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Deep learning the cis-regulatory code for gene expression in selected model plants

DOI

Please follow the steps below to reproduce the results from our work.

  1. Download this repository.
  2. Change directory into the model subdirectory, run the fetch_genomes_and_annotation.sh script. Firstly, this will create 3 new subdirectories: genomes, gene_models, tpm_counts. Then, it will download genomes and gene models for 4 plant species, uncompress them and store them in genomes and gene models subdirectories respectively.
  3. Download expression counts for the project from supplementary data from publication and save these files within the tpm_counts subdirectory. NB: you should have 8 files, corresponding to 4 plant species and 2 tissues. For example, for Arabidopsis thaliana you would have arabidopsis_counts.csv and arabidopsis_root_counts.csv for leaf and root tissues respectively.

Training convolutional neural networks

  • To train SSR and SSC models, run the train_ssr_ssc_models_leaf.py and train_ssr_ssc_models_root.py for leaf and root tissues respectively.
  • Train the MSR models using train_msr_models_leaf.py and train_msr_models_root.py.

Only after training CNN models can you run the scripts below that compute importance scores and generate motifs. Also note that deepLIFT which is used to compute importance scores is currently only compatible with tensorflow 1.x. So if you build models with tensorflow 2.x, you won't be able to use these scripts.

Computing importance scores and obtaining motifs

  • Run the motif_discovery_... scripts for respective tissue and models.
  • Then run extract_motifs_ssr.py or extract_motifs_msr.py to get the motifs out of the output produced by modisco.

Random forest models

  • Firstly, create the features using the create_generic_feature.py script.
  • Then run either random_forest_msr.py or random_forest_ssr.py for MSR and SSR models respectively.

Investigate effect of different sequence lengths

To investigate the effects of different UTR or promoter sequence lengths, use the effect_of_different_... scripts. These scripts will build several models based on different length specified within the scripts.

Generating validation_genes.pickle file

This file contains information of genes that have homologs only within their chromosomes, such that when we use chromosome level cross validation for training, we mitigate the effects of homologs leaking information between our training and test set. While I provide the pickle file used for this project, one can generate this themselves by firstly going into the data directory that sits as a sibling directory to model directory, then running the commands below in the terminal:

wget https://ftp.ebi.ac.uk/ensemblgenomes/pub/release-52/plants/fasta/arabidopsis_thaliana/pep/Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
gunzip Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
makeblastdb -in Arabidopsis_thaliana.TAIR10.pep.all.fa -dbtype prot -title arabidopsis -parse_seqids -hash_index -out arabidopsis
blastp -db arabidopsis -query Arabidopsis_thaliana.TAIR10.pep.all.fa -out Blast_ara_to_ara -outfmt 6

The above assume that you have blast installed on your computer. The above 4 lines are just for Arabidopsis thaliana but should be edited and repeated for the other 4 species. Once this is done, run the produce_non_homologous_val_sets.py script.

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Deep learning the cis-regulatory code for gene expression in selected model plants

