Repository files navigation

[alt text]

ProBioPred can predict the potential probiotic candidates from genome sequence based on Support Vector Machine (SVM) trained models. Preferable input for ProBioPred is complete genome for better results, but you can also provide draft genome assembly. Currently, ProBioPred supports prediction for only 9 genera viz. Bacillus, Clostridium, Lactobacillus, Leuconostoc, Streptococcus, Bifidobacterium, Enterococcus, Lactococcus and Pediococcus. The input genome should be in standard FASTA format to run this tool.

Theory

The ProBioPred uses available genetic information and Support Vector Machine (SVM) models for prediction of potential probiotic candidate. In brief, based on extensive literature survey and available databases, ProBioPred uses information on genes imparting probiotic properties, virulence factors and antibiotic resistance genes to generate and train models which eventually predicts a potential probiotic candidate. ProBioPred can also serves as a tool to predict probiotic genes, virulence factors and antibiotic resistance genes which can be browsed on the website or downloaded. These models can be used for analysis of genome sequences using ProBioPred either online or as a stand-alone tool.

Installing ProBioPred

# create conda environment
conda create -n probiopred python=3.10
conda activate probiopred
# install dependencies
# blast
conda install -c bioconda blast
# libsvm
conda install -c conda-forge libsvm
# install rgi
git clone https://github.com/arpcard/rgi.git
cd rgi
pip install .
# install rgi database
rgi auto_load
# install ProBioPred
git clone https://github.com/microDM/ProBioPred.git
cd ProBioPred
pip install .

Running ProBioPred

usage: proBioPred.py [-h] -i PATH -g GENUS [-o PATH] [-t THREADS]
Wrapper for running ProBioPred. Searches for probiotic, virulent and
antibiotic resistance genes in query genome. Then predicts the probability
score of genome being probiotic or non-probiotic based on SVM model.
optional arguments:
-h, --help show this help message and exit
-i PATH, --input_genome PATH
Query genome sequence in FASTA format
-g GENUS, --genus GENUS
Genus of query genome. Currently support only
following 9 genera.[bacillus, clostridium,
lactobacillus, leuconostoc, streptococcus,
bifidobacterium, enterococcus, lactococcus,
pediococcus]
-o PATH, --output_dir PATH
Path of output directory [Default: ProBioPred_out].
-t THREADS, --threads THREADS
Number of threads to run for BLAST and RGI.

Run ProBioPred on batch of genomes

# create tab-separated file with 3 columns:
1. genomeID: unique genome ID
2. genomeFile: file path to respective genome (fasta)
3. genus: one of the genus listed in ProBioPred help.

Output

ProBioPred generates output directory with several files and prints SVM score for probiotic/non-probiotic on standard output.

FileDescription
out.libsvmsvm-predict output (1/-1 refers to probiotic/non-probiotic class)
pro_hits.pfastaProbiotic genes (multi-FASTA file)
pro_outFiltered.blastBLAST outfmt6 for probiotic genes
resulTab.csvScores for each features (.csv format)
rgi_out.jsonRGI output (json format)
rgi_out.txtRGI output (tab-delimited format)
vfdb_hits.pfastaVirulent genes (multi-FASTA file)
vfdb_outFiltered.blastBLAST outfmt6 for virulent genes

About

No description, website, or topics provided.

Resources

Contributing

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
Skip to content

Repository files navigation

[alt text]

ProBioPred can predict the potential probiotic candidates from genome sequence based on Support Vector Machine (SVM) trained models. Preferable input for ProBioPred is complete genome for better results, but you can also provide draft genome assembly. Currently, ProBioPred supports prediction for only 9 genera viz. Bacillus, Clostridium, Lactobacillus, Leuconostoc, Streptococcus, Bifidobacterium, Enterococcus, Lactococcus and Pediococcus. The input genome should be in standard FASTA format to run this tool.

Theory

The ProBioPred uses available genetic information and Support Vector Machine (SVM) models for prediction of potential probiotic candidate. In brief, based on extensive literature survey and available databases, ProBioPred uses information on genes imparting probiotic properties, virulence factors and antibiotic resistance genes to generate and train models which eventually predicts a potential probiotic candidate. ProBioPred can also serves as a tool to predict probiotic genes, virulence factors and antibiotic resistance genes which can be browsed on the website or downloaded. These models can be used for analysis of genome sequences using ProBioPred either online or as a stand-alone tool.

