2b:T4de,intro = md` **Last update: June 8th, 2020 (data from June 7th, 2020); read analysis updates for important details**.
Which positions in the [SARS-CoV-2 genome](https://www.ncbi.nlm.nih.gov/nuccore/?term=COVID) **may be** subject to **positive** selection, i.e., involved in adaptation, or **negative** selection, i.e. conserved during evolution? We used several statistical techniques and other sources of information to identify potential candidates using unique genomic sequences ([details](ttps://github.com/veg/SARS-CoV-2)), with permissive thresholds to identify sets of sites that might merit a deeper look. The sites which **we think are the most interesting** are shown at the top. Each site receives _points_ for **eight** different categories, coded by icons and explained below, and sites with the most points are ranked higher. Details of what's in the table are below all the images on the page. The table below may take a little while to load, since it combines several "live" sources of data. The image below summarizes all the genomic sites which appear in any of the "interesting" categories detailed below (total # of categories = site score, click and drag in the bar below the plot to zoom in). ` 2c:T13f4,genome_view = vegalite({ "$schema": "https://vega.github.io/schema/vega-lite/v5.json", "config": { "view": { "stroke": null }, }, "data": { "values": _.filter (summary.plot, (r)=>r.score >= minimum_score && r.position >= 0 && (r.type != "Negative" || !ignoreConserved)) }, "vconcat": [{ "height": 90, "width": 750, "mark": { type: "point", filled : true, }, "encoding": { "y": { "field": "kind", "type": "nominal", "sort": null, "axis": { "domain": false, "offset": 10, "labelFontWeight": "bold", "ticks": false, "grid": true, "title": null } }, "shape" : { "field" : "type", "title" : "Selection", "scale" :{ "type" : "nominal", "domain" : ["Negative","Neutral","Positive"], "range" : ["triangle-down","circle","cross"] } }, "size" : { "value" :40, }, "x": { "field": "position", "type": "quantitative", "scale": { "domain": { "selection": "brush" } }, "axis": { "title": "Genomic Coordinate", "grid": false, "orient" : "top" } }, } }, { "height": 40, "width": 750, "layer": [{ "selection": { "brush": { "type": "interval", "encodings": ["x"], "mark": { "fill": "#fdbb84", "fillOpacity": 0.5, "stroke": "#e34a33" } } }, "transform": [{ "calculate": "3", "as": "y" }, { "calculate": "12", "as": "y2" }], "mark": { type: "rule" }, "encoding": { "tooltip": [{"field": "position", "type": "quantitative"}, {"field": "gene", "type": "nominal"}, {"field": "site_in_gene", "type": "quantitative"}, {"field": "score", "type": "quantitative"}, {"field": "type", "type": "nominal"}, {"field": "peptide", "type": "nominal"},], "x": { "field": "position", "type": "quantitative", "axis": null, }, "y": { "field": "y", "type": "quantitative", "axis": null, }, "y2": { "field": "y2", "type": "quantitative", "axis": null, }, "color": { "field": "score", "type": "nominal", "legend": { "title": "Site score", "orient": "bottom" }, "scale": { "scheme": "lightgreyred" } } } }, {"data": { "values": gene_coordinates(use_date) }, "transform": [{ "calculate": "(datum['3'] % 2) ? -1 : 1", "as": "coloring" }, { "calculate": "0", "as": "y" }, { "calculate": "15", "as": "y2" }, { "calculate": "-10 + (datum['3'] == 4 || (datum['3'] == 6 && datum['1'] != 'N') ? 26 : 0) + (datum['1'] == 'ORF8' ? 35 : 0) + (datum['3']>8? (datum['3'] % 2 ? 26 : 0) : 0) + (datum['1']=='S' ? 26 : 0) + (datum['1']=='leader' ? 26 : 0)", "as": "y3" }, { "calculate": "(datum['0']+datum['2'])*0.5", "as": "xm" }], "layer" : [ { "mark": { "type": "bar", "stroke": "#000", tooltip : false, }, "encoding": { "x": { "field": "0", "type": "quantitative", "axis": null }, "x2": { "field": "2" }, "y": { "field": "y", "type": "quantitative", "axis": null }, "y2": { "field": "y2" }, "fill": { "field": "coloring", "scale": { "range": ["lightgrey", "white"] }, "legend": null, "type": "nominal" }, "opacity": { "value": "0.5" } } }, { "mark": { "type": "text", "fontSize": 9, "baseline": "bottom", "tooltip" : false, "align" : "left" }, "encoding": { "x": { "field": "0", "type": "quantitative", }, "y": { "field": "y3" }, "text": { "field": "1", "legend" : null, }, } } ] } ] }, ] })2d:T1593,summary_table = { if(filtered_summary_table.length) { return table( filtered_summary_table, { limit: 5000, enableCSVDownload: true, sort: true, enableFilter: true, columns: [ { key: 'prior', name: 'First Detected', render: val => { if (val && val[1]) { let bgColor = d3.interpolateBlues(val[2]) if (val[0].length == 0) { return `New`; } else { return ' ' + date_format_short (date_reader(val[0][0])); } } return 'New'; } }, { key: 'site', name: 'Site' }, { key: 'classification', name: 'Categories', sortBy: (a,b,key, order) => { return Object.keys(a[key]).length > Object.keys(b[key]).length ? order : Object.keys(b[key]).length > Object.keys(a[key]).length ? -order : 0}, render: val => { let tags = []; if (val.fel) { if (val.kind == "negative") tags.push (''); else tags.push (''); } else { if (val.meme) { tags.push (''); } } if (val.MAF) { tags.push (''); } if (val.trend) { tags.push (''); } if (val.branches > 1) { tags.push (''); } if (val.variants > 0) { tags.push (''); } if (val.betas && val.betas.length > 0) { tags.push (''); } if (val.hla) { tags.push (''); } if (('evo_support' in val) && (val.evo_support == false)) { tags.push (''); } return `${tags.join (' ')}`;} }, { key: 'composition', name: 'Site composition
Evolutionary Credibility', render: val => {if(val["bloom-affinities"]) { debugger;}; return `${val.composition} ${val.variants.length > 0 ? '' : ''} `} }, { key: 'expanded_subs', name: 'Substitutions', render:val => { let tags = []; let decorate_key = (d)=>{ return d.key; } _.each (val, (d)=>{ let tag = "" + decorate_key(d); if (d.leaves) { tag += ' ' + d.leaves + ''; } if (d.internal) { tag += ' ' + d.internal + ''; } tag += "
"; tags.push (tag); //if (d[ }); return `${tags.join (' ')}`; } }, { key: 'annotation', name: 'Annotation', render:val => { let tags = []; _.each (val, (i) => { _.each (i, (detail, key)=> { let annotation_item = "

" + key + "

"; tags.push (annotation_item); _.each (detail['Impact'], (ignore,impact)=> { annotation_item = "

" + impact + "

"; tags.push (annotation_item); }); let references = []; _.each (detail['References'], (ingore, ref)=> { references.push ("[" + (1+references.length) + "] "); }); if (references.length) { tags.push ("

" + references.join("") + "

