Update images with ONT-DART nomenclature

README.md

@@ -20,32 +20,32 @@ The code as provided has also been modified to include a different set of amplic
 presents.
 
 # ONT-DART
-_nanopore Detection of Amplicons in Real-Time_
+_Oxford Nanopore Technologies Detection of Amplicons in Real-Time_
 
-The scripts in this repository are designed for a very specific amplicon use-case, and are part of the nanoDART (nanopore Detection of Amplicons in Real-Time) analysis pipeline.
+The scripts in this repository are designed for a very specific amplicon use-case, and are part of the ONT-DART (Oxford Nanopore Technologies Detection of Amplicons in Real-Time) analysis pipeline.
 The input directory should be the standard Oxford Nanopore Technologies (ONT) MinKNOW software output named `fastq_pass`.
 Each set of reads per barcode within this directory are aligned to an amplicon database with `blastn`.
 The analysis is meant to be in 'real-time', meaning as reads are output from the basecalling algorithm of guppy (or whatever the standard is at the time) into the `fastq_pass` directory, they will be concatentated and analyzed at a user-define interval.
 Visualization of output results from this processing pipeline are part of the front-end GUI application.
 
 ## Running ONT-DART Web Application in Development
 
-This is for testing out the nanoDART web application on your machine for development purposes.
+This is for testing out the ONT-DART web application on your machine for development purposes.
 
-Before going through the steps, make sure Docker is installed on your machine: https://docs.docker.com/get-docker/. You'll also need to have access to the nanoDART accessory files for the blastn database. Copy the nanoDART accessory files directory into the project's root directory.
+Before going through the steps, make sure Docker is installed on your machine: https://docs.docker.com/get-docker/. You'll also need to have access to the ONT-DART accessory files for the blastn database. Copy the ONT-DART accessory files directory into the project's root directory.
 
 - Open terminal and navigate to the project's root directory.
-- To build the Docker image for nanoDART, run the following command: `./build_image.sh`. This will properly bundle the frontend and backend web application files together and create a Docker image with the name **nanodart**.
-- Finally, to run the web application, run the following command: `docker run  -p 5000:5000 nanodart`. The web application can now be reached at the following URL: http://localhost:5000.
+- To build the Docker image for ONT-DART, run the following command: `./build_image.sh`. This will properly bundle the frontend and backend web application files together and create a Docker image with the name **ont-dart**.
+- Finally, to run the web application, run the following command: `docker run  -p 5000:5000 ont-dart`. The web application can now be reached at the following URL: http://localhost:5000.
 
-## Installing nanoDART Web Application to MK1C
+## Installing ONT-DART Web Application to MK1C
 
-Before going through the steps, make sure Docker is installed on your machine: https://docs.docker.com/get-docker/. You'll also need to have access to the nanoDART accessory files for the blastn database. Copy the nanoDART accessory files directory into the project's root directory.
+Before going through the steps, make sure Docker is installed on your machine: https://docs.docker.com/get-docker/. You'll also need to have access to the ONT-DART accessory files for the blastn database. Copy the ONT-DART accessory files directory into the project's root directory.
 
 - Open terminal and navigate to the project's root directory.
-- To build the Docker image for nanoDART that is compatible with the MK1C, run the following command: `./build_and_save_image_arm64.sh`. This will generate a file named **nanodart.tar.gz** in your current directory which is the nanoDART Docker image that has been saved as a file.
+- To build the Docker image for ONT-DART that is compatible with the MK1C, run the following command: `./build_and_save_image_arm64.sh`. This will generate a file named **ont-dart.tar.gz** in your current directory which is the ONT-DART Docker image that has been saved as a file.
 - Transfer the Docker image file to the MK1C. This can be done through methods like USB or SSH (user: minit, password: minit).
-- Through command line on the MK1C (either directly on the device or through SSH), run the following command: `docker load < /path/to/file/nanodart.tar.gz`. This will load the nanoDART Docker image onto the MK1C.
+- Through command line on the MK1C (either directly on the device or through SSH), run the following command: `docker load < /path/to/file/ont-dart.tar.gz`. This will load the ONT-DART Docker image onto the MK1C.
 
 ## NOTES:
 	The target environment for this code is an Oxford Nanopore Technologies (ONT) MinION Mk1C,
@@ -61,7 +61,7 @@ Before going through the steps, make sure Docker is installed on your machine: h
 
 ## Usage
 
-Upon opening the nanoDART website, you will be presented with three tabs.
+Upon opening the ONT-DART website, you will be presented with three tabs.
 To begin the process, select the "Analysis" tab.
 
