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<html><head>
<title>Riboswitch: DNA annotation</title>
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<body>
<div class="bg-white">
<div>
<main class="mx-auto px-4">
<h1 class="text-4xl font-bold tracking-tight text-gray-900">CompBio Lab
</h1><div class="flex items-baseline justify-between border-b border-gray-200 pt-12 pb-6">
<h1 class="text-4xl font-bold tracking-tight text-gray-900">
Riboswitch annotation tool
<span class="bg-blue-100 text-blue-800 text-xl font-semibold mr-2 px-2.5 py-0.5 rounded dark:bg-blue-200 dark:text-blue-800 hidden" id="annot-notif">
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</svg>
<span id="annot-text">Annotating: {name} - {1}/{no}</span>
</span>
</h1>
</div>
<section aria-labelledby="products-heading" class="pt-6 pb-24">
<div class="grid grid-cols-1 gap-x-8 gap-y-10 lg:grid-cols-4">
<!-- Filters -->
<div class="lg:block">
<!-- Replace with your content -->
<div class="p-4 rounded-lg border-4 border-dashed border-gray-200 h-full">
<h2 class="block text-xl font-medium text-gray-700">DNA sequence</h2>
<br>
<div>
<label class="block mb-2 text-sm font-medium text-gray-900 " for="file_input">Parse from a file</label>
<input class="block w-full text-sm text-gray-900 border border-gray-300 rounded-lg cursor-pointer bg-gray-50 focus:outline-none " aria-describedby="file_input_help" id="file_input" type="file" onchange="parse_file()">
<p class="mt-1 text-sm text-gray-500" id="file_input_help">Upload a GenBank, FASTA, JBEI, SnapGene, or SBOL file to parse sequence from it.</p>
</div>
<select id="sequence-list" class="bg-gray-50 border border-gray-300 text-gray-900 text-sm rounded-lg focus:ring-blue-500 focus:border-blue-500 block w-full p-2.5 mt-2" onchange="select_sequence()">
<option selected="" disabled="">Select a sequence</option>
</select>
<div class="relative flex py-5 items-center">
<div class="flex-grow border-t border-gray-400"></div>
<span class="flex-shrink mx-4 text-gray-400">or</span>
<div class="flex-grow border-t border-gray-400"></div>
</div>
<div>
<label for="about" class="block text-sm font-medium text-gray-700">DNA sequence from text</label>
<div class="mt-1">
<textarea id="dna_sequence_text" name="about" rows="3" class="mt-1 block w-full rounded-md border-gray-300 shadow-sm focus:border-indigo-500 focus:ring-indigo-500 sm:text-sm" placeholder="tcgcgcgtttcggtgatgacggtgaaaacctctgacacatgca">AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGCTGGCCCCGTGGGAGAGACCGGTCTTGTGACCGGCGCCGAAGGCGCAACACCGCCCGGAATCGCTCAGGCAAAAGGACCGTGGGGCCAATAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACCCTGTCGCTAGGGGTGCCCCCGTCAAGGGGCTGAGACGGCCGTGAAGCGGCCAACCCTCGGAACCTGAACCGGTTAATACCGGCGGAGGGAAGCGGCGGGCGCCCCACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAGCTGGCCCCGTGGGAGAGACCGGTCTTGTGACCGGCGCCGAAGGCGCAACACCGCCCGGAATCGCTCAGGCAAAAGGACCGTGGGGCCAATCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCTGTCGCTAGGGGTGCCCCCGTCAAGGGGCTGAGACGGCCGTGAAGCGGCCAACCCTCGGAACCTGAACCGGTTAATACCGGCGGAGGGAAGCGGCGGGCGCCCCAC</textarea>
</div>
<p class="mt-2 text-sm text-gray-500">Copy paste the DNA sequence in the textarea above. <b>This will be given preference over file.</b></p>
</div>
<br>
<div class="content-center">
<a href="#_" class="relative inline-block text-sm group" onclick="annotate_sequence()">
<span class="relative z-10 block px-5 py-3 overflow-hidden font-medium leading-tight text-gray-800 transition-colors duration-300 ease-out border-2 border-gray-900 rounded-lg group-hover:text-white">
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<span class="relative">Annotate DNA</span>
</span>
<span class="absolute bottom-0 right-0 w-full h-12 -mb-1 -mr-1 transition-all duration-200 ease-linear bg-gray-900 rounded-lg group-hover:mb-0 group-hover:mr-0" data-rounded="rounded-lg"></span>
</a>
</div>
</div>
</div>
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<div class="lg:col-span-3">
<!-- Replace with your content -->
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</div>
<!-- /End replace -->
</div>
</div>
</section>
<div class="text-2 tracking-tight text-gray-900">
<p>Guide: <i>Ashok Palaniappan</i></p>
<p><i>Krittika N, Mridula G, Vigneshwaran</i></p>
</div>
</main>
</div>
</div>
<div class="toast-panel absolute top-5 right-5" id="toasts">
</div>
<script src="https://unpkg.com/seqviz"></script>
<script src="https://unpkg.com/bio-parsers/umd/bio-parsers.js"></script>
