the database.
Databases available to download are as follows (see "iphop download -h" for how to download a specific database, or instructions in "Installation" for how to download the default one):
| Database version name | Database folder name | Minimum iPHoP version required | Notes |
|---|---|---|---|
| iPHoP_db_Jun25_rw | Jun_2025_pub_rw | >= 1.4.1 | Full database updated in June 2025 for version 1.4 and later. Host genomes extracted from GTDB r226, IMG unrestricted bins as of April 2025, MGnify MAG collections as of April 2025. This is the current default database, starting with the release of iPHoP version 1.4.1. |
| iPHoP_db_rw_1.4_for-test | Test_db_rw_v1.4 | >= 1.4.1 | Subset of the database, used with the test input file to check iPHoP installation for version 1.4 and later. |
| iPHoP_db_Aug23_rw | Aug_2023_pub_rw | >= 1.3.3 | Full database updated in Aug 2023 for version 1.3 and later. Host genomes extracted from GTDB r214, IMG published genomes as of Aug. 2023, MGnify MAG collections, and GEMv1. This was the default database starting with iPHoP v1.3.3. |
| iPHoP_db_Sept21_rw | Sept_2021_pub_rw | >= 1.3.0 | Full database updated in Sept 2021, for version 1.3 and later. Host genomes extracted from GTDB r202, IMG published genomes as of Sept. 2021, and GEMv1. This was the default database until v1.3. |
| iPHoP_db_rw_for-test | Test_db_rw | >= 1.3.0 | Subset of the database, used with the test input file to check iPHoP installation |
| iPHoP_db_Sept21 | Sept_2021_pub | <1.3.0 | Full database updated in Sept 2021. Host genomes extracted from GTDB r202, IMG published genomes as of Sept. 2021, and GEMv1 |
| iPHoP_db_for-test | Test_db | <1.3.0 | Subset of the database, used with the test input file to check iPHoP installation |
The content of the host database is as follows:
- **Jun_25_pub_rw: Current recommended database ** * GTDB release 226 (482,038 genomes, 143,614 representatives) * Public IMG bins, as of May 2025 (192,260 genomes, 43,100 representatives) * Public MGNify bins, as of May 2025 (481,678 genomes, 5,279 representatives)
The Jun_25_pub_rw database is currently the latest and downloaded by default. The current keywords for downloading this database are: "latest_rw" or "iPHoP_db_Jun25_rw"
- Test_db(_rw): Test database * Reduced database of 148 host genomes (for blast, CRISPR, and WIsH modules) * Host genomes were selected to be used with the phage genomes provided in the "test_input_phages.fna" file (see folder "test") The test_db_rw database is a smaller database that should be quick to download, and enable users to proceed with a test of their iPHoP installation before downloading the entire (and much larger) host database.
Stable versions of iPHoP are available as bioconda packages and Docker containers. The latest version of iPHoP can also be obtained by cloning this repository (see below).
- iPHoP is available on the Bioconda channel: -- Note - the bioconda installation is only possible for linux machines, not Mac OS X. For the latter, see the Docker container version below
$ conda create -n iphop_env python=3.8
$ conda activate iphop_env
$ conda install -c conda-forge -c bioconda iphop- For faster package installation, one can use mamba as follows:
$ conda create -c conda-forge -n iphop_env python=3.8 mamba
$ conda activate iphop_env
$ mamba install -c conda-forge -c bioconda iphop- We can now test that the installation was successful by running
$ iphop -h
iPHoP v1.4.1: integrating Host Phage Predictions
https://bitbucket.org/srouxjgi/iphop
usage: iphop <task> [options]
task:
predict run full pipeline to generate host prediction for some input phage genome(s)
download download and setup the latest host database
add_to_db add some host genomes to the database of hosts (e.g. MAGs derived from the same metagenome(s))
[...]
$ iphop predict -h
Run full pipeline to predict host of input bacteriophage/archaeal virus genomes
usage: iphop predict --fa_file <input genomes> --out_dir <output directory> [options]
optional arguments:
-h, --help show this help message and exit
--fa_file INPUT_FILE, -f INPUT_FILE
input fasta file of virus sequences
[...]-
From there on, the next step would typically be to download the iPHoP database if not already done.
-
You can also run a standard test dataset (provided in this repository) to verify that the installation was fully successful
$ mkdir iphop_db
$ iphop download --db_dir iphop_db/ -dbv iPHoP_db_rw_1.4_for-test
$ wget https://bitbucket.org/srouxjgi/iphop/raw/d27b6bbdcd39a6a1cb8407c44ccbcc800d2b4f78/test/test_input_phages.fna
$ mkdir iphop_test_results
$ iphop predict --fa_file test_input_phages.fna --db_dir iphop_db/Test_db_rw_v1.4/ --out_dir iphop_test_results/test_input_phages_iphop- The expected results for the test input with the test database are available in the "test" folder ("XXX-expected-results-iPHoP_db_for-test-db" files).
$ wget https://bitbucket.org/srouxjgi/iphop/raw/d27b6bbdcd39a6a1cb8407c44ccbcc800d2b4f78/test/Host_prediction_to_genus_m90.csv-expected-results-iPHoP_db_for-test-db
$ wget https://bitbucket.org/srouxjgi/iphop/raw/d27b6bbdcd39a6a1cb8407c44ccbcc800d2b4f78/test/Host_prediction_to_genome_m90.csv-expected-results-iPHoP_db_for-test-db
$ wget https://bitbucket.org/srouxjgi/iphop/raw/d27b6bbdcd39a6a1cb8407c44ccbcc800d2b4f78/test/Detailed_output_by_tool.csv-expected-results-iPHoP_db_for-test-db- The latest stable release of iPHoP is available on DockerHub:
$ docker pull simroux/iphop:latest- Running iPHoP in Docker is then similar to a standard installation, with the exception of the local directories that must be mounted in the container. See further instructions below.