 ![](./imgs/analysis.png)

build_and_save_image_arm64.sh

@@ -18,6 +18,6 @@ SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
 
 (cd $SCRIPT_DIR/frontend && npm run build)
 cp -r $SCRIPT_DIR/frontend/dist $SCRIPT_DIR/webapp
-docker build $SCRIPT_DIR -t nanodart
-docker buildx build --platform linux/arm64 $SCRIPT_DIR -t nanodart
-docker save nanodart | gzip > nanodart.tar.gz
+docker build $SCRIPT_DIR -t ont-dart
+docker buildx build --platform linux/arm64 $SCRIPT_DIR -t ont-dart
+docker save ont-dart | gzip > ont-dart.tar.gz

build_image.sh

@@ -18,4 +18,4 @@ SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
 
 (cd $SCRIPT_DIR/frontend && npm run build)
 cp -r $SCRIPT_DIR/frontend/dist $SCRIPT_DIR/webapp
-docker build $SCRIPT_DIR -t nanodart
+docker build $SCRIPT_DIR -t ont-dart

frontend/README.md

@@ -1,4 +1,4 @@
-# nanoDART Frontend
+# ONT-DART Frontend
 
 ## Building for Deployment
 
@@ -29,4 +29,4 @@ See [Configuration Reference](https://cli.vuejs.org/config/). This project uses
 
 ## Notes
 
-- In order for the nanoDART website to work properly, it's important that the website is deployed on the same machine as the backend shell scripts. This is because the file paths provided to the web page are passed directly to the shell scripts for further processing.
+- In order for the ONT-DART website to work properly, it's important that the website is deployed on the same machine as the backend shell scripts. This is because the file paths provided to the web page are passed directly to the shell scripts for further processing.

frontend/package.json

@@ -1,5 +1,5 @@
 {
-  "name": "nanodart",
+  "name": "ont-dart",
   "version": "0.1.0",
   "private": true,
   "scripts": {

frontend/src/App.vue

@@ -24,7 +24,7 @@
         max-width="40"
         contain
       ></v-img>
-      <v-toolbar-title><span style="color:white">nanoDART</span></v-toolbar-title>
+      <v-toolbar-title><span style="color:white">ONT-DART</span></v-toolbar-title>
 
       <v-spacer></v-spacer>
       <v-btn v-for="item in navItems" :key="item.title" :to="item.link" text tile color="white">

frontend/src/components/AnalysisForm.vue

@@ -130,7 +130,7 @@ export default {
     },
     data: () => ({
         valid: true,
-        inputDirectory: '/app/nanoDART_accessory_files/nanoDART_testdata-20200114_GXB01192_PHASE2-NB_FLG001-BA/BA_NB_Flongle/20200114_1957_X1_ABR474_dac2eaf0/fastq_pass/',
+        inputDirectory: '/app/EXAMPLE/fastq_pass/',
         barcodes: 'barcode10,barcode11,barcode12',
         analysisInterval: '10',
         threads: '1',

frontend/src/services/results.service.js

@@ -16,7 +16,7 @@ class ResultsService {
             data: data,
             responseType: 'blob'
         }).then((response) => {
-            fileDownload(response.data, 'nanodart-tables.zip')
+            fileDownload(response.data, 'ont-dart-tables.zip')
         })
     }
 
@@ -27,7 +27,7 @@ class ResultsService {
             data: data,
             responseType: 'blob'
         }).then((response) => {
-            fileDownload(response.data, 'nanodart.pdf')
+            fileDownload(response.data, 'ont-dart.pdf')
         })
     }
 }

frontend/src/views/Home.vue

@@ -18,16 +18,16 @@
   <div>
     <v-row>
       <v-col align="center">
-        <div class="text-h1">nanoDART</div>
+        <div class="text-h1">ONT-DART</div>
         <div class="text-subtitle-2">
-          <u>nano</u>pore <u>D</u>etection of <u>A</u>mplicons in <u>R</u>eal-<u>T</u>ime  
+          <u>Oxford Nanopore Technologies</u> <u>D</u>etection of <u>A</u>mplicons in <u>R</u>eal-<u>T</u>ime  
         </div>
       </v-col>
     </v-row>
     <v-row>
       <v-col>
         <div class="text-body-1">
-          The nanoDART (nanopore Detection of Amplicons in Real-Time) analysis pipeline is used to identify the presence of amplicons
+          The ONT-DART (Oxford Nanopore Technologies Detection of Amplicons in Real-Time) analysis pipeline is used to identify the presence of amplicons
           produced from a multiplex PCR reaction from sequence read output of an Oxford Nanopore Technologies (ONT) sequencing device 
           (e.g. MK1B, MK1C, or GridION). If all amplicon products from a particular organism are above the algorithm's three detection
           thresholds, the organism is flagged as present in the sample.