"); } }); }); return `${tags.join (' ')}`; } } ] } ) } else { return md`# No Sites Under this Filter Criteria` }}2e:T898,temporal_evolution_plot = vegalite({ "title" : "Temporal evolution of " + render_site.split (',').slice (0,2).join ("/") + " composition", width : 600, height: 300, autosize : "fit", data: {values: render_country=='World' ? _.flatten(_.map (_.groupBy (timing.data, "sample"), (v,d)=> { return _.map (_.groupBy (v, (v)=>v.residue), (counts, r)=> { return ({'residue' : r, 'sample' : d, 'count' : d3.sum (counts, (d)=>d.count)}); }); }), true) : _.filter (timing.data, (d)=> render_country == d.country) }, transform: [{ "window": [{ "op": "sum", "field": "count", "as": "total_count" }], "groupby": ["sample"], "frame" : [null, null] }, { "calculate": "datum.count/datum.total_count * 100", "as": "PercentOfTotal" }], mark: {type:"line", tooltip: true}, encoding: { x: {field: "sample", type: "temporal", axis: {"title": "Collection Date", titleFontSize : 14}}, "tooltip": [ {"field": "sample", "type": "temporal"}, {"field": "count", "type": "quantitative"}, {"field": "residue", "type": "nominal"} ] }, "layer": [{ "mark": {"type": "bar", "opacity" : 0.5}, "encoding": { "y": {"field": "count", "type": "quantitative", axis: { "title": "#Sequences with residue", "titleFontSize" : 14 }, "scale": { "type": "linear" }}, "color" : { "sort": {"field": "residue"}, "field" : "residue" , "scale": {"scheme": "set1"} } }, }, { "mark": {"type": "line", "interpolate" : "monotone", "strokeWidth" : 3}, "encoding": { "y": {"field": "PercentOfTotal", "type": "quantitative", axis: { "title": "% of total", "titleFontSize" : 14 }, "scale": { "type": "linear" }}, "color" : { "sort": {"field": "residue"}, "field" : "residue" , "scale": {"scheme": "set1"}, //"legend" : null } }, } ], "config": {"axisY": {"minExtent": 30}}, "resolve": {"scale": {"y": "independent"}} })2f:T49b,distribution_map = vegalite ( { "width": 800, "height": 400, "title" : "Distribution of " + use_residue + " at " + render_site.split (',').slice (0,2).join ('/'), "data": { "values" : world, "format": { "type": "topojson", "feature": "countries" } }, "transform": [{ "lookup": "id", "default" : "-0.0001", "from": { "data": { "values": subs_count }, "key": "id", "fields": ["rate","name","total","variant"] } }], "projection": { "type": "mercator", "center" : [5,20], "scale": 110, }, "mark": { type : "geoshape", }, "encoding": { "tooltip": [ {"field": "name", "type": "nominal"}, {"field": "variant", "type": "quantitative"}, {"field": "total", "type": "quantitative"}, {"field": "rate", "type": "quantitative"} ], "color": { "condition": { "test": "datum.rate < 0", "value": "#aaa" }, "scale" : { //domain : [0,1] }, "legend" : { "title" : "Residue frequency" }, "field": "rate", "type": "quantitative" } } } )30:Tfa0,rendered_tree = { var t = tree.render({ height:height, width:width, 'left-right-spacing': 'fit-to-size', 'top-bottom-spacing': 'fit-to-size', 'is-radial' : false, 'node_circle_size' : (n)=>1 }); const bs = (n) => { if (tree.is_leafnode (n)) { let bits = n.data.name.split ('_'); return 1+ Math.log(parseFloat (bits[bits.length-1])); } return 0; }; if ('usebl' in tree) { t.phylotree.branch_length_accessor = n=>tree.usebl[n.data.name]; } var c10 = d3.scaleOrdinal(d3.schemeSet1); t.node_bubble_size = bs; t.phylotree.node_bubble_size = bs; t.options["draw-size-bubbles"] = true; t.font_size = 8; let node_label_set = {}; t.node_label ((n)=> { try { if (node_labels[n.data.name][0] != '-') { node_label_set [node_labels[n.data.name][0]] = 1; } if (tip_names) { return n.data.name + "[" + node_labels[n.data.name][0] + "]"; } return node_labels[n.data.name][0]; } catch {} return ""; }); c10.domain (_.sortBy(_.keys(node_label_set))); function sort_nodes (asc) { tree.traverse_and_compute (function (n) { var d = 1; if (n.children && n.children.length) { d += d3.max (n.children, function (d) { return d["count_depth"];}); } n["count_depth"] = d; }); tree.resort_children (function (a,b) { return (a["count_depth"] - b["count_depth"]) * (asc ? 1 : -1); }); } sort_nodes (true); t.style_edges ((e,d) => { try { let to_aa = node_labels[d.target.data.name][0]; let from_aa = node_labels[d.source.data.name][0]; let from_codon = node_labels[d.source.data.name][1]; let to_codon = node_labels[d.target.data.name][1]; if (from_codon != to_codon && to_aa != "-") { e.style ('stroke-width' , '5px').style ('stroke', 'darkgrey'); //console.log ([from_aa + "->" + to_aa]); let title = e.selectAll("title").data ([from_codon + "(" + from_aa + ")->" + to_codon + "(" + to_aa + ")"]); title.enter().append("title"); title.text (d=>d); } else { e.style ('stroke-width' , '0.5px').style ('stroke', 'lightgrey'); } } catch { e.style ('stroke-width' , '0.5px').style ('stroke', 'lightgrey'); } }); t.style_nodes ((n,d) => { try { n.selectAll("title").data ([d.data.name + "(" + node_labels[d.data.name][1] + ")"]).enter().append("title").text (d=>d); if (node_labels[d.data.name][0] != '-') { let node_label = node_labels[d.data.name][0]; if (node_label) { let opacity = use_residue == node_label ? 1.0 : 0.5; n.style ('fill', c10 (node_label)).style ('opacity', opacity); n.selectAll ("circle").style ('fill', c10 (node_label)).style ('opacity', opacity); } } } catch {} }); /*tree.traverse_and_compute ((n) => { if (!tree.is_leafnode (n)) { if ("collapsed" in n) { delete n["collapsed"]; } n.tagged_view = false; } n.residues_below = {}; }); tree.traverse_and_compute ((n) => { try { if (tree.is_leafnode (n)) { n.residues_below [node_labels[n.data.name][0]] = 1; } if (n.parent) { _.extend (n.parent.residues_below, n.residues_below); } if (_.size (n.residues_below) > 1) { n.tagged_view = true; } if (n.tagged_view && n.parent) { n.parent.tagged_view = true; }} catch (e) {console.log (e);} }); tree.traverse_and_compute ((n) => { if (!tree.is_leafnode (n) && !n.tagged_view && n.parent && n.parent.tagged_view) { n.collapsed = true; } }); t.placenodes().layout(); */ t.update(); return t.show(); }31:T197e,documentation = md`### Analysis updates. Starting with the 2020-05-23 analysis, we split ORF1a and ORF1b into constituent peptides (e.g., nsp3, RdRp) for analyses to speed up execution. This loses some phylogenetic resolution, but because selection operates (largely) on the level of individual protein products, this is a standard approach to take. Therefore analyses before and after that date may yield somewhat different results, but **should** remain broadly comparable. ### What's in the selection table? Each row is a position in the SARV-CoV-2 genome, annotated by the gene, its position within that gene, and (if appropriate) peptide product, that was _inferred_ to be evolving **not-neutrally** using [MEME](http://hyphy.org/methods/selection-methods/#meme) and/or [FEL](http://hyphy.org/methods/selection-methods/#fel) methods, restricted only to internal tree branches, **or** carried a high minor allele frequency (MAF > 0.2) among uniue haplotypes. #### First detected The date this site first appeared on the list, or **New**. Note that sites that were detected in the past but are not detected at present are not shown in the table and you can choose to view the table as it appeared on a prior date by selecting it from the pull-down list. #### Categories Each site can _check_ one of the following five categories (the more it checks, the more interesting