ENH: add actions to filter MAGs

q2_moshpit/__init__.py

@@ -12,6 +12,7 @@
 from . import prodigal
 from ._version import get_versions
 from .dereplication import dereplicate_mags
+from .filtering import filter_derep_mags, filter_mags
 from .kaiju import classification as kaiju_class, database as kaiju_db
 from .kraken2 import (
     classification as kraken_class,
@@ -26,5 +27,6 @@
 __all__ = [
     'metabat2', 'bracken', 'kraken_class', 'kraken_db',
     'kaiju_class', 'kaiju_db', 'dereplicate_mags', 'eggnog',
-    'busco', 'prodigal', 'kraken_helpers', 'partition'
+    'busco', 'prodigal', 'kraken_helpers', 'partition',
+    'filter_derep_mags', 'filter_mags'
 ]

q2_moshpit/busco/types/_transformer.py

@@ -37,4 +37,11 @@ def _2(data: pd.DataFrame) -> BUSCOResultsFormat:
 def _3(ff: BUSCOResultsFormat) -> Metadata:
     with ff.open() as fh:
         df = _read_dataframe(fh)
+        # parse numeric columns as numbers (exclude the percent_gaps column)
+        columns = [
+            *BUSCOResultsFormat.HEADER[4:12],
+            *BUSCOResultsFormat.HEADER[13:]
+        ]
+        for col in columns:
+            df[col] = pd.to_numeric(df[col])
         return Metadata(df)

q2_moshpit/busco/types/tests/test_transformer.py

@@ -62,6 +62,11 @@ def test_result_to_metadata_transformer(self):
             self.fp, sep='\t', header=0, index_col=0, dtype='str'
         )
         df.index.name = 'id'
+        for col in [
+            'complete', 'single', 'duplicated', 'fragmented', 'missing',
+            'n_markers', 'scaffold_n50', 'contigs_n50', 'scaffolds', 'length'
+        ]:
+            df[col] = pd.to_numeric(df[col])
         exp = qiime2.Metadata(df)
 
         self.assertEqual(obs, exp)

q2_moshpit/filtering/__init__.py

@@ -0,0 +1,11 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2022-2023, QIIME 2 development team.
+#
+# Distributed under the terms of the Modified BSD License.
+#
+# The full license is in the file LICENSE, distributed with this software.
+# ----------------------------------------------------------------------------
+
+from .filter_mags import filter_derep_mags, filter_mags
+
+__all__ = ["filter_derep_mags", "filter_mags"]

