Update Course with more content #31
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Updated at 2023-07-20 with changes from f9404ef |
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tmooney
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Jul 21, 2023
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| ## Motivation | ||
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| 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). | ||
| 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 |
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| This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational approaches. We have built a computational | |
| This is a cross-disciplinary challenge involving genomics, proteomics, immunology, and computational approaches. We have built a computational |
(or if you really like commas, could do challenge, which involves 🙂 )
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| the data section of our precision medicine bioinformatics course: | ||
| [here](https://pmbio.org/module-02-inputs/0002/05/01/Data/). |
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This works fine. Could also consider making "the data section of our precision medicine bioinformatics course" the link!
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| - `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 |
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| 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 |
(Assuming "wild type" and "mutant" are both versions.)
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| 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 |
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| section of the pVACtools docs | |
| section of the pVACtools docs. |
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| For pVACfuse: | ||
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| - `agfusion_results`: A AGFusion output directory with annotated fusion calls |
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| - `agfusion_results`: A AGFusion output directory with annotated fusion calls | |
| - `agfusion_results`: An AGFusion output directory with annotated fusion calls |
| 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). |
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I don't have ready suggestions for replacement words this time, but I think it's better to have descriptive links instead of ones that say here. (It might be better for SEO, too 🙂 )
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| ### Transcript Support Level Filter | ||
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| The Transcript Support Level (TSL) Filter, removes neoantigen candidates for |
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| The Transcript Support Level (TSL) Filter, removes neoantigen candidates for | |
| The Transcript Support Level (TSL) Filter removes neoantigen candidates for |
| for each variants. | ||
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| 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 |
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| 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 |
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| - 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 ones passing the Anchor Criteria (explained in | ||
| 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. |
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We should be consistent on whether these end with .s
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| - 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. |
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