2b:null 2c:["$","title",null,{"children":"FEL analysis result visualization | Alexander Lucaci | Observable"}] 31:T77c,summary_table = html`

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

`32:T50c,plot_legends = ({ "Site-level dN/dS estimates" : "Maximum likelihood estimates of dN/dS at each site, together with estimated profile condifence intervals (if available). dN/dS = 1 (neutrality) is depicted as a horizontal gray line. Boundaries between partitions (if present) are shown as vertibal dashed lines.", "alpha/beta site-level estimates": "Maximum likelihood estimates of synonymous (α) and non-synonymous rates (β) at each site shown as bars. The line shows the estimates under the null model (α=β). Estimates above " + dyn_range_cap +" are censored at this value.", "Dense rate plot" : "Maximum likelihood estimates of synonymous (α) and non-synonymous rates (β) at each site. Estimates above " + dyn_range_cap +" are censored at this value. p-values are also shown", "Bootstrap vs asymptotic p-value" : "Comparison of site-level p-values for non-neutrality using parametric bootstrap and the asymptotic approximation. Rejection region (p ≤ " + pv + ") is shown as a shaded rectangle", "Rate density plots" : "Kernel density estimates of site-level rate estimates. Means are shown with red rules. Estimates above " + dyn_range_cap +" are censored at this value.", "Q-Q plots" : "Comparison of asymptotic vs boostrap LRT distributions (limited to 60 sites)." })33:T7dc,sites_table = { let results = []; const headers = _.clone(results_json.MLE.headers); let format = {}; format [headers[0][0]] = (d)=>d.toFixed (3); format [headers[1][0]] = (d)=>d.toFixed (3); format [headers[2][0]] = (d)=>d.toFixed (3); format [headers[3][0]] = (d)=>d.toFixed (3); format [headers[4][0]] = (d)=>d <= pv ? html`${d.toFixed (4)}` : d.toFixed (4); if (has_pasmt) { format[headers[headers.length-1][0]] = format [headers[4][0]]; } format [headers[5][0]] = (d)=>d.toFixed (3); headers.push (["class","Site classification at p<=" + pv]); format["class"] = (d)=>html`${d}`; _.each (results_json.MLE.content, (data, part)=> { const site_lookup = results_json["data partitions"][part].coverage[0]; _.each (data, (row, i)=> { let row_object = { 'partition' : (+part) + 1, 'codon' : site_lookup [i] + 1 }; row_object[headers[0][0]] = +row[0]; row_object[headers[1][0]] = +row[1]; row_object[headers[2][0]] = +row[2]; row_object[headers[3][0]] = +row[3]; row_object[headers[4][0]] = +row[4]; if (has_T) { row_object[headers[5][0]] = +row[5]; } if (has_ci) { row_object[headers[6][0]] = row[6]; row_object[headers[7][0]] = row[7]; row_object[headers[8][0]] = row[8]; } if (has_pasmt) { row_object [headers[headers.length-2][0]] = row[ headers.length-2]; } row_object[headers[headers.length-1][0]] = row[4] <= pv ? (row[0] < row[1] ? "Diversifying" : "Purifying") : (row[0] + row[1] ? "Neutral" : "Invariable"); results.push (row_object); }); }); return [format, results, headers]; }34:T859,rate_density = (data)=> { let rate_options = [["alpha","α"],["beta","β"], ["omega", "dN/dS"]]; return { "data" : {"values" : _.map (data, (d)=> { let dd = _.clone (d); _.each (["alpha","beta","dN/dS MLE"], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "transform" : [{"calculate" : "min(" + dyn_range_cap + ",datum.alpha > 0 ? datum.beta/datum.alpha : datum.beta > 0 ? 10000 : 0)", "as" : "omega"}], "vconcat" : _.map (rate_options, (rt)=>({"layer" : [{ "width": 800, "height": 100, "transform":[{ "density": rt[0], "bandwidth": 0.2 }], "mark": {type: "area", "opacity" : 0.5, tooltip : true, line : true}, "encoding": { "x": { "field": "value", "grid" : null, "title": rt[1], "type": "quantitative", "scale" : {"domain" : [0, dyn_range_cap]}, "axis": {"grid": false} }, "y": { "field": "density", "type": "quantitative", "title" : "", "axis": {"grid": false} }, "color" : {"value" : "grey"} }}, { "mark": "rule", "encoding": { "x": {"aggregate": "mean", "field": rt[0]}, "color": {"value": "firebrick"}, "size": {"value": 5}, } }, { "transform": [ { "aggregate": [{"op": "mean", "field": rt[0], "as": "rate_mean_" + rt[0]}] }, {"calculate": "format(datum['rate_mean_"+rt[0]+"'], '.2f')", "as": "fm1"} ], "mark": { "type": "text", "color": "gray", "size" : 12, "align": "left", "y": -5, "x": 2 }, "encoding": { "x" : {"field" : "rate_mean_" + rt[0], "type": "quantitative"}, "text": {"type": "nominal", "field": "fm1"} } }]}) ) } }35:T44a,qq_plot = (data, title)=> { return { "data": {"values": data}, "title" : title, "layer" : [{ "mark": {"type" : "line", "color" : "firebrick", "clip" : true}, "width" : 100, "height" : 100, "encoding": { "x": { "field": "bs", "type" : "quantitative", "scale" : {"domain" : [0,0.25]}, "axis" : {"grid" : false, "title" : "Bootstrap p-value", "labelFontSize" : 12, "titleFontSize" : 14} }, "y": {"field": "c2", "type" : "quantitative","axis" : {"grid" : false, "title" : "Asymptotic p-value", "labelFontSize" : 12, "titleFontSize" : 14}, "scale" : {"domain" : [0,0.25]}} }},{ "mark": { "type": "rule", "color": "grey", "strokeWidth": 1, "opacity" : 0.5, "clip" : true }, "encoding": { "x": { "datum": {"expr": "0"}, "type": "quantitative", }, "y": { "datum": {"expr": "0"}, "type": "quantitative", }, "x2": {"datum": {"expr": "1"}}, "y2": {"datum": {"expr": "1"}} }}] }}36:T75a,pv_plot = (data)=> { let color_d = []; let color_r = []; _.each (table_colors, (v,c)=> {color_d.push (c); color_r.push (v);}); return { "width": 500, "height": 500, "data" : {"values" : data}, "transform" : [{"calculate" : "(datum['p-value'] -" + pv + ")* (datum['p-asmp'] -" + pv + ") >= 0 ? 