Merge pull request #186 from openvax/use-new-isovar-api-but-old-effec…

…ts-logic

use new Isovar API but keep old logic for determining variant effects

.travis.yml

@@ -61,7 +61,7 @@ install:
   - source activate test-environment
   # install pysam from conda because I'm having trouble installing Cython
   # for Python 3 on Travis
-  - conda install -c bioconda pysam=0.9.0
+  - conda install -c bioconda pysam
   - pip install -r requirements.txt
   - pip install .
   - pip install coveralls

requirements.txt

@@ -3,7 +3,7 @@ numpy>=1.14.0
 pandas
 pyensembl>=1.5.0
 varcode>=0.5.9
-isovar>=0.8.5,<1.0.0
+isovar>=1.1.0
 mhctools>=1.5.0
 roman
 jinja2

setup.py

@@ -62,7 +62,7 @@
             'pandas',
             'pyensembl>=1.5.0',
             'varcode>=0.5.9',
-            'isovar>=0.8.5,<1.0.0',
+            'isovar>=1.1.0',
             'mhctools>=1.5.0',
             'roman',
             'jinja2',

test/test_mutant_protein_sequence.py

@@ -13,11 +13,9 @@
 from __future__ import absolute_import, print_function, division
 
 from nose.tools import eq_, assert_almost_equal
-from vaxrank.core_logic import run_vaxrank
+from vaxrank.cli import make_vaxrank_arg_parser, run_vaxrank_from_parsed_args
+from vaxrank.mutant_protein_fragment import MutantProteinFragment
 from mhctools import RandomBindingPredictor
-from isovar.cli.variant_sequences_args import make_variant_sequences_arg_parser
-from isovar.cli.rna_args import allele_reads_generator_from_args
-from varcode.cli import variant_collection_from_args
 
 from .testing_helpers import data_path
 
@@ -49,25 +47,19 @@ def test_mutant_amino_acids_in_mm10_chrX_8125624_refC_altA_pS460I():
     # there are two co-occurring variants in the RNAseq data but since
     # they don't happen in the same codon then we're considering the Varcode
     # annotation to be correct
-    # TODO: deal with phasing of variants explicitly so that both
-    # variant positions are considered mutated
-    arg_parser = make_variant_sequences_arg_parser()
+    # TODO:
+    #  deal with phasing of variants explicitly so that both
+    #  variant positions are considered mutated
+    arg_parser = make_vaxrank_arg_parser()
     args = arg_parser.parse_args([
         "--vcf", data_path("b16.f10/b16.f10.Wdr13.vcf"),
         "--bam", data_path("b16.f10/b16.combined.sorted.bam"),
+        "--vaccine-peptide-length", "15",
+        "--padding-around-mutation", "5",
+        "--mhc-predictor", "random",
+        "--mhc-alleles", "HLA-A*02:01",
     ])
-    reads_generator = allele_reads_generator_from_args(args)
-    variants = variant_collection_from_args(args)
-    results = run_vaxrank(
-        variants=variants,
-        reads_generator=reads_generator,
-        mhc_predictor=random_binding_predictor,
-        vaccine_peptide_length=15,
-        padding_around_mutation=5,
-        max_vaccine_peptides_per_variant=1,
-        min_alt_rna_reads=1,
-        min_variant_sequence_coverage=1,
-        variant_sequence_assembly=True)
+    results = run_vaxrank_from_parsed_args(args)
     ranked_list = results.ranked_vaccine_peptides
 
     for variant, vaccine_peptides in ranked_list:
@@ -85,23 +77,16 @@ def test_mutant_amino_acids_in_mm10_chr9_82927102_refGT_altTG_pT441H():
     # mentions the G>T variant but doesn't include the subsequent nucleotide
     # change T>G. To avoid having to think about phasing of variants I changed
     # the VCF in vaxrank to contain a GT>TG variant.
-    arg_parser = make_variant_sequences_arg_parser()
+    arg_parser = make_vaxrank_arg_parser()
     args = arg_parser.parse_args([
         "--vcf", data_path("b16.f10/b16.f10.Phip.vcf"),
         "--bam", data_path("b16.f10/b16.combined.sorted.bam"),
+        "--vaccine-peptide-length", "15",
+        "--padding-around-mutation", "5",
+        "--mhc-predictor", "random",
+        "--mhc-alleles", "HLA-A*02:01",
     ])
-    reads_generator = allele_reads_generator_from_args(args)
-    variants = variant_collection_from_args(args)
-    results = run_vaxrank(
-        reads_generator=reads_generator,
-        mhc_predictor=random_binding_predictor,
-        variants=variants,
-        vaccine_peptide_length=15,
-        padding_around_mutation=5,
-        min_alt_rna_reads=1,
-        min_variant_sequence_coverage=1,
-        variant_sequence_assembly=True,
-        max_vaccine_peptides_per_variant=1)
+    results = run_vaxrank_from_parsed_args(args)
     ranked_list = results.ranked_vaccine_peptides
 