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Repository files navigation
Deep learning the cis-regulatory code for gene expression in selected model plants
DOI
Please follow the steps below to reproduce the results from our work.
  1. Download this repository.
  2. Change directory into the model subdirectory, run the fetch_genomes_and_annotation.sh script. Firstly, this
will create 3 new subdirectories: genomes, gene_models, tpm_counts. Then, it will download genomes and gene models
for 4 plant species, uncompress them and store them in genomes and gene models subdirectories respectively.
  3. Download expression counts for the project from supplementary data from publication and save these files
within the tpm_counts subdirectory. NB: you should have 8 files, corresponding to 4 plant species and 2 tissues.
For example, for Arabidopsis thaliana you would have arabidopsis_counts.csv and arabidopsis_root_counts.csv
for leaf and root tissues respectively.
Training convolutional neural networks
  • To train SSR and SSC models, run the train_ssr_ssc_models_leaf.py and train_ssr_ssc_models_root.py for
leaf and root tissues respectively.
  • Train the MSR models using train_msr_models_leaf.py and train_msr_models_root.py.
Only after training CNN models can you run the scripts below that compute importance scores and generate motifs. Also
note that deepLIFT which is used to compute importance scores is currently only compatible with tensorflow 1.x. So if
you build models with tensorflow 2.x, you won't be able to use these scripts.
Computing importance scores and obtaining motifs
  • Run the motif_discovery_... scripts for respective tissue and models.
  • Then run extract_motifs_ssr.py or extract_motifs_msr.py to get the motifs out of the output produced by
modisco.
Random forest models
  • Firstly, create the features using the create_generic_feature.py script.
  • Then run either random_forest_msr.py or random_forest_ssr.py for MSR and SSR models respectively.
Investigate effect of different sequence lengths
To investigate the effects of different UTR or promoter sequence lengths, use the effect_of_different_...
scripts. These scripts will build several models based on different length specified within the scripts.
Generating validation_genes.pickle file
This file contains information of genes that have homologs only within their chromosomes, such that when we use
chromosome level cross validation for training, we mitigate the effects of homologs leaking information between our
training and test set. While I provide the pickle file used for this project, one can generate this themselves by
firstly going into the data directory that sits as a sibling directory to model directory, then running the commands
below in the terminal:
wget https://ftp.ebi.ac.uk/ensemblgenomes/pub/release-52/plants/fasta/arabidopsis_thaliana/pep/Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
gunzip Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
makeblastdb -in Arabidopsis_thaliana.TAIR10.pep.all.fa -dbtype prot -title arabidopsis -parse_seqids -hash_index -out arabidopsis
blastp -db arabidopsis -query Arabidopsis_thaliana.TAIR10.pep.all.fa -out Blast_ara_to_ara -outfmt 6
The above assume that you have blast installed on your computer.
The above 4 lines are just for Arabidopsis thaliana but should be edited and repeated for the other 4 species. Once
this is done, run the produce_non_homologous_val_sets.py script.
About
Deep learning the cis-regulatory code for gene expression in selected model plants
Topics
cis-regulatory-elementsdeep-learninggenomics
Resources
Readme
Activity
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Stars
26 stars
Watchers
1 watching
Forks
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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");
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Repository files navigation
Deep learning the cis-regulatory code for gene expression in selected model plants
DOI
Please follow the steps below to reproduce the results from our work.
  1. Download this repository.
  2. Change directory into the model subdirectory, run the fetch_genomes_and_annotation.sh script. Firstly, this
will create 3 new subdirectories: genomes, gene_models, tpm_counts. Then, it will download genomes and gene models
for 4 plant species, uncompress them and store them in genomes and gene models subdirectories respectively.
  3. Download expression counts for the project from supplementary data from publication and save these files
within the tpm_counts subdirectory. NB: you should have 8 files, corresponding to 4 plant species and 2 tissues.
For example, for Arabidopsis thaliana you would have arabidopsis_counts.csv and arabidopsis_root_counts.csv
for leaf and root tissues respectively.
Training convolutional neural networks
  • To train SSR and SSC models, run the train_ssr_ssc_models_leaf.py and train_ssr_ssc_models_root.py for
leaf and root tissues respectively.
  • Train the MSR models using train_msr_models_leaf.py and train_msr_models_root.py.
Only after training CNN models can you run the scripts below that compute importance scores and generate motifs. Also
note that deepLIFT which is used to compute importance scores is currently only compatible with tensorflow 1.x. So if
you build models with tensorflow 2.x, you won't be able to use these scripts.
Computing importance scores and obtaining motifs
  • Run the motif_discovery_... scripts for respective tissue and models.
  • Then run extract_motifs_ssr.py or extract_motifs_msr.py to get the motifs out of the output produced by
modisco.
Random forest models
  • Firstly, create the features using the create_generic_feature.py script.
  • Then run either random_forest_msr.py or random_forest_ssr.py for MSR and SSR models respectively.
Investigate effect of different sequence lengths
To investigate the effects of different UTR or promoter sequence lengths, use the effect_of_different_...
scripts. These scripts will build several models based on different length specified within the scripts.
Generating validation_genes.pickle file
This file contains information of genes that have homologs only within their chromosomes, such that when we use
chromosome level cross validation for training, we mitigate the effects of homologs leaking information between our
training and test set. While I provide the pickle file used for this project, one can generate this themselves by
firstly going into the data directory that sits as a sibling directory to model directory, then running the commands
below in the terminal:
wget https://ftp.ebi.ac.uk/ensemblgenomes/pub/release-52/plants/fasta/arabidopsis_thaliana/pep/Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
gunzip Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
makeblastdb -in Arabidopsis_thaliana.TAIR10.pep.all.fa -dbtype prot -title arabidopsis -parse_seqids -hash_index -out arabidopsis
blastp -db arabidopsis -query Arabidopsis_thaliana.TAIR10.pep.all.fa -out Blast_ara_to_ara -outfmt 6
The above assume that you have blast installed on your computer.
The above 4 lines are just for Arabidopsis thaliana but should be edited and repeated for the other 4 species. Once
this is done, run the produce_non_homologous_val_sets.py script.
About
Deep learning the cis-regulatory code for gene expression in selected model plants
Topics
cis-regulatory-elementsdeep-learninggenomics
Resources
Readme
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Stars
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Watchers
1 watching
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Report repository
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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('^' + ".*" + '

