gbff support

modelseedpy/core/msgenome.py

@@ -82,6 +82,27 @@ def parse_fasta_str(faa_str, split=DEFAULT_SPLIT, h_func=None):
     return features
 
 
+def read_gbff_records_from_file(filename: str):
+    if filename.endswith(".gbff"):
+        with open(filename, "r") as fh:
+            return read_gbff_records(fh)
+    elif filename.endswith(".gz"):
+        import gzip
+        from io import StringIO
+
+        with gzip.open(filename, "rb") as fh:
+            return read_gbff_records(StringIO(fh.read().decode("utf-8")))
+
+
+def read_gbff_records(handler):
+    from Bio import SeqIO
+
+    gbff_records = []
+    for record in SeqIO.parse(handler, "gb"):
+        gbff_records.append(record)
+    return gbff_records
+
+
 def extract_features(faa_str, split=DEFAULT_SPLIT, h_func=None):
     features = []
     active_seq = None
@@ -168,6 +189,45 @@ def from_fasta(filename, split=" ", h_func=None):
         genome.features += read_fasta2(filename, split, h_func)
         return genome
 
+    @staticmethod
+    def from_gbff_sequence(filename):
+        gbff_records = read_gbff_records_from_file(filename)
+        genome = MSGenome()
+        features = []
+        for rec in gbff_records:
+            feature = MSFeature(rec.id, str(rec.seq), description=rec.description)
+            features.append(feature)
+        genome.features += features
+        return genome
+
+    @staticmethod
+    def from_gbff_features(
+        filename, feature_id_qualifier="protein_id", description_qualifier="product"
+    ):
+        gbff_records = read_gbff_records_from_file(filename)
+        genome = MSGenome()
+        features = []
+        for rec in gbff_records:
+            for f in rec.features:
+                if f.type == "CDS":
+                    translations = f.qualifiers.get("translation", [])
+                    if len(translations) == 1:
+                        feature_id = f.qualifiers.get(feature_id_qualifier, [None])[0]
+                        description = f.qualifiers.get(description_qualifier, [None])[0]
+                        if feature_id:
+                            feature = MSFeature(
+                                feature_id, translations[0], description=description
+                            )
+                            features.append(feature)
+                        else:
+                            logger.warning(
+                                f"skip feature: unable to fetch id from qualifier {feature_id_qualifier}"
+                            )
+                    elif len(translations) > 1:
+                        logger.warning(f"skip feature: with multiple sequences {f}")
+        genome.features += features
+        return genome
+
     def to_fasta(self, filename, l=80, fn_header=None):
         to_fasta(self.features, filename, l, fn_header)
         return filename
@@ -203,3 +263,85 @@ def _repr_html_(self):
                 <td>{len(self.features)}</td>
             </tr>
         </table>"""
+
+
+class GenomeGff(MSGenome):
+    def __init__(self, contigs):
+        self.contigs = contigs
+        super().__init__()
+
+    @staticmethod
+    def read_sequence(feature_id, gff_record, expected_sequence, contigs):
+        from Bio.Seq import Seq
+        from Bio import Align
+
+        protein_seq_cds = expected_sequence
+        feature_contig = contigs.features.get_by_id(gff_record.contig_id)
+        seq = Seq(feature_contig.seq[gff_record.start - 1 : gff_record.end])
+        if gff_record.strand == "-":
+            seq = seq.reverse_complement()
+        seq_from_dna = str(seq.translate())
+        if len(seq_from_dna) > 0 and seq_from_dna[-1] == "*":
+            seq_from_dna = seq_from_dna[:-1]
+        if len(protein_seq_cds) > 0 and protein_seq_cds[-1] == "*":
+            protein_seq_cds = protein_seq_cds[:-1]
+        eq = protein_seq_cds == seq_from_dna
+
+        score = None
+        if not eq and len(seq_from_dna) > 0:
+            try:
+                aligner = Align.PairwiseAligner()
+                res = aligner.align(protein_seq_cds, seq_from_dna)
+                score = res.score
+            except ValueError as ex:
+                print("error", gff_record)
+                raise ex
+
+        feature = MSFeature(feature_id, protein_seq_cds)
+        feature.description = f"score: {score}"
+        feature.gff = gff_record
+        return feature
+
+    @staticmethod
+    def from_fna_faa_gff(
+        filename_fna, filename_faa, filename_gff, _fn_get_id, prodigal=False
+    ):
+        genome_gff_features = _read_gff_features(filename_gff)
+        genome_faa = MSGenome.from_fasta(filename_faa)
+        contigs = MSGenome.from_fasta(filename_fna)
+
+        feature_lookup = {}
+        if prodigal:
+            for feature in genome_faa.features:
+                attr = dict(
+                    x.split("=")
+                    for x in feature.description.split(" # ")[-1].split(";")
+                )
+                if attr["ID"] not in feature_lookup:
+                    feature_lookup[attr["ID"]] = feature
+                else:
+                    raise ValueError("")
+        else:
+            feature_lookup = {feature.id: feature for feature in genome_faa.features}
+
+        features = []
+        for gff_record in genome_gff_features:
+            if gff_record.feature_type == "CDS":
+                feature_id = gff_record.attr.get("ID")
+                if _fn_get_id:
+                    feature_id = _fn_get_id(gff_record)
+
+                feature_cds = feature_lookup.get(feature_id)
+
+                if feature_cds:
+                    protein_seq_cds = feature_cds.seq
+                    f = GenomeGff.read_sequence(
+                        feature_id, gff_record, protein_seq_cds, contigs
+                    )
+                    features.append(f)
+                else:
+                    print(f"not found {feature_id}")
+
+        genome = GenomeGff(contigs)
+        genome.features += features
+        return genome

modelseedpy/core/mstemplate.py

@@ -646,7 +646,7 @@ def from_dict(d, template):
             d.get("pigment", 0),
             d.get("carbohydrate", 0),
             d.get("energy", 0),
-            d.get("other", 0)
+            d.get("other", 0),
         )
         for item in d["templateBiomassComponents"]:
             biocomp = MSTemplateBiomassComponent.from_dict(item, template)

modelseedpy/core/rast_client.py

@@ -60,7 +60,7 @@ def __init__(self):
             },
         ]
 
-    def annotate_genome(self, genome):
+    def annotate_genome(self, genome, split_terms=True):
         p_features = []
         for f in genome.features:
             if f.seq and len(f.seq) > 0:
@@ -70,9 +70,13 @@ def annotate_genome(self, genome):
         for o in res[0]["features"]:
             feature = genome.features.get_by_id(o["id"])
             if "function" in o:
-                functions = re.split("; | / | @", o["function"])
-                for function in functions:
-                    feature.add_ontology_term("RAST", function)
+                rast_function = o["function"]
+                if split_terms:
+                    functions = re.split("; | / | @", rast_function)
+                    for function in functions:
+                        feature.add_ontology_term("RAST", function)
+                else:
+                    feature.add_ontology_term("RAST", rast_function)
 
         return res[0]["analysis_events"]
 

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [tool.black]
 line-length = 88
-python-version = ['py36']
+python-version = ['py38']
 include = '\.pyi?$'
 exclude = '''
 (