Installing ProBioPred

# create conda environment
conda create -n probiopred python=3.10
conda activate probiopred
# install dependencies
# blast
conda install -c bioconda blast
# libsvm
conda install -c conda-forge libsvm
# install rgi
git clone https://github.com/arpcard/rgi.git
cd rgi
pip install .
# install rgi database
rgi auto_load
# install ProBioPred
git clone https://github.com/microDM/ProBioPred.git
cd ProBioPred
pip install .

Running ProBioPred

usage: proBioPred.py [-h] -i PATH -g GENUS [-o PATH] [-t THREADS]
Wrapper for running ProBioPred. Searches for probiotic, virulent and
antibiotic resistance genes in query genome. Then predicts the probability
score of genome being probiotic or non-probiotic based on SVM model.
optional arguments:
-h, --help show this help message and exit
-i PATH, --input_genome PATH
Query genome sequence in FASTA format
-g GENUS, --genus GENUS
Genus of query genome. Currently support only
following 9 genera.[bacillus, clostridium,
lactobacillus, leuconostoc, streptococcus,
bifidobacterium, enterococcus, lactococcus,
pediococcus]
-o PATH, --output_dir PATH
Path of output directory [Default: ProBioPred_out].
-t THREADS, --threads THREADS
Number of threads to run for BLAST and RGI.

Run ProBioPred on batch of genomes

# create tab-separated file with 3 columns:
1. genomeID: unique genome ID
2. genomeFile: file path to respective genome (fasta)
3. genus: one of the genus listed in ProBioPred help.

Output

ProBioPred generates output directory with several files and prints SVM score for probiotic/non-probiotic on standard output.

FileDescription
out.libsvmsvm-predict output (1/-1 refers to probiotic/non-probiotic class)
pro_hits.pfastaProbiotic genes (multi-FASTA file)
pro_outFiltered.blastBLAST outfmt6 for probiotic genes
resulTab.csvScores for each features (.csv format)
rgi_out.jsonRGI output (json format)
rgi_out.txtRGI output (tab-delimited format)
vfdb_hits.pfastaVirulent genes (multi-FASTA file)
vfdb_outFiltered.blastBLAST outfmt6 for virulent genes

About

No description, website, or topics provided.

Resources

Contributing

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix"); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

[alt text]

ProBioPred can predict the potential probiotic candidates from genome sequence based on Support Vector Machine (SVM) trained models. Preferable input for ProBioPred is complete genome for better results, but you can also provide draft genome assembly. Currently, ProBioPred supports prediction for only 9 genera viz. Bacillus, Clostridium, Lactobacillus, Leuconostoc, Streptococcus, Bifidobacterium, Enterococcus, Lactococcus and Pediococcus. The input genome should be in standard FASTA format to run this tool.

Theory

The ProBioPred uses available genetic information and Support Vector Machine (SVM) models for prediction of potential probiotic candidate. In brief, based on extensive literature survey and available databases, ProBioPred uses information on genes imparting probiotic properties, virulence factors and antibiotic resistance genes to generate and train models which eventually predicts a potential probiotic candidate. ProBioPred can also serves as a tool to predict probiotic genes, virulence factors and antibiotic resistance genes which can be browsed on the website or downloaded. These models can be used for analysis of genome sequences using ProBioPred either online or as a stand-alone tool.

Installing ProBioPred

# create conda environment
conda create -n probiopred python=3.10
conda activate probiopred
# install dependencies
# blast
conda install -c bioconda blast
# libsvm
conda install -c conda-forge libsvm
# install rgi
git clone https://github.com/arpcard/rgi.git
cd rgi
pip install .
# install rgi database
rgi auto_load
# install ProBioPred
git clone https://github.com/microDM/ProBioPred.git
cd ProBioPred
pip install .

Running ProBioPred

usage: proBioPred.py [-h] -i PATH -g GENUS [-o PATH] [-t THREADS]
Wrapper for running ProBioPred. Searches for probiotic, virulent and
antibiotic resistance genes in query genome. Then predicts the probability
score of genome being probiotic or non-probiotic based on SVM model.
optional arguments:
-h, --help show this help message and exit
-i PATH, --input_genome PATH
Query genome sequence in FASTA format
-g GENUS, --genus GENUS
Genus of query genome. Currently support only
following 9 genera.[bacillus, clostridium,
lactobacillus, leuconostoc, streptococcus,
bifidobacterium, enterococcus, lactococcus,
pediococcus]
-o PATH, --output_dir PATH
Path of output directory [Default: ProBioPred_out].
-t THREADS, --threads THREADS
Number of threads to run for BLAST and RGI.

