Update course for 7.0 release

01-intro.Rmd

@@ -5,7 +5,7 @@ ottrpal::set_knitr_image_path()
 
 # Introduction
 
-This course was developed in Summer 2023 and updated in Fall 2025. We welcome any feedback at help@pvactools.org or by submission of [GitHub issues](https://github.com/griffithlab/pVACtools_Intro_Course/issues).
+This course was developed in Summer 2023 and last updated in Summer 2026. We welcome any feedback at help@pvactools.org or by submission of [GitHub issues](https://github.com/griffithlab/pVACtools_Intro_Course/issues).
 
 ## Motivation
 
@@ -24,7 +24,7 @@ prioritization, and selection using a graphical Web-based interface (pVACview),
 vaccines. pVACtools is available at [http://www.pvactools.org](http://www.pvactools.org).
 
 ```{r, fig.align='center', out.width="100%", echo = FALSE, fig.alt= "pVACtools is a cancer immunotherapy tools suite"}
-ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit#slide=id.g3a37485c18b_1_0")
+ottrpal::include_slide("https://docs.google.com/presentation/d/1uz39zaObDGKhEVCGzO0JO35CTbC0oRAM0mxgLcMAA9Y/edit?slide=id.g3e342b543ab_0_0#slide=id.g3e342b543ab_0_0")
 ```
 
 ## Background
@@ -43,7 +43,9 @@ stability and recognition by cytotoxic T cells [@Richters2019].
 pVACtools can be used as the final step in a well-established variant calling pipeline. It leverages existing tools with functionality related to variant annotation
 (Ensembl VEP [@McLaren2016]), identifying neoantigens from specific sources (e.g. fusions via star-fusion [@Haas2019], AGFusion [@Murphy2016], and Arriba [@Uhrig2021]),
 HLA typing (OptiType [@Szolek2014], PHLAT [@Bai2018]), peptide-MHC binding prediction (IEDB [@Vita2018], NetMHCpan [@Jurtz2017], MHCflurry [@ODonnell2018],
-MHCnuggets [@Shao2020]), peptide-MHC stability (NetMHCstabpan [@Rasmussen2016]], peptide processing (NetChop [@Nielsen2005]), manufacturability
+MHCnuggets [@Shao2020], MixMHCpred [@Gfeller2023]), presentation (IEDB [@Vita2018], BigMHC [@Albert2023], MHCflurry[@ODonnell2018], MixMHC2pred [@Racle2023]),
+immunogenicity (BigMHC [@Albert2023], DeepImmuno [@Li2021], ImmuoScope [@Shen2025], PRIME [@Gfeller2023]), peptide-MHC stability (NetMHCstabpan [@Rasmussen2016]],
+peptide processing (NetChop [@Nielsen2005]), manufacturability
 metrics (vaxrank [@Rubinsteyn2017]), and reference proteome similarity (BLAST [@Altschul1990]). Each of these tools tackles specific tasks within the broader goal of
 antigen analysis and is utilized by pVACtools to provide an end-to-end integration of novel algorithms and established tools needed to discover, characterize, prioritize,
 and utilize tumor-specific neoantigens in basic research and clinical applications. Combining pVACtools with existing variant calling pipelines provides an end-to-end

02-prerequisites.Rmd

@@ -70,27 +70,27 @@ install.packages("colourpicker", dependencies=TRUE)
 
 ## Data
 
-For this course, we have put together a set of input data generated from the breast 
+For this course, we have put together a set of input data generated from the breast
 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 
+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](https://pmbio.org/module-02-inputs/0002/05/01/Data/).
 
 The input data consists of the following files:
 
 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 versions of the full length protein sequences 
+  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 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.
 
 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) 
+[input file preparation](https://pvactools.readthedocs.io/en/latest/pvacseq/input_file_prep.html)
 section of the pVACtools docs.
 
 For pVACfuse:

