Isovar

Overview
Installation
Python API
Command line
Internal design
Documentation
Sequencing recommendations

Overview

Isovar determines mutant protein subsequences around mutations from cancer RNA-seq data.

Isovar works by:

collecting RNA reads spanning the location of a variant,
filtering the RNA reads to those which support the mutation,
assembling mutant reads into longer RNA sequences,
matching assembled RNA sequences against reference annotated reading frames, and
translating RNA-derived coding sequences into predicted protein subsequences.

The assembled sequences may incorporate nearby variants and observed splice junctions when the reads and reference context support them. Missing coverage or an unresolved reading frame remains uncertainty, not an unchanged protein.

Varcode generates transcript hypotheses and predicts coding consequences; Isovar reconstructs RNA-supported sequences and reconciles the evidence; Vaxrank evaluates protein/peptide candidates. See library responsibilities for the shared contract.

Installation

pip install isovar
# Optional figure rendering for `isovar plot` and `isovar fusion --plot-dir`:
pip install 'isovar[plot]'

Isovar requires Python 3.9 or later. Reference annotation comes from PyEnsembl; install the release matching your alignments before the first run, for example:

pyensembl install --release 75 --species human

Python API

isovar.run_isovar returns one isovar.IsovarResult per input variant, in input order. Each result holds the RNA evidence at that variant's locus and any mutant protein sequences assembled for it.

from isovar import run_isovar

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.
    if isovar_result.top_protein_sequence is not None:
        # Number of distinct fragments supporting the variant allele.
        print(isovar_result.variant, isovar_result.num_alt_fragments)

A collection of IsovarResult objects can also be flattened into a Pandas DataFrame:

from isovar import run_isovar, isovar_results_to_dataframe

df = isovar_results_to_dataframe(
    run_isovar(
        variants="cancer-mutations.vcf",
        alignment_file="tumor-rna.bam"))

Isovar logs through the standard logging module under the isovar logger and never configures logging itself; configure it in your application to see progress.

Collecting RNA reads

Create a ReadCollector to change how reads are selected. The defaults are shown:

from isovar import run_isovar, ReadCollector

read_collector = ReadCollector(
    min_mapping_quality=1,
    use_duplicate_reads=False,
    use_secondary_alignments=True,
    use_soft_clipped_bases=False,
    # Merge overlapping mates of one fragment into a single observation.
    merge_overlapping_fragments=True,
    # Keep reads without QUAL; their base qualities stay unknown.
    use_reads_without_base_qualities=True,
    # Optional predicate on each original pysam record.
    read_filter=None)

isovar_results = run_isovar(
    variants="cancer-mutations.vcf",
    alignment_file="tumor-rna.bam",
    read_collector=read_collector)

Read support counts sequenced segments, not their alternative SAM alignments. Segments are scoped by read group, and only complementary primary mates in the same read group are merged. A segment whose alternative placements support conflicting alleles is counted with the uncertain other reads rather than as ref or alt evidence, and incompatible placements of one segment cannot extend an assembly. Fragment counts (num_alt_fragments, etc.) are read-group-aware; the *_read_names properties are plain names for display. None of these counts establishes independent molecules or performs UMI deduplication.

Mates with conflicting alignment paths stay separate. For matching paths, disagreeing bases are resolved by quality, which can change allele support as well as assembled sequence. A read ending at an insertion supports the reference allele only if both flanking reference bases are aligned. use_soft_clipped_bases keeps unaligned read ends; it does not realign a clipped partner sequence.

Adapter/poly-A inference and optional end trimming are opt-in ReadCollector settings (infer_read_ends, read_end_profile, trim_adapters, trim_poly_a); original BAM records and aligned/inserted bases are never modified. See the read-end inference guide.

Assembly and translation

Create a ProteinSequenceCreator to change how reads are assembled into coding sequences, placed in a reading frame and grouped into proteins. The defaults are shown:

from isovar import run_isovar, ProteinSequenceCreator

protein_sequence_creator = ProteinSequenceCreator(
    # Peptide size K used to score context; the default target length is 2*K-1.
    protein_context_peptide_length=25,
    # None derives the target from the peptide size (49 aa for K=25).
    protein_sequence_length=None,
    # "balanced", "support" or "context"; see protein context selection below.
    protein_sequence_preference="balanced",
    # Balanced mode keeps candidates with at least this fraction of the best
    # candidate's compatible read support.
    min_protein_sequence_support_fraction=0.85,
    # Minimum number of reads covering each base of the coding sequence.
    min_variant_sequence_coverage=2,
    # Bases of reference transcript the cDNA must match before the variant
    # to establish a reading frame.
    min_transcript_prefix_length=10,
    # Mismatches allowed between the cDNA and the reference transcript.
    max_transcript_mismatches=2,
    # Also count mismatches after the variant toward max_transcript_mismatches.
    count_mismatches_after_variant=False,
    # Ranked protein sequences kept per variant; 0 keeps all.
    max_protein_sequences_per_variant=1,
    # Assemble overlapping reads; if False each sequence comes from one read.
    variant_sequence_assembly=True,
    # Minimum overlap, in nucleotides, before two reads are combined.
    min_assembly_overlap_size=30)