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Repository files navigation
Deep learning the cis-regulatory code for gene expression in selected model plants
DOI
Please follow the steps below to reproduce the results from our work.
  1. Download this repository.
  2. Change directory into the model subdirectory, run the fetch_genomes_and_annotation.sh script. Firstly, this
will create 3 new subdirectories: genomes, gene_models, tpm_counts. Then, it will download genomes and gene models
for 4 plant species, uncompress them and store them in genomes and gene models subdirectories respectively.
  3. Download expression counts for the project from supplementary data from publication and save these files
within the tpm_counts subdirectory. NB: you should have 8 files, corresponding to 4 plant species and 2 tissues.
For example, for Arabidopsis thaliana you would have arabidopsis_counts.csv and arabidopsis_root_counts.csv
for leaf and root tissues respectively.
Training convolutional neural networks
  • To train SSR and SSC models, run the train_ssr_ssc_models_leaf.py and train_ssr_ssc_models_root.py for
leaf and root tissues respectively.
  • Train the MSR models using train_msr_models_leaf.py and train_msr_models_root.py.
Only after training CNN models can you run the scripts below that compute importance scores and generate motifs. Also
note that deepLIFT which is used to compute importance scores is currently only compatible with tensorflow 1.x. So if
you build models with tensorflow 2.x, you won't be able to use these scripts.
Computing importance scores and obtaining motifs
  • Run the motif_discovery_... scripts for respective tissue and models.
  • Then run extract_motifs_ssr.py or extract_motifs_msr.py to get the motifs out of the output produced by
modisco.
Random forest models
  • Firstly, create the features using the create_generic_feature.py script.
  • Then run either random_forest_msr.py or random_forest_ssr.py for MSR and SSR models respectively.
Investigate effect of different sequence lengths
To investigate the effects of different UTR or promoter sequence lengths, use the effect_of_different_...
scripts. These scripts will build several models based on different length specified within the scripts.
Generating validation_genes.pickle file
This file contains information of genes that have homologs only within their chromosomes, such that when we use
chromosome level cross validation for training, we mitigate the effects of homologs leaking information between our
training and test set. While I provide the pickle file used for this project, one can generate this themselves by
firstly going into the data directory that sits as a sibling directory to model directory, then running the commands
below in the terminal:
wget https://ftp.ebi.ac.uk/ensemblgenomes/pub/release-52/plants/fasta/arabidopsis_thaliana/pep/Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
gunzip Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
makeblastdb -in Arabidopsis_thaliana.TAIR10.pep.all.fa -dbtype prot -title arabidopsis -parse_seqids -hash_index -out arabidopsis
blastp -db arabidopsis -query Arabidopsis_thaliana.TAIR10.pep.all.fa -out Blast_ara_to_ara -outfmt 6
The above assume that you have blast installed on your computer.
The above 4 lines are just for Arabidopsis thaliana but should be edited and repeated for the other 4 species. Once
this is done, run the produce_non_homologous_val_sets.py script.
About
Deep learning the cis-regulatory code for gene expression in selected model plants
Topics
cis-regulatory-elementsdeep-learninggenomics
Resources
Readme
Activity
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Stars
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Watchers
1 watching
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Report repository
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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" + '

