Results
Below you find for each supplied fasta file an individual tab. Each tab contains all the results and explanations to help you identify the possible phages.
Blue tabs also group the results. All the citations can be found in the results directory as a .bib file.
ERR575691_raw_assembly
Overview
Performance of each identification tool
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
How to read this plot
The total amount of identified phage contigs per tool is shown in blue bars on the left.
Black, vertical bars visualize the number of contigs that each tool or tool combination has uniquely identified.
Each tool combination is shown below the vertical barplot as a dot matrix. How to read the diagram: For example, 53 phage contigs are found by six tools (DeepVirFinder, Metaphinder-own-DB, Metaphinder, PPRmeta, Seeker and VirFinder).
Another 42 contigs are found by these tools but also virnet.
Phage annotations
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual representation of annotated genes via chromoMap
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.htmlPhage prediction by contig
Phage prediction table
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
How to interpret the data
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
Explanation tool output
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value | F1 scores by Ho et al. |
|---|---|---|---|
| deepvirfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| metaphinder | string: phage | 1 | >0.83 |
| metaphinder own | string: phage | 1 | N/A |
| phigaro | score: 0 to 1 | 0 to 1 | N/A |
| pprmeta | phage_score: 0 to 1 | 0 to 1 | 0.92 |
| seeker | score: 0 to 1 | 0 to 1 | <0.5 |
| sourmash | similarity: 0 to 1 | 0 to 1 | N/A |
| vibrant | prediction: virus | 1 | >0.83 |
| vibrant-virome | prediction: virus | 1 | N/A |
| virfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| virnet | score: 0 to 1 | 0 to 1 | N/A |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 | >0.83 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 | N/A |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 | 0.93 |
Extract contigs of interest
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contigs_of_interest -folder
# Copy the input_fasta to the contigs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d Taxonomic Phage classification
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
ERR575692_raw_assembly
Overview
Performance of each identification tool
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
How to read this plot
The total amount of identified phage contigs per tool is shown in blue bars on the left.
Black, vertical bars visualize the number of contigs that each tool or tool combination has uniquely identified.
Each tool combination is shown below the vertical barplot as a dot matrix. How to read the diagram: For example, 53 phage contigs are found by six tools (DeepVirFinder, Metaphinder-own-DB, Metaphinder, PPRmeta, Seeker and VirFinder).
Another 42 contigs are found by these tools but also virnet.
Phage annotations
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual representation of annotated genes via chromoMap
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.htmlPhage prediction by contig
Phage prediction table
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
How to interpret the data
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
Explanation tool output
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value | F1 scores by Ho et al. |
|---|---|---|---|
| deepvirfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| metaphinder | string: phage | 1 | >0.83 |
| metaphinder own | string: phage | 1 | N/A |
| phigaro | score: 0 to 1 | 0 to 1 | N/A |
| pprmeta | phage_score: 0 to 1 | 0 to 1 | 0.92 |
| seeker | score: 0 to 1 | 0 to 1 | <0.5 |
| sourmash | similarity: 0 to 1 | 0 to 1 | N/A |
| vibrant | prediction: virus | 1 | >0.83 |
| vibrant-virome | prediction: virus | 1 | N/A |
| virfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| virnet | score: 0 to 1 | 0 to 1 | N/A |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 | >0.83 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 | N/A |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 | 0.93 |
Extract contigs of interest
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contigs_of_interest -folder
# Copy the input_fasta to the contigs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d Taxonomic Phage classification
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
ERR576942_raw_assembly
Overview
Performance of each identification tool
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
How to read this plot
The total amount of identified phage contigs per tool is shown in blue bars on the left.
Black, vertical bars visualize the number of contigs that each tool or tool combination has uniquely identified.
Each tool combination is shown below the vertical barplot as a dot matrix. How to read the diagram: For example, 53 phage contigs are found by six tools (DeepVirFinder, Metaphinder-own-DB, Metaphinder, PPRmeta, Seeker and VirFinder).