q2_moshpit/filtering/filter_mags.py

@@ -0,0 +1,159 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2022-2023, QIIME 2 development team.
+#
+# Distributed under the terms of the Modified BSD License.
+#
+# The full license is in the file LICENSE, distributed with this software.
+# ----------------------------------------------------------------------------
+import os
+
+import pandas as pd
+from qiime2 import Metadata
+from qiime2.util import duplicate
+
+from q2_types.feature_data_mag import MAGSequencesDirFmt
+from q2_types.per_sample_sequences import MultiMAGSequencesDirFmt
+
+
+def _filter_ids(
+        ids: set,
+        metadata: Metadata = None,
+        where: str = None,
+        exclude_ids: bool = False
+) -> set:
+    """
+    Filters IDs based on the provided metadata.
+
+    Parameters:
+        ids (set): The set of IDs to filter.
+        metadata (Metadata, optional): The metadata to use for filtering.
+            Defaults to None.
+        where (str, optional): The condition to use for filtering.
+            Defaults to None.
+        exclude_ids (bool, optional): Whether to exclude the IDs that
+            match the condition. Defaults to False.
+
+    Returns:
+        set: The filtered set of IDs.
+    """
+    selected_ids = metadata.get_ids(where=where)
+    if not selected_ids:
+        print("The filter query returned no IDs to filter out.")
+    else:
+        if exclude_ids:
+            ids -= set(selected_ids)
+        else:
+            ids &= set(selected_ids)
+    print(f"Found {len(ids)} IDs to keep.")
+    return ids
+
+
+def _filter_manifest(
+        manifest: pd.DataFrame, ids_to_keep: set, on: str = 'mag'
+) -> pd.DataFrame:
+    """
+    Filters a manifest DataFrame based on a set of IDs.
+
+    Parameters:
+        manifest (pd.DataFrame): The manifest DataFrame to filter.
+        ids_to_keep (set): The set of IDs to keep.
+        on (str): The level on which to filter ('mag' or 'sample').
+            Defaults to 'mag'.
+
+    Returns:
+        pd.DataFrame: The filtered manifest DataFrame.
+    """
+    if on == 'mag':
+        lvl = 'mag-id'
+    elif on == 'sample':
+        lvl = 'sample-id'
+    else:
+        raise ValueError(f"Invalid value for 'on' parameter: {on}")
+
+    manifest["filename"] = \
+        manifest.index.get_level_values('sample-id') + "/" + \
+        manifest.index.get_level_values('mag-id') + ".fasta"
+
+    return manifest[manifest.index.get_level_values(lvl).isin(ids_to_keep)]
+
+
+def _mags_to_df(mags: MultiMAGSequencesDirFmt, on: str):
+    """
+    Converts a MultiMAGSequencesDirFmt object to a DataFrame.
+
+    Parameters:
+        mags (MultiMAGSequencesDirFmt): The MultiMAGSequencesDirFmt
+            object to convert.
+        on (str): The level on which to index the DataFrame
+            ('sample' or 'mag').
+
+    Returns:
+        pd.DataFrame: The converted DataFrame.
+    """
+    mags_df = pd.DataFrame.from_dict(mags.sample_dict(), orient="index")
+    mags_df = mags_df.stack().reset_index()
+    mags_df.columns = ["sample_id", "mag_id", "mag_fp"]
+    if on == 'sample':
+        mags_df.set_index("sample_id", inplace=True)
+    elif on == 'mag':
+        mags_df.set_index("mag_id", inplace=True)
+    return mags_df
+
+
+def filter_derep_mags(
+        mags: MAGSequencesDirFmt,
+        metadata: Metadata = None,
+        where: str = None,
+        exclude_ids: bool = False,
+) -> MAGSequencesDirFmt:
+    results = MAGSequencesDirFmt()
+    features = mags.feature_dict()
+    ids_to_keep = _filter_ids(
+        set(features.keys()), metadata, where, exclude_ids
+    )
+    try:
+        for _id in ids_to_keep:
+            duplicate(
+                features[_id], os.path.join(str(results), f"{_id}.fasta")
+            )
+    except KeyError:
+        raise ValueError(f"{_id!r} is not a MAG present in the input data.")
+
+    return results
+
+
+def filter_mags(
+        mags: MultiMAGSequencesDirFmt,
+        metadata: Metadata = None,
+        where: str = None,
+        exclude_ids: bool = False,
+        on: str = 'mag'
+) -> MultiMAGSequencesDirFmt:
+    results = MultiMAGSequencesDirFmt()
+    mags_df = _mags_to_df(mags, on)
+
+    ids_to_keep = _filter_ids(
+        set(mags_df.index), metadata, where, exclude_ids
+    )
+
+    filtered_mags = mags_df[mags_df.index.isin(ids_to_keep)]
+    filtered_manifest = _filter_manifest(
+        mags.manifest.view(pd.DataFrame), ids_to_keep, on=on
+    )
+    filtered_manifest.to_csv(
+        os.path.join(str(results), "MANIFEST"), sep=","
+    )
+    try:
+        for _id, row in filtered_mags.iterrows():
+            if on == 'mag':
+                sample_dir = os.path.join(str(results), row["sample_id"])
+                mag_dest = os.path.join(sample_dir, f"{_id}.fasta")
+            else:
+                sample_dir = os.path.join(str(results), _id)
+                mag_dest = os.path.join(sample_dir, f"{row['mag_id']}.fasta")
+            os.makedirs(sample_dir, exist_ok=True)
+            duplicate(row['mag_fp'], mag_dest)
+    except KeyError:
+        raise ValueError(f"{_id!r} is not a MAG present in the input data.")
+
+    return results