isovar_results = run_isovar(
    variants="cancer-mutations.vcf",
    alignment_file="tumor-rna.bam",
    protein_sequence_creator=protein_sequence_creator)

BAM-derived reads keep their aligned exon blocks and splice junctions. Each read is compatible with a set of annotated transcripts; overlapping reads are assembled only within shared compatible paths, and the resulting cDNA is translated only against those transcripts. Evidence that ends before an isoform-distinguishing junction stays ambiguous and can support every compatible branch without being counted twice. The reading frame is carried through the read's observed alignment, so an upstream indel shifts it (details).

Protein context selection

For peptide size K, Isovar targets 2*K-1 residues (15mers → 29 aa, 25mers → 49 aa), enough for every K-mer overlapping a centered single-residue change. The default balanced preference maximizes mutation-overlapping peptide windows among candidates with at least 85% of the best candidate's compatible read support. support ranks by read support first; context ignores the support budget. Actual context depends on RNA coverage, and no reference sequence fills missing RNA. See protein context selection for the exact rules and the tumor-RNA audit.

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. When the results are flattened into a DataFrame each filter becomes a filter:<name> column.

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, the latter being 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.

Phasing

Variants whose alt reads share at least min_shared_fragments_for_phasing (default 2) fragments with compatible placements are reported as phased. phased_variants_in_supporting_reads uses all alt reads and phased_variants_in_protein_sequence uses the reads behind the top protein sequence; phase_group_from_supporting_reads and phase_group_from_protein_sequence give the connected PhaseGroup, which may include variants linked only through others. Complementary mates, variants on one spliced alignment, and supplementary pieces whose reciprocal SA tags declare the same chimeric path can phase. Matching names in different read groups cannot. A group is connected pairwise evidence, not one resolved haplotype. IsovarReadPhasing and IsovarMutantTranscript expose these results through Varcode's phasing and mutant-transcript interfaces.

Structural variants and fusions

The small-variant pipeline 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_rna reconstructs exploratory RNA paths around one nominated SV from a BAM and annotated models, keeping sequence, frame and event-linkage evidence separate (guide). --predictions compares supplied protein predictions with the reconstructed paths; full reconciliation with Varcode hypotheses is #305.
isovar fusion / reconstruct_fusion validates a supplied fusion transcript's junction evidence and annotated coding frames (guide).

Command line

isovar run \
    --vcf somatic-variants.vcf \
    --bam rnaseq.bam \
    --output isovar-results.csv

isovar --help lists the subcommands; each subcommand's --help lists its options and defaults, which match the Python API. isovar --vcf ... --bam ... (without a subcommand) also runs the pipeline, and python -m isovar works too.

Command	Output
`isovar run`	One row per variant: read evidence, top protein sequence, predicted effect and filters
`isovar protein-sequences`	Ranked candidate protein sequences (`--max-protein-sequences-per-variant 0` keeps all)
`isovar translations`	Every translation of each assembled cDNA in each compatible reading frame, before grouping
`isovar variant-sequences`	Assembled cDNA sequences supporting each variant
`isovar reference-contexts`	Reference sequence and reading frame around each variant (no BAM needed)
`isovar allele-counts`	Read and fragment counts for the ref, alt and other alleles
`isovar allele-reads`	All reads overlapping each variant
`isovar variant-reads`	Reads supporting each variant's alt allele
`isovar plot`	Protein, coverage, read-overlap and transcript figures for one mutation (guide)
`isovar sv-rna`	Exploratory RNA paths around one nominated SV, as JSON
`isovar fusion`	Validated junction evidence and frames for a supplied fusion, as JSON

Except isovar run, the table and plot commands also install as hyphenated scripts such as isovar-protein-sequences and isovar-plot.

Every CSV starts with the same variant key: variant (as in chr9 g.82927102G>T) and chr, pos, ref and alt as given in the input, so tables from different commands can be joined. Counts are num_* columns, lists are ;-separated, and numbers are written with six significant digits. An empty result still has its header.

