diff --git a/01-intro.Rmd b/01-intro.Rmd index e34d4d2..b9eec92 100644 --- a/01-intro.Rmd +++ b/01-intro.Rmd @@ -10,7 +10,7 @@ This course has been developed recently (Summer 2023). We welcome any feedback a ## 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 +This is a cross-disciplinary challenge, which involves 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 diff --git a/02-prerequisites.Rmd b/02-prerequisites.Rmd index f1bd096..a827e43 100644 --- a/02-prerequisites.Rmd +++ b/02-prerequisites.Rmd @@ -71,8 +71,7 @@ For this course, we have put together a set of input data generated from the bre cancer cell line HCC1395 and a matched normal lymphoblastoid cell line HCC1395BL. Data from this cell line is commonly used as test data in bioinformatics applications. For more information on these lines and the generation of test data, please refer to -the data section of our precision medicine bioinformatics course: -[here](https://pmbio.org/module-02-inputs/0002/05/01/Data/). +the [data section of our precision medicine bioinformatics course](https://pmbio.org/module-02-inputs/0002/05/01/Data/). The input data consists of the following files: @@ -80,27 +79,28 @@ For pVACseq: - `annotated.expression.vcf.gz`: A somatic (tumor-normal) VCF and its tbi index file. The VCF has been annotated with VEP and has coverage and expression information added. It has also been annotated with - custom VEP plugins that provide wild type and mutant version of the full length protein sequences + custom VEP plugins that provide wild type and mutant versions of the full length protein sequences predicted to arise from each transcript annotated with each variant. - `phased.vcf.gz`: A phased tumor-germline VCF and its tbi index file to provide information about in-phase proximal variants that might alter the predicted peptide sequence around a somatic - mutation of interest -- `optitype_normal_result.tsv`: A OptiType file with HLA allele typing predictions + mutation of interest. +- `optitype_normal_result.tsv`: A OptiType file with HLA allele typing predictions. For more detailed information on how the variant input file is created, please refer to the [input file preparation](https://pvactools.readthedocs.io/en/latest/pvacseq/input_file_prep.html) -section of the pVACtools docs +section of the pVACtools docs. For pVACfuse: -- `agfusion_results`: A AGFusion output directory with annotated fusion calls +- `agfusion_results`: An AGFusion output directory with annotated fusion + calls. - `star-fusion.fusion_predictions.tsv`: A STARFusion prediction file with fusion read support - and expression information + and expression information. General: - `Homo_sapiens.GRCh38.pep.all.fa.gz`: A reference proteome peptide FASTA to use - for determining whether there are any reference matches of neoantigen candidates + for determining whether there are any reference matches of neoantigen candidates. To download this data, please run the following commands: diff --git a/04-outputs.Rmd b/04-outputs.Rmd index ab6e527..e68c9ea 100644 --- a/04-outputs.Rmd +++ b/04-outputs.Rmd @@ -95,9 +95,9 @@ patient's RNA. For pVACseq, this generally relies on your VCF being annotated with coverage and expression data. In our example, the VCF has already been annotated with -this data. For more information about how to add coverage and expression data -to your own VCFs, please see [here](https://pvactools.readthedocs.io/en/latest/pvacseq/input_file_prep/readcounts.html) -and [here](https://pvactools.readthedocs.io/en/latest/pvacseq/input_file_prep/expression.html). +this data. For more information about how to add [coverage](https://pvactools.readthedocs.io/en/latest/pvacseq/input_file_prep/readcounts.html) +and [expression data](https://pvactools.readthedocs.io/en/latest/pvacseq/input_file_prep/expression.html) +to your own VCFs, please see our docs. Additionally, filtering on the normal DNA depth and variant allele frequency (VAF) requires your VCF to be a tumor-normal sample VCF and the normal sample to be identifies in your pVACseq run using the `--normal-sample-name` @@ -130,7 +130,7 @@ The following thresholds are applied in pVACfuse by this filter: ### Transcript Support Level Filter -The Transcript Support Level (TSL) Filter, removes neoantigen candidates for +The Transcript Support Level (TSL) Filter removes neoantigen candidates for transcripts with a high TSL, as defined [by Ensembl](https://grch37.ensembl.org/info/genome/genebuild/transcript_quality_tags.html#tsl). The cutoff for this filter is set by the `--maximum-transcript-support-level` parameter. Transcripts with a TSL of NA will always be filtered out. @@ -147,16 +147,16 @@ The Top Score Filter will attempt to determine the best neoantigen candidate for each variants. For pVACseq it works as follows. Given a set of neoantigen candidates for a -variant we first group the transcripts into set where all transcripts in a set +variant we first group the transcripts into sets where all transcripts in a set code for the same set of neoantigen candidates. For each transcript set we then determine the best neoantigen candidate as