Run ProBioPred on batch of genomes

# create tab-separated file with 3 columns:
1. genomeID: unique genome ID
2. genomeFile: file path to respective genome (fasta)
3. genus: one of the genus listed in ProBioPred help.

Output

ProBioPred generates output directory with several files and prints SVM score for probiotic/non-probiotic on standard output.

FileDescription
out.libsvmsvm-predict output (1/-1 refers to probiotic/non-probiotic class)
pro_hits.pfastaProbiotic genes (multi-FASTA file)
pro_outFiltered.blastBLAST outfmt6 for probiotic genes
resulTab.csvScores for each features (.csv format)
rgi_out.jsonRGI output (json format)
rgi_out.txtRGI output (tab-delimited format)
vfdb_hits.pfastaVirulent genes (multi-FASTA file)
vfdb_outFiltered.blastBLAST outfmt6 for virulent genes

About

No description, website, or topics provided.

Resources

Contributing

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms"); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

[alt text]

ProBioPred can predict the potential probiotic candidates from genome sequence based on Support Vector Machine (SVM) trained models. Preferable input for ProBioPred is complete genome for better results, but you can also provide draft genome assembly. Currently, ProBioPred supports prediction for only 9 genera viz. Bacillus, Clostridium, Lactobacillus, Leuconostoc, Streptococcus, Bifidobacterium, Enterococcus, Lactococcus and Pediococcus. The input genome should be in standard FASTA format to run this tool.

Theory

The ProBioPred uses available genetic information and Support Vector Machine (SVM) models for prediction of potential probiotic candidate. In brief, based on extensive literature survey and available databases, ProBioPred uses information on genes imparting probiotic properties, virulence factors and antibiotic resistance genes to generate and train models which eventually predicts a potential probiotic candidate. ProBioPred can also serves as a tool to predict probiotic genes, virulence factors and antibiotic resistance genes which can be browsed on the website or downloaded. These models can be used for analysis of genome sequences using ProBioPred either online or as a stand-alone tool.

Installing ProBioPred

# create conda environment
conda create -n probiopred python=3.10
conda activate probiopred
# install dependencies
# blast
conda install -c bioconda blast
# libsvm
conda install -c conda-forge libsvm
# install rgi
git clone https://github.com/arpcard/rgi.git
cd rgi
pip install .
# install rgi database
rgi auto_load
# install ProBioPred
git clone https://github.com/microDM/ProBioPred.git
cd ProBioPred
pip install .

Running ProBioPred

usage: proBioPred.py [-h] -i PATH -g GENUS [-o PATH] [-t THREADS]
Wrapper for running ProBioPred. Searches for probiotic, virulent and
antibiotic resistance genes in query genome. Then predicts the probability
score of genome being probiotic or non-probiotic based on SVM model.
optional arguments:
-h, --help show this help message and exit
-i PATH, --input_genome PATH
Query genome sequence in FASTA format
-g GENUS, --genus GENUS
Genus of query genome. Currently support only
following 9 genera.[bacillus, clostridium,
lactobacillus, leuconostoc, streptococcus,
bifidobacterium, enterococcus, lactococcus,
pediococcus]
-o PATH, --output_dir PATH
Path of output directory [Default: ProBioPred_out].
-t THREADS, --threads THREADS
Number of threads to run for BLAST and RGI.

Run ProBioPred on batch of genomes

# create tab-separated file with 3 columns:
1. genomeID: unique genome ID
2. genomeFile: file path to respective genome (fasta)
3. genus: one of the genus listed in ProBioPred help.

Output

ProBioPred generates output directory with several files and prints SVM score for probiotic/non-probiotic on standard output.

FileDescription
out.libsvmsvm-predict output (1/-1 refers to probiotic/non-probiotic class)
pro_hits.pfastaProbiotic genes (multi-FASTA file)
pro_outFiltered.blastBLAST outfmt6 for probiotic genes
resulTab.csvScores for each features (.csv format)
rgi_out.jsonRGI output (json format)
rgi_out.txtRGI output (tab-delimited format)
vfdb_hits.pfastaVirulent genes (multi-FASTA file)
vfdb_outFiltered.blastBLAST outfmt6 for virulent genes

About

No description, website, or topics provided.