- First, clone the iPHoP repository to get the latest version of the scripts and classifiers
$ git clone https://bitbucket.org/srouxjgi/iphop.git
[...]
Resolving deltas: 100% (60/60), done- Next, create the corresponding conda environment and install the required packages
$ cd iphop
$ conda env create -f iphop_environment.yml
OR EVEN BETTER
$ mamba env create -f iphop_environment.yml
[...]
#
# To activate this environment, use
#
# $ conda activate iphop
#
# To deactivate an active environment, use
#
# $ conda deactivate(to install mamba, see - https://github.com/mamba-org/mamba)
- Note: if you have issues at the "git clone" step because git lfs is not installed on your system "git-lfs: command not found", you can do it the other way around, i.e.:
$ wget https://bitbucket.org/srouxjgi/iphop/raw/aa3075bf6b4640e8b39135e07b48b457948235f1/iphop_environment.yml
$ mamba env create -f iphop_environment.yml
$ conda activate iphop
$ git clone https://bitbucket.org/srouxjgi/iphop.git
$ cd iphop-
Note: in some cases, the conda environment must be modified to load the correct library paths. See "Known Issues" below.
-
Because classifiers are pretty large, these need to be downloaded using git lfs
$ git lfs install
$ git lfs pull- Next, let's install the last dependencies as well as iPHoP as a package in this environment
$ conda activate iphop
$ python -m pip install https://files.pythonhosted.org/packages/72/8a/033b584f8dd863c07aa8877c2dd231777de0bb0b1338f4ac6a81999980ee/tensorflow-2.7.0-cp38-cp38-manylinux2010_x86_64.whl -vv
$ python -m pip install https://files.pythonhosted.org/packages/2a/78/bf49937d0d9a36a19faca28dac470a48cfe4894995a70e73f3c0c1684991/tensorflow_decision_forests-0.2.2-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl -vv
$ python -m pip install .
[...]
running install_scripts
Successfully installed [...] iphop-1.4.1- We can now test that this was successful by running
$ iphop -h
iPHoP v1.4.1: integrating Host Phage Predictions
https://bitbucket.org/srouxjgi/iphop
usage: iphop <task> [options]
task:
predict run full pipeline to generate host prediction for some input phage genome(s)
download download and setup the latest host database
add_to_db add some host genomes to the database of hosts (e.g. MAGs derived from the same metagenome(s))
[...]
$ iphop predict -h
Run full pipeline to predict host of input bacteriophage/archaeal virus genomes
usage: iphop predict --fa_file <input genomes> --out_dir <output directory> [options]
optional arguments:
-h, --help show this help message and exit
--fa_file INPUT_FILE, -f INPUT_FILE
input fasta file of virus sequences
[...]- The next step is then to download the iPHoP database, if not already done
$ mkdir iphop_db
$ iphop download --db_dir iphop_db/
# OR
$ iphop download --db_dir iphop_db/ -dbv iPHoP_db_rw_for-test
# if you only want to download a small database to run the test sequences- You should be all set (yeah !). Now we can run a quick test as follows:
$ mkdir iphop_test_results
$ iphop predict --fa_file test/test_input_phages.fna --db_dir iphop_db/Test_db_rw/ --out_dir iphop_test_results/test_input_phages_iphop
# Note: you can replace "Test_db_rw" by the version of the database you have- The expected results for the test files with the test database are available in the "test" folder ("XXX-expected-results-iPHoP_db_for-test-db" files).
Note: if you don't get an output file, but an error with "wish_results/prediction.list" or "rafah_out/Full_Host_Predictions.tsv", this may be a known error with some libraries and conda environments (See "Known Issues" below.)
Regardless of how iPHoP was installed, you will always need to download the iPHoP database first before preforming predictions
- Downloading iPHoP database using the built-in utility:
$ iphop download --db_dir path_to_iPHoP_dbTo verify if the database integrity (after having downloaded and extracted)
$ iphop download --db_dir path_to_iPHoP_db --full_verifyNote: --full_verify can only be run if the database has already been downloaded, and to verify its integrity. If no database is available, the script will fail with a 'file not found' error
By default, iPHoP will always download the latest version of the database. Other specific versions currently available for download (specify the version name with "--db_version") include: iPHoP_db_for-test
- Downloading and extracting manually
$ wget https://portal.nersc.gov/cfs/m342/iphop/db/iPHoP.latest_rw.tar.gz
$ tar -zxvf iPHoP.latest_rw.tar.gz- Some users may wish to update the database using their own host genomes or MAGs (see below Adding bacterial and/or archaeal MAGs to the host database):
- When installed from Bioconda or manually from this repository, iPHoP can be run using a single command:
$ iphop predict --fa_file my_input_phages.fasta --db_dir path/to/iphop_db/Jun_2025_pub_rw/ --out_dir iphop_output/-
For a full listing of iPHoP programs and options, use:
iphop -handiphop <program> -h -
For more information on the result files and the interpretation of the results, see Main output files below
- Pull the image
$ docker pull simroux/iphop:latest- From there on, all iPHoP commands are available using
$ docker run --rm simroux/iphop:latestThe main specificity to running with Docker is that one needs to mount the local database and output directories in the container, and use the "no prompt" options if downloading the database (option "-n")
- If needed, download the database - note: the example here is for the smaller test database
$ docker run --rm -v /local/path/to/iphop_db:/iphop_db/:rw --user $(id -u):$(id -g) -t iphop:latest download --db_dir /iphop_db/ -dbv iPHoP_db_rw_for-test -n- Run a prediction (note: for convenience, the input fasta file was put in the output directory, this should not cause any issue)
$ docker run --rm -v /local/path/to/iphop_db:/iphop_db/:rw -v /local/path/to/iphop_results:/iphop_results/:rw --user $(id -u):$(id -g) -t iphop:latest predict --fa_file /iphop_results/test_input_phages.fna --db_dir /iphop_db/Test_db_rw/ --out_dir /iphop_results/-
When running iPHoP add_to_db, you get an error "args['tree_a'] = glob.glob(os.path.join(args['gtdb_dir'],"gtdbtk.ar[0-9]*.decorated.tree"))[0] IndexError: list index out of range"
This is most likely because the MAG set you are trying to add to the database did not include any archaea, and some versions of GTDB-tk will then not generate any of the archaea-specific files (which iPHoP expects). We are still fixing this from the iPHoP side, but in the meantime, one way to "fix" this is to add the MAGs provided to test the tool (https://bitbucket.org/srouxjgi/iphop/downloads/Data_test_add_to_db.tar.gz - folder Wetland_MAGs/) to your MAG set. Because our test MAGs include both bacteria and archaea, gtdbtk should generate both a bacteria and an archaea tree, and you should not see the error in iPHoP add_to_db anymore. -
iPhoP add_to_db seems stuck and does not complete Please verify if the names of the genomes added are composed only of numbers (e.g. 123.fna), and if so, add a prefix (e.g. MAG_123.fna).