Another 42 contigs are found by these tools but also virnet.
Phage annotations
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual representation of annotated genes via chromoMap
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.htmlPhage prediction by contig
Phage prediction table
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
How to interpret the data
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
Explanation tool output
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value | F1 scores by Ho et al. |
|---|---|---|---|
| deepvirfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| metaphinder | string: phage | 1 | >0.83 |
| metaphinder own | string: phage | 1 | N/A |
| phigaro | score: 0 to 1 | 0 to 1 | N/A |
| pprmeta | phage_score: 0 to 1 | 0 to 1 | 0.92 |
| seeker | score: 0 to 1 | 0 to 1 | <0.5 |
| sourmash | similarity: 0 to 1 | 0 to 1 | N/A |
| vibrant | prediction: virus | 1 | >0.83 |
| vibrant-virome | prediction: virus | 1 | N/A |
| virfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| virnet | score: 0 to 1 | 0 to 1 | N/A |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 | >0.83 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 | N/A |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 | 0.93 |
Extract contigs of interest
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contigs_of_interest -folder
# Copy the input_fasta to the contigs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d Taxonomic Phage classification
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
ERR576943_raw_assembly
Overview
Performance of each identification tool
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
How to read this plot
The total amount of identified phage contigs per tool is shown in blue bars on the left.
Black, vertical bars visualize the number of contigs that each tool or tool combination has uniquely identified.
Each tool combination is shown below the vertical barplot as a dot matrix. How to read the diagram: For example, 53 phage contigs are found by six tools (DeepVirFinder, Metaphinder-own-DB, Metaphinder, PPRmeta, Seeker and VirFinder).
Another 42 contigs are found by these tools but also virnet.
Phage annotations
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual representation of annotated genes via chromoMap
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.htmlPhage prediction by contig
Phage prediction table
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
How to interpret the data
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
Explanation tool output
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value | F1 scores by Ho et al. |
|---|---|---|---|
| deepvirfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| metaphinder | string: phage | 1 | >0.83 |
| metaphinder own | string: phage | 1 | N/A |
| phigaro | score: 0 to 1 | 0 to 1 | N/A |
| pprmeta | phage_score: 0 to 1 | 0 to 1 | 0.92 |
| seeker | score: 0 to 1 | 0 to 1 | <0.5 |
| sourmash | similarity: 0 to 1 | 0 to 1 | N/A |
| vibrant | prediction: virus | 1 | >0.83 |
| vibrant-virome | prediction: virus | 1 | N/A |
| virfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| virnet | score: 0 to 1 | 0 to 1 | N/A |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 | >0.83 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 | N/A |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 | 0.93 |
Extract contigs of interest
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contigs_of_interest -folder
# Copy the input_fasta to the contigs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d Taxonomic Phage classification
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
ERR576944_raw_assembly
Overview
Performance of each identification tool
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
How to read this plot
The total amount of identified phage contigs per tool is shown in blue bars on the left.
Black, vertical bars visualize the number of contigs that each tool or tool combination has uniquely identified.
Each tool combination is shown below the vertical barplot as a dot matrix. How to read the diagram: For example, 53 phage contigs are found by six tools (DeepVirFinder, Metaphinder-own-DB, Metaphinder, PPRmeta, Seeker and VirFinder).
Another 42 contigs are found by these tools but also virnet.
