2f:T8bc,viewof dashboard = { const brush = vl.selectInterval().encodings('x').init({x: [0, 40]}); const hover = vl.selectSingle('hover').on('mouseover').encodings("x").nearest(true).clear("mouseout"); const proteinList = Array.from(new Set(protein_data_viz.heatmap.map(d => d.n))); const scale = { domain: Array.from(new Set(protein_data_viz.heatmap.map(d => d.v))), range: d3.schemeSet3.concat(d3.schemeSet2).concat(['#ddd']) } const heatmap = vl .data(protein_data_viz.heatmap) .layer( vl.markRect() .params(hover) .encode( vl.y().fieldN('n').axis({title:null}), vl.x().fieldN('k').axis({domain: false, title: null, values: d3.range(0, protein_data.total_len + 1, 1), labelAngle: 0, orient: 'top'}), vl.color().fieldN('v').scale(scale).legend(null), ), vl.markText({align: 'center'}) .encode( vl.y().fieldN('n'), vl.x().fieldN('k'), vl.text().fieldN('v'), vl.tooltip([ {field:'n', title: 'Protein'}, {field: 'k', title: 'Position'}, {field: 'v', title: 'Sequence'}, {field: 'mode', title: 'Mode'}]) ), ) .width(800) .height(2400) .transform(vl.filter(brush)) const miniMap = vl.markTick() .data(protein_data_viz.heatmap) .encode( vl.x().fieldQ('k').axis(false), vl.y().fieldN('n').axis(false), vl.color().fieldN('v').legend(null), vl.color().value('lightgray') .if(brush, vl.color().fieldN('v').scale(scale).legend(null)), ) .height(150) .params(brush) .width(250) const barChart = vl.markBar() .data(protein_data_viz.bar) .encode( vl.x().fieldO('k').axis({ title: null, values: d3.range(0, protein_data.total_len, 20), labelAngle: 0}), vl.y().fieldQ('Conversion Rate'), vl.color().fieldQ('Conversion Rate').scale({scheme: 'yellowgreenblue'}).legend(null), vl.tooltip([ {field: 'Conversion Rate', format: '.2f'}, {field: 'k', title: 'Position'}]) ) .height(150) .width(500) .transform(vl.filter(brush)) return vl.vconcat(vl.hconcat(miniMap, barChart), heatmap) .config({view:{stroke:'transparent'}}) .render(); }30:T78c,summary_table = html`

${fel_results_json.input["number of sequences"]} sequences in the alignment	${fel_results_json.input["number of sites"]} codon sites in the alignment	${fel_results_json.input["partition count"]} partitions
${tested_branch_count} median branches/partition used for testing	${variable_site_count} non-invariant sites tested	${fel_results_json.simulated \|\| "N/A"} parametric bootstrap replicates
${_.filter (sites_table[1], (d)=>d.class == "Diversifying").length} Sites under diversifying positive selection at p≤${pv}	${_.filter (sites_table[1], (d)=>d.class == "Purifying").length} Sites under purifying selection at p≤${pv}

`31:T7d7,sites_table = { let results = []; const headers = _.clone(fel_results_json.MLE.headers); let format = {}; format [headers[0][0]] = (d)=>d.toFixed (3); format [headers[1][0]] = (d)=>d.toFixed (3); format [headers[2][0]] = (d)=>d.toFixed (3); format [headers[3][0]] = (d)=>d.toFixed (3); format [headers[4][0]] = (d)=>d <= pv ? html`${d.toFixed (4)}` : d.toFixed (4); if (has_pasmt) { format[headers[headers.length-1][0]] = format [headers[4][0]]; } format [headers[5][0]] = (d)=>d.toFixed (3); headers.push (["class","Site classification at p<=" + pv]); format["class"] = (d)=>html`${d}`; _.each (fel_results_json.MLE.content, (data, part)=> { const site_lookup = fel_results_json["data partitions"][part].coverage[0]; _.each (data, (row, i)=> { let row_object = { 'partition' : (+part) + 1, 'codon' : site_lookup [i] + 1 }; row_object[headers[0][0]] = +row[0]; row_object[headers[1][0]] = +row[1]; row_object[headers[2][0]] = +row[2]; row_object[headers[3][0]] = +row[3]; row_object[headers[4][0]] = +row[4]; if (has_T) { row_object[headers[5][0]] = +row[5]; } if (has_ci) { row_object[headers[6][0]] = row[6]; row_object[headers[7][0]] = row[7]; row_object[headers[8][0]] = row[8]; } if (has_pasmt) { row_object [headers[headers.length-2][0]] = row[ headers.length-2]; } row_object[headers[headers.length-1][0]] = row[4] <= pv ? (row[0] < row[1] ? "Diversifying" : "Purifying") : (row[0] + row[1] ? "Neutral" : "Invariable"); results.push (row_object); }); }); return [format, results, headers]; }32:T90c,dNdS_with_ci = (data, from, step)=> { let color_d = []; let color_r = []; _.each (table_colors, (v,c)=> {color_d.push (c); color_r.push (v);}); return { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i+1 >= from && i<= from + step), (d)=> { let dd = _.clone (d); _.each (["dN/dS LB","dN/dS UB", "dN/dS MLE"], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "transform" :[{"calculate": "1", "as": "neutral"}], "encoding": { "x": { "field": "codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon"} } }, "layer": [ { "mark": {"opacity": 1., "type": "line", "color": "steelblue"}, "encoding": { "y": { "field": "dN/dS LB", "scale" : {"type" : "sqrt"}, "type" : "quantitative", "axis": {"titleColor": "black", "grid" : false, "titleFontSize" : 14, "title" : "dN/dS"} }, "y2": { "field": "dN/dS UB", "type" : "quantitative" } } }, { "mark": {"stroke": "black", "type": "point", "filled" : true, "size" : 64, "stroke" : null, "tooltip" : {"contents" : "data"}}, "encoding": { "y": { "field": "dN/dS MLE", "type" : "quantitative", }, "color" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }, /*{ "mark": {"stroke": "lightgrey", "type": "line", "size" : 1, "opacity" : 1.}, "encoding": { "y": { "field": "dN/dS MLE", "type" : "quantitative", }, } }*/ { "mark" : {"opacity": 0.5, "type": "line", "color": "gray"}, "encoding" : { "y": { "field": "neutral", "type" : "quantitative", }, "x": { "field": "codon", "type" : "nominal"}, "size": {"value": 2}, } } ] }; }33:Tc29,html` `34:[{"id":"23af64c8854ab72561b97a10b609a0c69dd7884645365bcdf028189399e66eb493a5944140ddcfd9cc8cba558ad1c94f258ed124c0204b39de949e45c5380331","href":"https://static.observableusercontent.com/files/23af64c8854ab72561b97a10b609a0c69dd7884645365bcdf028189399e66eb493a5944140ddcfd9cc8cba558ad1c94f258ed124c0204b39de949e45c5380331","name":"BTG1_Human_f9746_unrelaxed_rank_001_alphafold2_ptm_model_2_seed_000.pdb","type":"application/octet-stream","lastModified":1702418522943.393,"size":109350},{"id":"263fe2523e3f3dd6a4710849755bab356ef0bd64bfad5802c9f79961cd76e3082883faf319a40330f957237290979e8a18791cf164c2f40fbd461b1534828a0f","href":"https://static.observableusercontent.com/files/263fe2523e3f3dd6a4710849755bab356ef0bd64bfad5802c9f79961cd76e3082883faf319a40330f957237290979e8a18791cf164c2f40fbd461b1534828a0f","name":"BTG1_orthologs.csv","type":"text/csv","lastModified":1702418538450.723,"size":28984},{"id":"dc3d01e0485b4eca67193dcd899171708f42986240fdf5ed8ab3a38bf7179e6e9e6b1758307d950a26e40b1e8c7fdd7d6946c474cdc288d1587fb0e2e9b8cedd","href":"https://static.observableusercontent.com/files/dc3d01e0485b4eca67193dcd899171708f42986240fdf5ed8ab3a38bf7179e6e9e6b1758307d950a26e40b1e8c7fdd7d6946c474cdc288d1587fb0e2e9b8cedd","name":"Mammalia-BTG1_codons@1.SA.TrimmedToHuman.Translated.fasta","type":"application/octet-stream","lastModified":1702476468609.056,"size":43940},{"id":"cf48d4e0f8a408d936d4ad2a0ee97c8d1d2c7aa770411e6743413597ab056efd5f012d99b1250d9a377354e53fb4e87dce667b35091d06c2971ea5d241077cd6","href":"https://static.observableusercontent.com/files/cf48d4e0f8a408d936d4ad2a0ee97c8d1d2c7aa770411e6743413597ab056efd5f012d99b1250d9a377354e53fb4e87dce667b35091d06c2971ea5d241077cd6","name":"Mammalia-BTG1_codons@1.SA.TrimmedToHuman.fasta.FEL.json","type":"application/json","lastModified":1702476479447.745,"size":131353},{"id":"6d9f6844ebd93f80017e369f15a4de53bdd60c130db697900307ead0cf78231b81fe0d71db09476601bb3e66230c0d