-
When running iPHoP predict, you get an error "FileNotFoundError: [...] wish_results/prediction.list" This is most likely due to the pre-compiled WIsH not working, because of libraries issues, especially if iPHoP was installed from the repo rather than via BioConda / run via Docker. The steps to solve these errors are similar to what is described at https://stackoverflow.com/questions/46826497/conda-set-ld-library-path-for-env-only : Locate the directory for the conda environment, such as /home/user/anaconda3/envs/iphop. Create the files needed to update the LD_LIBRARY_PATH variable:
$ touch ./etc/conda/activate.d/LD_env_vars.sh
$ touch ./etc/conda/deactivate.d/LD_env_vars.shEdit ./etc/conda/activate.d/LD_env_vars.sh as follows, while replacing the example path by the real path on your system:
export OLD_LD_LIBRARY_PATH=${LD_LIBRARY_PATH}
export LD_LIBRARY_PATH=${CONDA_PREFIX}/x86_64-conda-linux-gnu/lib/:${LD_LIBRARY_PATH}Edit ./etc/conda/deactivate.d/LD_env_vars.sh as follows
export LD_LIBRARY_PATH=${OLD_LD_LIBRARY_PATH}
unset OLD_LD_LIBRARY_PATHReload your environment, and (hopefully) WIsH should now be happy. Remember to either test on an empty directory, or remove "Wdir/wishparsed.csv" from your result directory, otherwise iPHoP will simply re-use this (likely empty) file
- When running iPHoP predict, you get an error "FileNotFoundError: [...] rafah_out/Full_Host_Predictions.tsv'" or "FileNotFoundError: [...] rafah_out/Full_CDS_Prediction.faa'"
This can actually be two distinct issues (i) if iPHoP was installed via the repository, then it may be a library issue, or (ii) RaFAH failed due to insufficient memory. - For (i), the steps to solve these errors are similar to what is described at https://stackoverflow.com/questions/46826497/conda-set-ld-library-path-for-env-only : Locate the directory for the conda environment, such as /home/user/anaconda3/envs/iphop. Create the files needed to update the PERL5LIB variable:
$ touch ./etc/conda/activate.d/update_bioperl.sh
$ touch ./etc/conda/deactivate.d/update_bioperl.shEdit ./etc/conda/activate.d/update_bioperl.sh as follows, while replacing the example path by the real path on your system:
export OLD_PERL5LIB=$PERL5LIB
export PERL5LIB=${CONDA_PREFIX}/lib/perl5/site_perl/5.22.0Edit ./etc/conda/deactivate.d/update_bioperl.sh as follows
export PERL5LIB=${OLD_PERL5LIB}
unset OLD_PERL5LIBReload your environment, and (hopefully) RaFAH should now be happy. Remember to either test on an empty directory, or remove "Wdir/rafahparsed.csv" from your result directory, otherwise iPHoP will simply re-use this (likely empty) file - For (ii), unfortunately there is no great solution except running on a machine with more memory or, in the future, using the KBase iPHoP app when available. In the meantime, after iPHoP failed the first time, you can run the same command line again, and this time the program will finish, while warning you that it did not find any RaFAH results:
[...]
[10/1] Preparing the detailed output...
Pblm - trying to use RaFAH, but the file iphop_test_results/test_input_phages_iphop/Wdir/rafahparsed.csv has no data, only the header ???
[10/2] Preparing the iPHoP-only result file, linking viruses to individual genomes (iphop_test_results/test_input_phages_iphop/Host_prediction_to_genome_m90.csv) ...
[10/3] Preparing the combined iPHoP / RaFAH output summarized at the genus rank (iphop_test_results/test_input_phages_iphop/Host_prediction_to_genus_m90.csv) ...
!#!#!#!#!#! WARNING --- SOME UNEXPECTED EVENTS HAPPENED -- WE LIST THEM BELOW, IT COULD BE NOTHING, BUT YOU SHOULD STILL DOUBLE-CHECK #!#!#!#!#!#!#
Pblm - trying to use RaFAH, but the file iphop_test_results/test_input_phages_iphop/Wdir/rafahparsed.csv has no data, only the header ???
!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!!#!#!#!#!#!#!
$You will not get results from RaFAH, but host prediction from other tools (including iPHoP-RF) will be available.