03-running_pvactools.Rmd

@@ -23,19 +23,19 @@ mkdir pVACtools_outputs
 docker run \
 -v ${PWD}/HCC1395_inputs:/HCC1395_inputs \
 -v ${PWD}/pVACtools_outputs:/pVACtools_outputs \
--it griffithlab/pvactools:6.0.3 \
+-it griffithlab/pvactools:7.0.0 \
 /bin/bash
 ```
 
-This will pull the 6.0.3 version of the griffithlab/pvactools Docker image and
+This will pull the 7.0.0 version of the griffithlab/pvactools Docker image and
 start an interactive session (`-it`) of that Docker image using the bash shell (`/bin/bash`). 
 The `-v ${PWD}/HCC1395_inputs:/HCC1395_inputs`
 part of the command will mount the
 `HCC1395_inputs` folder at `/HCC1395_inputs` inside of the Docker container
 so that you will have access to the input data from inside the Docker
 container. The `-v ${PWD}/pVACtools_outputs:/pVACtools_outputs` part of the command
 will mount the `pVACtools_outputs` folder you just created. We will write the
-outputs from pVACseq and pVACfuse to that folder so that you will have access
+outputs from pVACseq, pVACfuse, and pVACsplice to that folder so that you will have access
 to it once you exit the Docker image.
 
 ## Running pVACseq
@@ -120,17 +120,14 @@ your run. Here are a list of parameters we generally recommend:
   are considered by pVACseq. This flag will lead pVACseq to skip variants that
   have a FILTER applied in the VCF to, e.g., exclude variants that were marked
   as low quality by the variant caller.
-- `--percentile-threshold`: When considering the peptide-MHC binding affinity
-  for filtering and prioritizing neoantigen candidates, by default only the
-  IC50 value is being used. Setting this parameter will additionally also filter
-  on the predicted percentile. We recommend a value of 2 (2%) for this
-  threshold.
-- `--percentile-threshold-strategy`: When running pVACseq with a
-  `--percentile-threshold` set, this parameter will influence how both the
-  IC50 cutoff and the percentile cutoff are applied. The default,
-  `conservative`, will require a candidate to pass both the binding and the
-  percentile threshold, while the `exploratory` option will require a candidate
-  to only pass either the binding or the percentile threshold.
+- `--use-normalized-percentiles`: Not all prediction algorithms supported by
+  pVACseq output a percentile rank. This option will calculate normalized percentiles
+  for class I epitopes of length 8-11 and all class I algorithms and the 1,000
+  most common human class I MHC alleles based on the same set of 100,000 reference
+  peptides. These percentiles will be used in place of percentiles natively
+  calculated by some algorithms. This ensures that all class I algorithms will
+  return a percentile score since some do not do so natively. It also ensures
+  that the percentiles are calculated consistently between all algorithms.
 
 Additionally there are a number of parameters that might be useful depending
 on your specific analysis needs:
@@ -147,6 +144,12 @@ on your specific analysis needs:
   unstable. This parameter allows users to set their own rules as to which
   peptides are considered problematic and peptides meeting those rules will be marked in the
   pVACseq results and deprioritized.
+- `--percentile-threshold-strategy`: By default, pVACseq will
+  filter and prioritize neoantigen candidates on the binding, presentation,
+  and immunogenicity percentiles in addition to the raw IC50 binding affinity.
+  A candidate will need to pass all thresholds. However, setting this parameter
+  to `exploratory` will relax this behavior and only require a candidate to
+  pass one of the thresholds.
 - `--transcript-prioritization-strategy` and
   `--maximum-transcript-support-level`: Generally, multiple transcripts of a
   gene may code for a neoantigen candidate. When picking the best transcript
@@ -177,8 +180,8 @@ Given the considerations outlined above, let's run pVACseq on our sample data.
 
 From the `optitype_normal_result.tsv` we know that the patient's class I alleles are
 HLA-A\*29:02, HLA-B\*45:01, HLA-B\*82:02, and HLA-C\*06:02 (indicated that two of three class I
-alleles are homozygous in this sample). We also have clinical typing information that confirms 
-these class I alleles as well as identifying DQA1\*03:03, DQB1\*03:02, and DRB1\*04:05 as the 
+alleles are homozygous in this sample). We also have clinical typing information that confirms
+these class I alleles as well as identifying DQA1\*03:03, DQB1\*03:02, and DRB1\*04:05 as the
 patient's class II alleles.
 
 Note that where needed pVACseq will automatically create HLA class II dimer combinations using
@@ -274,17 +277,14 @@ usually apply. Here are a list of parameters we generally recommend:
   neoantigen candidate in the reference proteome and report any hits found.
   By default this is done using BLASTp but we recommend using a proteome FASTA
   file via the `--peptide-fasta` parameter to speed up this step.
-- `--percentile-threshold`: When considering the peptide-MHC binding affinity
-  for filtering and prioritizing neoantigen candidates, by default only the
-  IC50 value is being used. Setting this parameter will additionally also filter
-  on the predicted percentile. We recommend a value of 2 (2%) for this
-  threshold.
-- `--percentile-threshold-strategy`: When running pVACfuse with a