Resources

Contributing

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links"); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + '
Skip to content

Repository files navigation

[alt text]

ProBioPred can predict the potential probiotic candidates from genome sequence based on Support Vector Machine (SVM) trained models. Preferable input for ProBioPred is complete genome for better results, but you can also provide draft genome assembly. Currently, ProBioPred supports prediction for only 9 genera viz. Bacillus, Clostridium, Lactobacillus, Leuconostoc, Streptococcus, Bifidobacterium, Enterococcus, Lactococcus and Pediococcus. The input genome should be in standard FASTA format to run this tool.

Theory

The ProBioPred uses available genetic information and Support Vector Machine (SVM) models for prediction of potential probiotic candidate. In brief, based on extensive literature survey and available databases, ProBioPred uses information on genes imparting probiotic properties, virulence factors and antibiotic resistance genes to generate and train models which eventually predicts a potential probiotic candidate. ProBioPred can also serves as a tool to predict probiotic genes, virulence factors and antibiotic resistance genes which can be browsed on the website or downloaded. These models can be used for analysis of genome sequences using ProBioPred either online or as a stand-alone tool.

Installing ProBioPred

# create conda environment
conda create -n probiopred python=3.10
conda activate probiopred
# install dependencies
# blast
conda install -c bioconda blast
# libsvm
conda install -c conda-forge libsvm
# install rgi
git clone https://github.com/arpcard/rgi.git
cd rgi
pip install .
# install rgi database
rgi auto_load
# install ProBioPred
git clone https://github.com/microDM/ProBioPred.git
cd ProBioPred
pip install .

Running ProBioPred

usage: proBioPred.py [-h] -i PATH -g GENUS [-o PATH] [-t THREADS]
Wrapper for running ProBioPred. Searches for probiotic, virulent and
antibiotic resistance genes in query genome. Then predicts the probability
score of genome being probiotic or non-probiotic based on SVM model.
optional arguments:
-h, --help show this help message and exit
-i PATH, --input_genome PATH
Query genome sequence in FASTA format
-g GENUS, --genus GENUS
Genus of query genome. Currently support only
following 9 genera.[bacillus, clostridium,
lactobacillus, leuconostoc, streptococcus,
bifidobacterium, enterococcus, lactococcus,
pediococcus]
-o PATH, --output_dir PATH
Path of output directory [Default: ProBioPred_out].
-t THREADS, --threads THREADS
Number of threads to run for BLAST and RGI.

Run ProBioPred on batch of genomes

# create tab-separated file with 3 columns:
1. genomeID: unique genome ID
2. genomeFile: file path to respective genome (fasta)
3. genus: one of the genus listed in ProBioPred help.

Output

ProBioPred generates output directory with several files and prints SVM score for probiotic/non-probiotic on standard output.

FileDescription
out.libsvmsvm-predict output (1/-1 refers to probiotic/non-probiotic class)
pro_hits.pfastaProbiotic genes (multi-FASTA file)
pro_outFiltered.blastBLAST outfmt6 for probiotic genes
resulTab.csvScores for each features (.csv format)
rgi_out.jsonRGI output (json format)
rgi_out.txtRGI output (tab-delimited format)
vfdb_hits.pfastaVirulent genes (multi-FASTA file)
vfdb_outFiltered.blastBLAST outfmt6 for virulent genes

About

No description, website, or topics provided.

Resources

Contributing

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default"); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

[alt text]

ProBioPred can predict the potential probiotic candidates from genome sequence based on Support Vector Machine (SVM) trained models. Preferable input for ProBioPred is complete genome for better results, but you can also provide draft genome assembly. Currently, ProBioPred supports prediction for only 9 genera viz. Bacillus, Clostridium, Lactobacillus, Leuconostoc, Streptococcus, Bifidobacterium, Enterococcus, Lactococcus and Pediococcus. The input genome should be in standard FASTA format to run this tool.

Theory

The ProBioPred uses available genetic information and Support Vector Machine (SVM) models for prediction of potential probiotic candidate. In brief, based on extensive literature survey and available databases, ProBioPred uses information on genes imparting probiotic properties, virulence factors and antibiotic resistance genes to generate and train models which eventually predicts a potential probiotic candidate. ProBioPred can also serves as a tool to predict probiotic genes, virulence factors and antibiotic resistance genes which can be browsed on the website or downloaded. These models can be used for analysis of genome sequences using ProBioPred either online or as a stand-alone tool.