'Yes' : 'No'", "as": "agree"}], "layer" : [ { "mark" : {"opacity": 0.1, "type": "rect", "color": "#DDD"}, "encoding": { "x": { "datum": {"expr": "0"}, "type": "quantitative", }, "y": { "datum": {"expr": "0"}, "type": "quantitative", }, "x2": {"datum": {"expr": pv}}, "y2": {"datum": {"expr": pv}} } }, { "mark" : {"type" : "point", "size" : 96, "tooltip" : {"content" : "data"}, "filled" : true, "opacity" : 0.4}, "encoding" : { "x": { "field": "p-value", "type" : "quantitative", "scale" : {"type" : "sqrt"}, "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Bootstrap p-value"} }, "y": { "field": "p-asmp", "type" : "quantitative", "scale" : {"type" : "sqrt"}, "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Asymptotic p-value"} }, "color" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"}, "shape": { "field" : "agree", "title" : "Both p-values agree", "scale" : {"domain" : ["Yes","No"], "range" : ["circle", "cross"]} } } } ] } }37:T90c,dNdS_with_ci = (data, from, step)=> { let color_d = []; let color_r = []; _.each (table_colors, (v,c)=> {color_d.push (c); color_r.push (v);}); return { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i+1 >= from && i<= from + step), (d)=> { let dd = _.clone (d); _.each (["dN/dS LB","dN/dS UB", "dN/dS MLE"], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); return dd; })}, "transform" :[{"calculate": "1", "as": "neutral"}], "encoding": { "x": { "field": "codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon"} } }, "layer": [ { "mark": {"opacity": 1., "type": "line", "color": "steelblue"}, "encoding": { "y": { "field": "dN/dS LB", "scale" : {"type" : "sqrt"}, "type" : "quantitative", "axis": {"titleColor": "black", "grid" : false, "titleFontSize" : 14, "title" : "dN/dS"} }, "y2": { "field": "dN/dS UB", "type" : "quantitative" } } }, { "mark": {"stroke": "black", "type": "point", "filled" : true, "size" : 64, "stroke" : null, "tooltip" : {"contents" : "data"}}, "encoding": { "y": { "field": "dN/dS MLE", "type" : "quantitative", }, "color" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }, /*{ "mark": {"stroke": "lightgrey", "type": "line", "size" : 1, "opacity" : 1.}, "encoding": { "y": { "field": "dN/dS MLE", "type" : "quantitative", }, } }*/ { "mark" : {"opacity": 0.5, "type": "line", "color": "gray"}, "encoding" : { "y": { "field": "neutral", "type" : "quantitative", }, "x": { "field": "codon", "type" : "nominal"}, "size": {"value": 2}, } } ] }; }38:Ta40,alpha_beta_plot = (data, from, step)=> { let color_d = []; let color_r = []; _.each (table_colors, (v,c)=> {color_d.push (c); color_r.push (v);}); return { "width": {"step": 12}, "data" : {"values" : _.map ( _.filter (data, (d,i)=>i+1 >= from && i< from + step-1), (d)=> { let dd = _.clone (d); _.each (["alpha","beta", "alpha=beta"], (f)=> { dd[f] = Math.min (dyn_range_cap, dd[f]); }); dd.alpha = -dd.alpha; return dd; })}, "transform" :[{"calculate": "0", "as": "neutral"}, {"calculate": '-datum["alpha=beta"]', "as": "amb2"}], "encoding": { "x": { "field": "codon", "type" : "nominal", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Codon"} } }, "layer": [ { "mark" : {"opacity": 0.5, "type": "line", "color": "gray"}, "encoding" : { "y": { "field": "neutral", "type" : "quantitative", }, "x": { "field": "codon", "type" : "nominal"}, "size": {"value": 2}, } }, { "mark": {"type": "bar", "filled" : true, "stroke" : null, "opacity" : 0.5, "tooltip" : {"contents" : "data"}}, "encoding": { "y": { "field": "alpha", "type" : "quantitative", "axis": {"grid" : false, "titleFontSize" : 14, "title" : "Rate estimate", "labelExpr": "datum.label > 0 ? 'β = ' + datum.label: (toNumber(datum.label) == '0' ? '0' : 'α = ' + replace (datum.label, /[^0-9\.]/,''))"} }, "fill" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }, { "mark": { "type": "bar", "filled" : true, "opacity" : 0.5, "stroke" : null, "tooltip" : {"contents" : "data"}}, "encoding": { "y": { "field": "beta", "type" : "quantitative", }, "fill" : {"field" : "class", "scale" : {"domain" : color_d, "range" : color_r}, "title" : "Selection class"} } }, { "mark": {"opacity": 1., "type": "line", "color": "#444"}, "encoding": { "y": { "field": "alpha=beta", "type" : "quantitative" }, "y2": { "field": "amb2", "type" : "quantitative" } } } ] }; }39:T68f,display_tree = (i) => { let dim = treeDim.length ? _.map (treeDim.split ("x"), (d)=>+d) : null; let T = tree_objects[i]; var t = T.render({ height:dim && dim[0] || 1024, width:dim && dim[1] || 600, 'show-scale' : true, 'is-radial' : false, 'left-right-spacing': 'fit-to-size', 'top-bottom-spacing': 'fit-to-size', 'node_circle_size' : (n)=>0 } ); function sort_nodes (asc) { T.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; }); T.resortChildren (function (a,b) { return (a["count_depth"] - b["count_depth"]) * (asc ? 