.. image:: ../images/pVACseq_logo_trans-bg_sm_v4b.png :align: right :alt: pVACseq logo .. _filter_commands: Filtering Commands ============================= pVACseq currently offers four filters: a binding filter, a coverage filter, a transcript support level filter, and a top score filter. These filters are always run automatically as part of the pVACseq pipeline using default cutoffs. All filters can also be run manually on the filtered.tsv file to narrow the results down further, or they can be run on the all_epitopes.tsv file to apply different filtering thresholds. The binding filter is used to remove neoantigen candidates that do not meet desired peptide:MHC binding criteria. The coverage filter is used to remove variants that do not meet desired read count and VAF criteria (in normal DNA and tumor DNA/RNA). The transcript support level filter is used to remove variant annotations based on low quality transcript annotations. The top score filter is used to select the most promising peptide candidate for each variant. Multiple candidate peptides from a single somatic variant can be caused by multiple peptide lengths, registers, HLA alleles, and transcript annotations. Further details on each of these filters is provided below. .. note:: The default values for filtering thresholds are suggestions only. While they are based on review of the literature and consultation with our clinical and immunology colleagues, your specific use case will determine the appropriate values. Binding Filter -------------- .. program-output:: pvacseq binding_filter -h .. .. argparse:: :module: lib.binding_filter :func: define_parser :prog: pvacseq binding_filter The binding filter removes variants that don't pass the chosen binding threshold. The user can chose whether to apply this filter to the ``lowest`` or the ``median`` binding affinity score by setting the ``--top-score-metric`` flag. The ``lowest`` binding affinity score is recorded in the ``Best MT IC50 Score`` column and represents the lowest ic50 score of all prediction algorithms that were picked during the previous pVACseq run. The ``median`` binding affinity score is recorded in the ``Median MT IC50 Score`` column and corresponds to the median ic50 score of all prediction algorithms used to create the report. Be default, the binding filter runs on the ``median`` binding affinity. When the ``--allele-specific-binding-thresholds`` flag is set, binding cutoffs specific to each prediction's HLA allele are used instead of the value set via the ``--binding-threshold`` parameters. For HLA alleles where no allele-specific binding threshold is available, the binding threshold is used as a fallback. Alleles with allele-specific threshold as well as the value of those thresholds can be printed by executing the ``pvacseq allele_specific_cutoffs`` command. The binding filter also offers the option to filter on ``Fold Change`` columns, which contain the ratio of the MT score to the WT Score. This option can be activated by setting the ``--minimum-fold-change`` threshold (to require that the mutant peptide is a better binder than the corresponding wild type peptide). If the ``--top-score-metric`` option is set to ``lowest``, the ``Corresponding Fold Change`` column will be used (``Corresponding WT IC50 Score``/``Best MT IC50 Score``). If the ``--top-score-metric`` option is set to ``median``, the ``Median Fold Change`` column will be used (``Median WT IC50 Score``/``Median MT IC50 Score``). In addition to being able to filter on the IC50 score columns, the binding filter also offers the ability to filter on the percentile score using the ``--percentile-threshold`` parameter. When the ``--top-score-metric`` is set to ``lowest``, this threshold is applied to the ``Best MT Percentile`` column. When it is set to ``median``, the threshold is applied to the ``Median MT Percentile`` column. By default, entries with ``NA`` values will be included in the output. This behavior can be turned off by using the ``--exclude-NAs`` flag. Coverage Filter --------------- .. program-output:: pvacseq coverage_filter -h .. .. argparse:: :module: lib.coverage_filter :func: define_parser :prog: pvacseq coverage_filter If the input VCF contains readcount and/or expression annotations, then the coverage filter can be run again on the filtered.tsv report file to narrow down the results even further. You can also run this filter again on the all_epitopes.tsv report file to apply different cutoffs. The general goals of these filters are to limit variants for neoepitope prediction to those with good read support and/or remove possible sub-clonal variants. In some cases the input VCF may have already been filtered in this fashion. This filter also allows for removal of variants that do not have sufficient evidence of RNA expression. For more details on how to prepare input VCFs that contain all of these annotations, refer to the :ref:`prerequisites_label` section for more information. By default, entries with ``NA`` values will be included in the output. This behavior can be turned off by using the ``--exclude-NAs`` flag. Transcript Support Level Filter ------------------------------- .. program-output:: pvacseq transcript_support_level_filter -h This filter is used to eliminate variant annotations based on poorly-supported transcripts. By