2e:null 2f:["$","title",null,{"children":"From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences | CVR Bioinformatics | Observable"}] 34:Tc80,viewof form = Inputs.form({ ref:Inputs.select([null].concat(data.map(d => d.ref)), {value:null,label: "Select Reference Amino Acid",unique:true,sort:true}), alt:Inputs.select([null].concat(data.map(d => d.alt)), {value:null,label: "Select Mutant Amino Acid",unique:true,sort:true}), subunit:Inputs.select([null].concat(data.map(d => d.subunit)), {label: "Subunit",unique:true}), domain:Inputs.select([null].concat(data.map(d => d.domain)), {label: "Domain",unique:true}), ref_and_mask : Inputs.radio(["true","false"], {label: "Filter ref_and_mask tokens",unique:true,value:"true"}), highlight_voc_mutations:Inputs.radio(["True", "False"], {label: "Highlight VOC Mutations", value: "False"}), x_axis:Inputs.select( wuhan_esm2_data.columns, {value:"position",label: "Select X-Axis",unique:true}), y_axis:Inputs.select( wuhan_esm2_data.columns, {value:"relative_grammaticality",label: "Select Y-Axis",unique:true}), color_on:Inputs.select(wuhan_esm2_data.columns, {value:"semantic_rank",label: "Colour Mutations On Property",unique:true}), color:Inputs.select(new Map([ ["Blues (sequential, single-hue)", "blues"], ["Greens (sequential, single-hue)", "greens"], ["Greys (sequential, single-hue)", "greys"], ["Purples (sequential, single-hue)", "purples"], ["Reds (sequential, single-hue)", "reds"], ["Oranges (sequential, single-hue)", "oranges"], ["Turbo (sequential, multi-hue)", "turbo"], ["Viridis (sequential, multi-hue)", "viridis"], ["Magma (sequential, multi-hue)", "magma"], ["Inferno (sequential, multi-hue)", "inferno"], ["Plasma (sequential, multi-hue)", "plasma"], ["Cividis (sequential, multi-hue)", "cividis"], ["Cubehelix (sequential, multi-hue)", "cubehelix"], ["Warm (sequential, multi-hue)", "warm"], ["Cool (sequential, multi-hue)", "cool"], ["BuGn (sequential, multi-hue)", "bugn"], ["BuPu (sequential, multi-hue)", "bupu"], ["GnBu (sequential, multi-hue)", "gnbu"], ["OrRd (sequential, multi-hue)", "orrd"], ["PuBuGn (sequential, multi-hue)", "pubugn"], ["PuBu (sequential, multi-hue)", "pubu"], ["PuRd (sequential, multi-hue)", "purd"], ["RdPu (sequential, multi-hue)", "rdpu"], ["YlGnBu (sequential, multi-hue)", "ylgnbu"], ["YlGn (sequential, multi-hue)", "ylgn"], ["YlOrBr (sequential, multi-hue)", "ylorbr"], ["YlOrRd (sequential, multi-hue)", "ylorrd"], ["BrBG (diverging)", "brbg"], ["PRGn (diverging)", "prgn"], ["PiYG (diverging)", "piyg"], ["PuOr (diverging)", "puor"], ["RdBu (diverging)", "rdbu"], ["RdGy (diverging)", "rdgy"], ["RdYlBu (diverging)", "rdylbu"], ["RdYlGn (diverging)", "rdylgn"], ["Spectral (diverging)", "spectral"], ["BuRd (diverging)", "burd"], ["BuYlRd (diverging)", "buylrd"], ["Rainbow (cyclical)", "rainbow"], ["Sinebow (cylical)", "sinebow"]]), {label: "Color scheme", value: "turbo"}), log_y:Inputs.select( [true,false], {value:false,label: "Log Y-axis"}), // log_colour:Inputs.select( ['linear','log'], {value:false,label: "Log colour scale"}), })35:T67b,dms_plot = Plot.plot({ height: 800, width:2500, x:{nice:true,grid:true,labelAnchor: "center", labelArrow:true,}, y:{labelAnchor: "center", labelArrow:false,nice:true,grid:true,transform: (d) => {if(form.log_y == true){return Math.log(d)}else{return d}}}, style: {fontSize: "28px"}, marginLeft:100, marginRight:50, marginTop:40, marginBottom:60, marks: [ Plot.dot(filtered_data, {y: form.y_axis, x: form.x_axis, stroke:'black', strokeWidth:(d) => {if (form.highlight_voc_mutations == "True" && d.voc_mutation == false){return 0}else if (form.highlight_voc_mutations == "True" && d.voc_mutation == true){return 1}else{return 0}}, r:3, tip:true, // r:point_radius(form.opacity,filtered_data), sort:form.color_on, fillOpacity:(d) => {if (form.highlight_voc_mutations == "True" && d.voc_mutation == false){return 0.05}else if (form.highlight_voc_mutations == "True" && d.voc_mutation == true){return 1}else{return 1}}, fill:form.color_on, title: (d) => [ "Mutation: "+d.label, form.x_axis+": "+d[form.x_axis], form.y_axis+": "+d[form.y_axis], form.color_on+": "+d[form.color_on], ].join("\n\n")}), ], color: { scheme: form.color, legend: true, nice:true }, })36:T95d,lineage_plot = Plot.plot({ width:1900, height:800, marginRight:30, marginBottom:80, // style: {fontSize: "17px"}, x:{nice:true,label:lineage_form.x_axis,labelAnchor: "center", labelArrow:false,}, y:{nice:true,label:lineage_form.y_axis,labelAnchor: "center", labelArrow:false,}, style: {fontSize: "25px"}, marginLeft:90, marginTop:40, // marginBottom:50, grid:{x:true}, marks: [ Plot.dot(initial_lineage_sequences, { y:(d)=>{ if(lineage_form.y_axis == 'mutations'){ if(d.mutations === null){return 0} else{return d.mutations.split(', ').length} }else{return d[lineage_form.y_axis]} }, x: (d)=>{ if(lineage_form.x_axis == 'mutations'){ if(d.mutations === null){return 0} else{return d.mutations.split(', ').length} }else{return d[lineage_form.x_axis]} }, stroke:'black', strokeWidth:1, fill:'Voc', // r:(d)=>{if(lineage_form.opacity_and_fill == false){return 3}else{return Math.log(d['cluster_size'])}}, r:4.5, fillOpacity:1, sort: 'cluster_size', }), Plot.tip(initial_lineage_sequences, Plot.pointer({ y:(d)=>{ if(lineage_form.y_axis == 'mutations'){ if(d.mutations === null){return 0} else{return d.mutations.split(', ').length} }else{return d[lineage_form.y_axis]} }, x: (d)=>{ if(lineage_form.x_axis == 'mutations'){ if(d.mutations === null){return 0} else{return d.mutations.split(', ').length} }else{return d[lineage_form.x_axis]} }, // filter: (d) => d.info, title: (d) => ["Lineage: "+d.lineage, // lineage_form.x_axis+": "+d[lineage_form.x_axis], // lineage_form.y_axis+": "+d[lineage_form.y_axis], // lineage_form.color_on+": "+d[lineage_form.color_on], "Mutations: "+d['mutations'], ].join("\n\n") })), ], // color: { // scheme: form.color, // legend: true, // nice:true // }, color: { type: "categorical", range:['#1877B5','#FF7F09','#369D33','#D52627','#9667BC','#8C5D54','#FF8AB7'], // range:['#1877B5','#FF7F09','#369D33','Black','Black','#D52627','Black','Black','Black','Black','#9667BC','#8C5D54','Black'], legend:true }, })37:Td25,function haplotype_lineage_plot (haplotype_sequences,haplotype_lineage_form) { return Plot.plot({ width:1900, height:700, marginRight:30, marginBottom:80, // style: {fontSize: "17px"}, x:{nice:true,labelAnchor: "center", labelArrow:false,label:''}, y:{nice:true,labelAnchor: "center", labelArrow:false,label:'Dynamic Semantic Score'}, style: {fontSize: "25px"}, marginLeft:90, marginTop:40, // marginBottom:50, grid:{x:true}, marks: [ Plot.dot(haplotype_sequences.filter(d=>d.sliding_semantic_score > semantic_checkboxes), {y: haplotype_lineage_form.y_axis, x: haplotype_lineage_form.x_axis, stroke:'black', strokeWidth:1, fill:'Variant', // r:(d)=>{if(lineage_form.opacity_and_fill == false){return 3}else{return Math.log(d['cluster_size'])}}, r:4.5, fillOpacity:1, sort: 'cluster_size', }), Plot.tip(haplotype_sequences.filter(d=>d.sliding_semantic_score > semantic_checkboxes), Plot.pointer({ y: haplotype_lineage_form.y_axis, x: haplotype_lineage_form.x_axis, // filter: (d) => d.info, title: (d) => ["Lineage: "+d.lineage, // lineage_form.x_axis+": "+d[lineage_form.x_axis], // lineage_form.y_axis+": "+d[lineage_form.y_axis], // lineage_form.color_on+": "+d[lineage_form.color_on], "Mutations: "+d['mutations'], ].join("\n\n") })), // Plot.tip(haplotype_sequences.filter(d=>d.sample_date > start_date).filter(d=>d.sample_date < end_date).filter(d=>d.sliding_semantic_score > semantic_checkboxes), Plot.pointer({ // y: haplotype_lineage_form.y_axis, // x: haplotype_lineage_form.x_axis, // // filter: (d) => d.info, // title:(d) => ["Lineage: "+d.lineage, // "Date: "+d.sample_date, // // lineage_form.x_axis+": "+d[lineage_form.x_axis], // // lineage_form.y_axis+": "+d[lineage_form.y_axis], // // lineage_form.color_on+": "+d[lineage_form.color_on], // "Mutations: "+d['mutations'], // ].join("\n\n") // })), // Plot.areaY(moving_sliding_dates_all,{x:'date',y1:'lower',y2:'upper',fill:'orange',opacity:0.3}), // Plot.areaY(moving_sliding_dates_all,{x:'date',y1:'upper',y2:'max',fill:'red',opacity:0.3}), // Plot.lineY(moving_sliding_dates_all,{x:'date',y:'lower',stroke:'orange',strokeWidth:5,strokeDasharray:9}), // Plot.lineY(moving_sliding_dates_all,{x:'date',y:'upper',stroke:'red',strokeWidth:5,strokeDasharray:9}), // Plot.lineY(moving_sliding_dates_all,{x:'date',y:'max',stroke:'red',strokeWidth:5,strokeDasharray:9}), // Plot.ruleY([2.561407650727473],{stroke:'orange'}), // Plot.ruleY([3.514741745630979],{stroke:'red'}), ], color: { type: "categorical", range:[...['#9667BC','#1877B5','#FF7F09','#369D33','#D52627','#8C5D54'],...d3.quantize(d3.interpolateSpectral, 11).reverse(),...['Pink','Pink','Pink','Pink','Pink','Pink','Pink',]], // range:['#1877B5','#FF7F09','#369D33','#D52627','#9667BC','#8C5D54','#FF8AB7'], // range:['#1877B5','#FF7F09','#369D33','Black','Black','#D52627','Black','Black','Black','Black','#9667BC','#8C5D54','Black'], legend:true }, }) }38:T86a,function first_variants(moving_sliding_dates,first_variant_sequences,haplotype_sequences){ return Plot.plot({ width:500, height:500, // marginRight:30, marginBottom:40, // style: {fontSize: "17px"}, x:{grid:true,domain:[new Date(2019,12,1),new Date(2023,12,18)]}, y:{nice:true,grid:true,label:'Dynamic Semantic Score',labelAnchor: "center",labelArrow:false,}, style: {fontSize: "17px"}, marginLeft:70, marginTop:40, marks: [ Plot.areaY(moving_sliding_dates,{x:'date',y1:'lower',y2:'upper',fill:'orange',opacity:0.3}), Plot.areaY(moving_sliding_dates,{x:'date',y1:'upper',y2:'max',fill:'red',opacity:0.3}), Plot.lineY(moving_sliding_dates,{x:'date',y:'lower',stroke:'orange',strokeWidth:5,strokeDasharray:9}), Plot.lineY(moving_sliding_dates,{x:'date',y:'upper',stroke:'red',strokeWidth:4,strokeDasharray:9}), Plot.lineY(moving_sliding_dates,{x:'date',y:'max',stroke:'red',strokeWidth:4,strokeDasharray:9}), // Plot.density(first_haplotype_sequences, { x: "sliding_semantic_score", }), Plot.dot(first_variant_sequences.filter(d=>d.sliding_semantic_score > semantic_checkboxes), { x: "sample_date", y: "sliding_semantic_score", fill: "Variant", stroke:'black', strokeWidth:1, fill:'Variant', // r:(d)=>{if(lineage_form.opacity_and_fill == false){return 3}else{return Math.log(d['cluster_size'])}}, r:5, fillOpacity:1, sort: 'cluster_size', tip:true }), // Plot.ruleY([2.561407650727473],{stroke:'orange'}), // Plot.ruleY([3.514741745630979],{stroke:'red'}), ], color: { domain:[...new Set(haplotype_sequences.map(d=>d.Variant))].sort(), type: "categorical", range:[...['#9667BC','#1877B5','#FF7F09','#369D33','#D52627','#8C5D54'],...d3.quantize(d3.interpolateSpectral, 11).reverse(),...