Add propagate to predict prophage activity

assets/test-datasets/b_fragilis_checkv_contamination.tsv

@@ -0,0 +1,5 @@
+contig_id	contig_length	total_genes	viral_genes	host_genes	provirus	proviral_length	host_length	region_types	region_lengths	region_coords_bp	region_coords_genes	region_viral_genes	region_host_genes
+k141_126	3500	6	4	2	Yes	2000	1500	viral,host,viral	1000,1500,1000	1-1000,1001-2500,2501-3500	1-2,3-4,5-6	2,0,2	0,2,0
+k141_248	1000	3	1	2	Yes	500	500	host,viral,host	250,500,250	1-250,251-750,751-1000	1,2,3	0,1,0	1,0,1
+k141_50	1000	6	0	0	No	NA	NA	NA	NA	NA	NA	NA	NA
+k141_203	843	2	1	1	Yes	500	343	viral,host	500,343	1-500,501-843	1,2	1,0	0,1

assets/test-datasets/b_fragilis_clusters.tsv

@@ -0,0 +1,3 @@
+k141_126	k141_126,k141_248
+k141_50	k141_50
+k141_203	k141_203

assets/test-datasets/b_fragilis_genomad_virus_summary.tsv

@@ -0,0 +1,5 @@
+seq_name	length	topology	coordinates	n_genes	genetic_code	virus_score	fdr	n_hallmarks	marker_enrichment	taxonomy
+k141_126	3970	Provirus	1-3500	6	11	0.8408	0.0833	0	1.5749	Unclassified
+k141_248	3153	Provirus	1000-2000	3	11	0.7912	0.1337	0	1.7183	Viruses;Duplodnaviria;Heunggongvirae;Uroviricota;Caudoviricetes
+k141_50	3219	Provirus	1-1000	6	11	0.6643	0.1798	2	2.7775	Viruses;Duplodnaviria;Heunggongvirae;Uroviricota;Caudoviricetes
+k141_203	843	No terminal repeats	NA	2	11	0.9925	0.0075	1	3.0763	Viruses;Duplodnaviria;Heunggongvirae;Uroviricota;Caudoviricetes

