2b:null 2c:["$","title",null,{"children":"BioCompute Objects visualization | Jonathon Keeney | Observable"}] 31:T8b8,intro = md`This is a visualization for a BCO describing copy number variation detection in humans by Astling et al. (DOI: 10.1186/s12864-017-3976-z). A BioCompute Object (BCO) is a descriptive standard for human-readable explanations of the scientific rationale at each step of a workflow, as well as a high-level description of the work for context. This is extremely helpful for reviewers (regulatory or publication), or simply for lab records so others can follow the work (or so that you can follow your own work later!). But a visualization for a pipeline has not yet been built. The purpose of this notebook is to do that. BCOs have an IO Domain for declaring the inputs and outputs of a BCO, but they also have input and output fields within the Description Domain. A frequent question is what the difference between them is, and how the latter is used. The fields in the Description Domain are used to programmatically construct the pipeline, which is particularly helpful for branching pipelines. Because of this, the fields need not be human readable (there are other descriptive fields in that domain for annotating the step), so any identifier will work. However, if non-unique identifiers (e.g. \`"file:///dev/tmpfs"\`) is used for each step, it is not possible to reconstruct the pipeline, as in the below image. Rather, a unique identifer should be used (e.g. \`uuid\`), so that specific outputs can be connected to specific inputs. You can learn more about (and build) BCOs here. Note that if you're using BCO with a workflow language (e.g. the Nextflow plugin), then you may already have a way to visualize the pipeline (for example the Rabix Composer for use with Common Workflow Language (CWL).`32:T668,renderChart = (data, options={}) => { options.color ||= (d, i) => color(i) const tangleLayout = constructTangleLayout(_.cloneDeep(data), options); return svg``; }33:T57d,data3=[ [ { "id": "step1" } ], [ { "id": "step2", "parents": ["step1"] }, { "id": "step3", "parents": ["step1"] } ], [ { "id": "step4", "parents": ["step2","step3"] } ], [ { "id": "step5", "parents": ["step4"] }, { "id": "step6", "parents": ["step4"] }, { "id": "step7", "parents": ["step1"] }, { "id": "step8", "parents": ["step1"] }, { "id": "step9", "parents": ["step1"] } ], [ { "id": "step10", "parents": ["step5"] }, { "id": "step11", "parents": ["step5"] }, { "id": "step12", "parents": ["step8"] }, { "id": "step13", "parents": ["step8"] }, { "id": "step14", "parents": ["step8"] }, { "id": "step15", "parents": ["step9"] }, { "id": "step16", "parents": ["step9"] }, { "id": "step17", "parents": ["step9"] }, { "id": "step18", "parents": [] }, { "id": "step19", "parents": [] } ], [ { "id": "step20", "parents": ["step18"] }, { "id": "step21", "parents": ["step18"] }, { "id": "step22", "parents": ["step18"] }, { "id": "step23", "parents": ["step18"] }, { "id": "step24", "parents": ["step18"] }, { "id": "step25", "parents": ["step1", "step16"] }, { "id": "step26", "parents": ["step1", "step17"] }, { "id": "step27", "parents": ["step1", "step18"] }, { "id": "step28", "parents": ["step1", "step18", "step19"] }, { "id": "step29", "parents": ["step1"] } ] ]34:T562,data10 = [ [{ "id": "Gather data and dovetail mapping", "parents": [] }], [{ "id": "Scaffolding", "parents": ["Gather data and dovetail mapping"] }], [{ "id": "Manual scaffolding ", "parents": ["Scaffolding"] }], [{ "id": "Extract HiC reads ", "parents": ["Manual scaffolding "] }], [ { "id": "After manual intervention with Juicebox", "parents": ["Extract HiC reads "] } ], [ { "id": "Identify repeats in B. impatiens genome", "parents": ["After manual intervention with Juicebox"] } ], [ { "id": "Genome annotation ", "parents": ["Identify repeats