frontend/src/views/Results.vue

@@ -422,7 +422,7 @@ export default {
             this.renderHeatmap(this.plot.data)
         },
         downloadGraph() {
-            const config = { filename: 'nanodart_results' };
+            const config = { filename: 'ont-dart_results' };
             d3_save_svg.save(d3.select('svg').node(), config);
         },
         downloadAll() {

frontend/vue.config.js

@@ -22,7 +22,7 @@ module.exports = {
     config
       .plugin('html')
       .tap(args => {
-        args[0].title = "nanoDART"
+        args[0].title = "ONT-DART"
         return args
       })
   }

nanomonitor.sh

@@ -12,7 +12,7 @@ cat << EOF
 Help message for \`nanomonitor.sh\`:
 
 This algorithm is designed for a very specific amplicon use-case, and is part
-of the nanoDART (nanopore Detection of Amplicons in Real-Time) analysis pipeline.
+of the ONT-DART (Oxford Nanopore Technologies Detection of Amplicons in Real-Time) analysis pipeline.
 The input fastq_pass (-i) directory should have subfolders named for barcodes.
 For this use-case, subfolder names are expected to be from 'barcode01' to 'barcode12'.
 

webapp/app.py

@@ -34,7 +34,7 @@
 dist_path = os.path.abspath('dist')
 
 nanomonitor_path = Path.cwd().parent / 'nanomonitor.sh'
-ref_directory = '/app/nanoDART_accessory_files/blastn/unencrypted_db/blastdb_all_amplicons'
+ref_directory = '/app/EXAMPLE/Amplicon_blastdb/Amplicon_set'
 
 running_analysis = False
 output_directory: Optional[Path] = None
@@ -52,9 +52,15 @@ def run_nanomonitor(input_directory: Path, barcode_list: str, analysis_interval:
     logs_dir = 'logs'
     os.makedirs(logs_dir, exist_ok=True)
     timestamp = time.strftime('%Y%m%d-%H%M%S')
-    output_directory = input_directory.parent / 'nanodart-output-{}'.format(timestamp)
+    output_directory = input_directory.parent / 'ont-dart-output-{}'.format(timestamp)
     log_file = Path('{}/{}.log'.format(logs_dir, timestamp))
 
+    print(nanomonitor_path)
+    print(num_threads)
+    print(analysis_interval)
+    print(input_directory)
+    print(output_directory)
+    print(ref_directory)
     subprocess.Popen([nanomonitor_path, '-t', num_threads, '-a', analysis_interval,
                       '-i', input_directory, '-n', barcode_list, '-o', output_directory,
                       '-r', ref_directory], stdout=open(log_file, 'wb'), stderr=subprocess.STDOUT)
@@ -64,7 +70,7 @@ def run_nanomonitor(input_directory: Path, barcode_list: str, analysis_interval:
 @app.route('/')
 def index():
     """
-    Generates main index page for nanoDART from template.
+    Generates main index page for ONT-DART from template.
 
     Returns:
         render_template: renders index.html template
@@ -154,7 +160,7 @@ def results_zip():
 
     return send_file(memory_file,
                      mimetype='application/zip',
-                     attachment_filename='nanodart-tables.zip',
+                     attachment_filename='ont-dart-tables.zip',
                      as_attachment=True, cache_timeout=0)
 
 
@@ -170,11 +176,11 @@ def results_pdf():
     options = {'page-size': 'Letter', 'margin-top': '0.75in', 'margin-bottom': '0.75in',
                'margin-left': '0.75in', 'margin-right': '0.75in', 'quiet': ''}
     html_header = '<!doctype html><html lang="en"><head><meta charset="utf-8">' + \
-                  '<title>nanoDART</title></head><body>' + \
-                  '<h1>nanoDART Report: ' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '</h1>'
+                  '<title>ONT-DART</title></head><body>' + \
+                  '<h1>ONT-DART Report: ' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '</h1>'
     html_string = html_header + content['html'] + '</body></html>'
     pdf_bytes = pdfkit.from_string(html_string, options=options)
-    return send_file(io.BytesIO(pdf_bytes), attachment_filename='nanodart.pdf', as_attachment=True,
+    return send_file(io.BytesIO(pdf_bytes), attachment_filename='ont-dart.pdf', as_attachment=True,
                      mimetype='application/pdf')