Installing ProBioPred

# create conda environment
conda create -n probiopred python=3.10
conda activate probiopred
# install dependencies
# blast
conda install -c bioconda blast
# libsvm
conda install -c conda-forge libsvm
# install rgi
git clone https://github.com/arpcard/rgi.git
cd rgi
pip install .
# install rgi database
rgi auto_load
# install ProBioPred
git clone https://github.com/microDM/ProBioPred.git
cd ProBioPred
pip install .

Running ProBioPred

usage: proBioPred.py [-h] -i PATH -g GENUS [-o PATH] [-t THREADS]
Wrapper for running ProBioPred. Searches for probiotic, virulent and
antibiotic resistance genes in query genome. Then predicts the probability
score of genome being probiotic or non-probiotic based on SVM model.
optional arguments:
-h, --help show this help message and exit
-i PATH, --input_genome PATH
Query genome sequence in FASTA format
-g GENUS, --genus GENUS
Genus of query genome. Currently support only
following 9 genera.[bacillus, clostridium,
lactobacillus, leuconostoc, streptococcus,
bifidobacterium, enterococcus, lactococcus,
pediococcus]
-o PATH, --output_dir PATH
Path of output directory [Default: ProBioPred_out].
-t THREADS, --threads THREADS
Number of threads to run for BLAST and RGI.

Run ProBioPred on batch of genomes

# create tab-separated file with 3 columns:
1. genomeID: unique genome ID
2. genomeFile: file path to respective genome (fasta)
3. genus: one of the genus listed in ProBioPred help.

Output

ProBioPred generates output directory with several files and prints SVM score for probiotic/non-probiotic on standard output.

FileDescription
out.libsvmsvm-predict output (1/-1 refers to probiotic/non-probiotic class)
pro_hits.pfastaProbiotic genes (multi-FASTA file)
pro_outFiltered.blastBLAST outfmt6 for probiotic genes
resulTab.csvScores for each features (.csv format)
rgi_out.jsonRGI output (json format)
rgi_out.txtRGI output (tab-delimited format)
vfdb_hits.pfastaVirulent genes (multi-FASTA file)
vfdb_outFiltered.blastBLAST outfmt6 for virulent genes

About

No description, website, or topics provided.

Resources

Contributing

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers"); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + '
Skip to content

Repository files navigation

[alt text]

ProBioPred can predict the potential probiotic candidates from genome sequence based on Support Vector Machine (SVM) trained models. Preferable input for ProBioPred is complete genome for better results, but you can also provide draft genome assembly. Currently, ProBioPred supports prediction for only 9 genera viz. Bacillus, Clostridium, Lactobacillus, Leuconostoc, Streptococcus, Bifidobacterium, Enterococcus, Lactococcus and Pediococcus. The input genome should be in standard FASTA format to run this tool.

Theory

The ProBioPred uses available genetic information and Support Vector Machine (SVM) models for prediction of potential probiotic candidate. In brief, based on extensive literature survey and available databases, ProBioPred uses information on genes imparting probiotic properties, virulence factors and antibiotic resistance genes to generate and train models which eventually predicts a potential probiotic candidate. ProBioPred can also serves as a tool to predict probiotic genes, virulence factors and antibiotic resistance genes which can be browsed on the website or downloaded. These models can be used for analysis of genome sequences using ProBioPred either online or as a stand-alone tool.

Installing ProBioPred

# create conda environment
conda create -n probiopred python=3.10
conda activate probiopred
# install dependencies
# blast
conda install -c bioconda blast
# libsvm
conda install -c conda-forge libsvm
# install rgi
git clone https://github.com/arpcard/rgi.git
cd rgi
pip install .
# install rgi database
rgi auto_load
# install ProBioPred
git clone https://github.com/microDM/ProBioPred.git
cd ProBioPred
pip install .

Running ProBioPred

usage: proBioPred.py [-h] -i PATH -g GENUS [-o PATH] [-t THREADS]
Wrapper for running ProBioPred. Searches for probiotic, virulent and
antibiotic resistance genes in query genome. Then predicts the probability
score of genome being probiotic or non-probiotic based on SVM model.
optional arguments:
-h, --help show this help message and exit
-i PATH, --input_genome PATH
Query genome sequence in FASTA format
-g GENUS, --genus GENUS
Genus of query genome. Currently support only
following 9 genera.[bacillus, clostridium,
lactobacillus, leuconostoc, streptococcus,
bifidobacterium, enterococcus, lactococcus,
pediococcus]
-o PATH, --output_dir PATH
Path of output directory [Default: ProBioPred_out].
-t THREADS, --threads THREADS
Number of threads to run for BLAST and RGI.