in B. impatiens genome"] } ], [{ "id": "Alignment of BIMP_2.2 ", "parents": ["Genome annotation "] }], [{ "id": "Functional annotations", "parents": ["Alignment of BIMP_2.2 "] }], [ { "id": "calculate syntenic regions of the genome", "parents": ["Functional annotations"] } ], [ { "id": "Quality filter", "parents": ["calculate syntenic regions of the genome"] } ], [{ "id": "Align RNAseq data with STAR", "parents": ["Quality filter"] }], [ { "id": "Count alignment reads", "parents": ["Align RNAseq data with STAR"] } ], [{ "id": "Create snail plot ", "parents": ["Count alignment reads"] }], [{ "id": "Assess quality", "parents": ["Count alignment reads"] }] ] 35:T738,data12 = [ [{ "id": "Data Import ", "parents": [] }], [{ "id": "Data preprocessing (T-cell)", "parents": ["Data Import "] }], [ { "id": "APC_ms analysis and T-cell phenotyping", "parents": ["Data Import ", "Data preprocessing (T-cell)"] } ], [ { "id": "Training regression model to predict APC-ms stimulation dose", "parents": [ "APC_ms analysis and T-cell phenotyping", "APC_ms analysis and T-cell phenotyping", "APC_ms analysis and T-cell phenotyping", "Data preprocessing (T-cell)", "Data preprocessing (T-cell)" ] } ], [ { "id": "Cytotoxicity analysis CAR-T cells", "parents": [ "Data preprocessing (T-cell)", "Data preprocessing (T-cell)", "APC_ms analysis and T-cell phenotyping", "APC_ms analysis and T-cell phenotyping", "APC_ms analysis and T-cell phenotyping", "Training regression model to predict APC-ms stimulation dose", "Training regression model to predict APC-ms stimulation dose", "Training regression model to predict APC-ms stimulation dose" ] } ], [ { "id": "Predictive Modeling for CAR-T phenotype ", "parents": [ "Training regression model to predict APC-ms stimulation dose", "Cytotoxicity analysis CAR-T cells", "Cytotoxicity analysis CAR-T cells" ] } ], [ { "id": "Final validation and Summary", "parents": [ "Predictive Modeling for CAR-T phenotype ", "Predictive Modeling for CAR-T phenotype ", "Training regression model to predict APC-ms stimulation dose", "Predictive Modeling for CAR-T phenotype ", "Predictive Modeling for CAR-T phenotype ", "APC_ms analysis and T-cell phenotyping" ] } ] ] 36:T1089,constructTangleLayout = (levels, options={}) => { // precompute level depth levels.forEach((l, i) => l.forEach(n => (n.level = i))); var nodes = levels.reduce((a, x) => a.concat(x), []); var nodes_index = {}; nodes.forEach(d => (nodes_index[d.id] = d)); // objectification nodes.forEach(d => { d.parents = (d.parents === undefined ? [] : d.parents).map( p => nodes_index[p] ); }); // precompute bundles levels.forEach((l, i) => { var index = {}; l.forEach(n => { if (n.parents.length == 0) { return; } var id = n.parents .map(d => d.id) .sort() .join('-X-'); if (id in index) { index[id].parents = index[id].parents.concat(n.parents); } else { index[id] = { id: id, parents: n.parents.slice(), level: i, span: i - d3.min(n.parents, p => p.level) }; } n.bundle = index[id]; }); l.bundles = Object.keys(index).map(k => index[k]); l.bundles.forEach((b, i) => (b.i = i)); }); var links = []; nodes.forEach(d => { d.parents.forEach(p => links.push({ source: d, bundle: d.bundle, target: p }) ); }); var bundles = levels.reduce((a, x) => a.concat(x.bundles), []); // reverse pointer from parent to bundles bundles.forEach(b => b.parents.forEach(p => { if (p.bundles_index === undefined) { p.bundles_index = {}; } if (!(b.id in p.bundles_index)) { p.bundles_index[b.id] = []; } p.bundles_index[b.id].push(b); }) ); nodes.forEach(n => { if (n.bundles_index !