For example, use only primary alignments, include soft-clipped bases, and require at least three reads at every retained cDNA base:

isovar run --vcf somatic-variants.vcf --bam rnaseq.bam \
    --drop-secondary-alignments --use-soft-clipped-bases \
    --min-variant-sequence-coverage 3 --num-rna-decompression-threads 4 \
    --output isovar-results.csv

Progress messages go to stderr; set --log-level DEBUG for per-candidate detail or --log-level WARNING for quiet runs. Out-of-range options and unusable inputs (a missing file, an unindexed BAM, SAM input, a missing output directory, malformed JSON) are reported as one-line usage errors with exit status 2. The CLI applies the same default filters as run_isovar; the filter options set filter:* columns and passes_all_filters without removing rows. --reference-context-size belongs only to isovar reference-contexts; protein-producing commands derive their reference context from the requested cDNA length and minimum transcript prefix.

Internal design

The inputs to Isovar are one or more somatic variant call (VCF) files, along with a BAM file containing aligned tumor RNA reads. The following objects are used to aggregate information within Isovar:

LocusRead: Isovar examines each variant locus and extracts reads overlapping that locus, represented by LocusRead. The LocusRead representation allows filtering based on quality and alignment criteria (e.g. MAPQ > 0) which are thrown away in later stages of Isovar.
AlleleRead: Once LocusRead objects have been filtered, they are converted into a simplified representation called AlleleRead. Each AlleleRead contains only the cDNA sequences before, at, and after the variant locus.
ReadEvidence: The set of AlleleRead objects overlapping a mutation's location may support many different distinct alleles. The ReadEvidence type represents the grouping of these reads into ref, alt and other AlleleRead sets, where ref reads agree with the reference sequence, alt reads agree with the given mutation, and other reads contain all non-ref/non-alt alleles. The alt reads will be used later to determine a mutant coding sequence, but the ref and other groups are also kept in case they are useful for filtering.
VariantSequence: Overlapping AlleleReads containing the same mutation are assembled into a longer sequence by VariantSequenceCreator. The VariantSequence object represents this candidate coding sequence, as well as all the AlleleRead objects which were used to create it.
ReferenceContext: To determine the reading frame in which to translate a VariantSequence, Isovar looks at all Ensembl annotated transcripts overlapping the locus and collapses them into one or more ReferenceContext objects. Each ReferenceContext represents the cDNA sequence upstream of the variant locus and in which of the {0, +1, +2} reading frames it is translated.
VariantORF and Translation: A VariantORF places a VariantSequence in the reading frame of a ReferenceContext, and its translation into a protein fragment is represented by Translation.
ProteinSequence: Multiple distinct variant sequences and reference contexts can generate the same translations, so ProteinSequenceCreator aggregates those equivalent Translation objects into a ProteinSequence. TranscriptAssemblyEdit records the transcript-relative edits observed in its assemblies.
IsovarResult: Since a single variant locus might have reads which assemble into multiple incompatible coding sequences, an IsovarResult represents a variant and one or more ProteinSequence objects which are associated with it. Protein sequences are ranked by the configured context/support preference and the top sequence is made easy to access. Allele-support properties such as num_alt_fragments and fraction_ref_reads remain separate from the selected protein's compatible support.

Documentation

Guide	Contents
Protein context selection	Context target, the balanced/support/context preferences and their thresholds
Aligned reading frames	How observed alignments carry the coding frame
Read-end inference	Opt-in adapter and poly-A/T annotation and trimming
Mutation-evidence figures	`isovar plot` and the reproducible osteosarc figure examples
SV RNA reconstruction	`isovar sv-rna` inputs, outputs, ORF export and prediction comparison
ORF start evidence	Start-origin tiers and splice-linked inclusion for SV ORFs
Cell/UMI evidence	Input-scoped cell and UMI labels in SV support
ONT read lineage	Dorado split/duplex signal ancestry in SV support
Supplied fusion RNA	`isovar fusion` input/output contract
Library responsibilities	How Varcode, Isovar and Vaxrank divide the work
Minimal Sid test reads	The packaged, offline test-read bundle and its regeneration
Shared osteosarc data	The pinned 49-case BAM/index regression cache
Read-processing audit	Cross-platform read eligibility, native evidence tags and benchmarks
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.

Development Status
- 4 - Beta
Environment
- Console
Intended Audience
- Science/Research
Operating System
- OS Independent
Programming Language
- Python
Topic
- Scientific/Engineering :: Bio-Informatics

Release files for isovar 1.29.0

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