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Deep learning the cis-regulatory code for gene expression in selected model plants
DOI
Please follow the steps below to reproduce the results from our work.
  1. Download this repository.
  2. Change directory into the model subdirectory, run the fetch_genomes_and_annotation.sh script. Firstly, this
will create 3 new subdirectories: genomes, gene_models, tpm_counts. Then, it will download genomes and gene models
for 4 plant species, uncompress them and store them in genomes and gene models subdirectories respectively.
  3. Download expression counts for the project from supplementary data from publication and save these files
within the tpm_counts subdirectory. NB: you should have 8 files, corresponding to 4 plant species and 2 tissues.
For example, for Arabidopsis thaliana you would have arabidopsis_counts.csv and arabidopsis_root_counts.csv
for leaf and root tissues respectively.
Training convolutional neural networks
  • To train SSR and SSC models, run the train_ssr_ssc_models_leaf.py and train_ssr_ssc_models_root.py for
leaf and root tissues respectively.
  • Train the MSR models using train_msr_models_leaf.py and train_msr_models_root.py.
Only after training CNN models can you run the scripts below that compute importance scores and generate motifs. Also
note that deepLIFT which is used to compute importance scores is currently only compatible with tensorflow 1.x. So if
you build models with tensorflow 2.x, you won't be able to use these scripts.
Computing importance scores and obtaining motifs
  • Run the motif_discovery_... scripts for respective tissue and models.
  • Then run extract_motifs_ssr.py or extract_motifs_msr.py to get the motifs out of the output produced by
modisco.
Random forest models
  • Firstly, create the features using the create_generic_feature.py script.
  • Then run either random_forest_msr.py or random_forest_ssr.py for MSR and SSR models respectively.
Investigate effect of different sequence lengths
To investigate the effects of different UTR or promoter sequence lengths, use the effect_of_different_...
scripts. These scripts will build several models based on different length specified within the scripts.
Generating validation_genes.pickle file
This file contains information of genes that have homologs only within their chromosomes, such that when we use
chromosome level cross validation for training, we mitigate the effects of homologs leaking information between our
training and test set. While I provide the pickle file used for this project, one can generate this themselves by
firstly going into the data directory that sits as a sibling directory to model directory, then running the commands
below in the terminal:
wget https://ftp.ebi.ac.uk/ensemblgenomes/pub/release-52/plants/fasta/arabidopsis_thaliana/pep/Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
gunzip Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
makeblastdb -in Arabidopsis_thaliana.TAIR10.pep.all.fa -dbtype prot -title arabidopsis -parse_seqids -hash_index -out arabidopsis
blastp -db arabidopsis -query Arabidopsis_thaliana.TAIR10.pep.all.fa -out Blast_ara_to_ara -outfmt 6
The above assume that you have blast installed on your computer.
The above 4 lines are just for Arabidopsis thaliana but should be edited and repeated for the other 4 species. Once
this is done, run the produce_non_homologous_val_sets.py script.
About
Deep learning the cis-regulatory code for gene expression in selected model plants
Topics
cis-regulatory-elementsdeep-learninggenomics
Resources
Readme
Activity
Custom properties
Stars
26 stars
Watchers
1 watching
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4 forks
Report repository
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, '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); }
})();
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Deep learning the cis-regulatory code for gene expression in selected model plants
DOI
Please follow the steps below to reproduce the results from our work.
  1. Download this repository.
  2. Change directory into the model subdirectory, run the fetch_genomes_and_annotation.sh script. Firstly, this
will create 3 new subdirectories: genomes, gene_models, tpm_counts. Then, it will download genomes and gene models
for 4 plant species, uncompress them and store them in genomes and gene models subdirectories respectively.
  3. Download expression counts for the project from supplementary data from publication and save these files
within the tpm_counts subdirectory. NB: you should have 8 files, corresponding to 4 plant species and 2 tissues.
For example, for Arabidopsis thaliana you would have arabidopsis_counts.csv and arabidopsis_root_counts.csv
for leaf and root tissues respectively.
Training convolutional neural networks
  • To train SSR and SSC models, run the train_ssr_ssc_models_leaf.py and train_ssr_ssc_models_root.py for
leaf and root tissues respectively.
  • Train the MSR models using train_msr_models_leaf.py and train_msr_models_root.py.
Only after training CNN models can you run the scripts below that compute importance scores and generate motifs. Also
note that deepLIFT which is used to compute importance scores is currently only compatible with tensorflow 1.x. So if
you build models with tensorflow 2.x, you won't be able to use these scripts.
Computing importance scores and obtaining motifs
  • Run the motif_discovery_... scripts for respective tissue and models.
  • Then run extract_motifs_ssr.py or extract_motifs_msr.py to get the motifs out of the output produced by
modisco.
Random forest models