-  `--percentile-threshold` set, this parameter will influence how both the
-  IC50 cutoff and the percentile cutoff are applied. The default,
-  `conservative`, will require a candidate to pass both the binding and the
-  percentile threshold, while the `exploratory` option will require a candidate
-  to only pass either the binding or the percentile threshold.
+- `--use-normalized-percentiles`: Not all prediction algorithms supported by
+  pVACfuse output a percentile rank. This option will calculate normalized percentiles
+  for class I epitopes of length 8-11 and all class I algorithms and the 1,000
+  most common human class I MHC alleles based on the same set of 100,000 reference
+  peptides. These percentiles will be used in place of percentiles natively
+  calculated by some algorithms. This ensures that all class I algorithms will
+  return a percentile score since some do not do so natively. It also ensures
+  that the percentiles are calculated consistently between all algorithms.
 
 Additionally there are a number of parameters that might be useful depending
 on your specific analysis needs:
@@ -298,6 +298,12 @@ on your specific analysis needs:
   unstable. This parameter allows users to set their own rules as to which
   peptides are considered problematic and peptides meeting those rules will be marked in the
   pVACfuse results and deprioritized.
+- `--percentile-threshold-strategy`: By default, pVACfuse will
+  filter and prioritize neoantigen candidates on the binding, presentation,
+  and immunogenicity percentiles in addition to the raw IC50 binding affinity.
+  A candidate will need to pass all thresholds. However, setting this parameter
+  to `exploratory` will relax this behavior and only require a candidate to
+  pass one of the thresholds.
 - `--threads`: This argument will allow pVACfuse to run in multi-processing
   mode.
 - `--keep-tmp-files`: Setting this flag will save intermediate files created by pVACfuse.
@@ -312,7 +318,7 @@ Given the considerations outlined above, let's run pVACfuse on our sample data.
 
 As with pVACseq, we can use the `optitype_normal_result.tsv` file to identify the patient's
 class I HLA alleles. These are HLA-A\*29:02, HLA-B\*45:01, HLA-B\*82:02, and HLA-C\*06:02.
-We also have clinical typing information that confirms these class I alleles as well as 
+We also have clinical typing information that confirms these class I alleles as well as
 identified DQA1\*03:03, DQB1\*03:02, and DRB1\*04:05 as the patient's class II alleles.
 
 For pVACfuse the sample name is not used for any parsing so it doesn't need to
@@ -398,17 +404,14 @@ usually apply. Here is a list of parameters we generally recommend:
   neoantigen candidate in the reference proteome and report any hits found.
   By default this is done using BLASTp, but we recommend using a proteome FASTA
   file via the `--peptide-fasta` parameter to speed up this step.
-- `--percentile-threshold`: When considering the peptide-MHC binding affinity
-  for filtering and prioritizing neoantigen candidates, by default only the
-  IC50 value is being used. Setting this parameter will additionally filter
-  on the predicted percentile. We recommend a value of 2 (2%) for this
-  threshold.
-- `--percentile-threshold-strategy`: When running pVACsplice with a
-  `--percentile-threshold` set, this parameter will influence how both the
-  IC50 cutoff and the percentile cutoff are applied. The default,
-  `conservative`, will require a candidate to pass both the binding and the
-  percentile threshold, while the `exploratory` option will require a candidate
-  to only pass either the binding or the percentile threshold.
+- `--use-normalized-percentiles`: Not all prediction algorithms supported by
+  pVACsplice output a percentile rank. This option will calculate normalized percentiles
+  for class I epitopes of length 8-11 and all class I algorithms and the 1,000
+  most common human class I MHC alleles based on the same set of 100,000 reference
+  peptides. These percentiles will be used in place of percentiles natively
+  calculated by some algorithms. This ensures that all class I algorithms will
+  return a percentile score since some do not do so natively. It also ensures
+  that the percentiles are calculated consistently between all algorithms.
 
 Additionally there are a number of parameters that might be useful depending
 on your specific analysis needs:
@@ -422,6 +425,12 @@ on your specific analysis needs:
   unstable. This parameter allows users to set their own rules as to which
   peptides are considered problematic and peptides meeting those rules will be marked in the
   pVACsplice results and deprioritized.
+- `--percentile-threshold-strategy`: By default, pVACsplice will
+  filter and prioritize neoantigen candidates on the binding, presentation,
+  and immunogenicity percentiles in addition to the raw IC50 binding affinity.
+  A candidate will need to pass all thresholds. However, setting this parameter
+  to `exploratory` will relax this behavior and only require a candidate to
+  pass one of the thresholds.
 - `--transcript-prioritization-strategy` and
   `--maximum-transcript-support-level`: Generally, multiple transcripts of a
   gene may code for a neoantigen candidate. When picking the best transcript