it is). Note that each site must on the list must be **either** under selection (as determined by MEME/FEL) **or** have an minor allele at frequency of 20% or greater. ##### Selection kind * (Positive Pervasive): on average along interior tree branches, this site has a [dN/dS>1](https://en.wikipedia.org/wiki/Ka%2FKs_ratio) is accumulating non-synonyomous changes (some of which might have a functional impact, but most probably don't) faster relative to synonymous changes than would be expected under [neutral evolution](https://en.wikipedia.org/wiki/Neutral_mutation). * (Positive Episodic): only a fraction of interior tree branches has dN/dS > 1. This pattern is consistent with periods of evolutionary change followed by conservation or neutral evolution. Mouse over to the see the fraction of branches. * (Negative): on average along interior tree branches, this site has a dN/dS<1, meaning that it **conserved**, i.e. non-synonymous changes might be selected against. Note that sites with no changes (i.e. perfectly conserved sites) cannot be detected by dN/dS based methods ##### Multiple selective events If a site belongs to this category, then at least **two** internal branches in the tree are inferred to be subject to positive selection, implying several selective events (not just a single introduction) ##### Increasing allele frequency When a site belongs to this category, alternative amino-acids at this site has an increasing frequency trend (based on applying the [Jonckheere–Terpstra test](https://en.wikipedia.org/wiki/Jonckheere%27s_trend_test) to 10-day intervals) ##### High frequency variants When a site belongs to this category, the fraction of **unique viral hapoltypes in this gene** that carry a minor allele is ≥20%. ##### High frequency variants When a site belongs to this category, there is at least one [deep sequencing dataset](https://covid19.galaxyproject.org/genomics/4-Variation/) that shows the presence of intra-host variation at the same site at between 2 and 90%. ##### Evolutionarily unexpected variants When a site belongs to this category, there is at least one amino-acid present in more than one sequence that was not predicted to occur at this site based on the evolution of **other** betacoronaviruses. It might indicate evolutionary events that we have not observed in similar viruses before. ##### Non-neutral evolution in closely related beta-coronaviruses There is evidence of positive selection or differential selection acting on the bat/pangolin isolates closely related to the SARS-CoV-2 genome based on data from [Boni et al](https://www.biorxiv.org/content/10.1101/2020.03.30.015008v1) ##### Site belongs to one or more predicted CTL-epitopes This site belongs to one or more CTL linear epitopes that are predicted to be recognized by one or more HLA alleles reported in [Campbell et al](https://www.biorxiv.org/content/10.1101/2020.03.30.016931v1.abstract) #### Composition/Evolutionary Credibility How often does an amino-acid occur in the unique haplotypes and their inferred ancestral sequences (prior to 2020/04/12 updates), or all includes sequences (2020/04/12 and thereafter) ? \`[-]\` is used to denote a deletion (or missing sequence data). If the composition column is labeled with , then there are some deep sequencing samples where **intra-host ** (minor or major) allele variants are present at this position ([details](https://covid19.galaxyproject.org/genomics/4-Variation/)). Hover over this icon to see the SRR numbers and additional information. If there were **mixed bases** in the genome (e.g. AKT which maps to either AGT, coding for S or ATT, coding for I), you may see ambiguous amini-acids recorded in the table and the charts (e.g. SI₁) Evolutionary credibility is computed by determining whether the presence of a given residue is **expected** or **unexpected** based on the imputations under an evolutionary model fitted to an alignment of homologous sequences from betacoronaviruses. The color of the residue (green, yellow, orange, red) indicates what category the residue belongs to. Currently, these calculations are only done for S and some of the ORF1a, but we are working to extend them to the entire genome. #### Annotation Literature-based annotation of sites and residues. As with any statistical inference, sites may be incorrectly classified, i.e. truly selected sites may be missed, and detected sites may in fact be neutral. `32:T498,variation_plot = vegalite({ "title": null, //"title" : {text : "Temporal evolution of genomic variation", fontSize : 20, anchor : "middle"}, //transform: [{"filter" : "datum.gene=='S'"}], width : 200, height: 200, data: {values: v_data}, "mark": {"type": "line", "interpolate" : "monotone", "strokeWidth" : 3}, encoding: { "facet" : { "field" : "gene" , "type": "ordinal", "columns": 3, "title" : "Gene", "sort": {"field": "gene"} }, "x": {field: "date", type: "temporal", axis: {"title": "Analysis date", titleFontSize : 14, grid : false}}, "y": {"field": "fraction", "type": "quantitative", axis: { "title": "Fraction of total", "titleFontSize" : 14, "grid" : false }, "scale": { "type": "linear" }}, "color" : { "sort": {"field": "kind"}, "field" : "kind" , "type": "nominal", "scale": {"scheme": "set1"}, "legend" : {"title" : "Variable site kind", titleFontSize : 14, labelFontSize: 12, orient : "right"} }, } })33:T856,annotation_data = { /* POS: 24483 REF: "C" ALT: "T" AF: 0.330935 type: "S" syn: 0 non_syn: 1 ref_codon: "tCa" alt_codon: "tTa" ref_residue: "S" alt_residue: "L" Genomic_coord_start: 24482 Genomic_coord_end: 24484 cleanMain: "S" MainResidue: 974 cleanSecond: "na" SecondResidue: "na" NbSample: 1 MeanAF: 0.330935 Annotation: "“HR1” Strictly Conserved, position e in the heptad repeat abcdefg pattern" Impact: null Reference: "10.1016/j.virol.2005.02.022, doi.org/10.1016/S0140-6736(04)15788-7"*/ let annotations = {}; let retrieve = async function asyncCall () { let genes = [ ['S','https://raw.githubusercontent.com/galaxyproject/SARS-CoV-2/master/genomics/7-VariantsDescription/S/Annotation_variants_S.tab'], ['3C','https://raw.githubusercontent.com/galaxyproject/SARS-CoV-2/master/genomics/7-VariantsDescription/3CLpro/Annotation_variants_3C.tab'], ['RdRp', 'https://raw.githubusercontent.com/galaxyproject/SARS-CoV-2/master/genomics/7-VariantsDescription/RdRp/RdRp_variants.tab']]; for (var k in genes) { try { let gene_annotations = await d3.tsv (genes[k][1], d3.autoType); _.each (gene_annotations, (r)=>{ if (r.Annotation) { if (!