Isovar
Isovar finds the mutant protein sequence that tumor RNA actually encodes around each somatic mutation. Given variant calls (VCF) and aligned tumor RNA-seq reads (BAM), it assembles the reads that carry each mutation into cDNA and translates that cDNA in the reading frames of annotated transcripts.
The protein comes from the reads, not from the reference plus one edit, so it can include nearby variants and splice junctions that the reads show. Where coverage runs out or no reading frame can be established, the result is shorter or empty: Isovar never fills gaps with reference sequence.
Installation
pip install isovar
Isovar requires Python 3.9 or later. Reference annotation comes from PyEnsembl; install the Ensembl release that matches your alignments before the first run, for example:
pyensembl install --release 75 --species human
To choose a specific release or use your own GTF, see
choosing the reference annotation.
The figure commands (isovar plot and isovar fusion --plot-dir) need the
plotting extra: pip install 'isovar[plot]'.
Quick start
From Python:
from isovar import run_isovar, isovar_results_to_dataframe
isovar_results = run_isovar(
variants="cancer-mutations.vcf",
alignment_file="tumor-rna.bam")
for isovar_result in isovar_results:
# The protein preferred by the context/support policy, or None.
protein = isovar_result.top_protein_sequence
if protein is not None:
# num_alt_fragments: distinct fragments supporting the variant allele.
print(isovar_result.variant, isovar_result.num_alt_fragments, protein.amino_acids)
# One row per variant.
df = isovar_results_to_dataframe(isovar_results)
Isovar logs through the standard logging module under the isovar logger and
never configures logging itself; configure it in your application to see
progress.
From the command line:
isovar run \
--vcf somatic-variants.vcf \
--bam rnaseq.bam \
--output isovar-results.csv
The CSV has one row per variant, with the same columns as the DataFrame.
isovar --help lists the other commands; the
command-line guide
describes each one and its output.
Reading the results
run_isovar returns one IsovarResult per input variant, in input order. Each
holds the RNA evidence at that variant's locus and any mutant protein sequences
assembled for it.
IsovarResult property |
Table column | Meaning |
|---|---|---|
variant |
variant, chr, pos, ref, alt |
The input variant |
top_protein_sequence |
protein_sequence and other protein_sequence_* columns |
The top-ranked mutant protein assembled from RNA, or none |
sorted_protein_sequences |
Every protein kept, ranked; one by default | |
num_alt_reads, num_alt_fragments, fraction_ref_reads, ... |
Same names | Read evidence for the ref, alt and other alleles |
predicted_effect |
predicted_effect |
Varcode's prediction from the reference plus this one edit, for comparison |
passes_all_filters, filter_values |
passes_all_filters, filter:<name> |
Filter outcomes |
By default Isovar aims for 49 residues of protein: 2K−1 for peptide size K = 25, enough to contain every 25-mer overlapping a single changed residue. The actual length depends on RNA coverage. Protein context selection explains the target and how the top protein is chosen.
A read is one sequenced segment, such as one mate of a pair; a fragment is one template, so both mates of a pair count once. Fragment counts are usually the ones to filter on. Neither is a molecule count; see collecting and counting RNA reads.
Filtering results
run_isovar evaluates filters on each result; a failing result is kept, with
False in its filter_values dictionary and in passes_all_filters. In the
DataFrame and CSV each filter becomes a filter:<name> column. Filters never
remove a result or a row.
filter_thresholds maps names like 'min_num_alt_reads' or
'max_fraction_other_fragments' to numbers. The text after min_ or max_
names a numeric property of IsovarResult, and most read-evidence properties
follow the pattern {num|fraction}_{ref|alt|other}_{reads|fragments}. For
example, this requires at least 10 alt reads and at most 25% of fragments
supporting other alleles:
from isovar import run_isovar
isovar_results = run_isovar(
variants="cancer-mutations.vcf",
alignment_file="tumor-rna.bam",
filter_thresholds={"min_num_alt_reads": 10, "max_fraction_other_fragments": 0.25})
for isovar_result in isovar_results:
print(isovar_result.variant, isovar_result.passes_all_filters)
filter_flags names boolean properties of IsovarResult; prefix one with
not_ to negate it, as in not_protein_sequence_matches_predicted_mutation_effect.
Omitting either argument applies the defaults in
default_parameters.py:
DEFAULT_FILTER_THRESHOLDS, and DEFAULT_FILTER_FLAGS, which are
predicted_effect_modifies_protein_sequence,
has_mutant_protein_sequence_from_rna and protein_sequence_contains_mutation.
Passing a value replaces the corresponding defaults; to change one threshold,
copy DEFAULT_FILTER_THRESHOLDS and update it.
Structural variants and fusions
The pipeline above accepts literal nucleotide alleles, including
sequence-resolved indels. Symbolic structural variants (<DEL>, <DUP>, etc.),
breakends and varcode.StructuralVariant objects are rejected rather than
interpreted as small variants. Two separate workflows handle them:
isovar sv-rna/reconstruct_sv_rnareconstructs exploratory RNA paths around one nominated SV from a BAM and annotated transcript models (guide).isovar fusion/reconstruct_fusionchecks the junction evidence and annotated coding frames of a fusion transcript assembled by another tool (guide).