Phage annotations
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual representation of annotated genes via chromoMap
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.htmlPhage prediction by contig
Phage prediction table
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
How to interpret the data
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
Explanation tool output
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value | F1 scores by Ho et al. |
|---|---|---|---|
| deepvirfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| metaphinder | string: phage | 1 | >0.83 |
| metaphinder own | string: phage | 1 | N/A |
| phigaro | score: 0 to 1 | 0 to 1 | N/A |
| pprmeta | phage_score: 0 to 1 | 0 to 1 | 0.92 |
| seeker | score: 0 to 1 | 0 to 1 | <0.5 |
| sourmash | similarity: 0 to 1 | 0 to 1 | N/A |
| vibrant | prediction: virus | 1 | >0.83 |
| vibrant-virome | prediction: virus | 1 | N/A |
| virfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| virnet | score: 0 to 1 | 0 to 1 | N/A |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 | >0.83 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 | N/A |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 | 0.93 |
Extract contigs of interest
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contigs_of_interest -folder
# Copy the input_fasta to the contigs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d Taxonomic Phage classification
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
ERR576945_raw_assembly
Overview
Performance of each identification tool
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
How to read this plot
The total amount of identified phage contigs per tool is shown in blue bars on the left.
Black, vertical bars visualize the number of contigs that each tool or tool combination has uniquely identified.
Each tool combination is shown below the vertical barplot as a dot matrix. How to read the diagram: For example, 53 phage contigs are found by six tools (DeepVirFinder, Metaphinder-own-DB, Metaphinder, PPRmeta, Seeker and VirFinder).
Another 42 contigs are found by these tools but also virnet.
Phage annotations
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual representation of annotated genes via chromoMap
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.htmlPhage prediction by contig
Phage prediction table
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
How to interpret the data
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
Explanation tool output
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value | F1 scores by Ho et al. |
|---|---|---|---|
| deepvirfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| metaphinder | string: phage | 1 | >0.83 |
| metaphinder own | string: phage | 1 | N/A |
| phigaro | score: 0 to 1 | 0 to 1 | N/A |
| pprmeta | phage_score: 0 to 1 | 0 to 1 | 0.92 |
| seeker | score: 0 to 1 | 0 to 1 | <0.5 |
| sourmash | similarity: 0 to 1 | 0 to 1 | N/A |
| vibrant | prediction: virus | 1 | >0.83 |
| vibrant-virome | prediction: virus | 1 | N/A |
| virfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| virnet | score: 0 to 1 | 0 to 1 | N/A |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 | >0.83 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 | N/A |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 | 0.93 |
Extract contigs of interest
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contigs_of_interest -folder
# Copy the input_fasta to the contigs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d Taxonomic Phage classification
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
ERR576946_raw_assembly
Overview
Performance of each identification tool
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
How to read this plot
The total amount of identified phage contigs per tool is shown in blue bars on the left.
Black, vertical bars visualize the number of contigs that each tool or tool combination has uniquely identified.
Each tool combination is shown below the vertical barplot as a dot matrix. How to read the diagram: For example, 53 phage contigs are found by six tools (DeepVirFinder, Metaphinder-own-DB, Metaphinder, PPRmeta, Seeker and VirFinder).
Another 42 contigs are found by these tools but also virnet.
Phage annotations
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual representation of annotated genes via chromoMap
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.htmlPhage prediction by contig
Phage prediction table
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
How to interpret the data
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
Explanation tool output
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value | F1 scores by Ho et al. |
|---|---|---|---|
| deepvirfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| metaphinder | string: phage | 1 | >0.83 |
| metaphinder own | string: phage | 1 | N/A |
| phigaro | score: 0 to 1 | 0 to 1 | N/A |
| pprmeta | phage_score: 0 to 1 | 0 to 1 | 0.92 |
| seeker | score: 0 to 1 | 0 to 1 | <0.5 |
| sourmash | similarity: 0 to 1 | 0 to 1 | N/A |
| vibrant | prediction: virus | 1 | >0.83 |
| vibrant-virome | prediction: virus | 1 | N/A |
| virfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| virnet | score: 0 to 1 | 0 to 1 | N/A |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 | >0.83 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 | N/A |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 | 0.93 |
Extract contigs of interest
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contigs_of_interest -folder
# Copy the input_fasta to the contigs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d Taxonomic Phage classification
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
ERR579308_raw_assembly
Overview
Performance of each identification tool
Fig.1: UpSetR plot summarizes each tool’s performance. Additionally, it shows which tools (black dots) identified the same contigs (black bars).