  • Firstly, create the features using the create_generic_feature.py script.
  • Then run either random_forest_msr.py or random_forest_ssr.py for MSR and SSR models respectively.
Investigate effect of different sequence lengths
To investigate the effects of different UTR or promoter sequence lengths, use the effect_of_different_...
scripts. These scripts will build several models based on different length specified within the scripts.
Generating validation_genes.pickle file
This file contains information of genes that have homologs only within their chromosomes, such that when we use
chromosome level cross validation for training, we mitigate the effects of homologs leaking information between our
training and test set. While I provide the pickle file used for this project, one can generate this themselves by
firstly going into the data directory that sits as a sibling directory to model directory, then running the commands
below in the terminal:
wget https://ftp.ebi.ac.uk/ensemblgenomes/pub/release-52/plants/fasta/arabidopsis_thaliana/pep/Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
gunzip Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
makeblastdb -in Arabidopsis_thaliana.TAIR10.pep.all.fa -dbtype prot -title arabidopsis -parse_seqids -hash_index -out arabidopsis
blastp -db arabidopsis -query Arabidopsis_thaliana.TAIR10.pep.all.fa -out Blast_ara_to_ara -outfmt 6
The above assume that you have blast installed on your computer.
The above 4 lines are just for Arabidopsis thaliana but should be edited and repeated for the other 4 species. Once
this is done, run the produce_non_homologous_val_sets.py script.
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, 'i');
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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); }
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Deep learning the cis-regulatory code for gene expression in selected model plants
DOI
Please follow the steps below to reproduce the results from our work.
  1. Download this repository.
  2. Change directory into the model subdirectory, run the fetch_genomes_and_annotation.sh script. Firstly, this
will create 3 new subdirectories: genomes, gene_models, tpm_counts. Then, it will download genomes and gene models
for 4 plant species, uncompress them and store them in genomes and gene models subdirectories respectively.
  3. Download expression counts for the project from supplementary data from publication and save these files
within the tpm_counts subdirectory. NB: you should have 8 files, corresponding to 4 plant species and 2 tissues.
For example, for Arabidopsis thaliana you would have arabidopsis_counts.csv and arabidopsis_root_counts.csv
for leaf and root tissues respectively.
Training convolutional neural networks
  • To train SSR and SSC models, run the train_ssr_ssc_models_leaf.py and train_ssr_ssc_models_root.py for
leaf and root tissues respectively.
  • Train the MSR models using train_msr_models_leaf.py and train_msr_models_root.py.
Only after training CNN models can you run the scripts below that compute importance scores and generate motifs. Also
note that deepLIFT which is used to compute importance scores is currently only compatible with tensorflow 1.x. So if
you build models with tensorflow 2.x, you won't be able to use these scripts.
Computing importance scores and obtaining motifs
  • Run the motif_discovery_... scripts for respective tissue and models.
  • Then run extract_motifs_ssr.py or extract_motifs_msr.py to get the motifs out of the output produced by
modisco.
Random forest models
  • Firstly, create the features using the create_generic_feature.py script.
  • Then run either random_forest_msr.py or random_forest_ssr.py for MSR and SSR models respectively.
Investigate effect of different sequence lengths
To investigate the effects of different UTR or promoter sequence lengths, use the effect_of_different_...
scripts. These scripts will build several models based on different length specified within the scripts.
Generating validation_genes.pickle file
This file contains information of genes that have homologs only within their chromosomes, such that when we use
chromosome level cross validation for training, we mitigate the effects of homologs leaking information between our
training and test set. While I provide the pickle file used for this project, one can generate this themselves by
firstly going into the data directory that sits as a sibling directory to model directory, then running the commands
below in the terminal:
wget https://ftp.ebi.ac.uk/ensemblgenomes/pub/release-52/plants/fasta/arabidopsis_thaliana/pep/Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
gunzip Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
makeblastdb -in Arabidopsis_thaliana.TAIR10.pep.all.fa -dbtype prot -title arabidopsis -parse_seqids -hash_index -out arabidopsis
blastp -db arabidopsis -query Arabidopsis_thaliana.TAIR10.pep.all.fa -out Blast_ara_to_ara -outfmt 6
The above assume that you have blast installed on your computer.
The above 4 lines are just for Arabidopsis thaliana but should be edited and repeated for the other 4 species. Once
this is done, run the produce_non_homologous_val_sets.py script.
About
Deep learning the cis-regulatory code for gene expression in selected model plants
Topics
cis-regulatory-elementsdeep-learninggenomics
Resources
Readme
Activity
Custom properties
Stars
26 stars
Watchers
1 watching
Forks
4 forks
Report repository
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, '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); }
})();
})();