9640b639721ea8b0828e91612b69dce8a4","href":"https://static.observableusercontent.com/files/6d9f6844ebd93f80017e369f15a4de53bdd60c130db697900307ead0cf78231b81fe0d71db09476601bb3e66230c0d9640b639721ea8b0828e91612b69dce8a4","name":"Mammalia-BTG1_codons@1.SA.TrimmedToHuman.fasta.dst","type":"application/octet-stream","lastModified":1702476491248.773,"size":2590202},{"id":"9d8e898ea88150274359a201c7673d75fdb69a4ba2b8f4db2892a4e326b24edc9ece3c7c8f54da83397207d68b8fecd769cfe07fba93b475c32fae058b16c262","href":"https://static.observableusercontent.com/files/9d8e898ea88150274359a201c7673d75fdb69a4ba2b8f4db2892a4e326b24edc9ece3c7c8f54da83397207d68b8fecd769cfe07fba93b475c32fae058b16c262","name":"Mammalia-BTG1_codons@1.SA.TrimmedToHuman.fasta.treefile","type":"application/octet-stream","lastModified":1702476502783.526,"size":19841}] 2d:{"title":"Selection analyses of Mammalian BTG1","cells":[{"id":0,"value":"# Selection analyses of *${tag}* ${gene}\n\nWe provide an evolutionary analysis for ${tag} ${gene}, which includes ${btg_orthologs_table.length} species. The sequences were trimmed to correspond to the Human reference, therefore we ignore aspects of evolution in non-Human species.\n\nData was collected from NCBI Ortholog DB, (last accessed: 12.11.2023):\nhttps://www.ncbi.nlm.nih.gov/gene/694/ortholog/?scope=9443&term=BTG1 \n\nWorkflow: mini-AOC 2023","pinned":false,"hidden":false,"mode":"md"},{"id":421,"value":"## Table of species used (Orthologs in *${tag}*)","pinned":false,"hidden":false,"mode":"md"},{"id":9,"value":"//Inputs.table(btg1_orthologs_table)\n// Scientific name\tCommon name\tRefSeq Transcript accessions\tRefSeq Protein accessions\n\nInputs.table(btg_orthologs_table, {\n columns: [\n \"Scientific name\",\n \"Common name\",\n \"RefSeq Transcript accessions\",\n \"RefSeq Protein accessions\"\n ],\n header: {}\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"## Genetic distances (nucleotide-level)","pinned":false,"hidden":false,"mode":"md"},{"id":42,"value":"Plot.plot({\n y: {grid: true},\n color: {legend: true},\n marks: [\n Plot.rectY(tn93_data, Plot.binX({y: \"count\"}, {x: \"Distance\"})),\n Plot.ruleY([0])\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":31,"value":"## Multiple sequence alignment (protein-level)","pinned":false,"hidden":false,"mode":"md"},{"id":79,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":59,"value":"## Bootstrapped phylogenetic tree","pinned":false,"hidden":false,"mode":"md"},{"id":174,"value":"#### Adjust width","pinned":false,"hidden":false,"mode":"md"},{"id":118,"value":"viewof width = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":176,"value":"#### Adjust height","pinned":false,"hidden":false,"mode":"md"},{"id":120,"value":"viewof height = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":123,"value":"renderedTree.show()","pinned":false,"hidden":false,"mode":"ojs"},{"id":165,"value":"## Evidence of selection on individual codon sites","pinned":false,"hidden":false,"mode":"md"},{"id":423,"value":"**High-level overview**","pinned":false,"hidden":false,"mode":"md"},{"id":196,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":425,"value":"**Site by site evolutionary rate**","pinned":false,"hidden":false,"mode":"md"},{"id":252,"value":"viewof table_filter = Inputs.checkbox([\"Diversifying\", \"Purifying\", \"Neutral\",\"Invariable\"], {value: [\"Diversifying\", \"Purifying\", \"Neutral\", \"Invariable\"], label: html`Show`, format: x => html`${x}`})","pinned":false,"hidden":false,"mode":"ojs"},{"id":237,"value":"// plot_options = [[\"Site-level dN/dS estimates\",(d)=>d[\"confidence interval\"]],[\"alpha/beta site-level estimates\", (d)=>1], [\"Bootstrap vs asymptotic p-value\", (d)=>has_pasmt], [\"Rate density plots\", (d)=>1], [\"Q-Q