['Pink','Pink','Pink','Pink','Pink','Pink','Pink',]], // range:['#1877B5','#FF7F09','#369D33','#D52627','#9667BC','#8C5D54','#FF8AB7'], // range:['#1877B5','#FF7F09','#369D33','Black','Black','#D52627','Black','Black','Black','Black','#9667BC','#8C5D54','Black'], // legend:true }, }) }39:T7fc,function first_haplotype_plot(first_haplotype_sequences,haplotype_sequences, haplotype_lineage_form) { return Plot.plot({ width:1200, height:600, marginRight:30, marginBottom:50, // style: {fontSize: "17px"}, x:{grid:true,domain:[new Date(2019,12,1),new Date(2023,12,18)]}, y:{nice:true,grid:true,label:'Dynamic Semantic Score',labelAnchor: "center",labelArrow:false,}, style: {fontSize: "20px"}, marginLeft:70, marginTop:40, marks: [ // Plot.density(first_haplotype_sequences, { x: "sliding_semantic_score", }), Plot.areaY(moving_sliding_dates,{x:'date',y1:'lower',y2:'upper',fill:'orange',opacity:0.3}), Plot.areaY(moving_sliding_dates,{x:'date',y1:'upper',y2:'max',fill:'red',opacity:0.3}), Plot.lineY(moving_sliding_dates,{x:'date',y:'lower',stroke:'orange',strokeWidth:5,strokeDasharray:9}), Plot.lineY(moving_sliding_dates,{x:'date',y:'upper',stroke:'red',strokeWidth:5,strokeDasharray:9}), Plot.lineY(moving_sliding_dates,{x:'date',y:'max',stroke:'red',strokeWidth:5,strokeDasharray:9}), Plot.dot(first_haplotype_sequences.filter(d=>d.sliding_semantic_score > semantic_checkboxes), { x: "sample_date", y: "sliding_semantic_score", fill: "Variant", stroke:'black', strokeWidth:1, fill:'Variant', // r:(d)=>{if(lineage_form.opacity_and_fill == false){return 3}else{return Math.log(d['cluster_size'])}}, r:4.5, fillOpacity:1, sort: 'cluster_size', tip:true }), ], color: { type: "categorical", domain:[...new Set(haplotype_sequences.map(d=>d.Variant))].sort(), range:[...['#9667BC','#1877B5','#FF7F09','#369D33','#D52627','#8C5D54'],...d3.quantize(d3.interpolateSpectral, 11).reverse(),...['Pink','Pink','Pink','Pink','Pink','Pink','Pink',]], // range:['#1877B5','#FF7F09','#369D33','#D52627','#9667BC','#8C5D54','#FF8AB7'], // range:['#1877B5','#FF7F09','#369D33','Black','Black','#D52627','Black','Black','Black','Black','#9667BC','#8C5D54','Black'], // legend:true }, }) }3a:Tc6c,// moving_sliding_dates = { // var daysOfYear = []; // for (var date = new Date(2020, 1, 28); date <= new Date(2024, 3, 1); date.setDate(date.getDate()+90)) { // let next_date = new Date(date) // next_date.setDate(next_date.getDate()+90) // let prior_date = new Date(date) // prior_date.setDate(prior_date.getDate()-90) // let newDate = { // date:new Date(date), // upper:first_haplotype_sequences.filter( // d=>d.sample_date > prior_date).filter(d=>d.sample_date < date) // .map(d=>d.sliding_semantic_score) // .reduce((acc,v,i,a)=>(acc+v/a.length),0) + // (getStandardDeviation(first_haplotype_sequences // .filter(d=>d.sample_date > prior_date) // .filter(d=>d.sample_date d.sliding_semantic_score)))*2, // lower:first_haplotype_sequences.filter( // d=>d.sample_date > prior_date).filter(d=>d.sample_date < date) // .map(d=>d.sliding_semantic_score) // .reduce((acc,v,i,a)=>(acc+v/a.length),0) + // (getStandardDeviation(first_haplotype_sequences // .filter(d=>d.sample_date > prior_date) // .filter(d=>d.sample_date d.sliding_semantic_score))), // max: Math.max(...first_haplotype_sequences.filter( // d=>d.sample_date > new Date(2020, 1, 28)).filter(d=>d.sample_date < next_date) // .map(d=>d.sliding_semantic_score))+1.5, // } // daysOfYear.push(newDate) // } // return daysOfYear // } moving_sliding_dates = { const daysOfYear = []; const startDate = new Date(2020, 1, 28); const endDate = new Date(2023,12,18); const windowDays = 90; for (let date = new Date(startDate); date <= endDate; date.setDate(date.getDate() + 1)) { const currentDate = new Date(date); const nextDate = new Date(date); nextDate.setDate(nextDate.getDate() + windowDays); const priorDate = new Date(date); priorDate.setDate(priorDate.getDate() - windowDays); // Filter sequences within the window const windowSequences = first_haplotype_sequences .filter(d => d.sample_date > priorDate && d.sample_date < currentDate); const scores = windowSequences.map(d => d.sliding_semantic_score); // Handle empty data const mean = scores.length > 0 ? scores.reduce((sum, val) => sum + val, 0) / scores.length : 0; const stdDev = scores.length > 0 ? getStandardDeviation(scores) : 0; // Filter sequences for max calculation const maxSequences = first_haplotype_sequences .filter(d => d.sample_date > startDate && d.sample_date < nextDate); const maxScores = maxSequences.map(d => d.sliding_semantic_score); const maxScore = maxScores.length > 0 ? Math.max(...maxScores) + 1.5 : 1.5; daysOfYear.push({ date: currentDate, upper: mean + (stdDev * 2), lower: mean + stdDev, max: maxScore }); } return daysOfYear.filter(d=>d.date < new Date('2024-1-1')); }3b:T58b,function supplementary_figure_10_b(rbd_and_outwith){ return Plot.plot({ height: 900, width:2500, marginLeft:90, // marginRight:90, marginTop:50, marginBottom:100, x:{nice:true,grid:true,label:'', tickRotate: 90, domain:['B.1.1.7', 'B.1.351', 'P.1', 'B.1.617.2', 'BA.1', 'BA.2', 'BA.4', // 'BA.2.12.1', 'BA.2.75' ] }, y:{nice:true,grid:true,label:'',labelAnchor: "center", labelArrow:false,}, style: {fontSize: "10px"}, fx:{domain:[ 'semantic_score', 'relative_grammaticality', "masked_grammaticality", "mutated_grammaticality", 'relative_grammaticality_with_eve', 'Evescape Fitness', 'Evescape', 'IND Mutability Score','DCA Mutability Score', 'Entry', 'Binding', 'Escape', 'RBD Wuhan-Hu-1 Binding', 'RBD Variant Average Binding', 'RBD Wuhan-Hu-1 Expression', 'RBD Variant Average Expression' ],}, facet:{ data:rbd_and_outwith, x:'feature', y:'region' }, marks: [ Plot.ruleY([0]), Plot.barY(rbd_and_outwith, {x: "voc", y: "count",stroke:'black',strokeWidth:1,fill:'proportion',tip:true}) ], color:{ type:'ordinal', range:['blue','cornflowerblue','silver'], // domain:[...new Set(all.map(d=>d.feature))], legend:true } }) }3c:Td9a,function supplementary_figure_11_experimental(experimental_linear_correlations,colour_scheme){ return Plot.plot({ facet: { data:experimental_linear_correlations, x: "predictor", marginRight: 60,grid:true }, width:1700, height:700, marginLeft:140, marginBottom:210, marginTop:40, style: {fontSize: "17px"}, x: { axis: null, domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',] }, y: { grid: true, nice:true, // tickFormat: "s" label:'Correlation', domain:[-0.2,1.0], labelAnchor: "center", labelArrow:false, }, color: { domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',], legend:true, range: colour_scheme }, facet: { data: experimental_linear_correlations, x: "predictor", // y: "Method", }, fx: { tickRotate: -30, label:'', domain:[ 'Escape Dadonaite et al. (2023)', 'RBD Escape Yisimayi et al. (2024)', 'Entry Dadonaite et al. (2023)', 'Binding Dadonaite et al. (2023)', 'RBD Wuhan-Hu-1 Binding Starr et al. (2022)', 'RBD Variant Average Binding Starr et al. (2022)', 'RBD Wuhan-Hu-1 Expression Starr et al. (2022)', 'RBD Variant Average Expression Starr et al. (2022)', // 'Accessibility BEpro', // 'B-Factor PDB:6VXX', // 'ΔΔG FoldX', // 'PSSM DELTA-BLAST', // 'ESST CRESCENDO', // 'Entropy Alignment' ] }, marks: [ // Plot.frame(), Plot.ruleY([0]), Plot.barY( experimental_linear_correlations, Plot.groupX({ y: "mean" }, { x: "feature", y: 'correlation', fill: "feature", tip:true,stroke:'black' }) ), // Plot.link( // experimental_linear_correlations, // Plot.groupX( // { // y1: (d) => d3.mean(d) - d3.deviation(d), // y2: (d) => d3.mean(d) + d3.deviation(d) // }, // { // x: "feature", // y: 'correlation', // // stroke: "black", // strokeWidth: 3 // } // ) // ), Plot.dotX( experimental_linear_correlations, Plot.groupX( { y: "max" }, { x: "feature", y: "plot_significance", symbol:'asterisk', } )), Plot.dotX(experimental_linear_correlations,{ x: "feature", y: "correlation", // tip:true, // stroke:'black', r:0.5 }), ] }) }3d:Tda4,function supplementary_figure_11_computational(computational_linear_correlations,colour_scheme){ return Plot.plot({ facet: { data:computational_linear_correlations, x: "predictor", marginRight: 60,grid:true }, width:1700, height:700, marginLeft:90, marginBottom:190, marginTop:40, style: {fontSize: "17px"}, x: { axis: null, domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',] }, y: { grid: true, nice:true, // tickFormat: "s" label:'Correlation', domain:[-0.2,1.0], labelAnchor: "center", labelArrow:false, }, color: { domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',], legend:true, range: colour_scheme }, facet: { data: computational_linear_correlations, x: "predictor", // y: "Method", }, fx: { tickRotate: -30, label:'', domain:[ // 'Escape Dadonaite et al. (2023)', // 'RBD Escape Yisimayi et al. (2024)', // 'Entry Dadonaite et al. (2023)', // 'Binding Dadonaite et al. (2023)', // 'RBD Wuhan-Hu-1 Binding Starr et al. (2022)', // 'RBD Variant Average Binding Starr et al. (2022)', // 'RBD Wuhan-Hu-1 Expression Starr et al. (2022)', // 'RBD Variant Average Expression Starr et al. (2022)', 'Accessibility BEpro', 'B-Factor PDB:6VXX', 'ΔΔG FoldX', 'PSSM DELTA-BLAST', 'ESST CRESCENDO', 'Entropy Alignment' ] }, marks: [ // Plot.frame(), Plot.ruleY([0]), Plot.barY( computational_linear_correlations, Plot.groupX({ y: "mean" }, { x: "feature", y: 'correlation', fill: "feature", tip:true,stroke:'black' }) ), // Plot.link( // computational_linear_correlations, // Plot.groupX( // { // y1: (d) => d3.mean(d) - d3.deviation(d), // y2: (d) => d3.mean(d) + d3.deviation(d) // }, // { // x: "feature", // y: 'correlation', // // stroke: "black", // strokeWidth: 3 // } // ) // ), Plot.dotX( computational_linear_correlations, Plot.groupX( { y: "max" }, { x: "feature", y: "plot_significance", symbol:'asterisk', } )), Plot.dotX(computational_linear_correlations,{ x: "feature", y: "correlation", // tip:true, // stroke:'black', r:0.5 }), ] }) }3e:Tc31,function figure_4a_computational(computational_single_value_correlations, colour_scheme) { return Plot.plot({ facet: { data:computational_single_value_correlations, x: "predictor", marginRight: 60,grid:true }, width:1700, height:700, marginLeft:90, // marginRight:450, marginBottom:190, marginTop:40, style: {fontSize: "17px"}, x: { axis: null, domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',] }, y: { grid: true, nice:true, // tickFormat: "s" label:'Correlation', domain:[-0.4,0.75], labelAnchor: "center", labelArrow:false, }, color: { domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',], legend:true, range: colour_scheme }, facet: { data: computational_single_value_correlations, x: "predictor", // y: "Method", }, fx: { tickRotate: -30, label:'', domain:[ // 'Escape Dadonaite et al. (2023)', // 'RBD Escape Yisimayi et al. (2024)', // 