(r.POS in annotations)) { annotations[r.POS] = {}; } if (! (r.Annotation in annotations[r.POS])) { annotations[r.POS][r.Annotation] = { 'Impact' : {}, 'References' : {} } } if (r.Impact) { annotations[r.POS][r.Annotation]['Impact'][r.Impact] = 1; } if (r.Reference) { _.each (r.Reference.split (", "), (ref)=> annotations[r.POS][r.Annotation]['References'][ref] = 1); } } }); } catch (e) {console.log (e);} } }; retrieve (); return annotations; }34:T7f6,all_selection_data = { let retrieve = async function asyncCall (use_this_date) { let analysis_results = {}; let base_url = "https://data.hyphy.org/web/covid-19/selection-analyses/"; let genes = ["leader","nsp2","nsp3","nsp4","3C","nsp6","nsp7","nsp8","nsp9","nsp10","helicase","exonuclease","endornase","S","E","M","N","ORF3a","ORF6","ORF7a","ORF8","RdRp","methyltransferase"] for (var k in genes) { try { analysis_results[genes[k]] = await d3.json (base_url+use_this_date+"/sequences." + genes[k] + ".json"); if (! ('map' in analysis_results[genes[k]])) { analysis_results[genes[k]]['map'] = _.range (analysis_results[genes[k]].sites); } if ('branch_names' in analysis_results[genes[k]]) { // compressed format _.each (analysis_results[genes[k]]['selection'], (site)=> { let unpacked_labels = unpack_array(site.labels); site.labels = {}; _.each (unpacked_labels, (label,i)=> { site.labels [analysis_results[genes[k]]['branch_names'][i]] = label; }); let timing = {}; _.each (site.timing, (v,res)=> { timing[res] = _.unzip ([unpack_array(v[0]),unpack_array(v[1]),unpack_array(v[2]),v[3]]); }); site.timing = timing; }); } /*_.each (analysis_results[genes[k]]['selection'], (site)=> { _.each (site["labels"], (info, branch) => { if (info[0].length > 1){ // mixed base //console.log (info[0]); site["labels"][branch][0] = "amb"; } }); });*/ } catch (e) {console.log (e);} } return analysis_results; }; let raw_data = {}; raw_data[use_date] = await retrieve (use_date); return raw_data; }35:T6eb,timing = { var raw_timing = selection_data[0][render_site.split (',')[0] ]['selection'][render_site.split (',')[2]-1]['timing']; let time_parser = d3.timeParse ("%Y%m%d"); let rows = []; let do_aggregate = false; let countries = {'World' : 0}; _.each (raw_timing, (dates, residue) => { _.each (dates, (count, date_value) => { if (residue != '-') { if (_.isArray (count)) { do_aggregate = true; let row = {'residue' : residue, 'count' : count[3], 'sample' : time_parser (count[0]), 'country' : count[1] }; countries[count[1]] = count[3] + (countries[count[1]] ? countries[count[1]] : 0); rows.push (row); } else { let row = {'residue' : residue, 'count' : count, 'sample' : time_parser (date_value) }; rows.push (row); } } countries['World'] += count[3]; }) }); if (do_aggregate) { /*let by_date = _.groupBy (rows, (d)=>d.sample); let aggregated = []; _.each (by_date, (v,d)=> { _.each (_.groupBy (v, (v)=>v.residue), (counts, r)=> { aggregated.push ({'residue' : r, 'sample' : d, 'count' : d3.sum (counts, (d)=>d.count)}); }); });*/ return {'data' : rows, 'countries' : _.sortBy (_.map (countries, (v,k)=>[k,v]), (d)=>-d[1])}; } return {'data' : rows, 'countries' : countries}; }36:T704,subs_count = { let residue = use_residue; let raw_counts = current_site_info["timing"]; const parser = d3.timeParse ("%Y%m%d"); let counts_by_country = {}; const resolved_name = (name)=> { if (name == "England") { name = "United Kingdom"; } let c = _.find (country_equiv, (c)=>(c.name.common == name || c.name.official == name || c.cca3 == name || c.altSpellings.find ((e)=>e==name)!=undefined)); if (c) { return c.name.common; } return "N/A"; }; _.each (_.flatten(_.map (raw_counts, (residue)=>residue)), (d) => { let td = parser (d[0]); if (td >= date_range.range[0] && td <= date_range.range[1]) { const cn = resolved_name (d[1]); if (cn in counts_by_country) { counts_by_country [cn] += d[3]; } else { counts_by_country [cn] = d[3]; } } }); let residue_by_country = {}; _.each (raw_counts[residue],(d) => { let td = parser (d[0]); if (td >= date_range.range[0] && td <= date_range.range[1]) { const cn = resolved_name (d[1]); if (cn in residue_by_country) { residue_by_country [cn] += d[3]; } else { residue_by_country [cn] = d[3]; } } }); let fractions = _.map (counts_by_country, (d,c)=> { let cc = 0; if (c in residue_by_country) { cc = residue_by_country[c]; }; return [c,cc/d,cc,d] }); return _.map (fractions, (d) => { let name = d[0]; let country = _.find (country_equiv, (c)=>(c.name.common == name || c.name.official == name || c.cca3 == name || c.altSpellings.find ((e)=>e==name)!=undefined)); if (country) { return {'id' : country.ccn3, 'name' : name, 'rate' : d[1], 'total' : d[3], 'variant' : d[2]}; } return d; }); }37:T3d2c,summary = { let prior_dates = []; let support_format = d3.format (".2g"); let tabulate_for_date = (selection_info, this_date) => { let T = []; let for_plotting = []; let selectors = []; let epitopes = {}; _.each (selection_info, (record, gene)=> { let tree_object = new phylotree.phylotree (record['tree']); _.each (record['selection'], (info, site) => { let site_score = 0; let genomic_site = record.map[+site]+1; let ref_coord_start = _.find (gene_coordinates(this_date), (d)=> d[1] == gene)[0] + (genomic_site-1)*3; let prc = for_plotting.length; let plot_record = {'position' : ref_coord_start, 'gene' : gene, 'site_in_gene' : genomic_site}; let ref_coord = "" + ref_coord_start + "-" + (ref_coord_start+2) + ""; let peptide_site = _.find (peptide_coordinates, (d)=> (genomic_site >= d[1] && genomic_site <= d[2] && d[3] == gene)); let site_record = { 'site' : gene + " " + genomic_site + (peptide_site ? "
" + peptide_site[0] + " " + (genomic_site - peptide_site[1] + 1) : "") + "
Genomic " + ref_coord, 'classification' : {'fel' : info['fel'] <= record.p , 'meme' : info['meme'] <= record.p, 'fraction' : info['meme-fraction'], 'kind' : info['kind'], 'branches' : info['meme-branches'] }, 'substitutions' : info['substitutions'], 'position' : ref_coord_start, 'noise_coeff' : noiseCoeffDict[gene][genomic_site] }; // add bloom-affinities for each composition if(_.has(info, "bloom-affinities")) { site_record["affinities"] = info["bloom-affinities"]; } if (peptide_site) { plot_record['peptide'] = peptide_site[0] + " " + (genomic_site - peptide_site[1] + 1); } if ((site_record['classification']['fel']||site_record['classification']['meme'])) { site_score += 1; if (info['kind'] == "negative") { plot_record.kind = "Selection"; plot_record.type = "Negative"; for_plotting.push (_.clone(plot_record)); } else { plot_record.kind = "Selection"; plot_record.type = "Positive"; for_plotting.push (_.clone(plot_record)); } } else { plot_record.type = "Neutral"; } if (site_record['classification']['branches'] > 1) { site_score++; plot_record.kind = "Multiple Clades"; for_plotting.push (_.clone(plot_record)); } if ('MAF' in record) { if (info.MAF >= record.MAF) { site_record['classification']['MAF'] = info.MAF; site_score++; plot_record.kind = "Common Variant"; for_plotting.push (_.clone(plot_record)); } } if ('trend' in info) { if (info['trend'] >= 3) { site_record['classification']['trend'] = true; site_score++; plot_record.kind = "Increasing frequency"; for_plotting.push (_.clone(plot_record)); } } if (gene in shift_genes) { ref_coord_start++; } if ((ref_coord_start-1) in comparative_annotation) { site_record['comp'] = comparative_annotation[ref_coord_start-1]; if ('hla' in