How to read this plot
The total amount of identified phage contigs per tool is shown in blue bars on the left.
Black, vertical bars visualize the number of contigs that each tool or tool combination has uniquely identified.
Each tool combination is shown below the vertical barplot as a dot matrix. How to read the diagram: For example, 53 phage contigs are found by six tools (DeepVirFinder, Metaphinder-own-DB, Metaphinder, PPRmeta, Seeker and VirFinder).
Another 42 contigs are found by these tools but also virnet.
Phage annotations
Tab.1: Gene annotation of contigs based on Hmmer and Prodigal, using this database
Visual representation of annotated genes via chromoMap
Visual annotation of phage contigs and annotated protein-coding genes via chromoMap is stored by default here:
results/your_sample/sample_overview_large.html
results/your_sample/sample_overview_small.htmlPhage prediction by contig
Phage prediction table
Tab. 1: Interactive phage prediction table. The scores/p-values of each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.
How to interpret the data
WtP uses several phage prediction tools that work differently and generate different outputs. Contigs with the overall highest p-values/scores are displayed at the top, and contigs with low p-values/scores are at the bottom of the table. Each tool’s p-value/score can be individually adjusted and filtered in Table 1. Some tools don’t generate p-values or scores as output; instead, they generate categories with likelihoods or directly predict and assign the contigs as phage.
The tool’s output and what WtP assigns are shown in the table below.
Explanation tool output
Tab.2: The output of each tool and the values WtP assigns in Tab.1 .
| Tool | Standard output | WtP displayed value | F1 scores by Ho et al. |
|---|---|---|---|
| deepvirfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| metaphinder | string: phage | 1 | >0.83 |
| metaphinder own | string: phage | 1 | N/A |
| phigaro | score: 0 to 1 | 0 to 1 | N/A |
| pprmeta | phage_score: 0 to 1 | 0 to 1 | 0.92 |
| seeker | score: 0 to 1 | 0 to 1 | <0.5 |
| sourmash | similarity: 0 to 1 | 0 to 1 | N/A |
| vibrant | prediction: virus | 1 | >0.83 |
| vibrant-virome | prediction: virus | 1 | N/A |
| virfinder | score: 0 to 1 | 0 to 1 | >0.83 |
| virnet | score: 0 to 1 | 0 to 1 | N/A |
| virsorter | category 1, category 2, category 3 | 1, 0.5, 0 | >0.83 |
| virsorter-virome | category 1, category 2, category 3 | 1, 0.5, 0 | N/A |
| virsorter2 | dsDNAphage: 0 to 1 | 0 to 1 | 0.93 |
Extract contigs of interest
# Filter the Phage prediction by contig table to your liking
# Click on the CSV-Button (this will download the Phage prediction by contig table)
# Open your Linux-Terminal
mkdir contigs_of_interest
cd contigs_of_interest
# Copy the downloaded Phage prediction by contig table to the contigs_of_interest -folder
# Copy the input_fasta to the contigs_of_interest -folder
cp WtP_results/your_sample/Input_fasta/your_input_fasta.fa.gz /foo/bar/contigs_of_interest
# Get contig IDs of interest
tail -n+2 final_report.utf8.csv | tr -d '"' | cut -f2 -d"," > contig_IDs_of_interest.txt
# via Docker: use Seqkit to extract contigs of interest of your input fasta-file
docker run --rm -it -v $PWD:/input nanozoo/seqkit:0.13.2--cd66104
cd input
seqkit grep --pattern-file contig_IDs_of_interest.txt your_input_fasta.fa.gz > contigs_of_interest.fa
# Finally, close the docker with ctrl + d Taxonomic Phage classification
Tab. 1: Taxonomic classification of predicted phages based on sourmash using this database Each column can be filtered. The adjusted table can be exported as a .csv, .pdf or .excel.