follows: - Pick all neoantigens with a variant transcript that have a protein_coding Biotype - Of the remaining candidates, pick the ones with a variant transcript having a TSL less then the `--maximum-transcript-support-level`. -- Of the remaining candidates, pick the entries with no Problematic Positions +- Of the remaining candidates, pick the entries with no Problematic Positions. - Of the remaining candidates, pick the ones passing the Anchor Criteria (explained in - more detail further below) + more detail further below). - Of the remaining candidates, pick the one with the lowest MT IC50 Score (Median or Best depending on the `--top-score-metric`), lowest TSL, and longest transcript. @@ -185,10 +185,10 @@ are included in creating this report. In pVACseq, for each variant, all neoantigen candidates meeting the `--aggregate-inclusion-threshold` are evaluated as follows: -- Pick all entries with a variant transcript that have a protein_coding Biotype -- Of the remaining entries, pick the ones with a variant transcript having a Transcript Support Level <= `--maximum-transcript-support-level` -- Of the remaining entries, pick the entries with no Problematic Positions -- Of the remaining entries, pick the ones passing the Anchor Criteria (see Criteria Details section below) +- Pick all entries with a variant transcript that have a protein_coding Biotype. +- Of the remaining entries, pick the ones with a variant transcript having a Transcript Support Level <= `--maximum-transcript-support-level`. +- Of the remaining entries, pick the entries with no Problematic Positions. +- Of the remaining entries, pick the ones passing the Anchor Criteria (see Criteria Details section below). - Of the remaining entries, pick the one with the lowest MT IC50 score( Median or Best depending on the `--top-score-metric`), lowest Transcript Support Level, and longest transcript. diff --git a/05-pvacview_tour.Rmd b/05-pvacview_tour.Rmd index c06e548..6b56724 100644 --- a/05-pvacview_tour.Rmd +++ b/05-pvacview_tour.Rmd @@ -1,5 +1,5 @@ -# Tour of pVACview +# Review Your Results in pVACview ```{r, include = FALSE} ottrpal::set_knitr_image_path() @@ -7,24 +7,295 @@ ottrpal::set_knitr_image_path() ## Learning Objectives -This chapter will cover: +This chapter will cover: - Introduction to the pVACview module -- Demo of the pVACview interface +- How to start pVACview +- How to load your pVACseq data into pVACview +- The pVACview user interface +- How to re-tier results in pVACview +- How to add comments to the variants in the report +- How to export the reviewed results ## Introduction to the pVACview module -pVACview is a R shiny based tool designed to aid specifically in the prioritization and selection of neoantigen candidates for personalized cancer vaccines or other applications. It takes as inputs a pVACseq output aggregate report file (tsv format) and a corresponding pVACseq output metrics file (json). pVACview allows the user to launch an R shiny application to load and visualize the given neoantigen candidates with detailed information including that of the genomic variant, transcripts covering the variant, and strong-binding peptides predicted from the respective transcripts. It also incorporates anchor prediction data for a range of class I HLA alleles and peptides ranging from 8- to 11-mers. By taking all these types of information into account for the neoantigen candidates, researchers will be able to make more informed decisions when deciding final peptide candidates for experiments, personalized cancer vaccines, or T cell therapies designed to target neoantigens. +pVACview is a R shiny based tool designed to aid specifically in the prioritization and selection of +neoantigen candidates for personalized cancer vaccines or other applications. It takes as inputs a +pVACseq output aggregate report file (tsv format) and a corresponding pVACseq output metrics file (json). +pVACview allows the user to launch an R shiny application to load and visualize the given neoantigen +candidates with detailed information including that of the genomic variant, transcripts covering the +variant, and strong-binding peptides predicted from the respective transcripts. It also incorporates +anchor prediction data for a range of class I HLA alleles and peptides ranging from 8- to 11-mers. By +taking all these types of information into account for the neoantigen candidates, researchers will be +able to make more informed decisions when deciding final peptide candidates for experiments, personalized +cancer vaccines, or T cell therapies designed to target neoantigens. + +## Starting pVACview + +The pVACview R source code is distributed with every pVACseq run the in +MHC_Class_I and/or MHC_Class_II subdirectories, depending on which prediction +algorithms were run. For the HCC1395 pVACseq example run you did earlier, it +can be started by running the following command in your Terminal: + +```{r, engine = 'bash', eval = FALSE} +pvacview run ${PWD}/pVACtools_outputs/MHC_Class_I +``` + +This starts a process for the pVACview R shiny application. Do not close the +terminal window or