site_record['comp']) { let ec = _.size (site_record['comp']['hla']); if (ec) { site_record['classification']['hla'] = site_record['comp']['hla']; site_score++; plot_record.kind = "Predicted CTL epitope"; for_plotting.push (_.clone(plot_record)); } } if ('bFEL' in site_record['comp']) { if ( site_record['comp']['bFEL']['p'] <= record.p || site_record['comp']['bMEME']['p'] <= record.p || site_record['comp']['bCFEL']['p'] <= record.p) { if (site_record['comp']['bFEL']['p'] <= record.p) { site_record['classification']['betas'] = ((site_record['comp']['bFEL']['a'] < site_record['comp']['bFEL']['b']) ? "Positively" : "Negatively") + " selected in the closely related bat/pangolin clade";} else {site_record['classification']['betas'] = "";} if (site_record['comp']['bMEME']['p'] <= record.p) { if (site_record['classification']['betas'].length) { site_record['classification']['betas'] += "\n"; } site_record['classification']['betas'] += "Episodic positive selection in the closely related bat/pangolin clade"; } if (site_record['comp']['bCFEL']['p'] <= record.p) { if (site_record['classification']['betas'].length) { site_record['classification']['betas'] += "\n"; } site_record['classification']['betas'] = site_record['comp']['bCFEL']['b-nCOV'] > site_record['comp']['bCFEL']['b'] ? "Higher dN/dS in the closely related bat/pangolin clade" : "Lower dN/dS in the closely related bat/pangolin clade"; } site_score++; plot_record.kind = "Selected in animals"; for_plotting.push (_.clone(plot_record)); } } } let composition = _.sortBy(_.map (info['composition'], (v,k) => [k,v]), (d)=>-d[1]); if ('evolutionary_support' in info) { let support_by_residue = {}; _.each (info['evolutionary_support'], (e)=>{ if (! (e.aa in support_by_residue)) { support_by_residue[e.aa] = 0; } support_by_residue[e.aa] += e.support; }); _.each (composition, (d, i) => { if (d[0] in support_by_residue) { d[2] = support_by_residue[d[0]]; } else { d[2] = 0; } }); if (_.every (composition, (d,i)=> (i == 0 || d[2] > 0 || d[1] == 1 || d[0].length > 1))) { site_record['classification']['evo_support'] = true; //site_score++; } else { site_record['classification']['evo_support'] = false; site_score++; plot_record.kind = "Unexpected residues"; for_plotting.push (_.clone(plot_record)); } } site_record ['composition'] = { 'composition' : _.map (composition, (d,i)=>{ let style_string = ''; let support_text = ''; if (d.length>2 && d[0].length == 1 && d[0] != '-') { if (d[2] > 0.1) { style_string = 'style="color:darkgreen"'; support_text = "Very high evolutionary credibility"; } else { if (d[2] > 0.001) { style_string = 'style="color:#c2a90f"'; support_text = "High evolutionary credibility"; } else { if (d[2] > 0.0) { style_string = 'style="color:#ff8c00"'; support_text = "Medium evolutionary credibility"; } else { style_string = 'style="color:darkred"'; support_text = "Low evolutionary credibility"; } } } } let highlight_text = "" + (d[0] == '-' ? '[-]' : d[0]) + "_{" + d[1] + "}"; if(info["bloom-affinities"]){ let bloom_text = ""; if(_.has(info["bloom-affinities"]["mutants"], d[0])) { let mutant = info["bloom-affinities"]["mutants"][d[0]]; bloom_text = "Binding affinity Δ: " + mutant["bind_avg"] + ". Expression Δ: " + mutant["expr_avg"] + "."; highlight_text += "" } } return highlight_text; } ).join (""), 'variants' : site_record['variants'] = _.filter (intra_host, (d)=>d[0] == gene && d[3] >= ref_coord_start && d[3] <= ref_coord_start + 2 && genomic_site)}; if ('prime' in info) { site_record ['composition']['prime'] = info['prime']; } if (site_record ['composition'].variants.length > 0) { site_record['classification']['variants'] = site_record ['composition'].variants.length; site_score++; plot_record.kind = "Intra-host variants"; for_plotting.push (_.clone(plot_record)); } let substitutions = {}; let internal_node_names = {}; tree_object.traverse_and_compute ((n)=> { if (!tree_object.is_leafnode (n)) { internal_node_names [n.data.name] = 1; } }); let root_name = _.find (info['labels'], (n, k)=>{ return !(k in internal_node_names); }); info['labels']['root'] = (root_name); tree_object.traverse_and_compute ((n)=> { if (n.parent) { function gen_label (f,t) { var aac = (a)=>a.length <= 4 ? a : "MIX"; return f[1]+"("+aac(f[0])+"):"+t[1]+"("+aac(t[0])+")"; } let from_state = info['labels'][n.parent.data.name]; let to_state = info['labels'][n.data.name]; if (from_state[1] != to_state[1]) { let sub_key = from_state[1] < to_state[1] ? gen_label (from_state,to_state) : gen_label (to_state,from_state); if (! (sub_key in substitutions)) { substitutions[sub_key] = { 'leaves' : 0, 'internal' : 0, 'from_codon' : from_state[1], 'to_codon' : to_state[1], 'position' : _.map ( _.filter ( _.map (from_state[1], (c,i)=>{ return [c != to_state[1][i],i] }), (d)=>d[0]), (d)=>d[1])}; } if (tree_object.is_leafnode (n)) { substitutions[sub_key]['leaves'] ++; } else { substitutions[sub_key]['internal'] ++; } } } }); substitutions = _.sortBy (_.map (substitutions, (v,k)=> { v.key = k; return v; }), (d)=> -d['leaves']-100*d['internal']); site_record['expanded_subs'] = substitutions; site_record['annotation'] = []; _.each ([ref_coord_start,ref_coord_start+1,ref_coord_start+2], (c)=> { if (c in annotation_data) { site_record['annotation'].push (annotation_data[c]); } }); site_record['score'] = site_score; for (let i = prc; i < for_plotting.length; i++) { for_plotting[i]['score'] = site_score; } site_record['for_plot'] = _.clone(plot_record); site_record['for_plot'].score = site_score; T.push (site_record); selectors.push ([gene, genomic_site, +site+1,site_score]); epitopes [gene+"/"+genomic_site] = site_record['classification']['hla']; }); }); return {'table' : _.sortBy (T,(d)=>-d.score), 'selectors' : _.sortBy (selectors,(d)=>-d[3]), 'plot' : for_plotting, 'epitopes' : epitopes}; }; let current_data = tabulate_for_date (selection_data[0], use_date); _.each (current_data.table, (row)=>{ let item = firstDiscovered[[row.for_plot.gene,row.for_plot.site_in_gene].join('_')] let dates = _.map(item, d => d.date); row.prior = [dates , dates.length, noiseCoeffDict[row.for_plot.gene][row.for_plot.site_in_gene]]; if (dates.length==0) { let new_flag = row.for_plot; new_flag.kind = "New"; current_data.plot.push (new_flag); } }); return current_data; }38:[] 2a:{"title":"Natural selection analysis of SARS-CoV-2/COVID-19 enabled by data from ","cells":[{"id":0,"value":"md`# Natural selection analysis of SARS-CoV-2/COVID-19 enabled by data from $\"gisaid-logo\"$ \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":55,"value":"$2b","pinned":false,"hidden":false,"mode":"ojs"},{"id":910,"value":"viewof use_date = {\n const dd3 = select({\n title: \"View using data from\",\n options:\n analysis_dates\n });\n dd3.input.style.fontSize = \"30px\";\n dd3.input.style.marginTop = \"8px\";\n return dd3;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2655,"value":"viewof minimum_score = {\n const dd3 = number({\n value: 1,\n placeholder: _.max (_.map (_.uniqBy (summary.plot, \"score\"), (d)=>d.score)), \n title: \"Only display sites with at least this score\", \n submit: false});\n dd3.input.style.fontSize = \"30px\";\n dd3.input.style.marginTop = \"8px\";\n return dd3;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3142,"value":"viewof ignoreConserved = checkbox({\n description: \"Ignore conserved sites?