'Entry Dadonaite et al. (2023)', // 'Binding Dadonaite et al. (2023)', // 'RBD Wuhan-Hu-1 Binding Starr et al. (2022)', // 'RBD Variant Average Binding Starr et al. (2022)', // 'RBD Wuhan-Hu-1 Expression Starr et al. (2022)', // 'RBD Variant Average Expression Starr et al. (2022)', 'Accessibility BEpro', 'B-Factor PDB:6VXX', 'ΔΔG FoldX', 'PSSM DELTA-BLAST', 'ESST CRESCENDO', 'Entropy Alignment' ] }, marks: [ // Plot.frame(), Plot.ruleY([0]), // Plot.ruleY([0]), Plot.barY( computational_single_value_correlations, Plot.groupX({ y: "mean" }, { x: "feature", y:'correlation', fill: "feature", tip:true,stroke:'black' }) ), // Plot.text(computational_single_value_correlations, {x: "feature", y: 'correlation', text: 'bonferroni_max_significant', // lineAnchor: "bottom"}), Plot.dotX( computational_single_value_correlations, Plot.groupX( { y: "max" }, { x: "feature", y: "plot_significance", symbol:'asterisk', } )), // Plot.dot(computational_single_value_correlations, {x: "feature", y: (d)=>Math.abs(d.correlation)}), ], })}3f:Tc21,function figure4a_experimental(experimental_single_value_correlations,colour_scheme){ return Plot.plot({ facet: { data:experimental_single_value_correlations, x: "predictor", marginRight: 60,grid:true }, width:1700, height:700, marginLeft:140, // marginRight:140, marginBottom:210, marginTop:40, style: {fontSize: "17px"}, x: { axis: null, domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',] }, y: { grid: true, nice:true, // tickFormat: "s" label:'Correlation', domain:[-0.4,0.8], labelAnchor: "center", labelArrow:false, }, color: { domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',], legend:true, range: colour_scheme }, facet: { data: experimental_single_value_correlations, x: "predictor", // y: "Method", }, fx: { tickRotate: -30, label:'', domain:[ 'Escape Dadonaite et al. (2023)', 'RBD Escape Yisimayi et al. (2024)', 'Entry Dadonaite et al. (2023)', 'Binding Dadonaite et al. (2023)', 'RBD Wuhan-Hu-1 Binding Starr et al. (2022)', 'RBD Variant Average Binding Starr et al. (2022)', 'RBD Wuhan-Hu-1 Expression Starr et al. (2022)', 'RBD Variant Average Expression Starr et al. (2022)', // 'Accessibility BEpro', // 'B-Factor PDB:6VXX', // 'ΔΔG FoldX', // 'PSSM DELTA-BLAST', // 'ESST CRESCENDO', // 'Entropy Alignment' ] }, marks: [ // Plot.frame(), Plot.ruleY([0]), Plot.barY( experimental_single_value_correlations, Plot.groupX({ y: "mean" }, { x: "feature", y:'correlation', fill: "feature", tip:true,stroke:'black' }) ), Plot.link( experimental_single_value_correlations, Plot.groupX( { y1: (d) => d3.mean(d) - d3.deviation(d), y2: (d) => d3.mean(d) + d3.deviation(d) }, { x: "feature", y: 'correlation', // stroke: "black", strokeWidth: 3 } ) ), Plot.dotX( experimental_single_value_correlations, Plot.groupX( { y: "max" }, { x: "feature", y: "plot_significance", symbol:'asterisk', } )), ], })}40:Tf39,function figure_4b_experimental(experimental_rbf_svr_correlations,colour_scheme) { return Plot.plot({ facet: { data:experimental_rbf_svr_correlations, x: "predictor", marginRight: 60,grid:true }, width:1600, height:700, marginLeft:140, // marginRight:130, // marginRight:180, marginBottom:220, marginTop:40, style: {fontSize: "17px"}, x: { axis: null, domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',] }, y: { grid: true, nice:true, // tickFormat: "s" label:'Correlation', domain:[-0.2,1], labelAnchor: "center", labelArrow:false, }, color: { domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',], // legend:true, range: colour_scheme }, facet: { data: experimental_rbf_svr_correlations, x: "predictor", // y: 'Method', }, fx: { tickRotate: -30, label:'', domain:[ 'Escape Dadonaite et al. (2023)', 'RBD Escape Yisimayi et al. (2024)', 'Entry Dadonaite et al. (2023)', 'Binding Dadonaite et al. (2023)', 'RBD Wuhan-Hu-1 Binding Starr et al. (2022)', 'RBD Variant Average Binding Starr et al. (2022)', 'RBD Wuhan-Hu-1 Expression Starr et al. (2022)', 'RBD Variant Average Expression Starr et al. (2022)', // 'Accessibility BEpro', // 'B-Factor PDB:6VXX', // 'ΔΔG FoldX', // 'PSSM DELTA-BLAST', // 'ESST CRESCENDO', // 'Entropy Alignment' ] }, marks: [ // Plot.frame(), Plot.ruleY([0]), Plot.barY( experimental_rbf_svr_correlations, Plot.groupX({ y: "mean" }, { x: "feature", y: "correlation", fill: "feature", tip:true,stroke:'black' }) ), // Plot.link( // experimental_rbf_svr_correlations, // Plot.groupX( // { // y1: (d) => d3.mean(d) - d3.deviation(d), // y2: (d) => d3.mean(d) + d3.deviation(d) // }, // { // x: "feature", // y: "correlation", // // stroke: "black", // strokeWidth: 3 // } // ) // ), // Plot.text(experimental_rbf_svr_correlations, {x: "feature", // y: "correlation", // text: 'bonferroni_max_significant', // lineAnchor: "bottom" // }), Plot.dotX( experimental_rbf_svr_correlations, Plot.groupX( { y: "max" }, { x: "feature", y: "plot_significance", symbol:'asterisk', } )), Plot.dotX(experimental_rbf_svr_correlations,{ x: "feature", y: "correlation", // tip:true, // stroke:'black', r:0.5 }), ] }) }41:Tdba,function figure_4b_computational(computational_rbf_svr_correlations,colour_scheme){ return Plot.plot({ facet: { data:computational_rbf_svr_correlations, x: "predictor", marginRight: 60,grid:true }, width:1700, height:700, marginLeft:90, // marginRight:450, marginBottom:190, marginTop:40, style: {fontSize: "17px"}, x: { axis: null, nice:true, domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',] }, y: { grid: true, nice:true, // tickFormat: "s" label:'Correlation', domain:[-0.2,1], labelAnchor: "center", labelArrow:false, }, color: { domain:['semantic_score', 'relative_grammaticality', "masked_grammaticality", 'mutated_grammaticality', // 'relative_sequence_grammaticality', // 'ESM-1b Relative Grammaticality','relative_masked_grammaticality', 'relative_grammaticality_with_eve','Mean_Embedding','Logits','Evescape Fitness','Evescape', 'IND Mutability Score','DCA Mutability Score',], // legend:true, range: colour_scheme }, facet: { data: computational_rbf_svr_correlations, x: "predictor", // y: 'Method', }, fx: { tickRotate: -30, label:'', domain:[ // 'Escape Dadonaite et al. (2023)', // 'RBD Escape Yisimayi et al. (2024)', // 'Entry Dadonaite et al. (2023)', // 'Binding Dadonaite et al. (2023)', // 'RBD Wuhan-Hu-1 Binding Starr et al. (2022)', // 'RBD Variant Average Binding Starr et al. (2022)', // 'RBD Wuhan-Hu-1 Expression Starr et al. (2022)', // 'RBD Variant Average Expression Starr et al. (2022)', 'Accessibility BEpro', 'B-Factor PDB:6VXX', 'ΔΔG FoldX', 'PSSM DELTA-BLAST', 'ESST CRESCENDO', 'Entropy Alignment'] }, marks: [ // Plot.frame(), Plot.ruleY([0]), Plot.barY( computational_rbf_svr_correlations, Plot.groupX({ y: "mean" }, { x: "feature", y: "correlation", fill: "feature", tip:true,stroke:'black' }) ), // Plot.link( // computational_rbf_svr_correlations, // Plot.groupX( // { // y1: (d) => d3.mean(d) - d3.deviation(d), // y2: (d) => d3.mean(d) + d3.deviation(d) // }, // { // x: "feature", // y: "correlation", // // stroke: "black", // strokeWidth: 3 // } // ) // ), Plot.dotX( computational_rbf_svr_correlations, Plot.groupX( { y: "max" }, { x: "feature", y: "plot_significance", symbol:'asterisk', } )), Plot.dotX(computational_rbf_svr_correlations,{ x: "feature", y: "correlation", // tip:true, // stroke:'black', r:0.5 }), ] }) }42:Tf0e,function filter_data(uploaded_spike,data){ return data.filter((element, index) => { if (form.ref === null && form.alt === null){ return element.position >=slider_form.position[0] && element.position <=slider_form.position[1] && element["semantic_score"] >= slider_form.semantic[0] && element["semantic_score"] <= slider_form.semantic[1] && element["relative_grammaticality"] >= slider_form.ei[0] && element["relative_grammaticality"] <= slider_form.ei[1] && element["relative_sequence_grammaticality"] >= slider_form.relative_sequence_grammaticality[0] && element["relative_sequence_grammaticality"] <= slider_form.relative_sequence_grammaticality[1] }else if (form.ref !== null && form.alt === null){ return element.position >=slider_form.position[0] && element.position <=slider_form.position[1] && element.ref == form.ref && element["semantic_score"] >= slider_form.semantic[0] && element["semantic_score"] <= slider_form.semantic[1] && element["relative_grammaticality"] >= slider_form.ei[0] && element["relative_grammaticality"] <= slider_form.ei[1] && element["relative_sequence_grammaticality"] >= slider_form.relative_sequence_grammaticality[0] && element["relative_sequence_grammaticality"] <= slider_form.relative_sequence_grammaticality[1] }else if (form.ref === null && form.alt !== null){ return element.position >=slider_form.position[0] && element.position <=slider_form.position[1] && element.alt == form.alt && element["semantic_score"] >= slider_form.semantic[0] && element["semantic_score"] <= slider_form.semantic[1] && element["relative_grammaticality"] >= slider_form.ei[0] && element["relative_grammaticality"] <= slider_form.ei[1] && element["relative_sequence_grammaticality"] >= slider_form.relative_sequence_grammaticality[0] && element["relative_sequence_grammaticality"] <= slider_form.relative_sequence_grammaticality[1] }else{ return element.position >=slider_form.position[0] && element.position <=slider_form.position[1] && element.ref == form.ref && element.alt == form.alt && element["semantic_score"] >= slider_form.semantic[0] && element["semantic_score"] <= slider_form.semantic[1] && element["relative_grammaticality"] >= slider_form.ei[0] && element["relative_grammaticality"] <= slider_form.ei[1] && element["relative_sequence_grammaticality"] >= slider_form.relative_sequence_grammaticality[0] && element["relative_sequence_grammaticality"] <= slider_form.relative_sequence_grammaticality[1] } }).filter((element, index) => { if(form.subunit === null && form.domain === null){ return element }else if(form.subunit !== null && form.domain ===null){ return element.subunit == form.subunit }else if(form.subunit === null && form.domain !