pVAC-Seq¶
pVAC-Seq is a cancer immunotherapy pipeline for the identification of personalized Variant Antigens by Cancer Sequencing (pVAC-Seq) that integrates tumor mutation and expression data (DNA- and RNA-Seq). It enables cancer immunotherapy research by using massively parallel sequence data to predicting tumor-specific mutant peptides (neoantigens) that can elicit anti-tumor T cell immunity. It is being used in studies of checkpoint therapy response and to identify targets for cancer vaccines and adoptive T cell therapies. For more general information, see the manuscript published in Genome Medicine.
New in version 4.0.1¶
pVAC-Seq now supports local installs of IEDB MHC class I and class II binding prediction tools. This feature can be used by passing the directory that contains the local installations to the --iedb-install-directory parameter.
This version adds a new column Mutation Position to the report output. This column denotes the 1-based start position of the mutation in the MT Epitope Seq. If the value is 0 the mutation start position is before the first position in the epitope.
pVAC-Seq now allows the user to specify the number of retries after a request to the IEDB RESTful interface fails. The number of retries can be set by using the --iedb-retries parameter. Previously this number was hard-coded to 3. More retries might be necessary in order to get a successful response for complex queries (e.g., large number of variants, long frameshift downstream sequences, choice of compute-intensive prediction algorithms). This parameter should be used in conjunction with --fasta-size and --downstream-sequence-length for the hightest likelihood of sucess of finishing a pVAC-Seq run.
This release fixes an error that was introduced in the previous version which would occur when the user would try to rerun a process in the same output directory.
This version also fixes a bug with how to handle variants that are no-call or homozygous-reference. These variants will now be skipped.
Citation¶
Jasreet Hundal, Beatriz M. Carreno, Allegra A. Petti, Gerald P. Linette, Obi L. Griffith, Elaine R. Mardis, and Malachi Griffith. pVAC-Seq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Medicine. 2016, 8:11, DOI: 10.1186/s13073-016-0264-5. PMID: 26825632.