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Repository files navigation
Deep learning the cis-regulatory code for gene expression in selected model plants
DOI
Please follow the steps below to reproduce the results from our work.
  1. Download this repository.
  2. Change directory into the model subdirectory, run the fetch_genomes_and_annotation.sh script. Firstly, this
will create 3 new subdirectories: genomes, gene_models, tpm_counts. Then, it will download genomes and gene models
for 4 plant species, uncompress them and store them in genomes and gene models subdirectories respectively.
  3. Download expression counts for the project from supplementary data from publication and save these files
within the tpm_counts subdirectory. NB: you should have 8 files, corresponding to 4 plant species and 2 tissues.
For example, for Arabidopsis thaliana you would have arabidopsis_counts.csv and arabidopsis_root_counts.csv
for leaf and root tissues respectively.
Training convolutional neural networks
  • To train SSR and SSC models, run the train_ssr_ssc_models_leaf.py and train_ssr_ssc_models_root.py for
leaf and root tissues respectively.
  • Train the MSR models using train_msr_models_leaf.py and train_msr_models_root.py.
Only after training CNN models can you run the scripts below that compute importance scores and generate motifs. Also
note that deepLIFT which is used to compute importance scores is currently only compatible with tensorflow 1.x. So if
you build models with tensorflow 2.x, you won't be able to use these scripts.
Computing importance scores and obtaining motifs
  • Run the motif_discovery_... scripts for respective tissue and models.
  • Then run extract_motifs_ssr.py or extract_motifs_msr.py to get the motifs out of the output produced by
modisco.
Random forest models
  • Firstly, create the features using the create_generic_feature.py script.
  • Then run either random_forest_msr.py or random_forest_ssr.py for MSR and SSR models respectively.
Investigate effect of different sequence lengths
To investigate the effects of different UTR or promoter sequence lengths, use the effect_of_different_...
scripts. These scripts will build several models based on different length specified within the scripts.
Generating validation_genes.pickle file
This file contains information of genes that have homologs only within their chromosomes, such that when we use
chromosome level cross validation for training, we mitigate the effects of homologs leaking information between our
training and test set. While I provide the pickle file used for this project, one can generate this themselves by
firstly going into the data directory that sits as a sibling directory to model directory, then running the commands
below in the terminal:
wget https://ftp.ebi.ac.uk/ensemblgenomes/pub/release-52/plants/fasta/arabidopsis_thaliana/pep/Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
gunzip Arabidopsis_thaliana.TAIR10.pep.all.fa.gz
makeblastdb -in Arabidopsis_thaliana.TAIR10.pep.all.fa -dbtype prot -title arabidopsis -parse_seqids -hash_index -out arabidopsis
blastp -db arabidopsis -query Arabidopsis_thaliana.TAIR10.pep.all.fa -out Blast_ara_to_ara -outfmt 6
The above assume that you have blast installed on your computer.
The above 4 lines are just for Arabidopsis thaliana but should be edited and repeated for the other 4 species. Once
this is done, run the produce_non_homologous_val_sets.py script.
About
Deep learning the cis-regulatory code for gene expression in selected model plants
Topics
cis-regulatory-elementsdeep-learninggenomics
Resources
Readme
Activity
Custom properties
Stars
26 stars
Watchers
1 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages