Render bookdown

jhudsl-robot · jhudsl-robot · commit df442852459d · 2023-06-01T18:33:28.000Z
diff --git a/docs/01-intro.md b/docs/01-intro.md
@@ -3,6 +3,8 @@
 
 # Introduction
 
+This course is currently under development. The topics to be covered are outlined below.
+
 ## Motivation
 
 Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector–based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at [https://www.pvactools.org](https://www.pvactools.org).
diff --git a/docs/index.html b/docs/index.html
@@ -161,7 +161,7 @@ <h1>
 </div>
 <div id="header">
 <h1 class="title">Introduction to pVACtools</h1>
-<p class="date"><em>May, 2023</em></p>
+<p class="date"><em>June, 2023</em></p>
 </div>
 <div id="about-this-course" class="section level1 unnumbered">
 <h1>About this Course</h1>
diff --git a/docs/index.md b/docs/index.md
@@ -1,6 +1,6 @@
 ---
 title: "Introduction to pVACtools"
-date: "May, 2023"
+date: "June, 2023"
 site: bookdown::bookdown_site
 documentclass: book
 bibliography: [book.bib, packages.bib]
diff --git a/docs/introduction.html b/docs/introduction.html
@@ -161,6 +161,7 @@ <h1>
 </div>
 <div id="introduction" class="section level1" number="1">
 <h1><span class="header-section-number">Chapter 1</span> Introduction</h1>
+<p>This course is currently under development. The topics to be covered are outlined below.</p>
 <div id="motivation" class="section level2" number="1.1">
 <h2><span class="header-section-number">1.1</span> Motivation</h2>
 <p>Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector–based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at <a href="https://www.pvactools.org">https://www.pvactools.org</a>.</p>
diff --git a/docs/resources/images/01-intro_files/figure-html/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y_g2491f283519_0_0.png b/docs/resources/images/01-intro_files/figure-html/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y_g2491f283519_0_0.png
diff --git a/docs/resources/images/03-pvacview_tour_files/figure-html/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y_g2491f283519_0_8.png b/docs/resources/images/03-pvacview_tour_files/figure-html/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y_g2491f283519_0_8.png
diff --git a/docs/search_index.json b/docs/search_index.json
@@ -1 +1 @@
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We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector–based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at https://www.pvactools.org. 1.2 Target Audience The course is intended for anyone seeking a better understanding of current best practices in cancer vaccine design and neoantigen prioritization using pVACtools. It assumes that the learner is familiar with basic biology, genetics and immunology concepts. 1.3 Curriculum This course will teach learners to: Understand key concepts of immunogenomics and neoantigen identification How to run the pVACtools software suite How to visualize and prioritize neoantigen candidates with pVACview For more background on pVACtools please consult the core pVAC papers (Hundal et al. 2016, 2018, 2020). References "],["running-pvactools.html", "Chapter 2 Running pVACtools 2.1 Learning Objectives 2.2 Installation of pVACtools 2.3 Running pVACtools 2.4 Understanding pVAC outputs", " Chapter 2 Running pVACtools 2.1 Learning Objectives This chapter will cover: Installation of pVACtools Running pVACtools Understanding pVAC outputs 2.2 Installation of pVACtools This section will describe how to install pVACtools. 2.2.1 Using docker This subsection will cover how to run pVACtools from a docker container 2.3 Running pVACtools This section will explain how to run pVACtools either locally or on the cloud 2.4 Understanding pVAC outputs This section will review pVACtools outputs and explain how to correctly interpret them. 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It also incorporates anchor prediction data for a range of class I HLA alleles and peptides ranging from 8 to 11-mers. 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+[["index.html", "Introduction to pVACtools About this Course", " Introduction to pVACtools June, 2023 About this Course This course is part of a series of courses for the Informatics Technology for Cancer Research (ITCR) called the Informatics Technology for Cancer Research Education Resource. This material was created by the ITCR funded pVACtools resource. This initiative is funded by the following grant: National Cancer Institute (NCI) U01CA248235. "],["introduction.html", "Chapter 1 Introduction 1.1 Motivation 1.2 Target Audience 1.3 Curriculum", " Chapter 1 Introduction This course is currently under development. The topics to be covered are outlined below. 1.1 Motivation Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector–based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at https://www.pvactools.org. 1.2 Target Audience The course is intended for anyone seeking a better understanding of current best practices in cancer vaccine design and neoantigen prioritization using pVACtools. It assumes that the learner is familiar with basic biology, genetics and immunology concepts. 1.3 Curriculum This course will teach learners to: Understand key concepts of immunogenomics and neoantigen identification How to run the pVACtools software suite How to visualize and prioritize neoantigen candidates with pVACview For more background on pVACtools please consult the core pVAC papers (Hundal et al. 2016, 2018, 2020). 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Original file line number	Diff line number	Diff line change
