Merge pull request #300 from KwanLab/release-2.2.0

Release 2.2.0

Author: evanroyrees

.github/workflows/pytest_codecov.yml

@@ -26,7 +26,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python-version: [3.7]
+        python-version: [3.8]
     env:
       OS: ${{ matrix.os }}
       PYTHON: ${{ matrix.python-version }}

VERSION

@@ -1 +1 @@
-2.1.0
+2.2.0

autometa-env.yml

@@ -12,6 +12,7 @@ dependencies:
   - gdown
   - hdbscan
   - hmmer
+  - joblib==1.1.0 # See https://stackoverflow.com/a/73830525/12671809
   - numba>=0.47
   - numpy>=1.13
   - pandas>=1.1
@@ -23,8 +24,9 @@ dependencies:
   - rsync
   - samtools>=1.11
   - scikit-bio
-  - scipy==1.8 #force scipy 1.8 until scikit-bio updates to 1.9, https://github.com/KwanLab/Autometa/issues/285
+  - scipy==1.8.1 #force scipy 1.8 until scikit-bio updates to 1.9, https://github.com/KwanLab/Autometa/issues/285
   - scikit-learn==0.24 # prevent error from joblib in multiprocessing distance calculations
+  - seqkit
   - tqdm
   - trimap
   - tsne

autometa/binning/large_data_mode.py

@@ -39,7 +39,7 @@
 from autometa.common import kmers
 
 from autometa.common.exceptions import TableFormatError, BinningError
-from autometa.taxonomy.ncbi import NCBI
+from autometa.taxonomy.database import TaxonomyDatabase
 from autometa.binning.recursive_dbscan import get_clusters
 from autometa.binning.utilities import (
     write_results,
@@ -306,17 +306,26 @@ def cluster_by_taxon_partitioning(
     Raises
     -------
     TableFormatError
-        No marker information is availble for contigs to be binned.
+        No marker information is available for contigs to be binned.
     FileNotFoundError
         Provided `binning_checkpoints_fpath` does not exist
+    TableFormatError
+        No marker information is availble for contigs to be binned.
     """
+    if binning_checkpoints_fpath and not os.path.exists(binning_checkpoints_fpath):
+        raise FileNotFoundError(binning_checkpoints_fpath)
+
     if reverse_ranks:
         # species, genus, family, order, class, phylum, superkingdom
-        canonical_ranks = [rank for rank in NCBI.CANONICAL_RANKS if rank != "root"]
+        canonical_ranks = [
+            rank for rank in TaxonomyDatabase.CANONICAL_RANKS if rank != "root"
+        ]
     else:
         # superkingdom, phylum, class, order, family, genus, species
         canonical_ranks = [
-            rank for rank in reversed(NCBI.CANONICAL_RANKS) if rank != "root"
+            rank
+            for rank in reversed(TaxonomyDatabase.CANONICAL_RANKS)
+            if rank != "root"
         ]
     # if stage is cached then we can first look to the cache before we begin subsetting main...
     clustered_contigs = set()
@@ -326,15 +335,17 @@ def cluster_by_taxon_partitioning(
     starting_rank_name_txt = None
     # Retrieve appropriate starting canonical rank and rank_name_txt from cached binning checkpoints if cache was provided
     if cache:
-        if binning_checkpoints_fpath and not os.path.exists(binning_checkpoints_fpath):
-            raise FileNotFoundError(binning_checkpoints_fpath)
+        if not os.path.exists(cache):
+            os.makedirs(os.path.realpath(cache), exist_ok=True)
+            logger.debug(f"Created cache dir: {cache}")
+        if not os.path.isdir(cache):
+            raise NotADirectoryError(cache)
         if not binning_checkpoints_fpath:
             binning_checkpoints_fpath = os.path.join(
                 cache, "binning_checkpoints.tsv.gz"
             )
-        if os.path.exists(binning_checkpoints_fpath) and os.path.getsize(
-            binning_checkpoints_fpath
-        ):
+    if binning_checkpoints_fpath:
+        if os.path.exists(binning_checkpoints_fpath) and os.path.getsize(binning_checkpoints_fpath):
             checkpoint_info = get_checkpoint_info(binning_checkpoints_fpath)
             binning_checkpoints = checkpoint_info["binning_checkpoints"]
             starting_rank = checkpoint_info["starting_rank"]
@@ -392,9 +403,9 @@ def cluster_by_taxon_partitioning(
                 else None
             )
             # Create canonical rank cache outdir if it does not exist
-            rank_cache_outdir = os.path.join(cache, canonical_rank)
+            rank_cache_outdir = os.path.join(cache, canonical_rank) if cache else None
             if embedding_cache_fpath and not os.path.isdir(rank_cache_outdir):
-                os.makedirs(rank_cache_outdir)
+                os.makedirs(rank_cache_outdir, exist_ok=True)
             rank_embedding = get_kmer_embedding(
                 rank_counts,
                 cache_fpath=embedding_cache_fpath,
@@ -546,7 +557,7 @@ def cluster_by_taxon_partitioning(
             num_clusters += clustered.cluster.nunique()
             clusters.append(clustered)
             # Cache binning at rank_name_txt stage (rank-name-txt checkpointing)
-            if cache:
+            if binning_checkpoints_fpath:
                 binning_checkpoints = checkpoint(
                     checkpoints_df=binning_checkpoints,
                     clustered=clustered,