- When running iPHoP predict, you get an error ending with " raise KeyError(gpr) ... KeyError: 'N match" (reported by Zhichao Zhou, thanks! - https://bitbucket.org/srouxjgi/iphop/issues/5/keyerror-n-match)
This is usually because there was 0 blast hits found across the whole input dataset, which is an exception that is not well captured in iPHoP v1.1.0. While waiting for us to fix it in v1.2, a work-around is to add some sequences that will yield hits to your input file. A good set of sequences to concatenate to your own is the test set, i.e.
$ wget https://bitbucket.org/srouxjgi/iphop/raw/d27b6bbdcd39a6a1cb8407c44ccbcc800d2b4f78/test/test_input_phages.fna-
When running iPHoP via Docker on a custom database, you get an error "There seems to be a database incompatibility: this is iPHoP v1.3.1, but the database provided ..."
This is most likely because Docker can not read the symbolic links in "db" that iPHoP attempted to create when generating a new database. To circumvent this, you will need to remove two links to directories and copy them from the original iPHoP database into the custom database: "rafah_data" and "rewish_models". Similarly, you will need to remove the link to "Translate_genus_to_full_taxo.tsv" in "db_infos" and replace it with a full copy from the original database. Thanks CP Antony for reporting the error ! -
__When running iPHoP predict, you get an error "ValueError: Wrong number of items passed 3, placement implies 1" __ This seems to happen in some cases with large input files. Splitting your input files in smaller batches and running these separately should fix it (see https://bitbucket.org/srouxjgi/iphop/issues/65/an-error-occurred-while-running-the-wish). Thanks Zhiliang for reporting !
-
__When running iPHoP add_to_db, you get an error "The query file .DS_Store is empty, please double check the file.“ __ This is likely due to a hidden directory called ".DS_Store" present in your MAG folder. After removing this directory, add_to_db should complete successfully.
-
When running iPHoP add_to_db, the tool gets stuck at Step_06 This has happened in the past in cases where the MAG folder provided to iPHoP includes both MAGs (in fasta file) and one or several folders. iPHoP unfortunately mistakes these folder for fasta files and tries to read them as such. This should be corrected in the next release, but in the meantime removing any folder (and any file that is not a fasta file of a MAG) from the input MAG folder should fix it. Thanks Josue Rodriguez to report the issue and the solution !
-
__When running iPHoP, you get an error related to the version of tensorflow or another python package __ This is probably due to some incompatibility between your "global" packages and the ones in the conda environment. You can try to enter "export PYTHONNOUSERSITE=1" in a terminal before running iPHoP to fix these (thanks Haley for reporting !)
Found other issues ? Please report them to https://bitbucket.org/srouxjgi/iphop/issues
Host_prediction_to_genus_mXX.csv, where XX is the minimum score cutoff selected (default: Host_prediction_to_genus_m90.csv)
This contains integrated results from host-based and phage-based tools at the host genus level:
| Virus | AAI to closest RaFAH reference | Host genus | Confidence score | List of methods |
|---|---|---|---|---|
| IMGVR_UViG_3300029435_000002 | 48.49 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella | 98.50 | RaFAH;91.30 iPHoP-RF;89.50 CRISPR;70.20 |
| IMGVR_UViG_3300029435_000003 | 53.00 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Agathobacter | 92.20 | blast;94.40 |
| IMGVR_UViG_3300029435_000003 | 53.00 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Bacteroides_F | 90.90 | CRISPR;93.30 iPHoP-RF;51.70 |
| IMGVR_UViG_3300029435_000005 | 42.95 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Gemmiger | 95.30 | blast;96.70 CRISPR;92.70 iPHoP-RF;82.50 |
| IMGVR_UViG_3300029435_000007 | 35.09 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella | 98.40 | CRISPR;98.80 iPHoP-RF;95.40 blast;93.60 |
| IMGVR_UViG_3300029435_000009 | 99.62 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Lachnospira | 99.00 | CRISPR;98.80 blast;92.60 iPHoP-RF;70.90 RaFAH;65.80 |
| IMGVR_UViG_3300029435_000009 | 99.62 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Roseburia | 95.70 | CRISPR;97.00 iPHoP-RF;56.80 |
| IMGVR_UViG_3300029435_000010 | 22.47 | d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Burkholderiales;f__Burkholderiaceae;g__Sutterella | 97.60 | blast;98.30 CRISPR;80.00 iPHoP-RF;78.30 |
- This output file lists for each prediction the virus sequence ID, the level of amino-acid similarity (AAI) between the query and the genomes in the RaFAH phage database, the predicted host genus, the confidence score calculated from all tools, and the list of scores for individual classifiers obtained for this virus-host pair.
- For the detailed score by classifier, "RaFAH" represents the score derived from RaFAH (https://www.sciencedirect.com/science/article/pii/S2666389921001008), iPHoP-RF is the score derived from all host-based tools, CRISPR the score derived only from CRISPR hits, and blast the score derived only from blastn hits
- All virus-host pairs for which the confidence score is higher than the selected cutoff (default = 90) are included, so each virus may be associated with multiple predictions (e.g. IMGVR_UViG_3300029435_000003 and IMGVR_UViG_3300029435_000009).