Run ProBioPred on batch of genomes

# create tab-separated file with 3 columns:
1. genomeID: unique genome ID
2. genomeFile: file path to respective genome (fasta)
3. genus: one of the genus listed in ProBioPred help.

Output

ProBioPred generates output directory with several files and prints SVM score for probiotic/non-probiotic on standard output.

FileDescription
out.libsvmsvm-predict output (1/-1 refers to probiotic/non-probiotic class)
pro_hits.pfastaProbiotic genes (multi-FASTA file)
pro_outFiltered.blastBLAST outfmt6 for probiotic genes
resulTab.csvScores for each features (.csv format)
rgi_out.jsonRGI output (json format)
rgi_out.txtRGI output (tab-delimited format)
vfdb_hits.pfastaVirulent genes (multi-FASTA file)
vfdb_outFiltered.blastBLAST outfmt6 for virulent genes

About

No description, website, or topics provided.

Resources

Contributing

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode"); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })();
Skip to content

Repository files navigation

[alt text]

ProBioPred can predict the potential probiotic candidates from genome sequence based on Support Vector Machine (SVM) trained models. Preferable input for ProBioPred is complete genome for better results, but you can also provide draft genome assembly. Currently, ProBioPred supports prediction for only 9 genera viz. Bacillus, Clostridium, Lactobacillus, Leuconostoc, Streptococcus, Bifidobacterium, Enterococcus, Lactococcus and Pediococcus. The input genome should be in standard FASTA format to run this tool.

Theory

The ProBioPred uses available genetic information and Support Vector Machine (SVM) models for prediction of potential probiotic candidate. In brief, based on extensive literature survey and available databases, ProBioPred uses information on genes imparting probiotic properties, virulence factors and antibiotic resistance genes to generate and train models which eventually predicts a potential probiotic candidate. ProBioPred can also serves as a tool to predict probiotic genes, virulence factors and antibiotic resistance genes which can be browsed on the website or downloaded. These models can be used for analysis of genome sequences using ProBioPred either online or as a stand-alone tool.

Installing ProBioPred

# create conda environment
conda create -n probiopred python=3.10
conda activate probiopred
# install dependencies
# blast
conda install -c bioconda blast
# libsvm
conda install -c conda-forge libsvm
# install rgi
git clone https://github.com/arpcard/rgi.git
cd rgi
pip install .
# install rgi database
rgi auto_load
# install ProBioPred
git clone https://github.com/microDM/ProBioPred.git
cd ProBioPred
pip install .

Running ProBioPred

usage: proBioPred.py [-h] -i PATH -g GENUS [-o PATH] [-t THREADS]
Wrapper for running ProBioPred. Searches for probiotic, virulent and
antibiotic resistance genes in query genome. Then predicts the probability
score of genome being probiotic or non-probiotic based on SVM model.
optional arguments:
-h, --help show this help message and exit
-i PATH, --input_genome PATH
Query genome sequence in FASTA format
-g GENUS, --genus GENUS
Genus of query genome. Currently support only
following 9 genera.[bacillus, clostridium,
lactobacillus, leuconostoc, streptococcus,
bifidobacterium, enterococcus, lactococcus,
pediococcus]
-o PATH, --output_dir PATH
Path of output directory [Default: ProBioPred_out].
-t THREADS, --threads THREADS
Number of threads to run for BLAST and RGI.

Run ProBioPred on batch of genomes

# create tab-separated file with 3 columns:
1. genomeID: unique genome ID
2. genomeFile: file path to respective genome (fasta)
3. genus: one of the genus listed in ProBioPred help.

Output

ProBioPred generates output directory with several files and prints SVM score for probiotic/non-probiotic on standard output.

FileDescription
out.libsvmsvm-predict output (1/-1 refers to probiotic/non-probiotic class)
pro_hits.pfastaProbiotic genes (multi-FASTA file)
pro_outFiltered.blastBLAST outfmt6 for probiotic genes
resulTab.csvScores for each features (.csv format)
rgi_out.jsonRGI output (json format)
rgi_out.txtRGI output (tab-delimited format)
vfdb_hits.pfastaVirulent genes (multi-FASTA file)
vfdb_outFiltered.blastBLAST outfmt6 for virulent genes

About

No description, website, or topics provided.

Resources

Contributing

Stars

2 stars

Watchers

1 watching

Forks

Releases

Packages

Used by

Contributors

Languages