autometa/binning/recursive_dbscan.py

@@ -24,7 +24,7 @@
 from autometa.common.markers import load as load_markers
 
 from autometa.common.exceptions import TableFormatError, BinningError
-from autometa.taxonomy.ncbi import NCBI
+from autometa.taxonomy.database import TaxonomyDatabase
 from autometa.binning.utilities import (
     write_results,
     read_annotations,
@@ -628,10 +628,14 @@ def taxon_guided_binning(
     logger.info(f"Using {method} clustering method")
     if reverse_ranks:
         # species, genus, family, order, class, phylum, superkingdom
-        ranks = [rank for rank in NCBI.CANONICAL_RANKS if rank != "root"]
+        ranks = [rank for rank in TaxonomyDatabase.CANONICAL_RANKS if rank != "root"]
     else:
         # superkingdom, phylum, class, order, family, genus, species
-        ranks = [rank for rank in reversed(NCBI.CANONICAL_RANKS) if rank != "root"]
+        ranks = [
+            rank
+            for rank in reversed(TaxonomyDatabase.CANONICAL_RANKS)
+            if rank != "root"
+        ]
     starting_rank_index = ranks.index(starting_rank)
     ranks = ranks[starting_rank_index:]
     logger.debug(f"Using ranks: {', '.join(ranks)}")

autometa/binning/summary.py

@@ -17,7 +17,9 @@
 
 from Bio import SeqIO
 
+from autometa.taxonomy.database import TaxonomyDatabase
 from autometa.taxonomy.ncbi import NCBI
+from autometa.taxonomy.gtdb import GTDB
 from autometa.taxonomy import majority_vote
 from autometa.common.markers import load as load_markers
 
@@ -226,16 +228,16 @@ def get_metabin_stats(
 
 
 def get_metabin_taxonomies(
-    bin_df: pd.DataFrame, ncbi: NCBI, cluster_col: str = "cluster"
+    bin_df: pd.DataFrame, taxa_db: TaxonomyDatabase, cluster_col: str = "cluster"
 ) -> pd.DataFrame:
     """Retrieve taxonomies of all clusters recovered from Autometa binning.
 
     Parameters
     ----------
     bin_df : pd.DataFrame
         Autometa binning table. index=contig, cols=['cluster','length','taxid', *canonical_ranks]
-    ncbi : autometa.taxonomy.ncbi.NCBI instance
-        Autometa NCBI class instance
+    taxa_db : autometa.taxonomy.ncbi.TaxonomyDatabase instance
+        Autometa NCBI or GTDB class instance
     cluster_col : str, optional
         Clustering column by which to group metabins
 