end this process. You can now open pVACview in your browser +by navigating to [http://127.0.0.1:3333/](http://127.0.0.1:3333/). + +## Uploading pVACseq result data into pVACview + +The starting screen shows an upload form. You will want to upload the Class I +aggregated.tsv report under "1. Neoantigen Candidate Aggregate Report (tsv +required)". We generally recommend using Class I as the main report, as these +are more robust than Class II. However, either can be uploaded here as long as +the correct data type is selected under "Does this aggregate report file +correspond to Class I or Class II prediction data?" + +Upload the matching Class I metrics.json file under "2. Neoantigen Candidate +Metrics file (json required)". The Class II aggregated.tsv can now be uploaded +as a supplementary file under "3. Additional Neoantigen Candidate Aggregate +Report (tsv required)". + +This interface also optionally allows users to upload a gene-of-interest TSV +file to, e.g. highlight variants on known cancer genes. We have provided such +a TSV with your original HCC1395 downloaded data, called +"cancer_census_hotspot_gene_list.tsv". + +After uploading all files, you can visualize the result by clicking on the +"Visualize" button at the bottom of the page, or the "Visualize and Explore" +tab in the sidebar. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "pVACview data upload interface."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_2") +``` + +## The pVACview User Interface + +### Aggregate Report of Best Candidates by Variant + +The main table in the Aggregate Report of Best Candidates by Variant panel +shows the best neoantigen candidate for each variant. +It lists the gene and amino acid change of the variant as well as additional +information about the best peptide and the best transcript coding for it. These +include, from left to right, the transcript support level, the best-binding HLA +allele, the mutated positions of the best peptide, any positions in the peptide +where the amino acid might be problematic for manufacturing, and the total number +of neoantigen candidates passing the binding affinity threshold set by the user. +If a gene of interest list was uploaded, variants on those genes have their gene +highlighted with a green border. + +Next, this table lists the IC50 peptide MHC binding affinity for both the mutant +and the wild type. It also shows the percentile scores of the binding affinity values. +For the mutant values, a heatmap coloring is applied to make it easier to visually +identify well-binding peptides. + +The next few columns show the coverage and expression of the best transcript with +a bar plot background to represent where specific values fall across the entire +patient sample. + +The Tier column represents the tier assigned to the best peptide. The neoantigen +candidates in this view were all sorted into the Pass tier but tiers such as Low +Expression or Subclonal are applied to easily identify why a neoantigen candidate +might be unsuitable for vaccine selection. + +The Ref Match column reflects whether or not the best peptide was found in the +reference proteome which is undesired since such peptides are not novel and +including them in a vaccine might lead to an auto immune response. + +Users are able to set a status for each candidate in the Evaluation column to mark +them as Accept, Reject, or requiring further review. + +The Investigate button can be clicked to see more detail for a variant. This +will update the lower panels with details for the selected variant. ```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "Upon successfully uploading the relevant data files, you can explore the different aspects of your neoantigen candidates."} ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g2491f283519_0_8") ``` -## Tour of the pVACview interface +For candidates not sorted into the Pass tier, red borders visually highlight the +attributes failed by the candidate. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "Neoantigen candidates are binned into tiers depending on their suitability for vaccine creation."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_8") +``` + +### Variant Information + +The Variant Information panel shows more variant-level details of the selected +neoantigen candidate. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Variant Information tab shows more details for the selected variant."