`@@ -1 +1 @@`
`1`		-[["index.html", "Introduction to pVACtools About this Course", " Introduction to pVACtools May, 2023 About this Course This course is part of a series of courses for the Informatics Technology for Cancer Research (ITCR) called the Informatics Technology for Cancer Research Education Resource. This material was created by the ITCR funded pVACtools resource. This initiative is funded by the following grant: National Cancer Institute (NCI) U01CA248235. "],["introduction.html", "Chapter 1 Introduction 1.1 Motivation 1.2 Target Audience 1.3 Curriculum", " Chapter 1 Introduction 1.1 Motivation Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector–based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at https://www.pvactools.org. 1.2 Target Audience The course is intended for anyone seeking a better understanding of current best practices in cancer vaccine design and neoantigen prioritization using pVACtools. It assumes that the learner is familiar with basic biology, genetics and immunology concepts. 1.3 Curriculum This course will teach learners to: Understand key concepts of immunogenomics and neoantigen identification How to run the pVACtools software suite How to visualize and prioritize neoantigen candidates with pVACview For more background on pVACtools please consult the core pVAC papers (Hundal et al. 2016, 2018, 2020). 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You may want to try searching to find the page's new location, or use the table of contents to find the page you are looking for. "]]
	`1`	+[["index.html", "Introduction to pVACtools About this Course", " Introduction to pVACtools June, 2023 About this Course This course is part of a series of courses for the Informatics Technology for Cancer Research (ITCR) called the Informatics Technology for Cancer Research Education Resource. This material was created by the ITCR funded pVACtools resource. This initiative is funded by the following grant: National Cancer Institute (NCI) U01CA248235. "],["introduction.html", "Chapter 1 Introduction 1.1 Motivation 1.2 Target Audience 1.3 Curriculum", " Chapter 1 Introduction This course is currently under development. The topics to be covered are outlined below. 1.1 Motivation Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational framework called pVACtools that, when paired with a well-established genomics pipeline, produces an end-to-end solution for neoantigen characterization. pVACtools supports identification of altered peptides from different mechanisms, including point mutations, in-frame and frameshift insertions and deletions, and gene fusions. Prediction of peptide:MHC binding is accomplished by supporting an ensemble of MHC Class I and II binding algorithms within a framework designed to facilitate the incorporation of additional algorithms. Prioritization of predicted peptides occurs by integrating diverse data, including mutant allele expression, peptide binding affinities, and determination whether a mutation is clonal or subclonal. Interactive visualization via a Web interface allows clinical users to efficiently generate, review, and interpret results, selecting candidate peptides for individual patient vaccine designs. Additional modules support design choices needed for competing vaccine delivery approaches. One such module optimizes peptide ordering to minimize junctional epitopes in DNA vector vaccines. Downstream analysis commands for synthetic long peptide vaccines are available to assess candidates for factors that influence peptide synthesis. All of the aforementioned steps are executed via a modular workflow consisting of tools for neoantigen prediction from somatic alterations (pVACseq and pVACfuse), prioritization, and selection using a graphical Web-based interface (pVACviz), and design of DNA vector–based vaccines (pVACvector) and synthetic long peptide vaccines. pVACtools is available at https://www.pvactools.org. 1.2 Target Audience The course is intended for anyone seeking a better understanding of current best practices in cancer vaccine design and neoantigen prioritization using pVACtools. 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