\",\n options: [{ value: \"toggle\", label: \"On\" }],\n value: \"toggle\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1684,"value":"$2c","pinned":false,"hidden":false,"mode":"ojs"},{"id":2918,"value":"viewof filter_by_gene = {\n const dd3 = select({\n title: \"Only display sites within this gene or product\",\n options:\n [\"Any\"].concat(_.sortBy (_.map (_.uniqBy (summary.plot, \"gene\"), (d)=>d.gene)))\n });\n dd3.input.style.fontSize = \"30px\";\n dd3.input.style.marginTop = \"8px\";\n return dd3;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":3072,"value":"numEntries = md`Showing **${filtered_summary_table.length}** entries`","pinned":false,"hidden":false,"mode":"ojs"},{"id":276,"value":"$2d","pinned":false,"hidden":false,"mode":"ojs"},{"id":3204,"value":"[filtered_summary_table[0].for_plot.gene, filtered_summary_table[0].for_plot.site_in_gene]","pinned":false,"hidden":false,"mode":"ojs"},{"id":3173,"value":"filtered_summary_table","pinned":false,"hidden":false,"mode":"ojs"},{"id":1384,"value":"md`----\n### Temporal and geographic site trends\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":348,"value":"viewof render_site = {\n const dd3 = select({\n title: \"View annotated tree and time-series for a site\",\n options:\n _.map (filtered_summary_selectors, (d)=>{return {'label' : d[0] + \"/\" + d[1], \"value\" : d}})\n });\n dd3.input.style.fontSize = \"30px\";\n dd3.input.style.marginTop = \"8px\";\n return dd3;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2679,"value":"viewof render_country = {\n const dd3 = select({\n title: \"Country\",\n options:\n _.map (_.filter (timing.countries, (d)=>d[1]>=100), (d)=>{return {'label' : d[0], \"value\" : d[0]}})\n });\n dd3.input.style.fontSize = \"30px\";\n dd3.input.style.marginTop = \"8px\";\n return dd3;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":692,"value":"$2e","pinned":false,"hidden":false,"mode":"ojs"},{"id":1368,"value":"viewof use_residue = {\n const dd3 = select({\n title: \"Residue to view on map and highlight in the tree\",\n options:\n available_residues\n });\n dd3.input.style.fontSize = \"30px\";\n dd3.input.style.marginTop = \"8px\";\n return dd3;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1391,"value":"md`Select a date range on the sample count histogram below to restrict the data that the map is built on`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1378,"value":"viewof date_range = rangeSlider(sample_count, d=>d.date)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1388,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1828,"value":"md`---- \n### CTL epitopes\n> CTL epitopes (if any) that cover this site, based on the predictions of [Campbell et al](https://www.biorxiv.org/content/10.1101/2020.03.30.016931v1.abstract) and [Nelde et al](https://www.researchsquare.com/article/rs-35331/v1%20)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1858,"value":"epitope_table = table(\n detailed_epitopes,\n {\n limit: 5000,\n enableCSVDownload: true,\n sort: true,\n enableFilter: false,\n columns: [\n { \n key: 'epitope',\n name: 'Epitope / sequences presenting',\n render: val => {\n if (!_.isArray (val)) {\n return val;\n }\n let p = +val[1];\n return val[0].substr (0,p) + \"\" + val[0][p] + \"\" + val[0].substr (p+1) + \" / \" + val[2];\n }\n },\n {key: 'alleles',\n name: 'Alleles',\n render: val => {\n return _.map (val.score, (v, k)=>{\n let short_name = k.split ('-')[1].split(':')[0];\n return \"\" + k + \"\";}).join ('
');\n }\n }]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1381,"value":"md`---- \n### Tree view`","pinned":false,"hidden":false,"mode":"ojs"},{"id":397,"value":"viewof width = slider({\n min: 100, \n max: 4000,\n value: 800,\n step: 10, \n format: \"d\",\n description: \"Tree image width\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":401,"value":"viewof height = slider({\n min: 100, \n max: 10000,\n value: 2500,\n step: 10, \n format: \"d\",\n description: \"Tree image height\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":540,"value":"tree_howto = md`\n* Area of the bubble: how many isolates have this gene sequence (only unique sequences were used for this analysis); log scale for visualization clarity.\n* Branches with _codon_ changes (synonymous or non-synonymous) are drawn with thicker lines; hover over the branch to see the substitution. \n* Leaves are colored by the amino-acid at the end of the branch\n* Hover over the leaf to see representative sequence ID and codon.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":378,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":1503,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":2775,"value":"md`### Data usage notes.\n\nGISAID data provided on this page is subject to GISAID’s [Terms and Conditions](https://www.gisaid.org/registration/terms-of-use/).\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1137,"value":"md`--- \n### How does the number of variable positions in individual genes/ORFs change as we collect more data?`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1071,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":2609,"value":"counts_plot = vegalite({\n \"title\": null,\n //\"title\" : {text : \"Temporal evolution of genomic variation\", fontSize : 20, anchor : \"middle\"},\n //transform: [{\"filter\" : \"datum.gene=='S'\"}],\n width : 200,\n height: 200,\n data: {values: timeseries_summary},\n \"mark\": {\"type\": \"line\", \"interpolate\" : \"monotone\", \"strokeWidth\" : 3},\n\n encoding: {\n \"facet\" : {\n \"field\" : \"gene\" ,\n \"type\": \"ordinal\",\n \"columns\": 3,\n \"title\" : \"Gene\",\n \"sort\": {\"field\": \"gene\"}\n },\n \n \"x\": {field: \"num_seqs\", type: \"quantitative\", axis: {\"title\": \"Sequences\", titleFontSize : 14, grid : false}},\n \n \"y\": {\"field\": \"num_uniq_haps\", \"type\": \"quantitative\", axis: {\n \"title\": \"Unique Haplotypes\",\n \"titleFontSize\" : 14,\n \"grid\" : false\n },\n \"scale\": { \n \"type\": \"linear\"\n }},\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1511,"value":"dnds_plot = vegalite({\n \"title\": null,\n //\"title\" : {text : \"Temporal evolution of genomic variation\", fontSize : 20, anchor : \"middle\"},\n //transform: [{\"filter\" : \"datum.gene=='S'\"}],\n width : 200,\n height: 200,\n data: {values: timeseries_summary},\n \"mark\": {\"type\": \"line\", \"interpolate\" : \"monotone\", \"strokeWidth\" : 3, \"clip\": true},\n encoding: {\n \"facet\" : {\n \"field\" : \"gene\" ,\n \"type\": \"ordinal\",\n \"columns\": 