==null){ return element.domain == form.domain }else{ return element.domain == form.domain && element.subunit == form.subunit } }).filter((element, index) => { if(form.real === null){ return element }else{ return element.real == form.real } }).filter((element, index) => { if(form.convergent === null){ return element }else{ return element.convergent == form.convergent } }).filter((element, index) => { if(form.ref_and_mask === 'false'){ return element }else{ if(element.ref != element.alt){ if (element.alt != ''){ return element } } } }).filter((element, index) => { if(form.deletions === "True"){ return element }else{ return element.alt != "-" } }) }43:T502,spike ="MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSALEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGAAAYYVGYLQPRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT"44:Tccc,function figure_3_b (amino_sites_affected_by_mutations){ return Plot.plot({ height: 400, width: 1200, marginBottom:50, marginTop:20, x:{grid:false,tickRotate: -90,}, y:{grid:true, domain:['A','R','N','D','C','E','Q','G','H','I','L','K','M','F','P','S','T','W','Y','V']}, style: {fontSize: "17px"}, color: { legend:true, scheme: 'turbo', domain:['A','R','N','D','C','E','Q','G','H','I','L','K','M','F','P','S','T','W','Y','V'] }, marks: [ Plot.cell(amino_sites_affected_by_mutations, { x: 'position', y: 'reference_amino_acid', fill: "reference_amino_acid", tip:true }), // //FCS // Plot.rect([{x1:681,y1: 'V', x2: 685, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"purple",opacity:0.1}), // //S2' // Plot.rect([{x1:815,y1: 'V', x2: 815, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"orange",opacity:1}), // //NA // Plot.rect([{x1: 9,y1: 'V', x2: 9, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //NTD Plot.rect([{x1: 0,y1: 'V', x2: 305, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "forestgreen",fill:"forestgreen",opacity:0.3}), //NA Plot.rect([{x1: 306,y1: 'V', x2: 329, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //RBD Plot.rect([{x1: 330,y1: 'V', x2: 541, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "red",fill:"red",opacity:0.3}), //NA Plot.rect([{x1: 542,y1: 'V', x2: 680, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), // FCS Plot.rect([{x1:681,y1: 'V', x2: 685, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"purple",opacity:0.1}), //NA Plot.rect([{x1: 686,y1: 'V', x2: 787, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //FP Plot.rect([{x1: 788,y1: 'V', x2: 806, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "blue",fill:"blue",opacity:0.4}), //NA Plot.rect([{x1: 807,y1: 'V', x2: 911, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), // //NA // Plot.rect([{x1: 816,y1: 'V', x2: 911, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //HR1 Plot.rect([{x1: 912,y1: 'V', x2: 984, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "cornflowerblue",fill:"cornflowerblue",opacity:0.3}), //NA Plot.rect([{x1: 985,y1: 'V', x2: 1162, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //HR2 Plot.rect([{x1: 1163,y1: 'V', x2: 1213, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "cyan",fill:"cyan",opacity:0.5}), //TM Plot.rect([{x1: 1214,y1: 'V', x2: 1237, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "brown",fill:"brown",opacity:0.3}), //CTD Plot.rect([{x1: 1238,y1: 'V', x2: 1273, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "purple",fill:"orange",opacity:0.4}), ] })}45:Tcc6,function figure_3_d(amino_mutations_affect_sites){ return Plot.plot({ height: 400, width: 1200, marginBottom:50, marginTop:20, x:{grid:false,tickRotate: -90,}, y:{grid:true, domain:['A','R','N','D','C','E','Q','G','H','I','L','K','M','F','P','S','T','W','Y','V']}, style: {fontSize: "17px"}, color: { legend:true, scheme: 'turbo', domain:['A','R','N','D','C','E','Q','G','H','I','L','K','M','F','P','S','T','W','Y','V'] }, marks: [ Plot.cell(amino_mutations_affect_sites, { x: 'position', y: 'reference_amino_acid', fill: "reference_amino_acid", tip:true }), // //FCS // Plot.rect([{x1:681,y1: 'V', x2: 685, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"purple",opacity:0.1}), // //S2' // Plot.rect([{x1:815,y1: 'V', x2: 815, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"orange",opacity:1}), // //NA // Plot.rect([{x1: 9,y1: 'V', x2: 9, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //NTD Plot.rect([{x1: 0,y1: 'V', x2: 305, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "forestgreen",fill:"forestgreen",opacity:0.3}), //NA Plot.rect([{x1: 306,y1: 'V', x2: 329, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //RBD Plot.rect([{x1: 330,y1: 'V', x2: 541, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "red",fill:"red",opacity:0.3}), //NA Plot.rect([{x1: 542,y1: 'V', x2: 680, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), // FCS Plot.rect([{x1:681,y1: 'V', x2: 685, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"purple",opacity:0.1}), //NA Plot.rect([{x1: 686,y1: 'V', x2: 787, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //FP Plot.rect([{x1: 788,y1: 'V', x2: 806, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "blue",fill:"blue",opacity:0.4}), //NA Plot.rect([{x1: 807,y1: 'V', x2: 911, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), // //NA // Plot.rect([{x1: 816,y1: 'V', x2: 911, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //HR1 Plot.rect([{x1: 912,y1: 'V', x2: 984, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "cornflowerblue",fill:"cornflowerblue",opacity:0.3}), //NA Plot.rect([{x1: 985,y1: 'V', x2: 1162, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "sandybrown",fill:"sandybrown",opacity:0.1}), //HR2 Plot.rect([{x1: 1163,y1: 'V', x2: 1213, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "cyan",fill:"cyan",opacity:0.5}), //TM Plot.rect([{x1: 1214,y1: 'V', x2: 1237, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "brown",fill:"brown",opacity:0.3}), //CTD Plot.rect([{x1: 1238,y1: 'V', x2: 1273, y2: 'A',}],{x1:'x1',y1:'y1', x2: 'x2', y2: 'y2',stroke: "purple",fill:"orange",opacity:0.4}), ] }) }46:T50f,function benchmark_innocenti(ba1_rbd_positions,epi_compare_ranked,innocenti_labels_filtered){ return Plot.plot({ height: 500, width: 500, marginBottom:80, marginLeft:80, marginTop:20, style: {fontSize: "24px"}, // marginLeft: 60, y:{grid:true,nice:true,domain:ba1_rbd_positions,reverse:true, label:'Position' }, x:{grid:true,nice:true,domain:ba1_rbd_positions,tickRotate: -90,label:'Position' }, color: { // range:['blue','white','red'], // domain:[-0.8,0,0.8], scheme:'BuRd', type: "diverging", legend:true, label:'Pairwise Epistasis' }, marks: [ Plot.ruleY([0]), // Plot.ruleX([0]), // Plot.dot(identified_epi_compare_one_std,{x:d=>parseInt(d.labels.split(',')[0]), y: d=>parseInt(d.labels.split(',')[1]),fill:'epistasis',stroke:'black',r:10,tip:true}), // Plot.cell(divide_line_data.reverse(),{y:d=>parseInt(d.mutation.slice(1,-1)), x: d=>parseInt(d.position),fill:'grey',stroke:'black',title:'labels',tip:true}), Plot.dot(epi_compare_ranked,{y:d=>parseInt(d.labels.split(',')[0]), x: d=>parseInt(d.labels.split(',')[1]),fill:'epistasis',r:3,tip:true}), Plot.dot(innocenti_labels_filtered,{y:d=>parseInt(d.labels.split(',')[0]), x: d=>parseInt(d.labels.split(',')[1]),fill:'epistasis',stroke:'black',r:9}), ] })}47:T4ce,innocenti_labels = innocenti_labels_data.filter(d=>d.feature_codon_source>=319 && d.feature_codon_source<=541 && d.feature_codon_target>=319 && d.feature_codon_target<=541) .sort((a,b)=>a.feature_codon_source-b.feature_codon_source) .filter((item, index, arr) => { // Create a normalized key by sorting the pair const key = [item.feature_codon_source, item.feature_codon_target].sort((a, b) => a - b).join('-'); // Keep only the first occurrence of each unique pair return index === arr.findIndex(i => { const iKey = [i.feature_codon_source, i.feature_codon_target].sort((a, b) => a - b).join('-'); return iKey === key; }); }).map(item => { const [min, max] = [item.feature_codon_source, item.feature_codon_target].sort((a, b) => a - b); return { ...item, label: `${min},${max}` }; }) .map(d=>{return {labels:d.label}}) 48:T5fc,function likelihood_change_epistasis(all_ba1_pos,ba1_rbd_positions,divide_line_data,epistasis_data_std){ return Plot.plot({ height: 500, width: 500, marginBottom:80, marginLeft:120, marginTop:20, style: {fontSize: "24px"}, x:{grid:true,nice:true,domain:ba1_rbd_positions,tickRotate: -90, label:'Position'}, y:{grid:true,label:'',nice:true, label:'Mutation',domain:[ // "A67V","T95I","G142D","L212I", "G339D","S371L","S373P","S375F", "K417N","N440K","G446S", "S477N","T478K","E484A","Q493R","G496S","Q498R","N501Y","Y505H", // "T547K", // "D614G", // "H655Y","N679K","P681H", // "N764K", // "D796Y","N856K","N969K", // "Q954H","L981F" ].reverse()}, color: { label:'Likelihood Change', range: ['darkred','red','salmon','silver','cornflowerblue','blue','darkblue'].reverse(), scale:[-0.35, -0.05, -0.01, 0, 0.01, 0.05, 0.35], type: "diverging", legend:true }, marks: [ Plot.cell(divide_line_data,{x:'position',y:'mutation',fill:'grey',stroke:'black'}), Plot.dot(epistasis_data_std.filter(d=>all_ba1_pos.has(d.position) & d.single_significant_masked_change != null),{y: "mutation", x: "position",fill:d=>-d.single_significant_masked_change,r:6,tip:true,stroke:'grey'}), Plot.dot(epistasis_data.filter(d=>all_ba1_pos.has(d.position) & d.significant_masked_change != null),{y: "mutation", x: "position",fill:d=>-d.significant_masked_change,r:9,stroke:'black'}), ] })}49:T5da,function esm2_epistasis_change(epi_compare_ranked,ba1_rbd_positions,identified_epi_compare_two_std,identified_epi_compare_one_std) { return Plot.plot({ height: 500, width: 500, marginBottom:80, marginLeft:80, marginTop:20, style: {fontSize: "24px"}, // marginLeft: 60, y:{grid:true,nice:true,domain:ba1_rbd_positions,reverse:true, label:'Position' }, x:{grid:true,nice:true,domain:ba1_rbd_positions,tickRotate: -90,label:'Position' }, color: { // range:['blue','white','red'], // domain:[-0.8,0,0.8], scheme:'BuRd', type: "diverging", legend:true, label:'Pairwise Epistasis' }, marks: [ Plot.ruleY([0]), // Plot.ruleX([0]), // Plot.dot(identified_epi_compare_one_std,{x:d=>parseInt(d.labels.split(',')[0]), y: d=>parseInt(d.labels.split(',')[1]),fill:'epistasis',stroke:'black',r:10,tip:true}), // Plot.cell(divide_line_data.reverse(),{y:d=>parseInt(d.mutation.slice(1,-1)), x: d=>parseInt(d.position),fill:'grey',stroke:'black',title:'labels',tip:true}), Plot.dot(epi_compare_ranked,{y:d=>parseInt(d.labels.split(',')[0]), x: d=>parseInt(d.labels.split(',')[1]),fill:'epistasis',r:3,tip:true}), Plot.dot(identified_epi_compare_one_std,{y:d=>parseInt(d.labels.split(',')[0]), x: d=>parseInt(d.labels.split(',')[1]),fill:'epistasis',stroke:'grey',r:6}), Plot.dot(identified_epi_compare_two_std,{y:d=>parseInt(d.labels.split(',')[0]), x: d=>parseInt(d.labels.split(',')[1]),fill:'epistasis',stroke:'black',r:9}), ] })}4a:T62e,function esm2_absolute_epistasis_change(epi_compare_ranked, ba1_rbd_positions, identified_epi_compare_two_std, identified_epi_compare_one_std){ return Plot.plot({ height: 500, width: 500, marginBottom:80, marginLeft:80, marginTop:20, style: {fontSize: "24px"}, y:{grid:true,nice:true,domain:ba1_rbd_positions,reverse:true, label:'Position'}, x:{grid:true,nice:true,domain:ba1_rbd_positions,tickRotate: -90, label:'Position' }, color: { // range:['blue','white','red'], // domain:[-0.8,0,0.8], scheme:'YlOrRd', reverse: true, type: "linear", legend:true, label:'Pairwise Epistasis