q2_moshpit/filtering/tests/__init__.py

@@ -0,0 +1,7 @@
+# ----------------------------------------------------------------------------
+# Copyright (c) 2022-2023, QIIME 2 development team.
+#
+# Distributed under the terms of the Modified BSD License.
+#
+# The full license is in the file LICENSE, distributed with this software.
+# ----------------------------------------------------------------------------

q2_moshpit/filtering/tests/data/MANIFEST

@@ -0,0 +1,7 @@
+sample-id,mag-id,filename
+id1,mag1,id1/mag1.fasta
+id1,mag2,id1/mag2.fasta
+id2,mag3,id2/mag3.fasta
+id3,mag4,id3/mag4.fasta
+id3,mag5,id3/mag5.fasta
+id3,mag6,id3/mag6.fasta

q2_moshpit/filtering/tests/data/mags/MANIFEST

@@ -0,0 +1,4 @@
+sample-id,mag-id,filename
+sample1,24dee6fe-9b84-45bb-8145-de7b092533a1,sample1/24dee6fe-9b84-45bb-8145-de7b092533a1.fasta
+sample2,db03f8b6-28e1-48c5-a47c-9c65f38f7357,sample2/db03f8b6-28e1-48c5-a47c-9c65f38f7357.fasta
+sample2,d65a71fa-4279-4588-b937-0747ed5d604d,sample2/d65a71fa-4279-4588-b937-0747ed5d604d.fasta

q2_moshpit/filtering/tests/data/mags/sample1/24dee6fe-9b84-45bb-8145-de7b092533a1.fasta

@@ -0,0 +1,4 @@
+>NZ_00000000.1_contig1
+ATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATGTTTCCAGATGCAATGCGTGGGCACTGATG
+>NZ_00000000.1_contig2
+TTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGAAAACCATGCAGTGTTGACGTCAGTGA

q2_moshpit/filtering/tests/data/mags/sample2/d65a71fa-4279-4588-b937-0747ed5d604d.fasta