default, only transcripts with a `transcript support level (TSL) `_ of <=1 are kept. This threshold can be adjusted using the ``--maximum-transcript-support-level`` parameter. By default, entries with ``Not Supported`` values will be included in the output. These occur if VEP was run without the ``--tsl`` flag or if data is aligned to GRCh37 or older. Top Score Filter ---------------- .. program-output:: pvacseq top_score_filter -h This filter picks the top epitope for a variant. Epitopes with the same Chromosome - Start - Stop - Reference - Variant are identified as coming from the same variant. In order to account for different splice sites among the transcripts of a variant that would lead to different peptides, this filter also takes into account the different transcripts returned by VEP and bins the ones resulting in the same set of epitopes together into a transcript set. For each transcript set the filter will return the top epitope similar to how the Best Peptide is determined in the :ref:`aggregated report `: - 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 details below) - Of the remaining entries, pick the one with the lowest median/best MT IC50 score, lowest Transcript Support Level, and longest transcript. By default the ``--top-score-metric`` option is set to ``median`` which will apply this filter to the ``Median MT IC50 Score`` column. If the ``--top-score-metric`` option is set to ``lowest``, the ``Best MT IC50 Score`` column is used instead. **Anchor Criteria** This criteria is failed if all mutated amino acids of the entry (``Mutation Position``) are at an anchor position and the WT peptide has good binding ``(Best/Median WT IC50 Score < binding_threshold)``. When the ``--allele-specific-binding-thresholds`` flag is set, binding cutoffs specific to each prediction's HLA allele are used instead of the value set via the ``--binding-threshold`` parameters. For HLA alleles where no allele-specific binding threshold is available, the binding threshold is used as a fallback. Alleles with allele-specific threshold as well as the value of those thresholds can be printed by executing the ``pvacseq allele_specific_cutoffs`` command. **Additional Considerations** It is important to note that there are several reasons why a particular variant can lead to multiple peptides with different predicted binding affinities. The following can result in multiple peptides and/or binding predictions for a single variant: #. Different epitope lengths: specifying multiple epitope lengths results in similar but non-identical epitope sequences for each variant (e.g. KLPEPCPS, KLPEPCPST, KLPEPCPSTT, KLPEPCPSTTP). #. Different registers: pVACseq will test epitopes where the mutation is in every position (e.g. EPCPSTTP, PEPCPSTT, LPEPCPST, KLPEPCPS, ...). #. Different transcripts: in some case the peptide sequence surrounding a variant will depend on the reference transcript sequence, particularly if there are alternative splice sites near the variant position. #. Different HLA alleles: the HLA allele that produces the best predicted binding affinity is chosen. #. A homozygous somatic variant with heterozygous proximal variants nearby may produce multiple different peptides. The significance of choosing a single representative peptide can depend on your experimental or clinical aims. For example, if you are planning to use short peptide sequences exactly as they were assessed for binding affinity in pVACseq (e.g. specific 9-mers for in vitro experimental validation or perhaps a dendritic cell vaccine delivery approach) then the selection of a specific peptide from the possibilities caused by different lengths, registers, etc. is very important. In some cases you may wish to consider more criteria beyond which of these candidates has the best predicted binding affinity and gets chosen by the Top Score Filter. On the other hand, if you plan to use synthetic long peptides (SLPs) or encode your candidates in a DNA vector, you will likely include flanking amino acids. This means that you often get a lot of the different short peptides that correspond to slightly different lengths or registers within the longer containing sequence. In this scenario, pVACseq's choice of a single candidate peptide by the Top Score Filter isn't actually that critical in the sense of losing other good candidates, because you may get them all anyway. One important exception to this is the rare case where the same variant leads to different peptides in different transcripts (due to different splice site usage). If multiple transcripts are expressed and lead to distinct peptides, you may want to include both in your final list of candidates. The top score filter supports this case, as described above. This assumes you did not start with only a single transcript model for each gene (e.g. using the ``--pick`` option in VEP) and also that if you are requiring transcripts with TSL=1 that there are multiple qualifying transcripts that lead to different peptide sequences at the site of the variant. This will be fairly rare. Even though most genes have alternative transcripts, they often have only subtle differences in open reading frame and overall protein sequence, and only differences within the window that would influence a neoantigen candidate are consequential here.