3,\n \"title\" : \"Gene\",\n \"sort\": {\"field\": \"gene\"}\n //\"scale\": {\"scheme\": \"set1\"},\n },\n \"x\": {field: \"analysis_date\", type: \"temporal\", axis: {\"title\": \"Analysis date\", titleFontSize : 14, grid : false}},\n \n \"y\": {\"field\": \"mean_dnds_internal\", \"type\": \"quantitative\", axis: {\n \"title\": \"dN/dS on internal branches\",\n \"titleFontSize\" : 14,\n \"grid\" : false,\n },\n \"scale\": { \n \"type\": \"linear\",\n \"domain\": [0,5]\n }}, \n\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":5,"value":"md`----\n### Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":198,"value":"date_format = d3.timeFormat (\"%Y-%m-%d\") ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1039,"value":"date_format_short = d3.timeFormat (\"%d %b\") ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1031,"value":"date_reader = d3.timeParse (\"%Y-%m-%d\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2427,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":26,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":978,"value":"selection_data = [all_selection_data[use_date]]","pinned":false,"hidden":false,"mode":"ojs"},{"id":2870,"value":"summary_map = _.zipObject(_.map(summary.selectors, d => d[0] + ' ' + d[1]), _.map(summary.selectors, d=>d.join(',')))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1842,"value":"detailed_epitopes = {\n let site_info = render_site.split (',')[0] + \"/\" + render_site.split (',')[1];\n let epitope_list = summary.epitopes[ site_info];\n if (epitope_list) {\n return _.map (epitope_list, (p,k)=> {\n return {\n 'epitope' : [k, _.keys (p) [0], _.values(p)[0]],\n 'alleles' : campbell_epitopes [k]\n }})\n } \n return [{'epitope' : \"No overlapping epitopes found\"}]\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2673,"value":"shift_genes = {\n return {'ORF1b' : 1, 'RdRp' : 1, 'helicase' : 1, 'endornase' : 1, 'exonuclease' : 1, 'methyltransferase' : 1};\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":122,"value":"N_cutoff = 10","pinned":false,"hidden":false,"mode":"ojs"},{"id":367,"value":"tree = {\n var t = new phylotree.phylotree(selection_data[0][render_site.split (',')[0]]['tree']);\n if ('bl' in selection_data[0][render_site.split (',')[0]]) {\n t.usebl = selection_data[0][render_site.split (',')[0]]['bl'];\n }\n return t;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":458,"value":"node_labels = selection_data[0][render_site.split (',')[0]\n ]['selection'][render_site.split (',')[2]-1]['labels'];","pinned":false,"hidden":false,"mode":"ojs"},{"id":676,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":727,"value":"intra_host = {\n let raw_variants = await d3.tsv (\"https://raw.githubusercontent.com/galaxyproject/SARS-CoV-2/master/genomics/4-Variation/variant_list.05.tsv\", d3.autoType);\n /*\n Sample\tCHROM\tPOS\tREF\tALT\tDP\tAF\tSB\tDP4\tIMPACT\tFUNCLASS\tEFFECT\tGENE\tCODON\ttype\n \"*/\n let binned = _.countBy (raw_variants, (v)=>v.Sample);\n let iv = [];\n \n _.each (_.filter (raw_variants, (v)=>v.POS >= 265 && binned[v.Sample] <= 75 && v.AF >= 0.05 && v.AF <= .90), (v)=> {\n //console.log (v.POS);\n _.each (gene_coordinates('all'), (d,i)=> {\n if (d[0] <= v.POS && d[2] > v.POS) {\n let offset = 0;\n if (d[1] in shift_genes) {\n offset = 3; \n }\n iv.push ([d[1], Math.floor((v.POS-d[0]+offset+1)/3)+1,v.AF,v.POS,v[\"REF\"] + \":\" + v[\"ALT\"], v['EFFECT'], v['Sample']]);\n }\n \n });\n \n \n \n });\n return iv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":730,"value":"gene_coordinates = (d)=>{\n return _.map ([\n [266,'ORF1a',13483],\n [13471, 'ORF1b',21555],\n [21563,'S',25385],\n [25393,'ORF3a',26220],\n [26523,'M',27191],\n [27202,'ORF6',27387],\n [27394,'ORF7a',27759],\n [27894,'ORF8',28259],\n [28274,'N',29533],\n [266, \"leader\", 805],\n [806, \"nsp2\", 2719],\n [2720, \"nsp3\", 8554],\n [8555, \"nsp4\", 10054],\n [10055, \"3C\", 10972],\n [10973, \"nsp6\",11842],\n [11843, \"nsp7\", 12091],\n [12092, \"nsp8\", 12685],\n [12686, \"nsp9\", 13024],\n [13025, \"nsp10\",13442],\n [16237, \"helicase\",18039],\n [18040, \"exonuclease\", 19620],\n [19621, \"endornase\", 20658],\n [20659, \"methyltransferase\", 21552],\n [26244, \"E\",26471],\n [29557, \"ORF10\",29673],\n [13441,\"RdRp\",16235]], (d,i)=>{\n d.push (i);\n return d;\n })\n};\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2683,"value":"timeseries_summary = d3.csv(\"https://data.hyphy.org/web/covid-19/selection-analyses/report.csv\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2696,"value":"any_variation = _.filter(_.map(timeseries_summary, d => { return {date: d['analysis_date'], gene: d['gene'], kind: \"any\", fraction: d['codon_num_var_sites']/d['num_sites'], seqs: d['num_seqs']} }), d=> { return !_.isNaN(d.fraction) })","pinned":false,"hidden":false,"mode":"ojs"},{"id":2700,"value":"two_variation = _.filter(_.map(timeseries_summary, d => { return {date: d['analysis_date'], gene: d['gene'], kind: \"≥2x\", fraction: d['codon_num_var_sites_minor']/d['num_sites'], seqs: d['num_seqs']} }), d=> { return !_.isNaN(d.fraction) })","pinned":false,"hidden":false,"mode":"ojs"},{"id":2703,"value":"aa_two_variation = _.filter(_.map(timeseries_summary, d => { return {date: d['analysis_date'], gene: d['gene'], kind: \"≥2x amino-acid\", fraction: d['prot_num_var_sites_minor']/d['num_sites'], seqs: d['num_seqs']} }), d=> { return !_.isNaN(d.fraction) })","pinned":false,"hidden":false,"mode":"ojs"},{"id":2710,"value":"putative_selection = _.filter(_.map(timeseries_summary, d => { return {date: d['analysis_date'], gene: d['gene'], kind: \"Putatively Selected\", fraction: d['num_sites_pos_fel']/d['num_sites'], seqs: d['num_seqs']} }), d=> { return !_.isNaN(d.fraction) })","pinned":false,"hidden":false,"mode":"ojs"},{"id":2693,"value":"v_data = _.flatten([any_variation, two_variation, aa_two_variation, putative_selection])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1148,"value":"variation_data = {\n let variation_records = [];\n \n \n _.each (all_selection_data, (gene_info, date) => {\n let parsed_date = date_reader (date);\n _.each (gene_info, (fields, gene) => {\n \n variation_records.push ({'date' : parsed_date, 'gene' : gene, 'kind' : 'any', 'fraction' : fields['any variation'] / fields['sites'], 'seqs' : fields['total sequences']});\n variation_records.push ({'date' : parsed_date, 'gene' : gene, 'kind' : '≥2x', 'fraction' : fields['all variant sites'].length / fields['sites']});\n variation_records.push ({'date' : parsed_date, 'gene' : gene, 'kind' : '≥2x amino-acid', 'fraction' : fields['aminoacid variant sites'].length / fields['sites']});\n variation_records.push ({'date' : parsed_date, 'gene' : gene, 'kind' : 'Putatively Selected', 'fraction' : _.size(fields['selection']) / fields['sites']});\n \n });\n \n });\n \n return variation_records;\n}\n\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2611,"value":"counts_data = {\n let