- When multiple predictions are available for a query virus, typical standard practices is to use the one with the highest score
Host_prediction_to_genome_mXX.csv, where XX is the minimum score cutoff selected (default: Host_prediction_to_genome_m90.csv)
This contains integrated results from host-based tools only (i.e., no RaFAH) at the host genome representative level:
| Virus | Host genome | Host taxonomy | Main method | Confidence score | Additional methods |
|---|---|---|---|---|---|
| IMGVR_UViG_3300029435_000003 | RS_GCF_000020605.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Agathobacter;s__Agathobacter rectalis | blast | 94.40 | None |
| IMGVR_UViG_3300029435_000003 | RS_GCF_000155855.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Bacteroides_F;s__Bacteroides_F pectinophilus | CRISPR | 93.30 | iPHoP-RF;51.70 |
| IMGVR_UViG_3300029435_000003 | GB_GCA_000437735.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Lachnospira;s__Lachnospira sp000437735 | CRISPR | 91.00 | blast;79.50 |
| IMGVR_UViG_3300029435_000003 | RS_GCF_003478035.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__CAG-81;s__CAG-81 sp900066785 | blast | 90.80 | None |
| IMGVR_UViG_3300029435_000005 | RS_GCF_900167555.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Gemmiger;s__Gemmiger formicilis | blast | 96.70 | iPHoP-RF;82.50 CRISPR;68.10 |
| IMGVR_UViG_3300029435_000005 | GB_GCA_900540775.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Gemmiger;s__Gemmiger sp900540775 | blast | 96.50 | iPHoP-RF;82.50 |
| IMGVR_UViG_3300029435_000005 | RS_GCF_003324125.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Oscillospirales;f__Ruminococcaceae;g__Gemmiger;s__Gemmiger qucibialis | CRISPR | 92.70 | blast;81.60 iPHoP-RF;67.50 |
- This output file lists for each host-based prediction the virus sequence ID, the representative host genome ID, the corresponding host genome taxonomy, the main method supporting this prediction (i.e., highest score), the confidence score for this main method, and the list of additional methods and scores obtained for this virus-host pair. The first 3 contigs (IMGVR_UViG_3300029435_000002, IMGVR_UViG_3300029435_000003, and IMGVR_UViG_3300029435_000005) from the Host_prediction_to_genus_m90.csv are included in this example.
- Note: IMGVR_UViG_3300029435_000002 is not mentioned in this output file because no host-based predictions reached the minimum score of 90 (see above the example file "Host_prediction_to_genus_m90.csv")
- Note: For IMGVR_UViG_3300029435_000003, predictions to the genera g__Lachnospira and g__CAG-81 are not included in the Host_prediction_to_genus_m90.csv file (see above), because once aggregated at the genus level, these predictions do not reach the minimum score of 90
| Virus | Method | Host | Host taxonomy | Metric 1 | Score 1 | Metric 2 | Score 2 | Rank | Host representative |
|---|---|---|---|---|---|---|---|---|---|
| IMGVR_UViG_3300029435_000002 | crispr | GCA_003447235.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp003447235 | N_mismatches | 0.000 | Spacer_length | 30.0 | 1 | GB_GCA_003447235.1 |
| IMGVR_UViG_3300029435_000002 | crispr | GCF_003576475.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Prolixibacteraceae;g__Draconibacterium;s__Draconibacterium luteus | N_mismatches | 3.000 | Spacer_length | 36.0 | 2 | RS_GCF_003576475.1 |
| IMGVR_UViG_3300029435_000002 | crispr | GCF_001683355.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella scopos | N_mismatches | 4.000 | Spacer_length | 35.0 | 3 | RS_GCF_001683355.1 |
| IMGVR_UViG_3300029435_000002 | crispr | 3300029716_17 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Coprococcus;s__Coprococcus eutactus_A | N_mismatches | 4.000 | Spacer_length | 27.0 | 3 | RS_GCF_001404675.1 |
| IMGVR_UViG_3300029435_000002 | iPHoP-RF | RS_GCF_000179055.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella bryantii | Confidence_score | 0.954 | FDR | 0.105 | 1 | RS_GCF_000179055.1 |
| IMGVR_UViG_3300029435_000002 | iPHoP-RF | RS_GCF_001683355.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella scopos | Confidence_score | 0.946 | FDR | 0.118 | 2 | RS_GCF_001683355.1 |
| IMGVR_UViG_3300029435_000002 | iPHoP-RF | GB_GCA_003447235.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella sp003447235 | Confidence_score | 0.934 | FDR | 0.137 | 3 | GB_GCA_003447235.1 |
| IMGVR_UViG_3300029435_000002 | iPHoP-RF | RS_GCF_003576475.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Prolixibacteraceae;g__Draconibacterium;s__Draconibacterium luteus | Confidence_score | 0.774 | FDR | 0.332 | 4 | RS_GCF_003576475.1 |
| IMGVR_UViG_3300029435_000002 | iPHoP-RF | RS_GCF_000373185.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella paludivivens | Confidence_score | 0.726 | FDR | 0.394 | 5 | RS_GCF_000373185.1 |
| IMGVR_UViG_3300029435_000002 | rafah | Prevotella | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella | Confidence_score | 0.709 | FDR | 0.087 | 1 | NA |
| IMGVR_UViG_3300029435_000002 | rafah | Bacteroides | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Bacteroides | Confidence_score | 0.140 | FDR | 0.865 | 2 | NA |
| IMGVR_UViG_3300029435_000002 | rafah | Polynucleobacter | d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Burkholderiales;f__Burkholderiaceae;g__Polynucleobacter | Confidence_score | 0.019 | FDR | 0.991 | 3 | NA |
| IMGVR_UViG_3300029435_000002 | rafah | Alistipes | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes | Confidence_score | 0.016 | FDR | 0.994 | 4 | NA |