== undefined) { n.bundles = Object.keys(n.bundles_index).map(k => n.bundles_index[k]); } else { n.bundles_index = {}; n.bundles = []; } n.bundles.sort((a,b) => d3.descending(d3.max(a, d => d.span), d3.max(b, d => d.span))) n.bundles.forEach((b, i) => (b.i = i)); }); links.forEach(l => { if (l.bundle.links === undefined) { l.bundle.links = []; } l.bundle.links.push(l); }); // layout const padding = 8; const node_height = 22; const node_width = 70; const bundle_width = 14; const level_y_padding = 16; const metro_d = 4; const min_family_height = 22; options.c ||= 16; const c = options.c; options.bigc ||= node_width+c; nodes.forEach( n => (n.height = (Math.max(1, n.bundles.length) - 1) * metro_d) ); var x_offset = padding; var y_offset = padding; levels.forEach(l => { x_offset += l.bundles.length * bundle_width; y_offset += level_y_padding; l.forEach((n, i) => { n.x = n.level * node_width + x_offset; n.y = node_height + y_offset + n.height / 2; y_offset += node_height + n.height; }); }); var i = 0; levels.forEach(l => { l.bundles.forEach(b => { b.x = d3.max(b.parents, d => d.x) + node_width + (l.bundles.length - 1 - b.i) * bundle_width; b.y = i * node_height; }); i += l.length; }); links.forEach(l => { l.xt = l.target.x; l.yt = l.target.y + l.target.bundles_index[l.bundle.id].i * metro_d - (l.target.bundles.length * metro_d) / 2 + metro_d / 2; l.xb = l.bundle.x; l.yb = l.bundle.y; l.xs = l.source.x; l.ys = l.source.y; }); // compress vertical space var y_negative_offset = 0; levels.forEach(l => { y_negative_offset += -min_family_height + d3.min(l.bundles, b => d3.min(b.links, link => link.ys - 2*c - (link.yt + c)) ) || 0; l.forEach(n => (n.y -= y_negative_offset)); }); // very ugly, I know links.forEach(l => { l.yt = l.target.y + l.target.bundles_index[l.bundle.id].i * metro_d - (l.target.bundles.length * metro_d) / 2 + metro_d / 2; l.ys = l.source.y; l.c1 = l.source.level - l.target.level > 1 ? Math.min(options.bigc, l.xb-l.xt, l.yb-l.yt)-c : c; l.c2 = c; }); var layout = { width: d3.max(nodes, n => n.x) + node_width + 2 * padding, height: d3.max(nodes, n => n.y) + node_height / 2 + 2 * padding, node_height, node_width, bundle_width, level_y_padding, metro_d }; return { levels, nodes, nodes_index, links, bundles, layout }; }37:[] 2f:{"id":"b5accfac8cbf608a","slug":"biocompute-objects-visualization","title":"BioCompute Objects visualization","thumbnail":"4f8c435bd55fc33bf017d235af93a76c881a2c0cfbefcc2f7f8d732b8c979544","access_level":"public","create_time":"$D2024-12-06T20:55:03.377Z","update_time":"$D2025-02-21T18:18:29.923Z","view_time":null,"first_public_time":"$D2024-12-09T22:12:10.525Z","fork":{"id":"4cab522856cbfdde","slug":"tangled-tree-visualization-ii","title":"Tangled tree visualization","owner":{"id":"26c2ccc018e1ea02","login":"nitaku","name":"Matteo Abrate"}},"stars":0,"starred":false,"owner":{"type":"team","id":"a497d1ef2ce28acc","login":"neurogenomics","name":"Jonathon Keeney","avatar_url":null,"tier":"starter_2024"},"version":823,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"9d5322b35a731b90","login":"neurogenomics","name":"Jonathon Keeney","avatar_url":"https://avatars-next.observableusercontent.com/0b97a1e0fdca2e9eb5d751c94674364f41b99edb7e767c78c0e366f8c4e754e4","tier":null}],"members":[{"type":"individual","id":"9d5322b35a731b90","login":"neurogenomics","name":"Jonathon Keeney","avatar_url":"https://avatars-next.observableusercontent.com/0b97a1e0fdca2e9eb5d751c94674364f41b99edb7e767c78c0e366f8c4e754e4","tier":null,"role":"editor"},{"type":"team","id":"a497d1ef2ce28acc","login":"neurogenomics","name":"Jonathon Keeney","avatar_url":null,"tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":3,"collection":null,"type":"notebook","body":{"title":"BioCompute Objects visualization","cells":[{"id":0,"value":"md`# BioCompute Objects visualization`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":527,"value":"renderChart(data)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":226,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":674,"value":"renderChart(data2)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":693,"value":"md`The image at the top is a published workflow, but a fairly linear one. A visualizer like this can be more useful when viewing branching pipelines, like the fairly simple one below.