plots\", (d)=>has_pasmt], [\"Dense rate plot\", (d)=>1]]\n\nfigure1 = vegalite (plot_specs[\"Site-level dN/dS estimates\"])","pinned":false,"hidden":false,"mode":"ojs"},{"id":429,"value":"**Table of results for selection analysis**","pinned":false,"hidden":false,"mode":"md"},{"id":248,"value":"viewof table1 = Inputs.table (siteTableData, {\n rows : 40,\n format: sites_table[0]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":318,"value":"## Protein Structure for Human - ${gene}","pinned":false,"hidden":false,"mode":"md"},{"id":453,"value":"Via Colabfold: https://github.com/sokrypton/ColabFold","pinned":false,"hidden":false,"mode":"md"},{"id":443,"value":"**Color legend:**
Red = Site(s) with evidence of purifying selection;
Grey = Invariant or neutral sites;
\nCyan = Site(s) with evidence of positive selection;","pinned":false,"hidden":false,"mode":"md"},{"id":319,"value":"viewport = {\n let v = DOM.element(\"div\");\n v.id=\"viewport\";\n v.style.height = \"500px\"\n return v;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":438,"value":"# End of report ","pinned":false,"hidden":false,"mode":"md"},{"id":14,"value":"## Code behind the scenes -----------------------------------------","pinned":false,"hidden":false,"mode":"md"},{"id":494,"value":"##### File Imports","pinned":false,"hidden":false,"mode":"md"},{"id":7,"value":"btg_orthologs_table = FileAttachment(\"BTG1_orthologs.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":64,"value":"protein_file = FileAttachment(\"Mammalia-BTG1_codons@1.SA.TrimmedToHuman.Translated.fasta\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":127,"value":"nwk = FileAttachment(\"Mammalia-BTG1_codons@1.SA.TrimmedToHuman.fasta.treefile\").text()","pinned":false,"hidden":false,"mode":"ojs"},{"id":186,"value":"fel_results_json = FileAttachment(\"Mammalia-BTG1_codons@1.SA.TrimmedToHuman.fasta.FEL.json\").json()","pinned":false,"hidden":false,"mode":"ojs"},{"id":324,"value":"PDB = FileAttachment(\"BTG1_Human_f9746_unrelaxed_rank_001_alphafold2_ptm_model_2_seed_000.pdb\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":297,"value":"##### TN93\n","pinned":false,"hidden":false,"mode":"md"},{"id":40,"value":"//tn93_data = FileAttachment(\"Primates_codons.SA.fasta.dst\").csv({typed: true})\n//Primates_codons.SA.TrimmedToHuman.fasta.dst\ntn93_data = FileAttachment(\"Mammalia-BTG1_codons@1.SA.TrimmedToHuman.fasta.dst\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":54,"value":"// Forked from: https://observablehq.com/@howardyao/sequence-alignment-dashboard\nprotein_data = {\n const url = '' \n const raw = (\n protein_file === null \n ? a.fastaParser(await d3.text(url)) \n : a.fastaParser(await protein_file.text())\n ).map(d => ({...d, \n 'seq_len': d.seq.length,\n 'seq_name': d.header // .split('|')[2].split(' ')[0] \n }));\n \n const max_len = d3.max(raw, d => d.seq_len);\n \n const raw1 = raw.map(d => ({...d, \n 'seq_full_str': d.seq + '-'.repeat(max_len - d.seq_len),\n 'seq_full': [...(d.seq + '-'.repeat(max_len - d.seq_len))].map((n,i) => ({'k': i, 'v': n, 'n': d.seq_name})) \n }));\n\n const derive = raw1.map(k => k.seq_full.map(j => ({'n': j.n, 'k': j.k + 1, 'v': j.v }))).flat()\n\n return {'raw': raw1, 'derive': derive, 'total_len': max_len}\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":74,"value":"protein_data_viz = {\n const raw_len = protein_data.raw.length\n const countCol = d3.flatRollup(protein_data.derive, v => v.length, d => d.k, d => d.v)\n .map(([k, v, cnt]) => ({k, v, cnt}))\n\n const conversion = d3.groups(countCol, d=>d.k)\n .map(n => d3.greatest(n[1], a=>a.cnt))\n .map(d => ({...d, 'Conversion Rate': d.cnt / raw_len}))\n\n const derive2 = protein_data.derive.map(d => ({...d, 'mode': conversion.filter(j => j.k + 1 === d.k + 1)[0].v}))\n\n const data_viz = derive2.concat(conversion.map(d => ({'n': 'Consensus', 'k': d.k, 'v': d.v, 'mode': d.v})))\n\n return {'heatmap': data_viz, 'bar' : conversion}\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":98,"value":"renderedTree = tree.render({\n height:height, \n width:width,\n 'left-right-spacing': 'fit-to-size', \n 'align-tips':true,\n 'node-styler': colorNodesByName,\n 'edge-styler': colorEdgesByTarget\n});","pinned":false,"hidden":false,"mode":"ojs"},{"id":102,"value":"colorEdgesByTarget = function (element, data) {\n edgeColorizer (element, data);\n var m = (data.target.data.name).split (\"_\");\n element.style(\"stroke-width\", 5);\n if (m.length > 10) {\n element.style (\"stroke\", color_scale(_.lowerCase(m[9])));\n }\n };","pinned":false,"hidden":false,"mode":"ojs"},{"id":105,"value":"colorNodesByName = function (element, data) {\n nodeColorizer (element, data);\n var m = (data.data.name).split (\"_\");\n if (m.length > 10) {\n element.style (\"stroke\", color_scale(_.lowerCase(m[9])));\n }\n };","pinned":false,"hidden":false,"mode":"ojs"},{"id":107,"value":"edgeColorizer = function(element, data) {\n //console.log (data[current_selection_name]);\ntry {\n var count_class = 0;\n\n selection_set.forEach (function (d,i) { if (data[d]) {count_class ++; element.style (\"stroke\", color_scale(i), 'important');}});\n\n if (count_class > 1) {\n element.classed (\"branch-multiple\", true);\n } else\n if (count_class == 0) {\n element.style (\"stroke\", null)\n .classed (\"branch-multiple\", false);\n }\n}\ncatch (e) {\n}\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":109,"value":"nodeColorizer = function (element, data) {\ntry{\n var count_class = 0;\n\n selection_set.forEach (function (d,i) { if (data[d]) {count_class ++; element.style (\"fill\", color_scale(i), 'important');}});\n\n if (count_class > 1) {\n\n } else {\n if (count_class == 0) {\n element.style (\"fill\", null);\n }\n }\n}\ncatch (e) {\n\n}\n\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":111,"value":"selection_set = \"\";","pinned":false,"hidden":false,"mode":"ojs"},{"id":113,"value":"color_scale = d3.scaleOrdinal(d3.schemeCategory10);","pinned":false,"hidden":false,"mode":"ojs"},{"id":125,"value":"tree = new phylotree.phylotree(nwk);","pinned":false,"hidden":false,"mode":"ojs"},{"id":292,"value":"##### Newick","pinned":false,"hidden":false,"mode":"md"},{"id":184,"value":"##### FEL - variables","pinned":false,"hidden":false,"mode":"md"},{"id":192,"value":"viewof pv = Inputs.text({label: html`p-value threshold`, value: \"0.1\", submit: \"Update\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":202,"value":"has_pasmt = fel_results_json.MLE[\"LRT\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":205,"value":"table_colors = ({\n 'Diversifying' : '#e3243b',\n 'Neutral' : '#444',\n 'Purifying' : 'green',\n 'Invariable' : '#CCC'\n })","pinned":false,"hidden":false,"mode":"ojs"},{"id":214,"value":"has_srv = _.chain (fel_results_json.MLE.content).some ((d)=>_.some (d,(dd)=>dd[0] > 0 && dd[0] != 1)).value()","pinned":false,"hidden":false,"mode":"ojs"},{"id":212,"value":"has_ci = fel_results_json [\"confidence interval\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":209,"value":"has_T = _.some (_.map (fel_results_json.MLE.content, (d)=>_.some(d, (dd)=>dd[5] > 0.)))","pinned":false,"hidden":false,"mode":"ojs"},{"id":198,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":227,"value":"tested_branch_count = d3.median (_.chain (fel_results_json.tested).map ().map ((d)=>_.map (d, (dd)=>_.filter (dd, ddd=>ddd == \"test\"))).map ((d)=>d.length).value())","pinned":false,"hidden":false,"mode":"ojs"},{"id":230,"value":"variable_site_count = d3.sum(_.chain (fel_results_json.MLE.content).map ((d)=>_.filter (d, (dd)=>dd[0]+dd[1] > 0)).map (d=>d.length).value())","pinned":false,"hidden":false,"mode":"ojs"},{"id":243,"value":"plot_specs = (\n{\n \"Site-level dN/dS estimates\" : {\n \"width\": 800, \"height\": 200, \n \"vconcat\" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {\n return dNdS_with_ci (fig1data, d, 70)\n })}\n}\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":246,"value":"fig1data = {\n let in_set = new Set (_.map (table1, (d)=>d.codon));\n return _.filter (siteTableData, (x)=>in_set.has (x.codon));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":250,"value":"siteTableData = {\n return _.filter (sites_table[1], (x)=>table_filter.indexOf (x.class)>=0);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":256,"value":"dyn_range_cap = 10","pinned":false,"hidden":false,"mode":"ojs"},{"id":254,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":47,"value":"### NOTE: Recombination detection (Found no breakpoints)","pinned":false,"hidden":false,"mode":"md"},{"id":323,"value":"##### Protein Structure","pinned":false,"hidden":false,"mode":"md"},{"id":328,"value":"color_hash = {\n return {\n blue: 0x00FFFF,\n red: 0xcf5b5b,\n yellow: 0xFFFF00,\n green: 0x5fcf5b,\n pink: 0xFFC0CB,\n gray: 0x808080\n };\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":341,"value":"three_to_one = {\n return {\n \"ALA\": \"A\",\n \"CYS\": \"C\",\n \"ASP\": \"D\",\n \"GLU\": \"E\",\n \"PHE\": \"F\",\n \"GLY\": \"G\",\n \"HIS\": \"H\",\n \"ILE\": \"I\",\n \"LYS\": \"K\",\n \"LEU\": \"L\",\n \"MET\": \"M\",\n \"ASN\": \"N\",\n \"PRO\": \"P\",\n \"GLN\": \"Q\",\n \"ARG\": \"R\",\n \"SER\": \"S\",\n \"THR\": \"T\",\n \"VAL\": \"V\",\n \"TRP\": \"W\",\n \"TYR\": \"Y\"\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":344,"value":"stage.viewerControls.getOrientation()","pinned":false,"hidden":false,"mode":"ojs"},{"id":346,"value":"stage = {\n let s = new NGL.Stage( \"viewport\" ,{ backgroundColor: \"white\" })\n s.mouseControls.remove(\"hoverPick\");\n s.signals.hovered.add(function (pickingProxy) {\n if (pickingProxy && (pickingProxy.atom || pickingProxy.bond)){\n var atom = pickingProxy.atom || pickingProxy.closestBondAtom;\n var mp = pickingProxy.mouse.position;\n let residue = atom.residue;\n tooltip.innerText = residue.index + ' - index\\n' + \n residue.resno + ' - resno\\n' + \n residue.resname + ' - long resname\\n' +\n three_to_one[residue.resname] + ' - short resname\\n' +\n residue.chainname + ' - chain name\\n' +\n (selected_sites[residue.resno] ? selected_sites[residue.resno].annotation : '');\n tooltip.style.bottom = window.innerHeight - mp.y + 3 + \"px\";\n tooltip.style.left = mp.x + 3 + \"px\";\n tooltip.style.display = \"block\";\n } else {\n tooltip.style.display = \"none\";\n }\n })\n return s\n};","pinned":false,"hidden":false,"mode":"ojs"},{"id":360,"value":"pdbText = PDB.text()","pinned":false,"hidden":false,"mode":"ojs"},{"id":379,"value":"schemeId = {\n return NGL.ColormakerRegistry.addScheme(function (params) {\n this.atomColor = function (atom) {\n if(selected_sites[atom.residue.resno]) {\n \n console.log(\"# Site: \" + atom.residue.resno)\n \n let site_color = \"gray\"; // gray for Neutral\n \n console.log(\"Class: \" + selected_sites[atom.residue.resno].class)\n \n if (selected_sites[atom.residue.resno].class == \"Purifying\") {\n site_color = \"red\";\n }\n\n if (selected_sites[atom.residue.resno].class == \"Diversifying\") {\n site_color = \"blue\";\n }\n\n if (selected_sites[atom.residue.resno].class == \"Invariable\") {\n site_color = \"gray\";\n }\n\n return color_hash[site_color];\n //return color_hash[selected_sites[atom.residue.resno].color];\n }\n else {\n // gray\n //return 0x808080;\n return