| IMGVR_UViG_3300029435_000002 | rafah | Alistipes | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Rikenellaceae;g__Alistipes_A | Confidence_score | 0.016 | FDR | 0.994 | 4 | NA |
| IMGVR_UViG_3300029435_000002 | vhm | 3300019761_11 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Marinifilaceae;g__;s__ | S2star_similarity | 0.485 | NA | NA | 1 | 3300019761_11 |
| IMGVR_UViG_3300029435_000002 | vhm | GCA_003648395.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__UBA1556;g__4572-112;s__4572-112 sp003648395 | S2star_similarity | 0.434 | NA | NA | 2 | GB_GCA_003648395.1 |
| IMGVR_UViG_3300029435_000002 | vhm | GCA_002505585.1 | d__Archaea;p__Nanoarchaeota;c__Nanoarchaeia;o__SCGC-AAA011-G17;f__UBA489;g__UBA489;s__UBA489 sp002505585 | S2star_similarity | 0.433 | NA | NA | 3 | GB_GCA_002505585.1 |
| IMGVR_UViG_3300029435_000002 | vhm | GCF_003290445.1 | d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Francisellales;f__Francisellaceae;g__Francisella_A;s__Francisella_A adeliensis | S2star_similarity | 0.425 | NA | NA | 4 | RS_GCF_003290445.1 |
| IMGVR_UViG_3300029435_000002 | vhm | GCF_000764555.1 | d__Bacteria;p__Proteobacteria;c__Gammaproteobacteria;o__Francisellales;f__Francisellaceae;g__Francisella;s__Francisella sp000764555 | S2star_similarity | 0.421 | NA | NA | 5 | RS_GCF_000764555.1 |
| IMGVR_UViG_3300029435_000002 | wish | GCF_005217565.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Prolixibacteraceae;g__Puteibacter;s__Puteibacter caeruleilacunae | Log-likelihood | -1.364 | p-value | 0.196538 | 1 | RS_GCF_005217565.1 |
| IMGVR_UViG_3300029435_000002 | wish | GCF_001637225.1 | d__Bacteria;p__Firmicutes;c__Bacilli;o__Paenibacillales;f__Paenibacillaceae;g__Paenibacillus;s__Paenibacillus antarcticus | Log-likelihood | -1.365 | p-value | 0.17717 | 2 | RS_GCF_001637225.1 |
| IMGVR_UViG_3300029435_000002 | wish | GCF_002964605.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Vallitaleaceae_A;g__Vallitalea_A;s__Vallitalea_A okinawensis | Log-likelihood | -1.366 | p-value | 0.180156 | 3 | RS_GCF_002964605.1 |
| IMGVR_UViG_3300029435_000002 | wish | GCF_900142955.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Anaerosporobacter;s__Anaerosporobacter mobilis | Log-likelihood | -1.367 | p-value | 0.19196 | 4 | RS_GCF_900142955.1 |
| IMGVR_UViG_3300029435_000002 | wish | GCF_000373185.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella paludivivens | Log-likelihood | -1.367 | p-value | 0.177472 | 5 | RS_GCF_000373185.1 |
| IMGVR_UViG_3300029435_000002 | php | GCA_003045065.1 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Prolixibacteraceae;g__Puteibacter;s__Puteibacter caeruleilacunae | Score | 1442.476 | NA | 0.NA | 1 | GB_GCA_003045065.1 |
| IMGVR_UViG_3300029435_000002 | php | 3300019761_11 | d__Bacteria;p__Firmicutes;c__Bacilli;o__Paenibacillales;f__Paenibacillaceae;g__Paenibacillus;s__Paenibacillus antarcticus | Score | 1442.311 | NA | NA | 2 | 3300019761_11 |
| IMGVR_UViG_3300029435_000002 | php | GCF_000325745.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Vallitaleaceae_A;g__Vallitalea_A;s__Vallitalea_A okinawensis | Score | 1439.722 | NA | NA | 3 | RS_GCF_000325745.1 |
| IMGVR_UViG_3300029435_000002 | php | GCF_000373185.1 | d__Bacteria;p__Firmicutes_A;c__Clostridia;o__Lachnospirales;f__Lachnospiraceae;g__Anaerosporobacter;s__Anaerosporobacter mobilis | Score | 1439.305 | NA | NA | 4 | RS_GCF_000373185.1 |
| IMGVR_UViG_3300029435_000002 | php | 3300027828_61 | d__Bacteria;p__Bacteroidota;c__Bacteroidia;o__Bacteroidales;f__Bacteroidaceae;g__Prevotella;s__Prevotella paludivivens | Score | 1438.882 | NA | NA | 5 | 3300027828_61 |
- This output files lists the 5 best hits for each method for each input virus. The first contig (IMGVR_UViG_3300029435_000002) from the Host_prediction_to_genus_m90.csv is included in this example.
- When no hits were obtained, the corresponding method is not listed in this output file for the query virus. For instance, here, IMGVR_UViG_3300029435_000002 did not yield any significant and reliable blast hit, and no blast results are listed.
NOTE: We recommend all users to first run iPHoP with the standard database on the same viral sequences. We also recommend all MAGs to be carefully screened for contamination, as erroneous binning of a viral contig in a microbial MAG may lead to wrong host predictions associated with high confidence scores.
NOTE: For iPHoP < 1.2.0, the addition of custom MAGs in iPHoP expects an output from GTDB-tk v1.5.0, and is not compatible at this time with outputs from GTDB-tk v2. This should be fixed now in 1.2 and later versions.
- Users can add their own MAGs to the host database, for instance MAGs obtained from the same datasets or sampling sites from which the input phages were obtained. The module "add_to_db" in iPHoP can be used for this purpose, and requires fasta files for each MAG and the results of the "gtdb-tk infer" function applied to these same MAGs. An example set of files is available at https://bitbucket.org/srouxjgi/iphop/downloads/Data_test_add_to_db.tar.gz, based on data from Dalcin Martins et al., "Viral and metabolic controls on high rates of microbial sulfur and carbon cycling in wetland ecosystems"
- The full process to add MAGs to the host database is as follows:
$ wget https://bitbucket.org/srouxjgi/iphop/downloads/Data_test_add_to_db_v1.4.tar.gz
$ tar -xvf Data_test_add_to_db_v1.4.tar.gz
$ ls Data_test_add_to_db_v1.4The folder "Expected_results/" includes the expected results files from iPHoP using either the Jun25_rw database or the new database with additional MAGs.