`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":692,"value":"renderChart(data1)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":810,"value":"The more complicated the workflow, the more useful the BCO to manage complexity. When it becomes publicly available, I hope to update this with a neoantigen workflow comprised of over 100 steps! This will make it easier to trace the logical flow as you comprehend the rationale of what's being done.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":709,"value":"renderChart(data3)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":724,"value":"### Student Charts\nRepresentations of student pipelines.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined"},{"id":720,"value":"renderChart(data4)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":745,"value":"renderChart(data7)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":768,"value":"renderChart(data10)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":776,"value":"renderChart(data11)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":783,"value":"renderChart(data12)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":789,"value":"renderChart(data13)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":556,"value":"md`## Code`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":6,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":13,"value":"data = [\n [{ \"id\": \"Spike-In Simulate Reads\" }],\n [{ \"id\": \"Cutadapt\", \"parents\": [\"Spike-In Simulate Reads\"]}],\n [{ \"id\": \"Spike-In Trim and Filter Reads\", \"parents\": [\"Cutadapt\"]}],\n [{ \"id\": \"Spike-In Align Reads to the Genome Reference\", \"parents\": [\"Spike-In Trim and Filter Reads\"]}],\n [{ \"id\": \"Calculate Spike-In Coverage for each DUF1220 domain\", \"parents\": [\"Spike-In Align Reads to the Genome Reference\"]}],\n [{ \"id\": \"Baseline Simulate Reads\", \"parents\": [\"Calculate Spike-In Coverage for each DUF1220 domain\"]}],\n [{ \"id\": \"Baseline Trim and Filter Reads\", \"parents\": [\"Baseline Simulate Reads\"]}],\n [{ \"id\": \"Baseline Align Reads to the Genome Reference\", \"parents\": [\"Baseline Trim and Filter Reads\"]}],\n [{ \"id\": \"Calculate Baseline Coverage for each DUF1220 Domain\", \"parents\": [\"Baseline Align Reads to the Genome Reference\"]}],\n [{ \"id\": \"Result Analysis\", \"parents\": [\"Calculate Baseline Coverage for each DUF1220 Domain\"]}]\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":676,"value":"data1=[\n [\n { \"id\": \"wget\" }\n ],\n [\n { \"id\": \"FastQC\", \"parents\": [\"wget\"] },\n { \"id\": \"Trimmomatic\", \"parents\": [\"wget\"] }\n ],\n [\n { \"id\": \"Hexagon Alignment - strict\", \"parents\": [\"FastQC\", \"Trimmomatic\"] }\n ],\n [\n { \"id\": \"Heptagon Variant Caller\", \"parents\": [\"Hexagon Alignment - strict\"] },\n { \"id\": \"Hexagon Alignment - permissive\", \"parents\": [\"Hexagon Alignment - strict\"] },\n { \"id\": \"BLAST Alignment\", \"parents\": [\"Hexagon Alignment - strict\"] }\n ]\n ]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":699,"value":"data2=[\n [\n { \"id\": \"wget\" }\n ],\n [\n { \"id\": \"FastQC\", \"parents\": [\"wget\"] },\n { \"id\": \"Trimmomatic\", \"parents\": [\"wget\"] }\n ],\n [\n { \"id\": \"Hexagon Alignment - strict\", \"parents\": [\"wget\"] }\n ],\n [\n { \"id\": \"Heptagon Variant Caller\", \"parents\": [\"wget\"] },\n { \"id\": \"Hexagon Alignment - permissive\", \"parents\": [\"wget\"] },\n { \"id\": \"BLAST Alignment\", \"parents\": [\"wget\"] }\n ]\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":707,"value":"$33","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":726,"value":"data4 = [\n [{ \"id\": \"samtools\", \"parents\": [] }],\n [{ \"id\": \"Trimmomatic\", \"parents\": [\"samtools\", \"samtools\"] }],\n [{ \"id\": \"kallisto\", \"parents\": [\"Trimmomatic\", \"Trimmomatic\"] }],\n [{ \"id\": \"bowtie2\", \"parents\": [] }],\n [{ \"id\": \"RNAcofold\", \"parents\": [\"bowtie2\"] }],\n [\n {\n \"id\": \"Targeting Efficacy Calculation\",\n \"parents\": [\"kallisto\", \"bowtie2\"]\n }\n ],\n [{ \"id\": \"Pathway Analysis\", \"parents\": [\"Targeting Efficacy Calculation\"] }],\n [{ \"id\": \"EBV miRNA analysis in tumor immune signaling\", \"parents\": [] }]\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":729,"value":"data5 = [\n [{ \"id\": \"Download raw data to specified location \", \"parents\": [] }],\n [{ \"id\": \"Prepare Spatial Data\", \"parents\": [] }],\n [{ \"id\": \"Training Models\", \"parents\": [] }],\n [{ \"id\": \"Analyze Results\", \"parents\": [\"Prepare Spatial Data\"] }],\n [{ \"id\": \"Generate Figures\", \"parents\": [] }]\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":736,"value":"data6 = [\n [{ \"id\": \"RNA Sequencing\", \"parents\": [] }],\n [{ \"id\": \"ATAC Sequencing\", \"parents\": [] }],\n [{ \"id\": \"DNA Methylation Profiling\", \"parents\": [] }],\n [{ \"id\": \"CUT&RUN Analysis\", \"parents\": [] }],\n [{ \"id\": \"Multiomics Integration\", \"parents\": [\"RNA Sequencing\"] }]\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":742,"value":"data7 = [\n [{ \"id\": \"Integrate scRNAseq data sets\", \"parents\": [] }],\n [{ \"id\": \"Subclustering data\", \"parents\": [\"Integrate scRNAseq data sets\"] }],\n [\n {\n \"id\": \"Explore similarities and differences among fibroblast subclusters\",\n \"parents\": [\"Subclustering data\"]\n }\n ],\n [\n {\n \"id\": \"Spatial analysis of fibroblast clusters\",\n \"parents\": [\"Subclustering data\"]\n }\n ],\n [{ \"id\": \"Functional exploration of fibroblasts\", \"parents\": [] }],\n [{ \"id\": \"Generate spliced vs unspliced reads\", \"parents\": [] }],\n [\n {\n \"id\": \"RNA Velocity Analysis\",\n \"parents\": [\n \"Generate spliced vs unspliced reads\",\n \"Integrate scRNAseq data sets\"\n ]\n }\n ],\n [\n {\n \"id\": \"Evaluating fibroblast and immune cross-talk\",\n \"parents\": [\"Subclustering data\"]\n }\n ],\n [\n {\n \"id\": \"Determination of regulons\",\n \"parents\": [\"Integrate scRNAseq data sets\"]\n }\n ]\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":748,"value":"data8 = [\n [{ \"id\": \"Reference Genome. fasta\", \"parents\": [] }],\n [{ \"id\": \"Input dataset collection\", \"parents\": [] }],\n [{ \"id\": \"fastp\", \"parents\": [\"Input dataset collection\"] }],\n [{ \"id\": \"MultiQC\", \"parents\": [\"fastp\"] }],\n [{ \"id\": \"Kraken2\", \"parents\": [\"fastp\"] }],\n [{ \"id\": \"Shovill\", \"parents\": [] }],\n [{ \"id\": \"snippy\", \"parents\": [\"fastp\", \"Reference Genome. fasta\"] }],\n [{ \"id\": \"convert kraken\", \"parents\": [\"Kraken2\"] }],\n [{ \"id\": \"Quast\", \"parents\": [\"Shovill\"] }],\n [{ \"id\": \"staramr\", \"parents\": [\"Shovill\"] }],\n [{ \"id\": \"ABRicate\", \"parents\": [\"Shovill\"] }],\n [{ \"id\": \"Prokka\", \"parents\": [\"Shovill\"] }],\n [{ \"id\": \"SISTR\", \"parents\": [\"Shovill\"] }],\n [{ \"id\": \"snippy core\", \"parents\": [\"snippy\"] }],\n [{ \"id\": \"Krona Pie Chart\", \"parents\": [\"convert kraken\"] }],\n [{ \"id\": \"ABRicate summary\", \"parents\": [\"ABRicate\"] }],\n [{ \"id\": \"Roary\", \"parents\": [] }],\n [{ \"id\": \"SNP Distance Matrix\", \"parents\": [\"snippy core\"] }]\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":759,"value":"data9 = [\n [{ \"id\": \"Selection of Genetic Variants\", \"parents\": [] }],\n [{ \"id\": \"Data Filtering and Cohort Selection\", \"parents\": [] }],\n [\n {\n \"id\": \"Generation of Protein-Protein Interaction (PPI) Network\",\n \"parents\": []\n }\n ],\n [{ \"id\": \"Patient -Specific Edge Scores\", \"parents\": [] }],\n [{ \"id\": \"Clustering of Patients\", \"parents\": [] }],\n [{ \"id\": \" Genetic Profiling of Clusters \", \"parents\": [] }],\n [{ \"id\": \"Sociodemographic and Biomarker Analysis\", \"parents\": [] }],\n [{ \"id\": \" Neuroimaging Profiling\", \"parents\": [] }],\n [{ \"id\": \"Neurocognitive Profiling\", \"parents\": [] }],\n [{ \"id\": \"Results Analysis\", \"parents\": [] }]\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":765,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":779,"value":"data11 = [\n [{ \"id\": \"Data Imputation and Standardization\", \"parents\": [] }],\n [\n {\n \"id\": \"Phenotype Quality Control\",\n \"parents\": [\"Data Imputation and Standardization\"]\n }\n ],\n [\n {\n \"id\": \"Variant Filtering\",\n \"parents\": [\"Data Imputation and Standardization\"]\n }\n ],\n [\n {\n \"id\": \"Ancestry-Specific Meta-Analyses\",\n \"parents\": [\"Variant Filtering\", \"Phenotype Quality Control\"]\n }\n ],\n [\n {\n \"id\": \"Conditional Analysis\",\n \"parents\": [\"Ancestry-Specific Meta-Analyses\"]\n }\n ],\n [\n {\n \"id\": \"Trans-Ancestry Meta-Analyses\",\n \"parents\": [\"Ancestry-Specific Meta-Analyses\", \"Conditional Analysis\"]\n }\n ],\n [\n {\n \"id\": \"Fine-Mapping of Loci\",\n \"parents\": [\n \"Trans-Ancestry Meta-Analyses\",\n \"Ancestry-Specific Meta-Analyses\"\n ]\n }\n ],\n [\n {\n \"id\": \"Functional Annotation and Enrichment\",\n \"parents\": [\"Fine-Mapping of Loci\"]\n }\n ]\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":785,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":791,"value":"data13 = [\n [{ \"id\": \"Get input FASTQ file ready\", \"parents\": [] }],\n [\n {\n \"id\": \"Align to Human reference\",\n \"parents\": [\"Get input FASTQ file ready\"]\n }\n ],\n [\n {\n \"id\": \"Detect Viral Alignment\",\n \"parents\": [\"Get input FASTQ file ready\"]\n }\n ],\n [{ \"id\": \"Create Viral reference file\", \"parents\": [] }],\n [\n {\n \"id\": \"Genome level quantification\",\n \"parents\": [\"Align to Human reference\", \"Detect Viral Alignment\"]\n }\n ],\n [\n { \"id\": \"Filter Viral Species\", \"parents\": [\"Genome level quantification\"] }\n ],\n [\n {\n \"id\": \"Gene and CDS Quantification\",\n \"parents\": [\"Create Viral reference file\"]\n }\n ],\n [\n {\n \"id\": \"Viral Variant Calling\",\n \"parents\": [\"Create Viral reference file\"]\n }\n ]\n]\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":523,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":239,"value":"color = d3.scaleOrdinal(d3.schemeDark2)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":435,"value":"background_color = 'white'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":532,"value":"md`## Dependencies`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":242,"value":"d3 = require('d3-scale', 'd3-scale-chromatic', 'd3-array')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":521,"value":"_ = require(\"lodash\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":546,"value":"import {footnote} from \"@jashkenas/markdown-style-footnotes\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":535,"value":"md`## References`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"},{"id":539,"value":"footnote1 = footnote(\"intro\", 1, `A. Bezerianos, P. Dragicevic, J. Fekete, J. Bae and B. Watson, \"GeneaQuilts: A System for Exploring Large Genealogies,\" in IEEE Transactions on Visualization and Computer Graphics, vol. 16, no. 6, pp. 1073-1081, Nov.-Dec. 2010, doi: 10.1109/TVCG.2010.159.`)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined"}],"files":"$W37","stdlib":"1","titling":"auto"}} 30:"$2f:body"