@@ -0,0 +1,6 @@
+>NZ_CP018863.1_contig1
+GCCTCCTCCCAGTTCGTCTCAGCGCTGCTGCTGGTCGGCGCCAAATTCCGTAACGGGCTGCACCTCGAACATTCCGGCCAGAGCGTCCCCAGCCTGGACCACGTTGCCATGACCGTGGCGGTACTGCGCAGCGTCGGGGTGGAGGTAGACGATTCCCGGCAGAACCACTGGTGGTCCGGCCCGGACCGGTCAAGGCCTTCGACGTCACCGTCGAACAGGACCTTTCCAATGCCGGCCCCTTCCTCGCGGCAGCCCTGGCCACGAAGGGAACGGTTCGGATCCCAGGCTGGCCGGAGAAAACCACGCAGGTAGGTGACAAATGGCGCAGCATCCTGGCGCAACTCGGCGCCACTGTCAGCTACGAGAACGGCACCCTCACCGTAACCGGCGGGGCAGAGATCACCGGGGCGCAGCTCGCCGACACCAGCGAACTTGCCCCCACCACGGCGGCGCTCTGTGCCCTGGCCGGCAGCGAATCCAGGCTCACCGGAATTGCCCACTTGCGGGGACACGAAACAGACCGGCTGGCGGCTCTGGTCGCGGAAATCAATGCCTTGGGTGGCGACGCCGAAGAAACCGAAGACGGGTTGATCATCCGTCCGAGGCCACTGCATGGCGGGGTCTTCCATTCATATGAGGACCACAGGATGGCCACCGCCGGAGCCATTATCGGGCTGGCAGTCGAGGGCGTGGAAGTCGAAGACATCGGCACCACGGCCAAGACCATGCCCGAGTTCCCGCGGCTATGGCAGGACCTGTTCGAGACTTCCGTCCGCCAGTCCGAGGCGGGAGCGCTCTAAGGTGGTGCGCGGCAACCGTACGTGGGACGAGTCCGATGTCCGCATCCGTCCCAACAAGCGCGGCTCGCGTCCGCGTACCAAGGAACGGCCTGCCCACGAAGACGCCGTCATCGGGCGGATCATCACCGTGGACCGCGGCCGCTACACCGCGGTCGTCGATGAAGACACTGCCCGGGAACGGGTGGTTGTCGCCGCCCGTG
+>NZ_CP018863.1_contig2
+CCCGGGAGCTTCGTCGCAGTCCGGTGGTGGCCGGCGACTTCGTAGCGCTCGTCGGTGACATTACCGGTGAGCCGGATACGTTGGCCCGGCTGGTCCGGATTGAGGAACGCCGGACGCTGTTGCGCCGCAGCGCCGACGATACAGATCCCGTGGAGCGGGTAGTCGTCGCCAATGCAGACCAGCTGGTCATCGTCGTGGCCGCCGCAAACCCCGAGCCGCGCACCGGTTTCATCGACCGCGCCTTGGTAGCGGCGTACGACGCCGGTATCAGCCCGCTGCTGTGCGTCACCAAAGCGGACGTCAAGGATCCCGAAGAACTGCTCTCCAACTACCGGCACCTGGACCTGCCCGTGATCGTCAGCCGGACGGCCGGCACGGAGGGCTCCGGGGTGGATGCACGGTCCGCCGACGGGCTGTCTGCCCGTCTCGACCGTGACGCCGTAGCGGCGCTCCGTGGCTATCTGGATGGGATGGTCAGCGTCATGCTCGGCCATTCGGGCGTGGGCAAGTCCACCATGGTCAATGCCCTCACGGGGGCGGAGCGCGCCACGGGGGGAGTCAACGCGGTGACCGGGCGGGGCCGGCATACCTCCTCCTCGGCGCTGGCCCTGAAGCTGGCCGACGCTCCGGCTGGCAGCTGGATCATCGACACGCCCGGCATCCGTTCTTTTGGACTGGCCCACGTGGACCCGGACCGGATCATTTCCGCTTTTCCCGATTTGGAGCCCGGGACGGCGGACTGCGAGCGGGGCTGCAAGCACGACGACCATGCCGTCAACTGCGGCGTGGACGCCTGGGTGGCCTCCGGGCAGGCCGGCGAATCCGGCCCGGCACGGCTGGCCTCGCTGCGCCGTTTGCTGGGAACGGAAGAACGCGCCCAGGCGAAGGAACTCGGGTTCCAGTAGCACCGCCGTCGTCGGTCAGGGACTTCACATCCCGCATCCGGCCGCCAAATAAGGATAAGTTGAAGCCTATGACCCGTGACGTTCAAAGCTATAAC
+>NZ_CP018863.1_contig3
+GACGATCTGCGCCTGGCCCATGTGATGGCCGATTCCGTGGATTCGCAGACCATGGCCCGCTTCAAGGCGCTGGACCTGAAAATCGAGACCAAGCCGGATCTCACCCCTGTCACGGATGCGGACCGCGCCGCTGAAGAGGCCATCCGCGGCCAACTCTCCCGGGCCCGGCCGCGCGACGCGGTCCTCGGCGAGGAATACGGCAGCAGCGGCCACGGCTCCCGCCGCTGGATCATCGATCCCATCGACGGCACGAAGAACTTCGTCCGCGGGGTGCCGGTCTGGGCCACCTTGATCGCGCTGGTAGACGAAGACCGTCCCGTGGTCGGCCTGGTCAGCGCGCCGGCTCTGGGCAAGCGCTGGTGGGCCGCGACCGGAACCGGTGCCTACATGGGACGTTCGCTGTCCGCGGCCACCCGGCTCCGGGTATCCGATGTCAACCGGCTCGAGGACGCGTCCCTCTCCTATTCCAGCCTCACCGGCTGGCAGGAACGCGGCAACTTCCCGGAGTTCCTCGGCCTCACCGAATCCGTCTGGCGCACCCGTGCCTACGGGGACTTCTGGTCCTACTGCATGGTGGCCGAGGGCGCCGTCGACATTGCCTGCGAACCCGAACTCAACCTCTATGACATGGCGGCCCTCGTGCCGATCGTGACCGAGGCCGGCGGACGGTTCAGTTCGCTCGAGGGCGAGGACGGACCCTTCGGCGGCAACGCGTTGGCCACGAACGGCACGCTGCACGACGAGGTCCTCTACCGGCTCAATCCGCAGTTGCGCGGCCAGCGTCCGGCCGCACACCCGGAGGACGGGTCCCTGCCGGAAACCGCTCCGGAGGCCTCCATGGAGGCGGACGGCCTGCGCTGACGCTGTCTTTTGTGACGAATTACGACGGCGGCCGTCCCCATTCCGGGGATGGCCGCCTTTTCGTTCCCGTAACAAAGATGCGGCCCCTCCGGCCGGACAATAATCTCGATGGCAGGTCACGAGTGCCAGCGCTAAACCC