variation_records = [];\n \n \n _.each (all_selection_data, (gene_info, date) => {\n let parsed_date = date_reader (date);\n _.each (gene_info, (fields, gene) => {\n \n variation_records.push ({'date' : parsed_date, 'gene' : gene, 'haplo' : fields['sequences'], 'all' : fields['total sequences']});\n });\n \n });\n \n return variation_records;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1168,"value":"peptide_coordinates = {\n let absolute_coordinates = [\n // 1 based; name; from; to; parent ORF\n ['leader protein', 266, 805, 'ORF1a'],\n ['nsp2', 806, 2719, 'ORF1a'],\n ['nsp3', 2720, 8554, 'ORF1a'],\n ['nsp4', 8555, 10054, 'ORF1a'],\n ['3C-like proteinase', 10055, 10972,'ORF1a'],\n ['nsp6', 10973, 11842, 'ORF1a'],\n ['nsp7', 11843, 12091, 'ORF1a'],\n ['nsp8', 12092, 12685, 'ORF1a'],\n ['nsp9', 12686, 13024, 'ORF1a'],\n ['nsp10', 13025, 13441, 'ORF1a'],\n ['RdRp', 13441, 16236, 'ORF1b'],\n ['helicase', 16237, 18039, 'ORF1b'],\n [\"3'-to-5' exonuclease\",18040, 19620, 'ORF1b'],\n ['endoRNAse', 19621, 20658, 'ORF1b'],\n [\"2'-O-ribose methyltransferase\", 20659, 21552, 'ORF1b'],\n \n ];\n return _.map (absolute_coordinates, (d)=> d[3] == 'ORF1a' ? [d[0], (d[1]-266)/3 + 1, (d[2]-265)/3, d[3]] : [d[0], (d[1]-13468)/3, (d[2]-13467)/3-1, d[3]])\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1341,"value":"world = d3.json(\"https://unpkg.com/world-atlas@1/world/110m.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1345,"value":"country_codes = d3.json(\"https://raw.githubusercontent.com/lukes/ISO-3166-Countries-with-Regional-Codes/master/slim-3/slim-3.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1348,"value":"country_equiv = d3.json (\"https://raw.githubusercontent.com/mledoze/countries/master/dist/countries.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1355,"value":"current_site_info = selection_data[0][render_site.split (',')[0]\n ]['selection'][render_site.split (',')[2]-1]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1373,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":2773,"value":"tip_names = false","pinned":false,"hidden":false,"mode":"ojs"},{"id":1351,"value":"available_residues = _.map (_.sortBy (_.map (current_site_info[\"timing\"], (d,v)=>[v,current_site_info.composition[v]]), d=>-d[1]), d=>d[0])","pinned":false,"hidden":false,"mode":"ojs"},{"id":1366,"value":"sample_count = {\n let raw_counts = current_site_info[\"timing\"];\n let residue_by_date = [];\n const parser = d3.timeParse (\"%Y%m%d\");\n _.each (raw_counts[use_residue],(d) => {\n let td = parser(d[0]);\n residue_by_date.push ({'date' : td});\n });\n return residue_by_date;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1791,"value":"comparative_annotation = await d3.json (\"https://raw.githubusercontent.com/veg/SARS-CoV-2/segmented/data/comparative-annotation.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1836,"value":"campbell_epitopes = await d3.json (\"https://data.hyphy.org/web/covid-19/selection-analyses/ctl/epitopes.json\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2820,"value":"start = moment('2020-03-30')","pinned":false,"hidden":false,"mode":"ojs"},{"id":2826,"value":"end = moment('2020-06-30')","pinned":false,"hidden":false,"mode":"ojs"},{"id":2822,"value":" analysis_dates = \n{\n let items = await d3.text(\"https://data.hyphy.org/web/covid-19/selection-analyses/valid_runs.csv\")\n return _.filter(items.split('\\r\\n')).reverse()\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2907,"value":"filtered_summary_table = _.filter(summary.table, d=> d.score >= minimum_score && (d.for_plot.gene == filter_by_gene || filter_by_gene == \"Any\") && (d.classification.kind != \"negative\" || !ignoreConserved))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3132,"value":"filtered_summary_selectors = _.filter(summary.selectors, d => _.includes(filtered_items,_.join([d[0],d[1]], \",\")))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3129,"value":"filtered_items = _.map(filtered_summary_table, d => _.join([d.for_plot.gene, d.for_plot.site_in_gene], \",\"))","pinned":false,"hidden":false,"mode":"ojs"},{"id":2942,"value":"priors = d3.csv(\"https://data.hyphy.org/web/covid-19/selection-analyses/priors-report.csv\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2967,"value":"priorDates = _.uniqBy(_.sortBy(priors, d => moment(d.date)), (elem) => { return [elem.region, elem.site].join(); });","pinned":false,"hidden":false,"mode":"ojs"},{"id":241,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":2992,"value":"firstDiscovered = _.groupBy(priors, d => [d.region, d.site].join('_'))","pinned":false,"hidden":false,"mode":"ojs"},{"id":3209,"value":"import {noiseCoeffDict} from \"@stevenweaver/comparative-runs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":3216,"value":"noiseCoeffDict","pinned":false,"hidden":false,"mode":"ojs"},{"id":10,"value":"md`### Dependencies`","pinned":false,"hidden":false,"mode":"ojs"},{"id":17,"value":"d3=require(\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":2831,"value":"Moment = require('moment');","pinned":false,"hidden":false,"mode":"ojs"},{"id":3026,"value":"unpack_array = (array_object)=> {\n if (!_.isArray (array_object)) {\n return _.map (array_object.values, (d)=>{\n return array_object.keys[d];\n });\n }\n return array_object;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2834,"value":"momentRange = require('moment-range');","pinned":false,"hidden":false,"mode":"ojs"},{"id":2837,"value":"moment = momentRange.extendMoment(Moment);","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"_ = require('lodash')","pinned":false,"hidden":false,"mode":"ojs"},{"id":22,"value":"vegalite = require(\"vega-embed@5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":227,"value":"import { table } from \"@stevenweaver/table\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":314,"value":"render_property = (value) => {\n let tag = value[0].split (' ');\n if (tag.length > 2) {\n tag = tag.slice (2).join (' ');\n } else {\n tag = value[0]; \n }\n if (value[2] > 0) {\n return '' + tag + '';\n } \n if (value[2] < 0) {\n return '' + tag + '';\n } \n return tag;\n }","pinned":false,"hidden":false,"mode":"ojs"},{"id":334,"value":"import {select, slider, checkbox, number} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":360,"value":"resolve(\"phylotree@0.1/phylotree.css\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":364,"value":"phylotree=require (\"phylotree@1.0.0-alpha.26\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":1375,"value":"import {rangeSlider} from \"@bumbeishvili/utils\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1593,"value":"md`#### CSS`","pinned":false,"hidden":false,"mode":"ojs"},{"id":310,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":1591,"value":"stylesheet = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":2510,"value":"html``","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}