Absolute Rank' }, marks: [ Plot.ruleY([0]), // Plot.ruleX([0]), // Plot.dot(identified_epi_compare_one_std,{x:d=>parseInt(d.labels.split(',')[0]), y: d=>parseInt(d.labels.split(',')[1]),fill:'rank',stroke:'black',r:10,strokeWidth:3,tip:true}), // Plot.cell(divide_line_data.reverse(),{y:d=>parseInt(d.mutation.slice(1,-1)), x: d=>parseInt(d.position),fill:'grey',stroke:'black',title:'labels',tip:true}), Plot.dot(epi_compare_ranked,{y:d=>parseInt(d.labels.split(',')[0]), x: d=>parseInt(d.labels.split(',')[1]),fill:'rank',r:3,tip:true}), Plot.dot(identified_epi_compare_one_std,{y:d=>parseInt(d.labels.split(',')[0]), x: d=>parseInt(d.labels.split(',')[1]),fill:'rank',stroke:'grey',strokeWidth:2,r:6,tip:true}), Plot.dot(identified_epi_compare_two_std,{y:d=>parseInt(d.labels.split(',')[0]), x: d=>parseInt(d.labels.split(',')[1]),fill:'rank',stroke:'black',strokeWidth:2,r:9}), ] 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32:{"id":"007ba3bbc922f346","slug":"from-a-singlesequence-nature-communications","title":"From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences","thumbnail":"9721cf2445d2dfc2aedfa71a26bca37f5e19f99e0cb548fa21c88a5dc6caf964","access_level":"public","create_time":"$D2026-03-03T13:38:34.309Z","update_time":"$D2026-03-03T13:38:34.309Z","view_time":null,"first_public_time":"$D2026-03-03T13:40:47.015Z","has_unpublished_edits":null,"fork":{"id":"37790e3850c983b6","slug":"from-a-single-sequencenature-communications","title":"From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences","owner":{"id":"1157d8a0e7ace7c6","login":"cvr-bioinfo","name":"CVR Bioinformatics"}},"stars":0,"starred":false,"owner":{"type":"team","id":"1157d8a0e7ace7c6","login":"cvr-bioinfo","name":"CVR 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Bioinformatics","avatar_url":"https://avatars-next.observableusercontent.com/022d43de01273d3d19941f51b9b2c412d919f4a74262d41e14c5379b08c7d5f2","tier":"pro_2024","role":"viewer"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"title":"From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences","cells":[{"id":0,"value":"# From a single sequence to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2 protein sequences","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1455,"value":"The following notebook describes the plots and includes the data for the paper. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":98,"value":"# Introduction","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":102,"value":"### Figure 1.\nSchematic summary of our methodology. Deep mutational scanning involves taking a sequence and mutating every position to each of the possible alternative amino acids. These are then passed to the PLM to produce embeddings and logits which can be used for downstream tasks or to produce the metrics relative grammaticality and semantic score. Identifying epistatic interactions involves reverting the mutations from a SARS-CoV-2 variant (here BA.1) and measuring the effect this has on the other likelihoods. Embeddings and log-likelihoods (logits) can be used for surveillance by producing metrics over timescales for newly emergent protein sequences of concern, or by looking at evolutionary trajectories such as with evo-velocity. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":104,"value":"FileAttachment(\"Figure_1_Observable.png\").image({width:800})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":112,"value":"# ESM-2 captures evolutionary potential ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":261,"value":"### Figure 2A\nGraph of the amino acid sequence alignment entropy at each position in the SARS-CoV-2 spike protein. S1 contains the majority of the sites with high entropy, while S2 contains only a few. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":258,"value":"\n Plot.plot({\n height: 300,\n width:1200,\n x:{nice:true,},\n y:{nice:true,transform: (d) => {if(form.log_y == true){return Math.log(d)}else{return d}}},\n grid:{x:true},\n style: {fontSize: \"14px\"},\n marginLeft:85,\n marginTop:40,\n marginBottom:50,\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(filtered_data.sort((a,b) => (b.position) - (a.position)), {x: \"position\", y: \"Entropy\",stroke:'subunit',tip:true,})\n ],\n color: {\n range:['salmon','cornflowerblue'],\n legend: true,\n nice:true\n },\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":110,"value":"### Figure 2B \nScatter plot of spike protein DMS. Relative grammaticality is shown on the y-axis, with the amino acid position on the x-axis. Points are coloured on the semantic rank of each change\n\n Use X-axis \"position\" and Y-axis \"relative_grammaticality\".\n\n### Supplementary Figure 2\nDMS plot with only the substitutions observed in SARS-CoV-2 VOC sequences highlighted. Most of the mutations occur in the spike protein S1 region which the model predicts has a higher likelihood of mutations, i.e., a higher grammaticality.\n\n Use X-axis \"position\" and Y-axis \"relative_grammaticality\".\n\n Also select \"highlight VOC mutations\".\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":117,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":120,"value":"viewof slider_form = Inputs.form({ \n \n position: rangeSlider({step:1,min: 1,max: 1273,value: this ? this.value : [1, 1273],title: 'Position Filter',description: '',}),\n \n semantic: rangeSlider({step:0.5,min:0 ,max: 20,value: this ? this.value : [0,20],title: 'Semantic Score Filter',description: '',}),\n \n ei: rangeSlider({step:1,min: -35,max: 5,value: this ? this.value : [-35,5],title: 'Relative Grammatically',description: '',}),\n \n relative_sequence_grammaticality: rangeSlider({step:1,min:-40,max:10,value: this ? this.value : [ -40,10],title: 'Relative Sequence Grammaticality Filter',description: '',}),\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":123,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":125,"value":"**Spearman ρ** between ${form.x_axis} and ${form.y_axis} = ${rhoval}","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":115,"value":"#### Upload Spike Sequence as filter\nSpike sequences must be 1273 in length (i.e a complete spike sequence). Spike Sequences will be checked for mutations, and the following plots will then filter and display only mutations present in the uploaded spike. Alternatively the Pango lineage can be entered to display a lineage from the initial spike sequences.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":143,"value":"viewof uploaded_spike= Inputs.text({label: \"Submit Spike Sequence Here\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":145,"value":"viewof search = Inputs.text({label: 'Or select a lineage to use', placeholder: \"Type a lineage here\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":147,"value":"{\n if (uploaded_spike.length == 0 & lineage_mutations.length == 0){\n return 'Spike not uploaded'\n }else if (uploaded_spike.length != 1273 & lineage_mutations.length == 0){\n return \"Invalid spike, must be of length 1273\"\n }else if (uploaded_spike.length == 1273){\n return uploaded_spike\n }else if(uploaded_spike.length != 1273 & lineage_mutations.length > 0){\n return \"Using Spike protein from lineage \"+search\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":149,"value":"Inputs.table([selected_lineage_entry],{})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":151,"value":"spike","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":153,"value":"wuhan_esm2_data = dms_zip.file(\"DMS_S_annotated_with_counts.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":255,"value":"### Figure 2 (C) \nAverage relative grammaticality at each position on the spike protein plotted on three structures, the full Spike protein, the spike monomer, and the spike receptor binding domain (RBD) bound to the ACE-2 receptor in purple.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":265,"value":"FileAttachment(\"Figure_2_C_Observable.png\").image({width:800})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1378,"value":"## Supplementary Figure 1. \nViolin plots for the distributions of entropy and relative grammaticality for each of the SARS-CoV-2 spike protein subunits S1 and S2. The black line shows the mean while the green shows the median. For both entropy and grammaticality, the S2 subunit has on average lower scores compared to the S1.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1346,"value":"vegalite(({hconcat:[subunit_boxplot_relative_grammaticality,subunit_boxplot_entropy]}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1355,"value":"subunit_boxplot_entropy= ({\n width:450,\n height:300,\n // \"title\": \"1D Distances\",\n labelFontSize:20,\n titleFontSize:20,\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:filtered_data.filter(d=>d.Entropy !=0).map(obj => ({ ...obj, log_entropy: Math.log(obj.Entropy)}))\n // values:filtered_data.filter(d=>d.Entropy !=0).map(obj => ({ ...obj, log_entropy: obj.Entropy}))\n },\n encoding:{\n y:{field:\"log_entropy\",type:\"quantitative\",\"title\": \"Log Entropy\",},\n x:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\"},\n color:{field:\"subunit\",type:\"nominal\", scale:{range:['#E74421','#4068FE']}}\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1343,"value":"subunit_boxplot_relative_grammaticality = ({\n width:450,\n height:300,\n // \"title\": \"1D Distances\",\n labelFontSize:20,\n titleFontSize:20,\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:filtered_data\n },\n encoding:{\n y:{field:\"relative_grammaticality\",type:\"quantitative\",\"title\": \"Relative Grammaticality\",},\n x:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\"},\n color:{field:\"subunit\",type:\"nominal\", scale:{range:['#E74421','#4068FE']}}\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":269,"value":"# PLMs Identify epistatic interaction sites","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":385,"value":"### Figure 3. (A)\nMonomeric structures showing the changes in probabilities for three example substitutions in SARS-CoV-2's spike protein: E484A, D614G and N969K. This is the negative of the reversion likelihood. The mutation site is coloured yellow, red sites increase in probability while blue sites decrease. Mutation probabilities were only shown if they were outside two standard deviations of the mean change from all BA.1 reversions. (B) Likelihood changes of Omicron RBD mutations on mutation sites. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":275,"value":"FileAttachment(\"Figure_3_A_Observable.