color_hash[\"gray\"]\n }\n }\n })\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":350,"value":"tooltip = {\n let tt = document.createElement(\"div\");\n document.body.appendChild(tt);\n Object.assign(tt.style, {\n display: \"none\",\n position: \"absolute\",\n zIndex: 10,\n pointerEvents: \"none\",\n backgroundColor: \"rgba(1, 1, 1, 1)\",\n color: \"lightgrey\",\n padding: \"0.5em\",\n fontFamily: \"sans-serif\"\n });\n return tt;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":382,"value":"component = {\n let str;\n stage.removeAllComponents();\n var stringBlob = new Blob( [ pdbText ], { type: 'text/plain'} );\n stage.loadFile(stringBlob, { ext: \"pdb\" }).then(d=>{\n d.addRepresentation(\"rope\", {color: schemeId})\n //d.addRepresentation(\"hyperball\", { sele: selection_string , color: schemeId});\n d.addRepresentation(\"licorice\", { sele: selection_string , color: schemeId});\n\n stage.viewerControls.orient([17.550527072492336, 77.73142323575034, -35.35459236339453, 0, 76.29412120673659, 1.939498913978275, 42.137723873208586, 0, 38.35789916992212, -39.42342635625532, -67.63584394065052, 0, 3.367788314819336, 0.33123016357421875, 3.3393964767456055, 1]);\n stage.setSpin(false);\n str = d;\n })\n return str;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":348,"value":"selected_sites = {\n let site_table = sites_table;\n \n let site_hash = {};\n \n site_table[1].forEach(site_datum => site_hash[+site_datum.codon] = site_datum);\n \n return site_hash;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":386,"value":"selection_string = _.map (\n Object.keys(selected_sites), (i, s) => ['A'].map(chain => i + ':' + chain).join(\" OR \")\n).join (\" OR \")","pinned":false,"hidden":false,"mode":"ojs"},{"id":67,"value":"## Imports","pinned":false,"hidden":false,"mode":"md"},{"id":69,"value":"a = require('https://stephenshank.com/bundle.js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":96,"value":"stylesheet = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":384,"value":"html`

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":100,"value":"phylotree = require('phylotree@1.0.0')","pinned":false,"hidden":false,"mode":"ojs"},{"id":115,"value":"d3 = require('d3')","pinned":false,"hidden":false,"mode":"ojs"},{"id":233,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":241,"value":"vegalite = require (\"vega-embed\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":330,"value":"NGL = require(\"ngl@2.0.0-dev.38\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":332,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":334,"value":"_ = require (\"lodash\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":336,"value":"ReactDOM = r('react-dom')","pinned":false,"hidden":false,"mode":"ojs"},{"id":393,"value":"React = r('react')","pinned":false,"hidden":false,"mode":"ojs"},{"id":338,"value":"r = require.alias({\n react: \"react@16/umd/react.production.min.js\",\n \"react-dom\": \"react-dom@16/umd/react-dom.production.min.js\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":389,"value":"bioseq = require(\"bioseq\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":486,"value":"## Supplemental Figs\n","pinned":false,"hidden":false,"mode":"md"},{"id":487,"value":"Plot.plot({\n inset: 8,\n grid: true,\n color: {\n legend: true,\n },\n marks: [\n Plot.dot(protein_data[\"raw\"], {x: \"header\", y: \"seq_len\"})\n ]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":161,"value":"## Gene-wide selection","pinned":false,"hidden":false,"mode":"md"},{"id":514,"value":"tag = \"Mammalian\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":523,"value":"gene = \"BTG1\"","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W34","stdlib":"1","titling":"auto"}