The file "Input_viral_contigs.fasta" is the input file.
The folder "Wetland_MAGs/" includes the fasta files of all MAGs.
The folder "Wetland_MAGs_GTDB-tk_results/" includes the result files from gtdb-tk that iPHoP will use.
- This was generated by running:
$ gtdbtk de_novo_wf --genome_dir Data_test_add_to_db_v1.4/Wetland_MAGs/ --bacteria --outgroup_taxon p__Patescibacteriota --out_dir Wetland_MAGs_GTDB-tk_results/ --cpus 32 --force --extension fa
$ gtdbtk de_novo_wf --genome_dir Data_test_add_to_db_v1.4/Wetland_MAGs/ --archaea --outgroup_taxon p__Altiarchaeota --out_dir Wetland_MAGs_GTDB-tk_results/ --cpus 32 --force --extension fa- Next, a new iPHoP database must be created, which will include GTDB genomes and the additional MAGs provided by the user, but not the GEM or IMG genomes
$ cd Data_test_add_to_db
$ iphop add_to_db --fna_dir Data_test_add_to_db_v1.4/Wetland_MAGs/ --gtdb_dir Wetland_MAGs_GTDB-tk_results/ --out_dir Jun_2025_pub_rw_w_Wetland_hosts --db_dir /path/to/iphop_db/Jun_2025_pub_rw/Note: to avoid copying large files, the new database is based in part on symbolic links to the original database. That means that if the original database (here "iphop_db/Jun_2025_pub_rw/") is modified and/or deleted, the new database will also not be working anymore. This also means that the full path of the original database should be provided as "db_dir" argument.
The folder "Jun_2025_pub_rw_w_Wetland_hosts" can then be used as an iPHoP database, for instance:
$ iphop predict --fa_file Input_viral_contigs.fasta --db_dir Jun_2025_pub_rw_w_Wetland_hosts/ --out_dir test_add_db -t 4 --min_score 75**Q: To be continued **
A: To be continued
The iPHoP publication is now available at https://doi.org/10.1371/journal.pbio.3002083. The full citation is:
@article{roux_iphop_2023,
abstract = {The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived sequences lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e., for a number of viruses they yield erroneous predictions or no prediction at all. Here, we describe iPHoP, a two-step framework that integrates multiple methods to reliably predict host taxonomy at the genus rank for a broad range of viruses infecting bacteria and archaea, while retaining a low false discovery rate. Based on a large dataset of metagenome-derived virus genomes from the IMG/VR database, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses.},
author = {Roux, Simon and Camargo, Antonio Pedro and Coutinho, Felipe H. and Dabdoub, Shareef M. and Dutilh, Bas E. and Nayfach, Stephen and Tritt, Andrew},
doi = {10.1371/journal.pbio.3002083},
issn = {1545-7885},
journal = {PLOS Biology},
number = {4},
title = {{iPHoP}: {An} integrated machine learning framework to maximize host prediction for metagenome-derived viruses of archaea and bacteria},
volume = {21},
year = {2023},
}
In addition, please cite the following tools upon which iPHoP is built:
- Coutinho, F. H. et al. RaFAH: Host prediction for viruses of Bacteria and Archaea based on protein content. Patterns 2, (2021). 10.1016/j.patter.2021.100274
- Galiez, C. et al. WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs. Bioinformatics 33, 3113–14 (2017). 10.1093/bioinformatics/btx383
- Ahlgren, N., Ren, J., Lu, Y. Y., Fuhrman, J. A. & Sun, F. Alignment-free d2∗ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Res. 45, 39–53 (2016). 10.1093/nar/gkw1002
- Lu, C. et al. Prokaryotic virus Host Predictor: A Gaussian model for host prediction of prokaryotic viruses in metagenomics. BMC Biol. 19, 1–11 (2021). 10.1101/2020.12.02.408310
The complete list of entries in bibtex format is:
@article{Coutinho2021,
abstract = {Culture-independent approaches have recently shed light on the genomic diversity of viruses of prokaryotes. One fundamental question when trying to understand their ecological roles is: which host do they infect? To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), that uses scores to 43,644 protein clusters to assign hosts to complete or fragmented genomes of viruses of Archaea and Bacteria. RaFAH displayed performance comparable with that of other methods for virus-host prediction in three different benchmarks encompassing viruses from RefSeq, single amplified genomes, and metagenomes. RaFAH was applied to assembled metagenomic datasets of uncultured viruses from eight different biomes of medical, biotechnological, and environmental relevance. Our analyses led to the identification of 537 sequences of archaeal viruses representing unknown lineages, whose genomes encode novel auxiliary metabolic genes, shedding light on how these viruses interfere with the host molecular machinery. RaFAH is available at https://sourceforge.net/projects/rafah/.},
author = {Coutinho, Felipe Hernandes and Zaragoza-Solas, Asier and L{\'{o}}pez-P{\'{e}}rez, Mario and Barylski, Jakub and Zielezinski, Andrzej and Dutilh, Bas E. and Edwards, Robert and Rodriguez-Valera, Francisco},
doi = {10.1016/j.patter.2021.100274},
issn = {26663899},
journal = {Patterns},
number = {7},
title = {{RaFAH: Host prediction for viruses of Bacteria and Archaea based on protein content}},
volume = {2},
year = {2021}
}
@article{Galiez2017,
abstract = {WIsH predicts prokaryotic hosts of phages from their genomic sequences. It achieves 63% mean accuracy when predicting the host genus among 20 genera for 3 kbp-long phage con-tigs. Over the best current tool, WisH shows much improved accuracy on phage sequences of a few kbp length and runs hundreds of times faster, making it suited for metagenomics studies. Availability and implementation: OpenMP-parallelized GPL-licensed C þþ code available at},