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":337,"value":"#### Extra interactive plot: View points from Figure 3C","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":279,"value":"viewof epistasis_mutation_select = Inputs.select([\"E484A\", \"D614G\",\"N969K\"], {label: \"Pick mutation\", value: [\"E484A\"]})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":282,"value":"Plot.plot({\n height: 300,\n width:1200,\n style: {fontSize: \"20px\"},\n marginBottom:50,\n marginLeft:80,\n marginTop:0,\n y:{\n domain:[-0.35,0.35],\n label:'Likelihood Change',\n labelAnchor: \"center\",\n labelArrow:false,\n grid:true,\n nice:true},\n x:{domain:[0,1273],\n label:'Position',\n labelAnchor: \"center\",\n labelArrow:false,},\n marks: [\n Plot.ruleY([0]),\n Plot.dot(epistasis_dot_data.filter(d=>d.mutation == epistasis_mutation_select), {x: \"position\", y:d=>-d.significant_masked_change,fill:d=>-d.significant_masked_change,tip:true,r:3,stroke:'black',strokeWidth:0.5},)\n ],\n color: {\n label:'Likelihood Change',\n range: ['darkred','red','salmon','silver','cornflowerblue','blue','darkblue'].reverse(),\n domain:[-0.35,0.35],\n scale:[-0.35,\n -0.05,\n -0.01,\n 0,\n 0.01,\n 0.05,\n 0.35],\n type: \"diverging\",\n // ticks: 5,\n legend:true\n },\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":285,"value":"epistasis_data = epistasis_zip.file(\"Revisions_Omicron_Epistasis_with_single_std.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1558,"value":"epistasis_data.map(d=>d.source_sequence)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":299,"value":"epistasis_dot_data = epistasis_data.filter(d=>d.mutation == epistasis_mutation_select & (d.significant_masked_change >0 |d.significant_masked_change <0))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":513,"value":"### Figure 3 B-D\n(B) Heatmap of BA.1 RBD mutations and the change in likelihoods at other BA.1 mutation sites. Larger points indicate the sites were 2 standard deviations away from the average change, smaller points indicate 1 deviation away.(C) Experimental epistasis data from Moulana et al.21 showing the pairwise effect of mutations on ACE-2 binding affinity. Positive values indicate a pairwise increase in ACE-2 binding affinity. Larger points indicate the sites were identified by ESM using 2 standard deviations away from the average change, smaller points indicate 1 deviation away. The smallest points were not identified by ESM. (D) Experimental epistasis data coloured based on the ranking of the absolute epistatic effect values. This allows large negative values to be counted as high ranking and vice versa.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":394,"value":"

\n ${likelihood_change_epistasis(all_ba1_pos, ba1_rbd_positions, divide_line_data, epistasis_data)}\n ${esm2_epistasis_change(epi_compare_ranked,ba1_rbd_positions,identified_epi_compare_two_std,identified_epi_compare_one_std)}\n ${esm2_absolute_epistasis_change(epi_compare_ranked, ba1_rbd_positions, identified_epi_compare_two_std, identified_epi_compare_one_std)}\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":547,"value":"### Supplementary Figure 12.\nResults from Moulana et al. filtered by the RBD epistatic interactions identified by Innocenti et al.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":536,"value":"

\n ${benchmark_innocenti(ba1_rbd_positions,epi_compare_ranked,innocenti_labels_filtered)}\n\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":529,"value":"innocenti_labels_filtered = epi_compare_ranked.filter(d=>new Set(innocenti_labels.map(d=>d.labels)).has(d.labels))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":627,"value":"## Figure 4. \n(A) The significantly changed logits across the whole DMS were identified, with positions that were repeatedly identified (called critical sites) as being affected by the in-silico mutations counted. These were then mapped onto the spike structure and coloured on their domains.
(B) Amino acids of each consistently affected reference residues from the DMS data. The NTD and RBD contain mostly Prolines(P) and Cystines(C), while the rest of spike has a wider distribution of amino acids.
(C) Sites that when mutated affect a significantly greater number of other positions (called impactful sites) in the sequence from the DMS data. \n
(D) Reference residues for each of the impactful sites. An even larger proportion of impactful sites are cystines and are distributed throughout the protein. Arginines are enriched in the FCS. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":557,"value":"FileAttachment(\"Figure_4_A_C_Observable.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":569,"value":"

\n BD${figure_3_b(amino_sites_affected_by_mutations)}\n ${figure_3_d(amino_mutations_affect_sites)}\n\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1312,"value":"### Supplementary Figure 3\n(A)\nBoxplots showing the distribution of distances between mutations and the positions affected by mutations in each subunit.\n
(B) \n Boxplots showing the distribution of distances between mutations and the positions for each mutation.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1314,"value":"

\n A\n ${await vegalite(({hconcat:[one_dimensional_boxplot,three_dimensional_boxplot]}))}\n B\n ${await vegalite(({vconcat:[mutation_one_dimensional_boxplot,mutation_three_dimensional_boxplot]}))}\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1307,"value":"### Supplementary Figure 4\nNumber of sites with a significant (+/-2 deviations from mean) change in probability for each SARS-CoV-2 BA.1 reversion change.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1271,"value":"Plot.plot({\n marginLeft:80,\n width:1200,\n height:400,\n // marginBottom:30,\n // marginTop:10,\n x:{tickRotate:0, label:'Mutation',nice:true},\n y:{tickRotate:0, label:'Sites\\n Affected',nice:true,grid:true},\n marks: [\n Plot.barY([...new Map(epistasis_distances.map(item =>[item['mutation'], item])).values()], {x: \"mutation\", y: \"Sites Affected\",fill:'subunit', sort: {x: \"y\", reverse: false}}),\n Plot.ruleY([0])\n ],\n color: {\n type: \"categorical\", \n range:['#E74421','#4168FE'],\n legend:true\n },\n\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1273,"value":"epistasis_distances = FileAttachment(\"significant_distance_data_BA1_epistasis_revisions.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":689,"value":"## PLMs provide orthogonal metrics for assessing mutational impact","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":742,"value":"### Figure 5. \n(A) Spearman's Rank correlations between the semantic score, grammaticality and relative sequence grammaticality and traditional metrics. Bars with an asterix means the correlation was found to be significant after a Bonferroni correction (multiple test correction). \n
(B) Spearman's Rank correlations between the language model metric and the traditional metrics. Each pair was fitted using a support vector regression model using an RBF kernel, with 5-fold cross validation which was repeated on 3 randomised splits of the data. Bars represent the mean of the correlations, with the error bar +/- 1 standard deviation of the correlations. Bars with an asterix mean the correlation was found to be significant after a Bonferroni correction. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":677,"value":"

\n A\n
\n ExperimentalComputational\n ${figure4a_experimental(experimental_single_value_correlations, colour_scheme)}\n ${figure_4a_computational(computational_single_value_correlations, colour_scheme)}\nB\n
\n ExperimentalComputational\n ${figure_4b_experimental(experimental_rbf_svr_correlations, colour_scheme)}\n ${figure_4b_computational(computational_rbf_svr_correlations, colour_scheme)}\n\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":761,"value":"## Supplementary Figure 11 \nLinear regression Spearmans rank correlation coefficients for each of the different feature sets. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":774,"value":"

\n ExperimentalComputational${supplementary_figure_11_experimental(experimental_linear_correlations, colour_scheme)\n}\n ${supplementary_figure_11_computational(computational_linear_correlations, colour_scheme)\n}\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":828,"value":"## Supplementary Figure 10. \nData from each of the mutational scans were filtered so that there is a measurement for every mutation in each dataset to allow for equivalence. The full spike mutations exclude the RBD Experimental mutations since the mutation set could only increase selection of RBD mutants given the lack of other regions in this data. \n
(A) Barchart for each feature showing the number of mutations from variant of concern sequences present in the top 10% of shared feature predictions. The top set of bars shows the shared mutations within the RBD, while the bottom shows mutations shared across the whole of the spike protein. \n
(B) A cumulative sum of shared DMS mutations that have appeared at least 100 times during the pandemic that appear in the top 10 % of all features. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":798,"value":"

\n A${supplementary_figure_10_a(full_spike_common, supplementary_colour_scheme) }\nB\n ${supplementary_figure_10_b(rbd_and_outwith)}\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1438,"value":"## Supplementary Figure 6: \nBoxplots showing Jensen Shannon distances (JSDs) between semantic score or grammaticality and other computational metrics. JSD was calculated for every site, colours show the distribution of scores in different protein regions. Comparisons between S1 regions and S2 are all significant by Mann Whitney U test following Bonferroni correction (p<0.05) except for the following comparisons: FoldX ΔΔG vs semantic score; all S1 vs S2 comparisons, Log Likelihood vs semantic score; S1 C-terminal vs S2, ESST probability vs semantic score; S1 RBD vs S2, S1 C-terminal vs S2.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1440,"value":"FileAttachment(\"Supplementary_Figure_6_Observable.png\").image({width: 500})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":864,"value":"## Language models recapitulate evolutionary relationships between related sequences","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":925,"value":"## Figure 6. \n\n(A) UMAP of initial spike sequence embeddings for SARS-CoV-2 PANGO lineages and a selection of other known Sarbecovirus spike sequences. Each lineage is represented by one spike embedding. Points are coloured on VOC classification. Arrows represent the evo-velocity through the embedding space, which shows a “directionality” of evolution. \n
\n(B) shows the sequences coloured by pseudotime inferred using sequence embedding probabilities to order sequences in time using an inferred root and an endpoint.