1 : -1); }); } sort_nodes (true); t.style_nodes ((e,n) => { if (n.children && n.children.length) return; /*if (variants.indexOf (n.data.name) >= 0) { e.style ("fill", "firebrick"); } else { e.style ("fill", null); }*/ e.selectAll ("title").data ([n.data.name]).join ("title").text ((d)=>d); }); t.style_edges ((e,n) => { const is_tested = results_json["tested"][i][n.target.data.name] == "test"; if (is_tested) { e.style ("stroke", "firebrick"); } else { e.style ("stroke", null); } }); t.placenodes(); t.update(); return t; } 3a:T820,function multiAutoSelect(config = {}) { const { value, title, description, disabled, autocomplete = "off", placeholder, size, options, list = "variants" } = Array.isArray(config) ? { options: config } : config; const optionsSet = new Set(options); const form = input({ type: "text", title, description, attributes: { disabled }, action: fm => { const addSelectedOption = d => { const ele = html`${d} `; ele.querySelector("button").addEventListener("click", b => { fm.value.splice(fm.value.indexOf(d), 1); renderSelection(); fm.dispatchEvent(new CustomEvent("input")); fm.input.focus(); }); return ele; }; function renderSelection() { fm.output.innerHTML = ""; for (let o of fm.value) { fm.output.appendChild(addSelectedOption(o)); } } fm.input.value = ""; fm.value = value || []; fm.onsubmit = e => e.preventDefault(); fm.input.oninput = function(e) { e.stopPropagation(); if ( optionsSet.has(fm.input.value) && fm.value.indexOf(fm.input.value) === -1 ) { fm.value.push(fm.input.value); renderSelection(); fm.dispatchEvent(new CustomEvent("input")); fm.input.value = ""; } }; renderSelection(); }, form: html` ` }); form.output.style["margin-left"] = "0px"; return form; }3b:Tc29,html` `3c:[] 2f:{"id":"0842d80c30a36dc8","slug":null,"title":"FEL analysis result visualization","thumbnail":"d498aec27ef9cd1c556ef748f2096fceb728cce88e82f8cece68296389c759db","access_level":"public","create_time":"$D2023-12-04T19:18:29.383Z","update_time":"$D2023-12-04T19:18:29.383Z","view_time":null,"first_public_time":"$D2023-12-04T19:18:29.383Z","has_unpublished_edits":null,"fork":{"id":"a639365e667d8feb","slug":"fel","title":"FEL analysis result visualization","owner":{"id":"9bf992815a7b66f2","login":"spond","name":"Sergei Pond"}},"stars":0,"starred":false,"owner":{"type":"team","id":"0022ea5182eedc15","login":"aglucaci","name":"Alexander Lucaci","avatar_url":"https://avatars-next.observableusercontent.com/31da2eec8e58cf73fded4c73b820d7f6c0e73dc86ee1ef2bf16742992286ad77","tier":"starter_2024"},"version":2303,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"f610d52ff7f59ec1","login":"aglucaci","name":"Alexander Lucaci","avatar_url":"https://avatars-next.observableusercontent.com/31da2eec8e58cf73fded4c73b820d7f6c0e73dc86ee1ef2bf16742992286ad77","tier":null}],"members":[{"type":"individual","id":"f610d52ff7f59ec1","login":"aglucaci","name":"Alexander Lucaci","avatar_url":"https://avatars-next.observableusercontent.com/31da2eec8e58cf73fded4c73b820d7f6c0e73dc86ee1ef2bf16742992286ad77","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":null,"type":"notebook","body":{"titling":"auto","title":"FEL analysis result visualization","cells":[{"id":0,"value":"md`# FEL analysis result visualization`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":89,"value":"viewof gard_results_file = params.get (\"url\") ? localFileInput({accept: \".json\", value : params.get (\"url\"), disable: true}) : localFileInput({accept: \".json\"}) ","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1302,"value":"intro = md`FEL analysis was performed on the alignment from ${results_json.input[\"file name\"]}. Statistical significance is evaluated based on ${results_json.simulated ? \"\" + results_json.simulated + \" site-level parametric bootstrap replicates\" : \"the asymptotic χ² distribution\"}. This analysis **${has_srv? \"includes\" : \"does not include\"}** site to site synonymous rate variation. ${has_ci ? \"Profile approximate confidence intervals for site-level dN/dS ratios have been computed\" : \"\"}.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1673,"value":"citation = md`**Suggested citation**: ${results_json.analysis[\"citation\"]}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1119,"value":"viewof pv = Inputs.text({label: html`p-value threshold`, value: \"0.1\", submit: \"Update\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":592,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1456,"value":"viewof table_filter = Inputs.checkbox([\"Diversifying\", \"Purifying\", \"Neutral\",\"Invariable\"], {value: [\"Diversifying\", \"Purifying\", \"Neutral\", \"Invariable\"], label: html`Show`, format: x => html`${x}`})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1646,"value":"viewof plot_type = Inputs.select(_.map (_.filter (plot_options, (d)=>d[1](results_json)), d=>d[0]),{label: html`Plot type`})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":538,"value":"fig1caption=md`**Figure 1**. ${plot_legends[plot_type]}`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1328,"value":"figure1 = vegalite (plot_specs[plot_type])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1111,"value":"table1caption=md`**Table 1**. Detailed site-by-site results from the FEL analysis`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1114,"value":"viewof table1 = Inputs.table (siteTableData, {\n rows : 15,\n format: sites_table[0]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1186,"value":"table1legend = html`\n\n