q2_moshpit/filtering/tests/data/mags/sample2/db03f8b6-28e1-48c5-a47c-9c65f38f7357.fasta

@@ -0,0 +1,4 @@
+>NZ_CP007255.1_contig1
+GGCGTGCCCTCAGCGGTCTCCTGCCCATTTACCCGGTCCATCAGACCGCGGACATACTCTTCTGCAAAGGCAACCTCGTTCCAGTCGGGAAAGACAACCTCCCACACGTCGAAATGACCCGTGTCATCGCGCGACGCTTCAACGACCGCTACGGCCAGGTCTTCCCCGAACCTCAAGCGCTGCTTACCTCGAGCCCCGAAGTACCTGAACTCGACGGCAGAAAGATATCCAAGAGCTACGGAAACGCCATCACACTGTCGATGACCGCCGACGACACCGCAGCGGCAATCCGCAAGGCACCGACCGACTCACAACGCCGCATCACCTTCGACCCGATCAACCGCCCCGGGGTATCCGCACTGCTGACCACAGCAGCATTGTGCTCAGGTCGCGACGAGAATGACCTCGCCGACCAGATCGGCGACGCCGGCGCAGGCGCACTCAAAAAGCTCACCACCACGCTCGGGAACGACTTCCTCGCCCCTCACCGGCAACGCAGAAACGACCTCGCCCAAGACCCCCACCATGTCGCCGACGTCCTGCGCATCGGAAACCAGCGCGCGAACGAAATCGCCCAACAGACCCTCGAGGAAGTCCGAACCGCAATGGGCACCATCTACTGATCGACTCCTACCCATAGCGCACTCCAGTAGTTTGGACATTCACCGGGTTGCGACGATCCCTGGAATCCGCCATCGGGAGTGTCTGATCAACGGTGGATCCGACCTCGGTATCGGTTAATTACTGTTGAGGGTGATGCGTGCTTCGAAGGTGATAGCGAACGCGTTCAGCGCTGGCTTCCACCTCATCGCCCATCGCGCCTTACCTTTCCCAGTCGGGTCCAGAGCCCGAGTCGCCAGATACAAGCATTCGAGCGCCGCCTGCTCCGTCGGGAAGTGCCCACGAGCACGCACAGCACGCCGGTAACGAGCGTTGACGGACTCGATCGCATTGGTCGAGCAGATCACCCGCAGGATCTCTTTCGTCGTAATCCAAGAAC
+>NZ_CP007255.1_contig3
+ATGATCGATCCTTCCCACCAAGCTCAGCTCGGCGTGTCGAACCAAGACCGGATCAACCGTTTATCTGACAGTCCCCTCACTAGCGAGTAAGTCGTCAGAGCGATTCTTCGGGTTTGGGTGTGCGTTTTTGACCATCGCCATACCGGACAGCACGGAACGGGCTTCTGGGCGTGACTGACGTGGAGAAACTTAGCGAAAAGTTTCCAATTGGGTGTCCCGTCGGCGAACGTGGGTGTGAAGATCGGGTGTCGAGATGCAACACCGCCGGCAGAACACGGGATCCAGGTTGATCTTCTGGTGAAGGCGGGTGTGGATCGCGACCAGGCTTATGTCGATCATCGTGTCAGCGGCGGGCAGGCTCGTCGGCCAGGTTTGGATGATGCGATCAACGCGGCTCGGGCCGGCGACGTCCTTTGTGTGACGATGCCTCATTAACTGTCGCGCTCGGCGAAGGATCTGCATGAGCCGGTAGAACGGATCGCGGAGAAGGTGTATCGCTGTCAATTGACGGTCAGTTGTACGACCCGCGCGATCCGATGGGGAAGATGTTCATCGGCTTGCTGGGGATTGATGACGGAGTTTCGAGTCGGATCTGATTCGTGGCCGCACTCGTGACGCCCTGGCCGCCGCGACAGCTGCTGAGAAGAGAAAGGTGCGTCGGACAAGCTCACCGGCTGGAGAAAAAAGTCAGTCACGATCGGGTCTTTTCTTCCAGAGGCTCCCGGTCTGGAGCCTCACCGGTTGCCGCGGGTCGGGCTCCTTCGATGTGTGGGTCAGCTCGTTACGCCGCGTTAAGAATCTGAAAACTTGGTGAGCAAACCTTCCTTTCCCAGGTGAAGCGGCGGATTATCGAAGTGGCCGCGCATCTGGTTACCGCGCGGCATGTCGAGCGCGCCTGAAGGCACAACGGGCCGGGAGGTCACATGCTGCTACACCAATCGATAGGGTTGGAGCGCTTCAACGAACTACCGCGACAGAAGGCGGTTCACGCACTGTTCGA

q2_moshpit/filtering/tests/data/metadata-derep.tsv

@@ -0,0 +1,4 @@
+mag_id	sample_id	input_file	dataset	complete	single	duplicated	fragmented	missing	n_markers	scaffold_n50	contigs_n50	percent_gaps	scaffolds	length
+24dee6fe-9b84-45bb-8145-de7b092533a1	sample1	24dee6fe-9b84-45bb-8145-de7b092533a1.fasta	bacteria_odb10	28.2	27.4	0.8	8.9	62.9	124	4785	4785	0.000%	265	1219165
+d65a71fa-4279-4588-b937-0747ed5d604d	sample2	d65a71fa-4279-4588-b937-0747ed5d604d.fasta	bacteria_odb10	1.6	1.6	0.0	1.6	96.8	124	3548	3548	0.000%	67	245922
+db03f8b6-28e1-48c5-a47c-9c65f38f7357	sample2	db03f8b6-28e1-48c5-a47c-9c65f38f7357.fasta	bacteria_odb10	26.6	26.6	0.0	3.2	70.2	124	78679	78679	0.000%	17	714893

q2_moshpit/filtering/tests/data/metadata-sample.tsv

@@ -0,0 +1,3 @@
+id	input_file	dataset	metric
+sample1	24dee6fe-9b84-45bb-8145-de7b092533a1.fasta	bacteria_odb10	28.2
+sample2	d65a71fa-4279-4588-b937-0747ed5d604d.fasta	bacteria_odb10	1.6