<script src="https://cdnjs.cloudflare.com/ajax/libs/axios/1.2.1/axios.min.js" integrity="sha512-zJYu9ICC+mWF3+dJ4QC34N9RA0OVS1XtPbnf6oXlvGrLGNB8egsEzu/5wgG90I61hOOKvcywoLzwNmPqGAdATA==" crossorigin="anonymous" referrerpolicy="no-referrer"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
<script type="module">
import ColorHash from 'https://cdnjs.cloudflare.com/ajax/libs/color-hash/2.0.2/esm.min.js';
window.ColorHash = ColorHash;
</script>
<script>
var parsed_file = {
sequences: null,
selected_sequence: null,
};
var annotate_seq;
var annotate_seq_name;
const options = document.getElementById("sequence-list")
var annotate_settings = {
start: 0,
width: 256,
skip: 50,
uri: "http://localhost:8080/predict",
}
const models = {}
function predict_riboswitch_from_server() {
seq = annotate_seq
len = seq.length;
n = Math.ceil((len - annotate_settings.start) / (annotate_settings.skip))
m = 1
annotations = []
const colorHash = new ColorHash()
function _continue_prediction(seq, m, n) {
annotate_notif(annotate_seq_name, m, n)
if (m > n) {
if (annotations.length < 1) {
notification("No riboswitch sequence found")
} else {
notification(`Found ${annotations.length} riboswitch sequences`)
}
viewer.setState({
annotations: annotations
})
return
}
s = seq.substring(0, annotate_settings.width)
seq = seq.substring(annotate_settings.skip)
axios.post(annotate_settings.uri, {
seq: s,
})
.then(function(r){
resp = r.data
if (resp.is_riboswitch) {
end = (m + 1)*(annotate_settings.skip) + annotate_settings.width
end = end > len ? len : end
annotations.push(
{ name: resp.annotations, start: m * (annotate_settings.skip), end: end, direction: 1, color: colorHash.hex(resp.annotations) }
)
}
_continue_prediction(seq, m+1, n)
})
.catch(
function(e) {
notification("Unable to annotate sequence.")
console.log(e)
}
);
}
_continue_prediction(seq, m, n)
}
function notification(msg) {
let element = document.createElement("toast")
element.innerHTML = `<div id="toast" class="flex items-center p-4 m-1 w-full max-w-xs text-gray-100 bg-black rounded-lg shadow-lg " role="alert"> <div class="text-sm font-normal"> ${msg} </div> <div class="flex items-center ml-auto space-x-2"> <button type="button" class="close-btn bg-black text-gray-400 hover:text-gray-900 rounded-lg focus:ring-2 focus:ring-gray-300 p-1.5 hover:bg-gray-100 inline-flex h-8 w-8" aria-label="Close" onclick="this.parentElement.parentElement.classList.add('hidden')" > <span class="sr-only">Close</span> <svg aria-hidden="true" class="w-5 h-5" fill="currentColor" viewBox="0 0 20 20" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" d="M4.293 4.293a1 1 0 011.414 0L10 8.586l4.293-4.293a1 1 0 111.414 1.414L11.414 10l4.293 4.293a1 1 0 01-1.414 1.414L10 11.414l-4.293 4.293a1 1 0 01-1.414-1.414L8.586 10 4.293 5.707a1 1 0 010-1.414z" clip-rule="evenodd"></path></svg> </button> </div> </div>`;
document.getElementById("toasts").appendChild(element);
setTimeout(function() {
element.remove()
}, 15000);
}
function annotate_notif(name, m, n) {
document.getElementById("annot-notif").classList.remove("hidden");
document.getElementById("annot-text").innerText = !!m ? (`Annotating: ${name} - ${m} / ${n}`) : (`Annotating: ${name}`)
if (m == 1) {
notification(`Annotating <b>${name}</b>`)
}
if (m == n+1) {
document.getElementById("annot-notif").classList.add("hidden");
notification(`Completed annotating <b>${name}</b>`)
}
}
async function parse_file() {
if (file_input.files < 1) {
return;
}
notification("Parsing file for sequences")
options.classList.add("hidden");
try {
file_content = await file_input.files.item(0).text()
parsed_seq = await bioParsers.anyToJson(file_content)
if (parsed_seq.length < 1) {
notification("No sequences found in the file.")
return;
}
parsed_file.sequences = []
parsed_file.selected_sequence = null
options.options.length = 1
for (i = 0; i < parsed_seq.length; i++) {
s = parsed_seq[i];
n = s.parsedSequence.name.split(" ")[0]
parsed_file.sequences.push({
name: n,
seq: s.parsedSequence.sequence,
});
options.add(new Option(`${i+1}. ${n}`, i))
}
notification(`Parsed ${parsed_seq.length} sequences.`);
options.classList.remove("hidden");
} catch(e) {
notification("Failed to parse file.");
console.log(e);
}
}
function select_sequence() {
seq_i = Number(options.value);
parsed_file.selected_sequence = parsed_file.sequences[seq_i];
notification(`Selected sequence: ${parsed_file.selected_sequence.name} for annotation. `)
}
async function annotate_sequence() {
dna = dna_sequence_text.value.trim()
name = 'Sequence'
if (dna) {
notification("Using sequence from text.")