author = {Galiez, Clovis and Siebert, Matthias and Enault, Fran{\c{c}}ois and Vincent, Jonathan and S{\"{o}}ding, Johannes and Enault, Fran{\c{c}}ois and Vincent, Jonathan and S{\"{o}}ding, Johannes},
doi = {10.1093/bioinformatics/btx383},
issn = {1367-4803},
journal = {Bioinformatics},
number = {19},
pages = {3113--14},
pmid = {28957499},
title = {{WIsH: who is the host? Predicting prokaryotic hosts from metagenomic phage contigs}},
url = {https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btx383},
volume = {33},
year = {2017}
}
@article{Ahlgren2016,
abstract = {Viruses and their host genomes often share simi- lar oligonucleotide frequency (ONF) patterns, which can be used to predict the host of a given virus by finding the host with the greatest ONF similar- ity. We comprehensively compared 11 ONF metrics using several k -mer lengths for predicting host tax- onomy from among ∼32 000 prokaryotic genomes for 1427 virus isolate genomes whose true hosts are known. The background-subtracting measure d 2 at ∗ k = 6gave the highest host prediction accuracy (33%, genus level) with reasonable computational times. Requiring a maximum dissimilarity score for mak- ing predictions (thresholding) and taking the consen- sus of the 30 most similar hosts further improved accuracy. Using a previous dataset of 820 bacte- riophage and 2699 bacterial genomes, d 2 host pre- ∗ diction accuracies with thresholding and consensus methods (genus-level: 64%) exceeded previous Eu- clidian distance ONF (32%) or homology-based (22- 62%) methods. When applied to metagenomically- assembled marine SUP05 viruses and the human gut virus crAssphage, d 2-based predictions overlapped ∗ (i.e. some same, some different) with the previously inferred hosts of these viruses. The extent of overlap improved when only using host genomes or metage- nomic contigs from the same habitat or samples as the query viruses. The d 2 ONF method will greatly ∗ improve the characterization of novel, metagenomic viruses.},
archivePrefix = {arXiv},
arxivId = {1611.06654},
author = {Ahlgren, NA and Ren, Jie and Lu, Yang Young and Fuhrman, Jed A. and Sun, Fengzhu},
doi = {10.1093/nar/gkw1002},
eprint = {1611.06654},
isbn = {2076792171},
issn = {0305-1048},
journal = {Nucleic Acids Research},
number = {1},
pages = {39--53},
pmid = {27899565},
title = {{Alignment-free d<sub>2</sub><sup>∗</sup> oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences}},
url = {http://fdslive.oup.com/www.oup.com/pdf/production_in_progress.pdf%5Cnhttp://www.ncbi.nlm.nih.gov/pubmed/27899565},
volume = {45},
year = {2016}
}
@article{Lu2021,
abstract = {Background: Viruses are ubiquitous biological entities, estimated to be the largest reservoirs of unexplored genetic diversity on Earth. Full functional characterization and annotation of newly-discovered viruses requires tools to enable taxonomic assignment, the range of hosts, and biological properties of the virus. Here we focus on prokaryotic viruses, which include phages and archaeal viruses, and for which identifying the viral host is an essential step in characterizing the virus, as the virus relies on the host for survival. Currently, the method for determining the viral host is either to culture the virus, which is low-throughput, time-consuming, and expensive, or to computationally predict the viral hosts, which needs improvements at both accuracy and usability. Here we develop a Gaussian model to predict hosts for prokaryotic viruses with better performances than previous computational methods. Results: We present here Prokaryotic virus Host Predictor (PHP), a software tool using a Gaussian model, to predict hosts for prokaryotic viruses using the differences of k-mer frequencies between viral and host genomic sequences as features. PHP gave a host prediction accuracy of 34% (genus level) on the VirHostMatcher benchmark dataset and a host prediction accuracy of 35% (genus level) on a new dataset containing 671 viruses and 60,105 prokaryotic genomes. The prediction accuracy exceeded that of two alignment-free methods (VirHostMatcher and WIsH, 28%-34%, genus level). PHP also outperformed these two alignment-free methods much (24%-38% vs 18%-20%, genus level) when predicting hosts for prokaryotic viruses which cannot be predicted by the BLAST-based or the CRISPR-spacer-based methods alone. Requiring a minimal score for making predictions (thresholding) and taking the consensus of the top 30 predictions further improved the host prediction accuracy of PHP. Conclusions: The Prokaryotic virus Host Predictor software tool provides an intuitive and user-friendly API for the Gaussian model described herein. This work will facilitate the rapid identification of hosts for newly-identified prokaryotic viruses in metagenomic studies.},
author = {Lu, Congyu and Zhang, Zheng and Cai, Zena and Zhu, Zhaozhong and Qiu, Ye and Wu, Aiping and Jiang, Taijiao and Zheng, Heping and Peng, Yousong},
doi = {10.1101/2020.12.02.408310},
issn = {26928205},
journal = {BMC biology},
number = {5},
pages = {1--11},
publisher = {BMC Biology},
title = {{Prokaryotic virus Host Predictor: A Gaussian model for host prediction of prokaryotic viruses in metagenomics}},
volume = {19},
year = {2021}
}
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