     for variant, vaccine_peptides in ranked_list:
@@ -112,25 +97,19 @@ def test_mutant_amino_acids_in_mm10_chr9_82927102_refGT_altTG_pT441H():
             mutant_protein_fragment)
 
 def test_keep_top_k_epitopes():
-    arg_parser = make_variant_sequences_arg_parser()
+    arg_parser = make_vaxrank_arg_parser()
+    keep_k_epitopes = 3
     args = arg_parser.parse_args([
         "--vcf", data_path("b16.f10/b16.f10.Phip.vcf"),
         "--bam", data_path("b16.f10/b16.combined.sorted.bam"),
+        "--vaccine-peptide-length", "15",
+        "--padding-around-mutation", "5",
+        "--num-epitopes-per-vaccine-peptide", str(keep_k_epitopes),
+        "--mhc-predictor", "netmhc",
+        "--mhc-alleles", "HLA-A*02:01",
     ])
-    reads_generator = allele_reads_generator_from_args(args)
-    variants = variant_collection_from_args(args)
-    keep_k_epitopes = 3
-    results = run_vaxrank(
-        reads_generator=reads_generator,
-        mhc_predictor=random_binding_predictor,
-        variants=variants,
-        vaccine_peptide_length=15,
-        padding_around_mutation=5,
-        min_alt_rna_reads=1,
-        min_variant_sequence_coverage=1,
-        variant_sequence_assembly=True,
-        max_vaccine_peptides_per_variant=1,
-        num_mutant_epitopes_to_keep=keep_k_epitopes)
+    results = run_vaxrank_from_parsed_args(args)
+
     ranked_list = results.ranked_vaccine_peptides
 
     for variant, vaccine_peptides in ranked_list:
@@ -141,3 +120,25 @@ def test_keep_top_k_epitopes():
         mutant_epitope_score = sum(
             p.logistic_epitope_score() for p in vaccine_peptide.mutant_epitope_predictions)
         assert_almost_equal(mutant_epitope_score, vaccine_peptide.mutant_epitope_score)
+
+def test_mutant_protein_fragment_serialization():
+    arg_parser = make_vaxrank_arg_parser()
+    keep_k_epitopes = 3
+    args = arg_parser.parse_args([
+        "--vcf", data_path("b16.f10/b16.f10.Phip.vcf"),
+        "--bam", data_path("b16.f10/b16.combined.sorted.bam"),
+        "--vaccine-peptide-length", "15",
+        "--padding-around-mutation", "5",
+        "--num-epitopes-per-vaccine-peptide", str(keep_k_epitopes),
+        "--mhc-predictor", "netmhc",
+        "--mhc-alleles", "HLA-A*02:01",
+    ])
+    results = run_vaxrank_from_parsed_args(args)
+
+    ranked_list = results.ranked_vaccine_peptides
+
+    for _, vaccine_peptides in ranked_list:
+        mutant_protein_fragment = vaccine_peptides[0].mutant_protein_fragment
+        json_str = mutant_protein_fragment.to_json()
+        deserialized = MutantProteinFragment.from_json(json_str)
+        eq_(mutant_protein_fragment, deserialized)

test/test_shell_script.py

@@ -30,7 +30,7 @@
     "--mhc-predictor", "random",
     "--mhc-alleles", "H2-Kb,H2-Db",
     "--padding-around-mutation", "5",
-    "--include-mismatches-after-variant"
+    "--count-mismatches-after-variant",
 ]
 
 cli_args_for_b16_seqdata_real_predictor = [
@@ -41,7 +41,7 @@
     "--mhc-alleles", "H2-Kb,H2-Db",
     "--mhc-epitope-lengths", "8",
     "--padding-around-mutation", "5",
-    "--include-mismatches-after-variant"
+    "--count-mismatches-after-variant"
 ]
 
 

vaxrank/__init__.py

@@ -1 +1 @@
-__version__ = "1.2.0"
+__version__ = "1.3.0"