\n(C) Shows an unrooted nucleotide phylogenetic tree of the spike sequences, coloured again by VOC. ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":918,"value":"FileAttachment(\"Figure_6_A_B_C.png\").image({width: 700})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":923,"value":"## Figure 6D \nShows the spike protein sequences plotted using their sample date and semantic score coloured consistently to Figure 1B.\n\nUse X-axis \"sample_date\" and Y-axis \"semantic_score\"\n\n\n### Supplementary Figure 7\n\nUse X-axis \"semantic_score\" and Y-axis \"relative_grammaticality\"\n\n \n### Supplementary Figure 8\n\nUse X-axis \"sample_date\" and Y-axis \"relative_grammaticality\"\n\n\n### Supplementary Figure 9\n\nUse X-axis \"mutations\" and Y-axis \"semantic_score\"\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":931,"value":"viewof lineage_form =Inputs.form({\n x_axis:Inputs.select(initial_lineage_sequences.columns, {value:\"sample_date\",label: \"Select X-Axis\",unique:true}), \n y_axis:Inputs.select(initial_lineage_sequences.columns, {value:\"relative_grammaticality\",label: \"Select Y-Axis\",unique:true}),\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":934,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1436,"value":"## Supplementary Figure 5. \nPredicted root nodes and endpoints identified by running Markov diffusion process over the weighted edges of the evo-velocity network. The root nodes are correctly identified as the Sarbecovirus spike sequences, with Omicron VOC sequences predicted as the end nodes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1433,"value":"FileAttachment(\"Supplementary_Figure_5_Observable.png\").image({width: 700})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1005,"value":"# Detecting the distinct nature of the variants of concern on emergence","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":947,"value":"## Figure 7\n(A) UK SARS-CoV-2 spike sequences through the pandemic. Each point represents a sequence cluster with 99.9% sequence similarity. Dynamic semantic scores were calculated for each sequence cluster, with the black line showing the mean sliding score. \n
(B) Haplotypes are filtered on first occurrence of a new PANGO lineage with standard deviations computed for each 3-month window. The orange warning area covers sequences above 1 deviation from the mean, while the red warning area covers >2 standard deviations. \n
(C) Haplotypes filtered on the first appearance of a VOC/major variant, with deviation thresholds showing if they were detected on first appearance.\n
(D) Site mutation frequencies for sequences identified by warning thresholds mapped onto the spike monomer.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":977,"value":"viewof haplotype_lineage_form =Inputs.form({\n x_axis:Inputs.select(haplotype_sequences.columns, {value:\"sample_date\",label: \"Select X-Axis\",unique:true}), \n y_axis:Inputs.select(haplotype_sequences.columns, {value:\"sliding_semantic_score\",label: \"Select Y-Axis\",unique:true}),\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":974,"value":"viewof semantic_checkboxes = Inputs.range([0, 10], {label: \"Filter by sliding semantic score\", step: 0.1,value:0})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1036,"value":"

\n A\n ${haplotype_lineage_plot(haplotype_sequences, haplotype_lineage_form) }\n

\n BC\n ${first_haplotype_plot(first_haplotype_sequences, haplotype_sequences, haplotype_lineage_form) }\n ${first_variants(moving_sliding_dates, first_variant_sequences, haplotype_sequences) }\n\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":958,"value":"haplotype_sequences = haplotype_sequences_zip.file('updated_90_proper_Mafft_Haplotype_Spikes_Annotated.tsv').tsv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1099,"value":"## Figure 7 (D) \nSite mutation frequencies for sequences identified by warning thresholds mapped onto the spike monomer.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1213,"value":"

\n\n ${await FileAttachment(\"Figure_7_D_Observable@1.png\").image({width: 450})}\n ${bonus_plot_7d (haplotype_warning_counts)}\n

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1726,"value":"haplotype_warning_counts = haplotype_warning_positions.map(d=>{return {position:d,count:countOccurrences([...first_haplotype_mutation_subset].map(d=>parseInt(d.slice(1,-1))),d)}}).sort((a,b)=>b.count-a.count)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1756,"value":"haplotype_warning_positions = [...new Set([...first_haplotype_mutation_subset].map(d=>parseInt(d.slice(1,-1))))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1823,"value":"{\n let set_1 = new Set(haplotype_warning_positions)\n let set_2 = new Set([...variant_mutation_subset].map(d=>parseInt(d.slice(1,-1))))\n return set_1.intersection(set_2)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1113,"value":"warning_mutation_counts = [...new Set(haplotype_warning_position_occurences)].map(d=>{return {position:d,count:countOccurrences(haplotype_warning_position_occurences,d)}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":138,"value":"# Imports and Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1003,"value":"### Detecting the distinct nature of the variants of concern on emergence","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1017,"value":"### Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":953,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":996,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":982,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1132,"value":"function bonus_plot_7d (warning_mutation_counts){\n return Plot.plot({\n height: 650,\n width:300,\n x:{nice:true,grid:true,labelAnchor: \"center\",\n labelArrow:false,label:'Count'},\n y:{nice:true,grid:true,domain:[1,1273].reverse(),labelAnchor: \"center\",\n labelArrow:false,label:'Position'},\n style: {fontSize: \"16px\"},\n marginLeft:80,\n marginRight:50,\n marginTop:40,\n marginBottom:50,\n marks: [\n // Plot.ruleY([0]),\n Plot.dotY(warning_mutation_counts, {y: \"position\", x: \"count\",fill:\"count\",tip:true,r:3,stroke:'black'})\n ],\n color: {\n range: [\"grey\",\"#FEFFB2\",\"#FED976\",\"#FEB34B\",\"#FD8D3C\",\"#F03B21\",\"#BD0026\"],\n domain:[0,1,2,3,4,5,6],\n // legend: true,\n nice:true\n },\n})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":964,"value":"function getStandardDeviation (array) {\n const n = array.length\n const mean = array.reduce((a, b) => a + b) / n\n return Math.sqrt(array.map(x => Math.pow(x - mean, 2)).reduce((a, b) => a + b) / n)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1104,"value":"interpolatedReference = d3.scaleLinear()\n .domain(moving_sliding_dates.map(d => d.date))\n .range(moving_sliding_dates.map(d => d.lower));","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1117,"value":"countOccurrences = (arr, val) =>\n arr.reduce((a, v) => (v === val ? a + 1 : a), 0);","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1015,"value":"### Variables","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":960,"value":"haplotype_sequences_zip = FileAttachment(\"updated_90_proper_Mafft_Haplotype_Spikes_Annotated 2-5.tsv.zip\").zip()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":988,"value":"first_haplotype_sequences = {\n let sorted_sequences = haplotype_sequences.sort((a,b) => Date.parse(a.sample_date) - Date.parse(b.sample_date))\n let seen = new Set([])\n let sequences = []\n for (const sequence of sorted_sequences) {\n if (seen.has(sequence.lineage) == false){\n sequences.push(sequence)\n seen.add(sequence.lineage)\n }\n }\n return sequences\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":998,"value":"first_variant_sequences = {\n let sorted_sequences = haplotype_sequences.sort((a,b) => Date.parse(a.sample_date) - Date.parse(b.sample_date))\n let seen = new Set([])\n let sequences = []\n for (const sequence of sorted_sequences) {\n if (seen.has(sequence.Variant) == false){\n if (sequence.Variant != 'A Non-VOC'){\n sequences.push(sequence)\n seen.add(sequence.Variant)\n }\n }\n }\n return sequences\n}\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1152,"value":"first_haplotype_mutation_subset = new Set(first_haplotype_sequences.filter(d => d.sliding_semantic_score > interpolatedReference(d.sample_date)).map(d=>d.mutations.split(',')).flat(1).map(d=>d.replace(/\\s/g, '')))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1817,"value":"haplotype_warning_variant_intersection = variant_mutation_subset.intersection(first_haplotype_mutation_subset)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1720,"value":"##### Mutations in first Variants","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1106,"value":"variant_mutation_subset = new Set([...new Set(first_variant_sequences.filter(d=>d.sliding_semantic_score > semantic_checkboxes).map(d=>d.mutations.split(', ')).join().split(','))])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1723,"value":"##### Mutations in fist Variants","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1102,"value":"warning_mutations = new Set([...variant_mutation_subset.intersection(new Set(first_haplotype_sequences.filter(d => d.sliding_semantic_score > interpolatedReference(d.sample_date)).map(d=>d.mutations.split(',')).flat(1).map(d=>d.replace(/\\s/g, ''))))])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1110,"value":"warning_mutations_annotated = filtered_data.filter(d=>warning_mutations.has(d.label))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1155,"value":"haplotype_warning_position_occurences = [...first_haplotype_mutation_subset].map(d=>parseInt(d.slice(1,-1))).filter(d=>warning_positons.has(d))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1143,"value":"warning_positons = new Set(warning_mutations_annotated.map(d=>d.pos))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":984,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":866,"value":"## Benchmarking","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":893,"value":"### Supplementary Figure 10","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":895,"value":"#### Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":785,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":807,"value":"function supplementary_figure_10_a(full_spike_common,supplementary_colour_scheme){\n return Plot.plot({\n marginTop:50,\n height: 500,\n width:1200,\n margin:50,\n x:{nice:true,grid:true,label:''},\n y:{nice:true,grid:true,label:'Proportion of top 10% mutations observed',labelAnchor: \"center\",\n labelArrow:false,},\n style: {fontSize: \"14px\"},\n facet:{\n data:full_spike_common,\n x:'method',\n y:'region'\n },\n fx: {padding:0.3,domain:['ESM','Eve','Mutability','Experimental Full Spike','Experimental']}, \n marks: [\n \n Plot.ruleY([0]),\n Plot.lineY(full_spike_common, {x: \"date\", y: \"percentage\",stroke:'feature',tip:true,strokeWidth:3,strokeDasharray:1})\n ],\n color:{\n range:supplementary_colour_scheme,\n domain:[...new Set(full_spike_common.map(d=>d.feature))],\n legend:true\n }\n})\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":897,"value":"#### Variables","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":812,"value":"supplementary_colour_scheme = d3.schemeOranges[6].slice(1,4).concat(d3.schemeReds[4].slice(0,4).reverse()).concat(d3.schemeBlues[7].slice(1,7)).concat(d3.schemeGreys[3].slice(1,3)).concat(d3.schemeGreens[3].slice(1,3))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":794,"value":"rbd_and_outwith = FileAttachment(\"voc_indentification_updated_common_measurements_rbd_spike.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":819,"value":"full_spike_common = FileAttachment(\"common_monthly_muts_cumulative_all.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":901,"value":"### Supplementary Figure 11","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":903,"value":"#### Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":759,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":771,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":869,"value":"### Figure 4","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":874,"value":"##### Variables","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":671,"value":"all_correlations = FileAttachment(\"all_significant_correlations.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":669,"value":"single_value_correlations = all_correlations.filter(d=>d.Model == 'Value').map(obj => ({ ...obj, plot_significance:obj.bonferroni_max_significant == \"*\" ? obj.correlation*1.1 : null }))\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":710,"value":"computational_rbf_svr_correlations = rbf_svr_correlations.filter(d=>d.predictor.includes('20') == false)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":708,"value":"experimental_rbf_svr_correlations = rbf_svr_correlations.filter(d=>d.predictor.includes('20') == true)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":712,"value":"rbf_svr_correlations = all_correlations.filter(d=>d.Model == 'SVR').map(obj => ({ ...obj, plot_significance:obj.bonferroni_max_significant == \"*\" ? obj.correlation*1.1 : null }))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":667,"value":"computational_single_value_correlations = single_value_correlations.filter(d=>d.predictor.includes('20') == false)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":665,"value":"experimental_single_value_correlations = single_value_correlations.filter(d=>d.predictor.includes('20') == true)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":681,"value":"colour_scheme = d3.schemeBlues[7].slice(1,6).concat(d3.schemeOranges[4].slice(2,4)).concat(d3.schemeGreens[3].slice(1,3)).concat(d3.schemeGreys[3].slice(0,2))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":753,"value":"computational_linear_correlations = updated_linear_correlations.filter(d=>d.predictor.includes('20') == false)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":757,"value":"experimental_linear_correlations = updated_linear_correlations.filter(d=>d.predictor.includes('20') == true)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":755,"value":"updated_linear_correlations = all_correlations.filter(d=>d.Model == 'Linear').map(obj => ({ ...obj, plot_significance:obj.bonferroni_max_significant == \"*\" ? obj.correlation*1.1 : null }))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":871,"value":"##### Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":662,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":656,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":697,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":703,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":172,"value":"## DMS","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":157,"value":"### DMS Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":182,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":160,"value":"function get_lineage_mutations(search,data){\n for (const lineage of data) {\n if (lineage.lineage == search){\n\n if(lineage.mutations !