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_14") +``` + +On the left is a three-tab section. The first tab, "Transcript Sets of Selected +Variant", shows a list of transcript sets. pVACtools bins transcripts that code for +the same set of neoantigen candidates into a set +because the neoantigen-level information for all transcripts in a set will be +identical. The transcript set containing the Best Peptide is highlighted in +green. This table shows the number of transcripts in each set, the number of +well-binding peptides the transcripts in the set code for, and the total +expression of all the transcripts in the set. + +The second tab, titled Reference Matches, shows the details for any reference matches of the neoantigen +candidate. It repeats information about the Best Peptide sequence, the amino acid +change, the mutated position, and the gene for easy reference. The mutated +positions are notated in red in the Best Peptide sequence and the Query +Sequence. The Query Sequence is a longer sequence around the somatic mutation. +The Best Peptide subsequence is highlighted in yellow in the Query Sequence. For +any 8mer subsequence of the query sequence, pVACtools looks for matches in the +reference proteome. Any matches found are reported in the Hits table + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Reference Matches tab shows details of reference matches of the neoantigen candidate."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_20") +``` + +The "Additional Data" tab shows the additional data for the variant if a Additional Neoantigen Candidate Aggregate +Report was uploaded. It shows the Best Peptide and its information for this variant from the +additional report. This can be used, e.g., when a Class I neoantigen candidate +is a bad binder but all other metrics look good. Oftentimes this variant can +be rescued by considering the best Class II neoantigen candidate instead. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Additional Data shows data from the Additional Aggregate Report for the variant."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_26") +``` + +The next section, "Variant & Gene Info", shows coverage and expression information as well as +the genomic coordinates for the variant. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Variant & Gene INfo section show coverage and expression information for the variant."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_75") +``` + +The last section, "Peptide Evaluation Overview", shows counts for how many peptides have been accepted, +rejected, or marked for review. For most vaccines a certain minimum number of +neoantigen candidates is desired so this panel makes it easy to review how +many neoantigen candidates are still needed. -Here is a brief tour of the [pVACview](https://pvactools.readthedocs.io/en/latest/pvacview.html){target="_blank"} interface: - -```{r, echo=FALSE} -knitr::include_url("https://www.youtube.com/embed/SMcXSV1cp1U") +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Peptide Evaluation Overview section shows how many neoantigen candidates have been accepted, rejected, marked for review, or are pending."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_80") ``` +### Transcript Set Detailed Data + +When selecting a transcript set in the Variant Info panel, this panel will +show details about the neoantigen candidates the transcripts in the set code +for and as well as details on the transcripts themselves. + +The Peptide Candidates from Selected Transcript Set tab shows a list of +mutant and matched wildtype peptides and their IC50 binding +affinity to the patient HLA alleles. Only neoantigen candidates where at least +one peptide-MHC binding prediction falls within the `--aggregate-inclusion-threshold` +will be shown in this table. For HLA alleles where the peptide is not +well-binding the prediction details will show `X`. This table also shows the +mutant position, whether or not the neoantigen candidate has any problematic +positions, and whether or not it failed the anchor criteria. This helps in +determining whether a neoantigen candidate was deprioritized when selecting the +Best Peptide. The Best Peptide is highlighted in green. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Transcript Set Detailed Data panel shows binding prediction details for the neoantigens the transcripts in the set code for."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_32") +``` + +The Transcripts in Set tabs shows details of the transcripts in the selected +set such as the transcript Ensembl ID, the transcript expression, the +transcript support level, the biotype, and the transcript length. This +reflects the criteria used in determining the Best Transcript. The Best +Transcript is highlighted in green. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Transcripts in Set tab shows details about the transcripts in the set."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_38") +``` + +### Additional Peptide Information + +In the Additional Peptide Information panel, users can see more information +for the neoantigen candidate selected in the Transcript Set Detailed Data +panel. + +The first tab, "IC50 Plot", shows violin plots of the predicted IC50 binding +affinity for each prediction algorithm for both the neoantigen candidate and +its matched wildtype peptide. This can be used to check concordance +of predictions between the different algorithms. It also allows for a +detailed comparison between the mutant and wildtype predictions in addition to +the median or lowest IC50 binding affinity used elsewhere. A solid line is used +to represent the median score. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Additional Peptide Information panel shows more information for the peptide selected in the Transcript Set Detailed Data panel."