Map Ribo-TISH's colon-qualified ORF types on their location

modules/nf-core/custom/orfnormalise/meta.yml

@@ -35,8 +35,10 @@ description: |
   `orf_type_native` carries the caller's own ORF-type label verbatim, so every
   harmonisation decision below stays auditable without re-running the caller.
   ORF-type tokens are matched exactly (casefolded) against each caller's closed
-  vocabulary. Tokens matching no entry cause the process to fail; successful
-  outputs report `unmapped_orf_type=0` on the `# parser_columns:` provenance line.
+  vocabulary, on the part before any colon so that a qualified label such as
+  Ribo-TISH's `Novel:CDSFrameOverlap` matches on `Novel`. Tokens whose location
+  matches no entry cause the process to fail; successful outputs report
+  `unmapped_orf_type=0` on the `# parser_columns:` provenance line.
 
   `orf_class` is purely positional: it records where the ORF sits relative to
   the annotated CDS and never encodes its length. Select small ORFs with the

modules/nf-core/custom/orfnormalise/templates/orfnormalise.py

@@ -413,6 +413,8 @@ def write_outputs(bed_path, tsv_path, bed_lines, tsv_rows, parser_columns, unmap
 # Every caller's ORF-type vocabulary is a closed enum, so tokens are matched
 # exactly (casefolded) rather than by substring. Substring matching mis-fired
 # on the overlap forms, because "uorf" is a substring of "overlap_uorf".
+#
+# No key may contain a colon: classify() matches only the part before one.
 # ----------------------------------------------------------------------------
 
 CLASS_TOKENS = {
@@ -498,13 +500,17 @@ def write_outputs(bed_path, tsv_path, bed_lines, tsv_rows, parser_columns, unmap
 def classify(caller, orf_type):
     """Map a caller's native ORF-type token to the harmonised class.
 
-    Returns (orf_class, matched); `matched` is False when a non-empty token
-    matched no entry, so unmapped labels can be counted rather than silently
-    absorbed into `other`.
+    Only the part before the first colon is matched; the full token survives in
+    `orf_type_native`.
+
+    Returns (orf_class, matched); `matched` is False when a non-empty token's
+    location matched no entry, so unmapped labels can be counted rather than
+    silently absorbed into `other`.
     """
     if not orf_type:
         return "other", True
-    cls = CLASS_TOKENS[caller].get(orf_type.strip().casefold())
+    location = orf_type.split(":", 1)[0]
+    cls = CLASS_TOKENS[caller].get(location.strip().casefold())
     if cls is None:
         return "other", False
     return cls, True

modules/nf-core/custom/orfnormalise/tests/main.nf.test

@@ -431,4 +431,50 @@ nextflow_process {
         }
     }
 
+    test("ribotish composite ORF types map on the location, keeping the qualifier native") {
+
+        when {
+            process {
+                """
+                input[0] = channel
+                    .of(
+                        'Tid\tGid\tGenomePos\tTisType\tAALen\tFisherPvalue',
+                        't1\tg1\tchr1:1000-1100:+\tNovel:CDSFrameOverlap\t30\t0.001',
+                        "t1\tg1\tchr1:1100-1200:+\t3'UTR:CDSFrameOverlap\t30\t0.001",
+                        't1\tg1\tchr1:1200-1300:+\tInternal:CDSFrameOverlap\t30\t0.001',
+                        't1\tg1\tchr1:1300-1400:+\tNovel:Known\t30\t0.001'
+                    )
+                    .collectFile(name: 'composite.ribotish.pred.txt', newLine: true, sort: false)
+                    .map { orfs -> [[id: 'sample1'], orfs, 'ribotish'] }
+                input[1] = channel
+                    .of('chr1\ttest\texon\t1001\t2000\t.\t+\t.\tgene_id "g1"; transcript_id "t1";')
+                    .collectFile(name: 'composite.gtf', newLine: true)
+                    .map { gtf -> [[id: 'reference'], gtf] }
+                """
+            }
+        }
+
+        then {
+            def lines = path(process.out.tsv[0][1]).text.readLines()
+            def header = lines.find { it.startsWith('orf_id') }.split('\t') as List
+            def data = lines.findAll { !it.startsWith('#') && !it.startsWith('orf_id') }
+            def i_class = header.indexOf('orf_class')
+            def i_native = header.indexOf('orf_type_native')
+            def classes = data.collect { it.split('\t')[i_class] } as Set
+            def natives = data.collect { it.split('\t')[i_native] }
+            def qualified = natives.findAll { it.contains(':') }
+            def provenance = lines.find { it.startsWith('# parser_columns:') }
+
+            assertAll(
+                // The part before the colon selects the class; the qualifier survives
+                // in orf_type_native.
+                { assert process.success },
+                { assert classes == ['novel_u', 'dORF', 'intORF'] as Set },
+                { assert qualified.size() == natives.size() },
+                { assert natives.contains('Novel:CDSFrameOverlap') },
+                { assert provenance.contains('unmapped_orf_type=0') }
+            )
+        }
+    }
+
 }