Relax wildcard constraints for TPM.

The goal is to also allow TPM computed on protein-coding genes only.

Degradome-seq/Degradome-seq.snakefile

@@ -413,7 +413,7 @@ rule compute_TPM:
             f"all_on_{genome}", "{biotype}_{orientation}_TPM.txt"),
     # The sum must be done over all counted features
     wildcard_constraints:
-        biotype = "alltypes"
+        biotype = "|".join(["alltypes", "protein_coding"]),
     # run:
     #     rpk = pd.read_table(input.rpk_file, index_col="gene")
     #     tpm = 1000000 * rpk / rpk.sum()

PRO-seq/PRO-seq.snakefile

@@ -992,7 +992,7 @@ rule compute_TPM:
             "all_on_C_elegans", "{biotype}_{orientation}_TPM.txt"),
     # The sum must be done over all counted features
     wildcard_constraints:
-        biotype = "|".join(["alltypes"])
+        biotype = "|".join(["alltypes", "protein_coding"])
     # run:
     #     rpk = pd.read_table(input.rpk_file, index_col="gene")
     #     tpm = 1000000 * rpk / rpk.sum()

RNA_Seq_Cecere/RNA-seq.snakefile

@@ -1143,7 +1143,7 @@ rule compute_TPM:
             "all_{mapped_type}", "{biotype}_{orientation}_TPM.txt"),
     # The sum must be done over all counted features
     wildcard_constraints:
-        biotype = "|".join(["alltypes"])
+        biotype = "|".join(["alltypes", "protein_coding"])
     # run:
     #     rpk = pd.read_table(input.rpk_file, index_col="gene")
     #     tpm = 1000000 * rpk / rpk.sum()

Ribo-seq/Ribo-seq.snakefile

@@ -1292,7 +1292,7 @@ rule compute_TPM:
             "all_{read_type}_on_%s" % genome, "{biotype}_{orientation}_TPM.txt"),
     # The sum must be done over all counted features
     wildcard_constraints:
-        biotype = "alltypes"
+        biotype = "|".join(["alltypes", "protein_coding"]),
     run:
         rpk = pd.read_table(input.rpk_file, index_col="gene")
         tpm = 1000000 * rpk / rpk.sum()