Features

Splice Site Analysis

pVACsplice offers epitope binding predictions for splice site variants predicted by RegTools.

No local install of epitope prediction software needed

pVACsplice utilizes the IEDB RESTful web interface. This means that none of the underlying prediction software, like NetMHC, needs to be installed locally.

Warning

We only recommend using the RESTful API for small requests. If you use the RESTful API to process large VCFs or to make predictions for many alleles, epitope lengths, or prediction algorithms, you might overload their system. This can result in the blacklisting of your IP address by IEDB, causing 403 errors when trying to use the RESTful API. In that case please open a ticket with IEDB support to have your IP address removed from the IEDB blacklist.

Support for local installation of the IEDB Analysis Resources

pVACsplice provides the option of using a local installation of the IEDB MHC class I and class II binding prediction tools.

Warning

Using a local IEDB installation is strongly recommended for larger datasets or when the making predictions for many alleles, epitope lengths, or prediction algorithms. More information on how to install IEDB locally can be found on the Installation page (note: the pvactools docker image now contains IEDB).

MHC Class I and Class II predictions

Both MHC Class I and Class II predictions are supported. Simply choose the desired prediction algorithms and HLA alleles during processing and Class I and Class II prediction results will be written to their own respective subdirectories in your output directory. pVACsplice supports binding affinity algorithms as well as presentation and immunogenicity algorithms.

By using the IEDB RESTful web interface, pVACsplice leverages their extensive support of different prediction algorithms.

In addition to IEDB-supported prediction algorithms, we’ve also added support for a variety of additional algorithms.

Algorithm	Version(s)	MHC Class	Prediction Type	Supports Percentile Ranks?	Supports Normalized Percentile Ranks?
BigMHC_EL		MHC Class I	Presentation	no	yes
BigMHC_IM		MHC Class I	Immunogenicity	no	yes
DeepImmuno		MHC Class I	Immunogenicity	no	yes
ImmuoScope_IM		MHC Class II	Immunogenicity	no	no
MHCflurry		MHC Class I	Binding	yes	yes
MHCflurryEL		MHC Class I	Presentation, Processing	yes (Presentation only)	yes (Presentation and Processing)
MHCnuggetsI		MHC Class I	Binding	yes	yes
MHCnuggetsII		MHC Class II	Binding	yes	no
MixMHC2pred		MHC Class II	Presentation	yes	no
MixMHCpred		MHC Class I	Binding	yes	yes
NNalign	2.3	MHC Class II	Binding	yes	no
NetMHC	4.0	MHC Class I	Binding	yes	yes
NetMHCIIpan	4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3	MHC Class II	Binding	yes	no
NetMHCIIpanEL	4.0 (not supported by standalone IEDB), 4.1 (default), 4.2., 4.3	MHC Class II	Presentation	yes	no
NetMHCcons	1.1	MHC Class I	Binding	yes	yes
NetMHCpan	4.1	MHC Class I	Binding	yes	yes
NetMHCpanEL	4.1	MHC Class I	Presentation	yes	yes
PRIME		MHC Class I	Immunogenicity	yes	yes
Pickpocket	1.1	MHC Class I	Binding	yes	yes
SMM	1.0	MHC Class I	Binding	yes	yes
SMMPMBEC	1.0	MHC Class I	Binding	yes	yes
SMMalign	1.1	MHC Class II	Binding	yes	no

Calculation of normalized percentiles

Not all prediction algorithms supported by pVACsplice output a percentile rank. In order to alleviate this issue, and to provide percentile ranks that have been consistently calculated, we have run predictions for all class I algorithms supported by pVACtools on 100,000 reference peptides each in lengths 8-11 and for the most common 1,000 human class I MHC alleles. These predictions allow pVACsplice to support the calculation of normalized percentiles. This feature is enable be setting the --use-normalized-percentiles parameter. With this option enabled, pVACsplice will calculate normalized percentiles scores for all predicted neoantigen candidates and selected prediction algorithms. These normalized percentile ranks will be used in place of percentile ranks calculated by the algorithms natively. The algorithms supporting this feature are noted in the table above.

Comprehensive filtering

Automatic filtering on the binding affinity IC50 (nm) value, binding percentile, presentation percentile, and immunogenicity percentile narrows down the results to only include “good” candidate peptides. The binding filter thresholds can be adjusted by the user for each pVACsplice run. pVACsplice also support the option of filtering on allele-specific binding thresholds as recommended by IEDB. Additional filtering on the binding affitinity can be manually done by the user by running the standalone binding filter on the filtered result file to narrow down the candidate epitopes even further or on the unfiltered all_epitopes file to apply different cutoffs.

Readcount and expression data are extracted from an annotated VCF to automatically filter with adjustable thresholds on depth, VAF, and/or expression values. The user can also manually run the standalone coverage filter to further narrow down their results from the filtered output file.

If the input VCF is annotated with Ensembl transcript support levels (TSLs), MANE Select, and Canonical status, pVACseq will filter on these to only keep high-confidence transcripts. This filter can also be run standalone.

As a last filtering step, pVACsplice applies the top score filter to only keep the top scoring epitope for each variant. As with all previous filters, this filter can also be run standalone. Please also see that section for more details about how the top scoring epitope is determines.

NetChop and NetMHCstab integration

Cleavage position predictions are added with optional processing through NetChop.

Stability predictions can be added if desired by the user. These predictions are obtained via NetMHCstabpan.

Reference proteome similarity analysis

This optional feature will search for an epitope in the reference proteome using BLAST or a reference proteome FASTA file to determine if the epitope occurs elsewhere in the proteome and is, therefore, not tumor-specific.

Problematic amino acids

This optional feature allows users to specify a list of amino acids that would be considered problematic to occur either everywhere or at specific positions in a neoepitope. This can be useful when certain amino acids would be problematic during peptide manufacturing.