} else if (parsed_file.selected_sequence) {
dna = parsed_file.selected_sequence.seq
name = parsed_file.selected_sequence.name
notification("Using sequence from file.")
} else {
notification("No sequence to annotate.")
}
annotate_seq_name = name
annotate_seq = dna
annotate_notif(annotate_seq_name)
viewer.setState({
seq: dna,
name: name,
bpColors: { A: "#FF0000", T: "blue", 12: "#00FFFF" },
annotations: [],
})
setTimeout(function() {
predict_riboswitch()
}, 10)
}
const viewer = window.seqviz.Viewer("root",
{
name: "L09136",
seq: "tcgcgcgtttcggtgatgacggtgaaaacctctgacacatgca",
style: { height: "500px"},
showPrimers: true,
showComplement: false,
showIndex: true,
viewer: "both",
}
);
viewer.render();
(async function () {
models.detector = await tf.loadLayersModel('models/detector-js/model.json')
models.classifier = await tf.loadLayersModel('models/classifier-js/model.json')
})()
function character_mapping(x) {
mapping = {'R' : 'G', 'Y' : 'T', 'M' : 'A', 'K' : 'G', 'S' : 'G', 'W' : 'A', 'H' : 'A', 'B' : 'G', 'V' : 'G', 'D' : 'G'}
x = x.toUpperCase()
for (k in mapping) {
x = x.replace(k, mapping[k])
}
return x.split('')
}
function letter_to_index(letter) {
TOKEN_MAPPING = "ATGCN"
for (i = 0; i < TOKEN_MAPPING.length; i++) {
if (TOKEN_MAPPING[i] == letter)
return i
}
return null
}
function tokenize(i) {
mapping = {'a': 1, 't': 2, 'g': 3, 'c': 4, 'n': 5}
return mapping[i.toLowerCase()]
}
function pad_sequences(a, m) {
if (a.length > m) {
return a.slice(-m)
} else {
fill = Array(m - a.length).fill(0)
return [...fill, ...a]
}
}
function process_sequence(seq, for_classifier) {
seq = character_mapping(seq)
if (for_classifier) {
seq = seq.map(letter_to_index)
} else {
seq = seq.map(tokenize)
}
return pad_sequences(seq, (for_classifier ? 250 : 256))
}
async function predict_detector(seq) {
seq = tf.tensor2d([process_sequence(seq)])
p = await models.detector.predict(seq)
p = (await p.array())[0]
f = p[0], t = p[1]
return {
is_riboswitch: t > f,
annotations: null,
confidence: ((t > f ? t : f) * 100).toFixed(),
}
}
async function predict_classifier(seq) {
seq = tf.tensor2d([process_sequence(seq, true)])
p = await models.classifier.predict(seq)
p = (await p.array())[0]
c = p.indexOf(Math.max(...p))
return {
is_riboswitch: true,
annotations: `Ribo-${c}`,
confidence: (p[c] * 100).toFixed(),
}
}
async function predict_ribo_fn(seq) {
det = await predict_detector(seq)
if (det.is_riboswitch) {
return await predict_classifier(seq)
} else
return det
}
function predict_riboswitch() {
seq = annotate_seq
len = seq.length;
n = Math.ceil((len - annotate_settings.start) / (annotate_settings.skip))
m = 1
annotations = []
const colorHash = new ColorHash()
async function _continue_prediction(seq, m, n, start) {
annotate_notif(annotate_seq_name, m, n)
console.log("called annotate_notif")
if (m > n) {
console.log(annotations.length, annotations)
if (annotations.length < 1) {
notification("No riboswitch sequence found")
} else {
notification(`Found ${annotations.length} riboswitch sequences`)
}
viewer.setState({
annotations: annotations
})
return
}
s = seq.substring(0, annotate_settings.width)
seq = seq.substring(annotate_settings.skip)
predict_ribo_fn(s)
.then(function(r){
resp = r
console.log("resp", resp)
if (resp.is_riboswitch) {
end = start + annotate_settings.width
end = end > len ? len : end
annotations.push(
{ name: resp.annotations, start:start, end: end, direction: 1, color: colorHash.hex(resp.annotations) }
)
}
setTimeout(function() {
_continue_prediction(seq, m+1, n, start + annotate_settings.skip)
}, 1)
})
.catch(
function(e) {
notification("Unable to annotate sequence.")
console.log(e)
}
);
}
setTimeout(function() {
_continue_prediction(seq, m, n, 0)
}, 1)
}
</script>
</body></html>