How it works
For each variant, Isovar:
- collects the RNA reads that overlap it,
- keeps the reads that carry the mutant allele,
- assembles overlapping mutant reads into longer cDNA sequences,
- places those sequences in the reading frames of annotated transcripts, and
- translates them into mutant protein sequences.
Internal design describes the objects behind each step.
Isovar is one of three OpenVax libraries that share this work. Varcode generates transcript hypotheses and predicts coding consequences; Isovar reconstructs RNA-supported sequences and reconciles the evidence; Vaxrank evaluates protein and peptide candidates. See library responsibilities.
Documentation
Running the pipeline
| Guide | What it covers |
|---|---|
| Choosing the reference annotation | --genome, Ensembl releases and your own GTF |
| Command line | Every isovar command, its output table, logging and errors |
| Collecting and counting RNA reads | ReadCollector options, reads versus fragments, and the RNA support record in every JSON export |
| Read eligibility across platforms | Which alignments are used, and what Illumina, ONT and PacBio quality tags mean |
| Assembly and translation | ProteinSequenceCreator options, and other variants found in the assembled RNA |
| Protein context selection | How much protein context is reported and which candidate comes first |
| Reading frames from aligned reads | How an indel upstream of the variant carries into the reading frame |
| Phasing | Which variants share RNA molecules, the cis/trans test, and matched germline variants |
Exports and evidence for small variants
| Guide | What it covers |
|---|---|
| Protein hypothesis export | Every protein and translation with scoped RNA evidence, for tools that combine sources |
| Competing allele interpretations | Which of several callers' versions of one locus the RNA supports |
| Mutation-evidence figures | isovar plot, how to read its figures, and the osteosarc gallery |
| Adapter and poly-A trimming | Opt-in annotation and trimming of technical read ends |
| Base-quality evidence | Optional focal-allele quality categories reported alongside raw counts |
| Partial-read support | Fractional support scores that divide each fragment among compatible hypotheses |
| Cell/UMI evidence | Cell barcode and UMI labels behind small-variant alleles and SV support |
Structural variants and fusions
| Guide | What it covers |
|---|---|
| SV RNA reconstruction | isovar sv-rna: RNA paths around an SV call, their frames and ORFs, export and comparison |
| Supplied fusion RNA | isovar fusion: checking a fusion transcript assembled by another tool |
| ORF start evidence | Where an SV ORF's start codon comes from, in four tiers |
| ONT read lineage | Split and duplex nanopore reads in SV support counts |
| uORF evidence | Offline human upstream-ORF models, protein sequences, native peptide evidence and mutation-position annotation |
| Portable audit bundles | Installed Python API and CLI for preserving inputs, backup/restore and offline SV RNA replay |
Project
| Guide | What it covers |
|---|---|
| Library responsibilities | How Varcode, Isovar and Vaxrank divide the work |
| Internal design | The objects that carry reads to protein sequences |
| Changelog | Behavior changes by release |
Sequencing recommendations
Isovar works best with high-quality, high-coverage poly-A-selected mRNA sequencing, for example >100M paired-end reads on a current Illumina short-read platform. The depth needed depends on RNA degradation and tumor purity.
With short reads, read length bounds the recoverable protein. Assembly only uses reads overlapping the variant, so 100 bp reads give at most 199 bp of sequence around a somatic SNV, about 66 amino acids. Without assembly, one 100 bp read determines at most 33.
Overlap assembly requires exact sequence matches, which suits short reads with
low error rates. Long reads (PacBio, Oxford Nanopore) often span the whole
context without assembly, but noisy reads may not join by exact overlap; SV
reconstruction (isovar sv-rna) uses noise-tolerant extension. Coverage
trimming assumes that read coverage falls off away from the variant, which reads
spanning splice junctions can violate.
Project reports and test data
The notes directory holds maintainer working notes: Sid analysis reports, design specifications and benchmark records, with their data. They are not usage guides.
- Sid test reads: the original Sid osteosarcoma reads packaged with Isovar for its tests, and how to make them again.
- Tumor-RNA audit: a sample-by-sample RNA and protein audit of six osteosarc loci in two RNA sources, made for Isovar 1.8.0.
Citing Isovar
Isovar is part of the OpenVax pipeline described in:
Rubinsteyn, A., Kodysh, J., Hodes, I., et al. Computational Pipeline for the PGV-001 Neoantigen Vaccine Trial. Frontiers in Immunology 8, 1807 (2018).
Kodysh, J. & Rubinsteyn, A. OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction. Methods Mol. Biol. 2120, 147–160 (2020).
License
Isovar is released under the Apache License 2.0.
Release files for isovar 1.54.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| isovar-1.54.3.tar.gz | 12.0 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| isovar-1.54.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 23.5 MB
Release files / isovar-1.54.3.tar.gz
| Download URL | isovar-1.54.3.tar.gz |
|---|---|
| Size | 12.0 MB |
| Tags | Source |
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