Table column definitions

\n\n

\"+d[0]+\": \" + d[1] + \"

\n\n

\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1549,"value":" viewof tree_id = Inputs.select(_.map (_.range (1,tree_objects.length+1), (d)=>\"Partition \" + d),{label: html`View tree for `})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1793,"value":"viewof treeDim = text({placeholder : \"1024 x 800\", description: \"Tree dimension (height x width in pixels), leave blank to auto-scale\", submit: \"Resize\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1772,"value":"treeLegend = md`Branches that are shown in red color are those that were included in testing for selection`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1565,"value":"figure2 = display_tree((-1) + (+tree_id.split (\" \")[1])).show()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2225,"value":"#### Code, data, and libraries","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":15,"value":"html`\n \n

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":638,"value":"floatFormat = d3.format (\"2g\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2257,"value":"results_json = await get_json (params.get (\"url\"))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":99,"value":"get_json = async (json_source)=> {\n if (json_source) {\n return d3.json (json_source);\n }\n return gard_results_file.json();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1638,"value":"fig1data = {\n let in_set = new Set (_.map (table1, (d)=>d.codon));\n return _.filter (siteTableData, (x)=>in_set.has (x.codon));\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1653,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1665,"value":"plot_specs = (\n{\n \"Site-level dN/dS estimates\" : {\n \"width\": 800, \"height\": 200, \n \"vconcat\" : _.map (_.range (1, fig1data.length + 1, 70), (d)=> {\n return dNdS_with_ci 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