@@ -246,7 +248,9 @@ def get_metabin_taxonomies(
         Indexed by cluster
     """
     logger.info(f"Retrieving metabin taxonomies for {cluster_col}")
-    canonical_ranks = [rank for rank in NCBI.CANONICAL_RANKS if rank != "root"]
+    canonical_ranks = [
+        rank for rank in TaxonomyDatabase.CANONICAL_RANKS if rank != "root"
+    ]
     is_clustered = bin_df[cluster_col].notnull()
     bin_df = bin_df[is_clustered]
     outcols = [cluster_col, "length", "taxid", *canonical_ranks]
@@ -277,11 +281,13 @@ def get_metabin_taxonomies(
             taxonomies[cluster][canonical_rank].update({taxid: length})
         else:
             taxonomies[cluster][canonical_rank][taxid] += length
-    cluster_taxonomies = majority_vote.rank_taxids(taxonomies, ncbi)
+    cluster_taxonomies = majority_vote.rank_taxids(taxonomies, taxa_db=taxa_db)
     # With our cluster taxonomies, let's place these into a dataframe for easy data accession
     cluster_taxa_df = pd.Series(data=cluster_taxonomies, name="taxid").to_frame()
     # With the list of taxids, we'll retrieve their complete canonical-rank information
-    lineage_df = ncbi.get_lineage_dataframe(cluster_taxa_df.taxid.tolist(), fillna=True)
+    lineage_df = taxa_db.get_lineage_dataframe(
+        cluster_taxa_df.taxid.tolist(), fillna=True
+    )
     # Now put it all together
     cluster_taxa_df = pd.merge(
         cluster_taxa_df, lineage_df, how="left", left_on="taxid", right_index=True
@@ -323,11 +329,18 @@ def main():
         required=True,
     )
     parser.add_argument(
-        "--ncbi",
-        help="Path to user NCBI databases directory (Required for retrieving metabin taxonomies)",
+        "--dbdir",
+        help="Path to user taxonomy database directory (Required for retrieving metabin taxonomies)",
         metavar="dirpath",
         required=False,
     )
+    parser.add_argument(
+        "--dbtype",
+        help="Taxonomy database type to use (NOTE: must correspond to the same database type used during contig taxon assignment.)",
+        choices=["ncbi", "gtdb"],
+        required=False,
+        default="ncbi",
+    )
     parser.add_argument(
         "--binning-column",
         help="Binning column to use for grouping metabins",
@@ -377,14 +390,17 @@ def main():
     logger.info(f"Wrote metabin stats to {args.output_stats}")
     # Finally if taxonomy information is available then write out each metabin's taxonomy by modified majority voting method.
     if "taxid" in bin_df.columns:
-        if not args.ncbi:
+        if not args.dbdir:
             logger.warn(
-                "taxid found in dataframe. --ncbi argument is required to retrieve metabin taxonomies. Skipping..."
+                "taxid found in dataframe. --dbdir argument is required to retrieve metabin taxonomies. Skipping..."
             )
         else:
-            ncbi = NCBI(dirpath=args.ncbi)
+            if args.dbtype == "ncbi":
+                taxa_db = NCBI(dbdir=args.dbdir)
+            elif args.dbtype == "gtdb":
+                taxa_db = GTDB(dbdir=args.dbdir)
             taxa_df = get_metabin_taxonomies(
-                bin_df=bin_df, ncbi=ncbi, cluster_col=args.binning_column
+                bin_df=bin_df, taxa_db=taxa_db, cluster_col=args.binning_column
             )
             taxa_df.to_csv(args.output_taxonomy, sep="\t", index=True, header=True)
 

autometa/binning/utilities.py

@@ -33,7 +33,7 @@
 
 from typing import Iterable, Tuple
 
-from autometa.taxonomy.ncbi import NCBI
+from autometa.taxonomy.database import TaxonomyDatabase
 
 
 logger = logging.getLogger(__name__)
@@ -98,7 +98,7 @@ def filter_taxonomy(df: pd.DataFrame, rank: str, name: str) -> pd.DataFrame:
         Provided `name` not found in `rank` column.
     """
     # First clean the assigned taxa by broadcasting lowercase and replacing any whitespace with underscores
-    for canonical_rank in NCBI.CANONICAL_RANKS:
+    for canonical_rank in TaxonomyDatabase.CANONICAL_RANKS:
         if canonical_rank not in df.columns:
             continue
         df[canonical_rank] = df[canonical_rank].map(
@@ -395,7 +395,11 @@ def write_results(
         outcols.extend(annotation_cols)
         # Add in taxonomy columns if taxa are present
         # superkingdom, phylum, class, order, family, genus, species
-        taxa_cols = [rank for rank in reversed(NCBI.CANONICAL_RANKS) if rank != "root"]
+        taxa_cols = [
+            rank
+            for rank in reversed(TaxonomyDatabase.CANONICAL_RANKS)
+            if rank != "root"
+        ]
         taxa_cols.append("taxid")
         # superkingdom, phylum, class, order, family, genus, species, taxid
         for taxa_col in taxa_cols:

autometa/common/external/bedtools.py

@@ -104,7 +104,7 @@ def parse(bed: str, out: str = None, force: bool = False) -> pd.DataFrame:
     df = pd.read_csv(bed, sep="\t", names=names, index_col="contig")
     df = df[df.index != "genome"]
     df = df.assign(depth_product=lambda x: x.depth * x.bases)
-    dff = df.groupby("contig")["depth_product", "bases"].sum()
+    dff = df.groupby("contig")[["depth_product", "bases"]].sum()
     dff = dff.assign(coverage=lambda x: x.depth_product / x.bases)
     if out and (not os.path.exists(out) or (os.path.exists(out) and force)):
         dff.to_csv(out, sep="\t", index=True, header=True)

autometa/common/utilities.py

@@ -27,6 +27,25 @@
 logger = logging.getLogger(__name__)
 
 
+def is_gz_file(filepath) -> bool:
+    """
+    Check if the given file is gzipped compressed or not.
+
+    Parameters
+    ----------
+    filepath : str
+        Filepath to check
+
+    Returns
+    -------
+    bool
+        True if file is gzipped else False
+    """
+    # https://stackoverflow.com/a/47080739
+    with open(filepath, "rb") as test_f:
+        return test_f.read(2) == b"\x1f\x8b"
+
+
 def unpickle(fpath: str) -> Any:
     """Load a serialized `fpath` from :func:`make_pickle`.