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_44") +``` + +The %ile Plot tab shows a similar violin plot but for the predicted percentile +scores as opposed to the IC50 binding affinity. A solid line is also used here +to represent the median score. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The %ile Plot tab shows violin plots of the percentile score predicted by each algorithm."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_50") +``` + +The next tab, "Binding Data", shows the IC50 binding affinity and +percentile score but in table format. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Binding Data tab shows a table of the IC50 binding affinity and percentile predicted by each algorithm."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_56") +``` + +The Elution Table tab shows the predicted elution scores and percentiles, if +the appropriate prediction algorithm(s) were chosen. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Elution Table tab shows elution prediction scores and precentiles for the selected peptide."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_62") +``` + +Lastly, the Anchor Heatmap tab shows a heatmap overlayed over each neoantigen +candidate from the selected transcript set. A darker color represents a higher +probability that a position in the peptide is an anchor. Mutated positions are +represented by red letters. More information +about how to interpret the heatmap can be found in the graph on the right of this +panel. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "The Anchor heatmaps show which positions in a peptide are likely to be anchors."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_68") +``` + +## Regenerate Tiers with Custom Parameters + +During review of your data it might become apparent that different tiering +thresholds would've been more approriate. pVACview allows you to re-tier your +data with custom parameters by adjusting the sliders and inputs in the +"Advanced Options: Regenerate Tiering with different parameters" panel and +pressing the "Recalculate Tiering with new parameters" button. + +The parameters that were used in the original pVACseq run can still be viewed +in the "Original Parameters for Tiering" panel and the tiers can be reset to +those parameters by pressing the "Reset to original parameters" button. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "Users can re-tier the neoantigen candidates by adjusting the tiering thresholds."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_87") +``` + +## Adding Comments to Variants + +When reviewing neoantigen candidates in pVACseq, users are able to add +comments on each variant, for example, describing what additional review is +necessary. Please note that comments are not saved until the "Update Comment +Section" button is pressed. + + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "Users can leave comments on each variant."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_93") +``` + +## Exporting the Aggregated Table + +Once review of the neoantigen candidates has been completed, the results can +be exported by switching to the Export interface via the sidebar. This will +export the Aggregated Report with the updated Evaluation column and comments +added. The report can be exported in either TSV or Excel format. + +```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "Users can leave comments on each variant."} +ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g25ad9ce8c9b_0_99") +``` diff --git a/06-conclusions.Rmd b/06-conclusions.Rmd index 82de4ec..d6fddbb 100644 --- a/06-conclusions.Rmd +++ b/06-conclusions.Rmd @@ -12,9 +12,18 @@ This chapter will summarize: - Key conclusions of the course - Additional learning resources -## Key conclusions +## Key conclusions +In this course you will have gained a better understanding of the current best +practices for neoantigen indentification and prioritization. You will have +learned how to run pVACtools, interpret pVACtools results, and select neoantigen +candidates suitable for vaccine manufacturing using pVACview. ## Additional Resources - +- [pVACtools documentation](https://pvactools.readthedocs.io/en/latest/) +- [Email help alias](mailto:help@pvactools.org) +- [Report an issue with + pVACtools](https://github.com/griffithlab/pVACtools/issues) +- [Report an issue with this + course](https://github.com/griffithlab/pVACtools_Intro_Course/issues) diff --git a/HCC1395_inputs.zip b/HCC1395_inputs.zip index 590d271..405831b 100644 Binary files a/HCC1395_inputs.zip and b/HCC1395_inputs.zip differ diff --git a/resources/dictionary.txt b/resources/dictionary.txt index fda81b8..4ea6712 100644 --- a/resources/dictionary.txt +++ b/resources/dictionary.txt @@ -8,6 +8,7 @@ BLASTp Bloomberg Bookdown bioinformatics +biotype CHROM CLI ClinVar @@ -25,6 +26,8 @@ Dockerfile Dockerhub deprioritized dropdown +Elution +elution epitope epitopes Ensembl @@ -46,12 +49,14 @@ HCC HLA histocompatibility homozygous +hotspot https http IC IEDB ITCR ITN +ile immunotherapies immunotherapy isoform