=null){\n return lineage.mutations.replaceAll(\"'\",\"\").replaceAll(\"[\",\"\").replaceAll(\"]\",\"\").split(\", \")\n }else{\n return []\n }\n }\n }\n return []\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":163,"value":"function mutate_spike(lineage_mutations,spike){\n if (lineage_mutations.length>0){\n let mutated_spike = spike.split(\"\")\n for (let mutation of lineage_mutations){\n let ref = mutation.slice(0,1)\n let alt = mutation.slice(-1)\n let pos = parseInt(mutation.slice(1,-1))\n console.log(ref)\n console.log(pos)\n console.log(alt)\n \n mutated_spike[pos-1] = alt\n }\n return mutated_spike.join(\"\")\n }\n return \"\"\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":170,"value":"### DMS Variables","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":166,"value":"mutated_lineage_spike = mutate_spike(lineage_mutations,spike)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":168,"value":"$43","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":177,"value":"selected_lineage_entry = {\n if(search !=\"\"){\n for (const lineage of initial_lineage_sequences) {\n if (lineage.lineage == search){\n return lineage\n }\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":180,"value":"lineage_mutations = get_lineage_mutations(search,initial_lineage_sequences)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":188,"value":"dms_spike_upload_filter = {\n return wuhan_esm2_data.filter((element, index) => {\n if (uploaded_mutations.length > 0){\n return uploaded_mutations.includes(element.label)\n }\n })\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":192,"value":"uploaded_mutations_positions = uploaded_mutations.map((d)=>parseInt(d.slice(1,-1)))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":194,"value":"uploaded_mutations = { \n if (uploaded_spike.split(\"\").length == 1273) {\n return spike.split(\"\").map((d, i) => {\n if(d != uploaded_spike[i]){\n return d+(i+1)+uploaded_spike[i]\n }else{\n return false\n } \n }).filter((d)=>d!=false)\n }else if(mutated_lineage_spike.split(\"\").length == 1273){\n return spike.split(\"\").map((d, i) => {\n if(d != mutated_lineage_spike[i]){\n return d+(i+1)+mutated_lineage_spike[i]\n }else{\n return false\n }\n }).filter((d)=>d!=false)\n }else{\n console.log(mutated_lineage_spike.length)\n return []\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":155,"value":"dms_zip = FileAttachment(\"DMS_S_annotated_with_counts-1.csv.zip\").zip()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":199,"value":"data = wuhan_esm2_data.map(obj => ({ ...obj, voc_mutation:voc_mutations.has(obj.label)}))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":197,"value":"filtered_data = { \n if (uploaded_spike.split(\"\").length ==1273 ){ \n return filter_data(uploaded_spike,dms_spike_upload_filter)\n }else if(mutated_lineage_spike.split(\"\").length == 1273){\n return filter_data(uploaded_spike,dms_spike_upload_filter)\n }\n return filter_data(uploaded_spike,data)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":231,"value":"## Lineage Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":233,"value":"initial_lineage_sequences = FileAttachment(\"initial_sequences_with_sliding_windows.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1386,"value":"voc_mutations_2 = new Set(initial_lineage_sequences.filter(d=>d.Voc == 'Non-VOC').map(d=>{\n if (d.mutations !=null){\n return d.mutations.split(', ')\n }else{\n return \"\"\n }\n}).flat(1).filter(d=>d.includes('del') == false))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1803,"value":"voc_mutations = new Set(voc_sequences.map(d=>{\n if (d.mutations !=null){\n return d.mutations.split(', ')\n }else{\n return \"\"\n }\n}).flat(1).filter(d=>d.includes('del') == false))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1793,"value":"voc_sequences = {\n let sorted_sequences = initial_lineage_sequences.sort((a,b) => Date.parse(a.sample_date) - Date.parse(b.sample_date))\n let seen = new Set([])\n let sequences = []\n for (const sequence of sorted_sequences) {\n if (seen.has(sequence.Voc) == false){\n if (sequence.Voc != 'Non-VOC'){\n sequences.push(sequence)\n seen.add(sequence.Voc)\n }\n }\n }\n return sequences\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":381,"value":"## Epistasis","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":444,"value":"identified_interactions_std = FileAttachment(\"Omicron_Interactions_Std.zip\").zip()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":290,"value":"epistasis_zip = FileAttachment(\"Revisions_Omicron_Epistasis_with_single_std.csv.zip\").zip()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":486,"value":"### Variables","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":599,"value":"#### Critical and Impactful Sites","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":561,"value":"$44","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":586,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":567,"value":"amino_sites_affected_by_mutations = FileAttachment(\"amino_acids_positions_affected_by_mutations.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":592,"value":"amino_mutations_affect_sites = FileAttachment(\"amino_acids_mutations_affect_positions.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":490,"value":"##### BA.1 Info","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":465,"value":"BA1_Additional_Mutation = new Set(['Y505H',\"N501Y\",'Q498R','G496S','Q493R','E484A','T478K','S477N','G446S','N440K','K417N','S375F','S373P','S371L','G339D'])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":467,"value":"BA1_Additional_Mutation_Positions = new Set([505,501,498,496,493,484,478,477,446,440,417,375,373,371,339])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":448,"value":"ba1_rbd_positions = [...all_ba1_pos].slice(4,-11)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":463,"value":"BA1_Additional = epistasis_data.filter(d=>{\n if(BA1_Additional_Mutation.has(d.mutation) \n & d.significant_masked_change != null\n & BA1_Additional_Mutation_Positions.has(d.position)\n )\n {return d} })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":446,"value":"all_ba1_pos = new Set(epistasis_data.map(d=>parseInt(d.mutation.slice(1,-1))))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":450,"value":"divide_line_data = [...BA1_Additional_Mutation].map(d=> {return {mutation:d,position:parseInt(d.slice(1,-1))}})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":505,"value":"##### Identified Interactions ESM2","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":428,"value":"identified_sites_two_std = new Set(identified_interactions_std_two.map(d=>d.position_1.toString()+','+d.position_2.toString()))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":440,"value":"identified_interactions_std_two = identified_interactions_std.file(\"Omicron_Two_Std_Interactions.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":435,"value":"identified_epi_compare_two_std = epi_compare_ranked.filter(d=>identified_sites_two_std.has(d.labels))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":430,"value":"identified_sites_one_std = new Set(identified_interactions_std_one.map(d=>d.position_1.toString()+','+d.position_2.toString()))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":437,"value":"identified_epi_compare_one_std = epi_compare_ranked.filter(d=>identified_sites_one_std.has(d.labels))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":442,"value":"identified_interactions_std_one = identified_interactions_std.file(\"Omicron_One_Std_Interactions.csv\").csv({typed: true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":553,"value":"##### Benchmark","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":526,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":531,"value":"// innocenti_labels = [\n// {labels:'444,460'},\n// {labels:'445,460'},\n// {labels:'346,339'},\n// {labels:'339,368'},\n// {labels:'339,373'},\n// {labels:'371,373'},\n// {labels:'373,375'},\n// {labels:'373,498'},\n// {labels:'498,501'},\n// {labels:'498,505'},\n// {labels:'484,498'},\n// {labels:'477,484'},\n// {labels:'417,440'},\n// {labels:'486,490'},\n// {labels:'408,410'},\n// {labels:'376,405'},\n// {labels:'405,408'},\n// {labels:'446,452'},\n// {labels:'452,493'},\n// ]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2074,"value":"innocenti_labels_data = FileAttachment(\"13059_2024_3355_MOESM1_ESM_Spike_filtered.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2080,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":498,"value":"##### Experimental Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":425,"value":"epi_compare = FileAttachment(\"2ndorder_structure_summaryACE2.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":432,"value":"epi_compare_ranked = epi_compare.sort((a, b) => Math.abs(b.epistasis) - Math.abs(a.epistasis)) // Sort by absolute epistasis\n .map((item, index) => ({ ...item, rank: index + 1 }))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":479,"value":"### Functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":396,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":409,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":421,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":132,"value":"## Spearmans Rank","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":237,"value":"SpearmanRHO = require('https://bundle.run/spearman-rho@1.0.6')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":129,"value":"rho = new SpearmanRHO(\n filtered_data.filter(obj => obj[form.y_axis] !== null).filter(obj => obj[form.x_axis] !== null).map(obj => obj[form.x_axis]), \n filtered_data.filter(obj => obj[form.y_axis] !== null).filter(obj => obj[form.x_axis] !== null).map(obj => obj[form.y_axis])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":127,"value":"rhoval= rho.calc()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1278,"value":"### Boxplots","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1280,"value":"vegalite = require(\"@observablehq/vega-lite@0.2\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1283,"value":"one_dimensional_boxplot = ({\n width:450,\n height:300,\n \"title\": \"1D Distances\",\n labelFontSize:20,\n titleFontSize:20,\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:epistasis_distances\n },\n encoding:{\n y:{field:\"1d_distance\",type:\"quantitative\",\"title\": \"Distance\",},\n x:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\"},\n color:{field:\"subunit\",type:\"nominal\", scale:{range:['#E74421','#4068FE']}}\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1285,"value":"three_dimensional_boxplot = ({\n width:500,\n height:300,\n \"title\": \"3D Distances\",\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:epistasis_distances\n },\n encoding:{\n y:{field:\"3d_distance\",type:\"quantitative\",\"title\": \" \",},\n x:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\"},\n color:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\", scale:{range:['#E74421','#4068FE']}}\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1287,"value":"mutation_three_dimensional_boxplot = ({\n width:1000,\n height:150,\n \"title\": \"3D Distances\",\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:epistasis_distances\n },\n encoding:{\n y:{field:\"3d_distance\",type:\"quantitative\",\"title\": \" Distance\",},\n x:{field:\"mutation\",\n type:\"ordinal\",\n \"title\": \"Mutation\",\n scale: {\"domain\": ['A67V', 'T95I', 'G142D', 'L212I', 'G339D', 'S371L', 'S373P', 'S375F',\n 'K417N', 'N440K', 'G446S', 'S477N', 'T478K', 'E484A', 'Q493R', 'G496S',\n 'Q498R', 'N501Y', 'Y505H', 'T547K', 'D614G', 'H655Y', 'N679K', 'P681H',\n 'N764K', 'D796Y', 'N856K', 'Q954H', 'N969K', 'L981F']}\n },\n color:{field:\"subunit\",\n type:\"nominal\",\n title: \"Subunit\", \n scale:{range:['#E74421','#4068FE']}\n }\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1289,"value":"mutation_one_dimensional_boxplot = ({\n width:1000,\n height:150,\n \"title\": \"1D Distances\",\n mark:{\n type:\"boxplot\",\n ticks:true,\n },\n data:{\n values:epistasis_distances\n },\n encoding:{\n y:{field:\"1d_distance\",type:\"quantitative\",\"title\": \"Distance\",},\n x:{field:\"mutation\",type:\"ordinal\",\"title\": \"Mutation\",\n scale: {\"domain\": ['A67V', 'T95I', 'G142D', 'L212I', 'G339D', 'S371L', 'S373P', 'S375F',\n 'K417N', 'N440K', 'G446S', 'S477N', 'T478K', 'E484A', 'Q493R', 'G496S',\n 'Q498R', 'N501Y', 'Y505H', 'T547K', 'D614G', 'H655Y', 'N679K', 'P681H',\n 'N764K', 'D796Y', 'N856K', 'Q954H', 'N969K', 'L981F']}},\n color:{field:\"subunit\",type:\"nominal\",\"title\": \"Subunit\", scale:{range:['#E74421','#4068FE']}}\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":206,"value":"## Misc Imports","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":220,"value":"import {ForceGraph} from \"@d3/force-directed-graph\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":215,"value":"import {toSVG} from \"@fil/plot-figure-to-svg\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":210,"value":"import { rangeSlider as rangeSlider } from '@mootari/range-slider'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4b","stdlib":"1","titling":"auto"}} 33:"$32:body"