assets/test-datasets/b_fragilis_propagate_contigs.fa

@@ -0,0 +1,6 @@
+>k141_126
+CATAAACTACGCTCGGCACAGCCTCTGCTCGCCGTGCTGAACCGCCTGGAACAGAGAAAACCGGTGGGGCTGCGCTACGATCCGCAAGCACAATCGCTGGTGTGCCTGCCCACGCAAACCCGGACGGGCTGGAATCTCAATGGCTTTGAGGTGGGGTTCAGGCCATGCGTCAGGCTGATGATTTACGGACGTTCTCTTGAGGCGCAGGCAACCGCGAGTCTTGCAGCAGCCACAGGCTATGACAGCCATATCTTCGATCTTTTTCCGGCCTCAGCCAGCGCTCAGATCGATACCGATACGGCGGTCATTTTGCTGTGCCATGATCTCAACCGGGAGCTGCCAGTGTTGCAGGCCGCGCGAGAAGCAAAACCCTTTTATCTCGGCGCATTGGGCAGCTATCGAACCCACACGTTACGTCTGCAAAAGCTCCACGAGCTGGGATGGTCCAGGGAGGAAACAACGCAAATCCGGGCACCCGTCGGGATATTCCCCAAAGCCCGGGATGCGCATACTCTGGCACTCTCCGTGCTGGCAGAAGTCGCCTCTGTACGTCTCCATCAGGAGGAGGATTCATGCCTGCCCCCGTCGTCCTGATCCTTGCGGCCGGGCGTGGAGAGTGCTTTCTCGCCTCCGGGGGAAATACCCATAAGTGTATCGGCTGGCGTCAGTCCCCGGAGGTTGCGCCTTATCGCTGGCCATTTGAAGAAAACGGGAGAACTTTCGACCTTGCGATTGAACCGCAGATTACGACTAATGATCTGCGTCTGATGTTGAGGCTGGCTCTTGCCGGCGGAGGAATAACAATTGCCACTCAGGAAACTTTCAGGCCATATATTGAAAGCGGTAAGCTTGTATCGCTGCTTGATGACTTTCTTCCACAATTTCCGGGCTTCTATCTGTATTTCCCACAGCGTCGCAATATTGCACCAAAGCTCCGCGCCCTGATTGACTACGTCAAAGAATGGCGGCAGCAATTGGCTTAAATGTCTGCACCTGCACTGCCTGATGTCAGAACAGTATTTTGATGAATTGCCAAGGTTACAATGGCACAAATACGGCACAGGAGGAAACGTGGTGTATTTAAATATGGGGTAACTCTTTGATTTTAATGGTGCCGATAATAGGAGTCGAACCTACGACCTTCGCATTACGAATGCGATGTTAATGCATTTTTAACTACTTGAGCAATCAATCAAAACAACATACTTTATATTATAAAACACCAAGTTATAGCAATAAACCCGTTGATTGAAATTAACAAAAGTTGATGTTTATTCCCTCCATTTACTTACACCAGCAGTTACATTATTGTAAGACAGCAATTCTCAACTGGAGCCGAAAAATGGCGCTTTCAAGACAAAAATTTACCTTCGAAAGACTTCGCAGATTCACCTTACCGGAAGGGAAAAAACAAACTTTTCTTTGGGATGCAGATGTAACAACCCTGGCATGCCGAGCAACTAGCGGAGCAAAAGCCTTTGTATTCCAAAGCGTATATGCGGGGAAAACCCTTCGCATGACTATTGGCAACATTAACGACTGGAAGATTGATGATGCGAGAGCCGAGGCAAGACGGTTACAAACATTGATCGATACAGGGATAGATCCACGAATTGCTAAGGCTGTAAAAATCGCAGAAGCAGAATCCCTGCAGGCAGAATCACGTAAAACAAAAGTGACTTTCTCCGTCGCCTGGGAAGACTATCTTCAAGAATTGAGAACCGGTATCAGTGCAAAAACTAAACGCCCATATTCTACTCGATACATTGCCGATCACATTAACTTGTCCAGTCGTGGAGGCGAAAGTAAAAAAAGAGGCCAAGGCCCGACTTCGGCTGGACCATTGGCTAGTTTGCTCAACCTGCCGTTATCGGAGCTAACCCCAGATTACATAGCAGCGTGGCTGAGTACAGAAAGGCAAAATAGACCTACCGTCACTGCTCACGCTTATCGCCTACTACGTGCTTTCATCAAATGGAGTAATTATCAGAAAAAATATCAAGGGATCATTCCTGGCGATCTGGCACAAGATTACAACGTAAGAAAAATGGTTCCCGTGTCAGCGAGTAAAGCTGATGATTGCCTGCAAAAGGAACAACTAAAAAGCTGGTTTAGTGCCGTGCGTAGCCTCAATAATCCTATTGCATCGGCCTATCTCCAAGTACTTTTGCTCACTGGTGCTCGGCGTGAAGAAATTGCGTCGCTTCGCTGGTCAGACGTAGATTTCAAATGGTCAAGCATGCGAATTAAAGACAAGATCGAAGGTGAACGTATCATCCCTCTCACTCCTTATGTTTCTGAATTGTTAAATGTGCTAGCGCAATCCCCAAATTCTGACGTAAATAAGGAGGGTTGGGTTTTCAGAAGTAACAGTAAAAGTGGCAAAATTATTGAGCCGCGTTCAGCGCACAACAGAGCATTAGTGCTGGCTGAGTTACCACATATCAGCCTTCACGGTTTACGTCGTAGTTTTGGTACTTTGGCCGAGTGGGTTGAAGTTCCCACTGGTATTGTTGCTCAAATTATGGGACACAAACCCAGCGCTCTTGCCGAAAAACACTATCGCCGTCGTCCGTTAGATCTGTTACGAAAATGGCACGAGAAAATTGAGACATGGATCTTAAATGAAGCAGGTATTACCATAAAAAACAACGTTGATATGCGTTGATTCCATTAAAAATCAACATATTACAAAATATCATCAACTATTGATCAAGATAGATTTTCATGTATCGTAATACACAGTTTAGTCAATGATACAGCAACTACACAGGAGATAAGCCAATGGCAACCCCAGCAACTGTATCCATAGAACCCACTCTGGCAGCTATCAGAGCTCGCTGGTGTATTAATTCAAGTAAAACAACTCAATCCTTTAACGATCCTGCGTCCATGGAAGAGGTTGTCGAGTATCTCAAAGGAACATACTCAGCTCTTCGCAAGTCTGTCGCATGCGCCAAACTGAAAATTTTACATCTTAAACAAAGAATGCAAAATGCTACTAACTTTCTCGCGCGTCTGATGTCATGTAAAAATCAGGCATCCAGATCGCATCACAGTACGGCTAAATCAGCTAAAAGTGCCTTATCATCAGATTCAGGTGATGGTAGTGACCCCGACCCCGAGCCCGAAACGTTTCCTTCTGCCTTCATTACTACCCCTACTAATTCAATAATGCTTAAAGCTTTCTTTGCCAATATCTCAATCACTGAGGTGGCAAAATGAGCGCATTCAAACTCCCGGATACATCTCAATCACAGCTCATTTCAACAGCTGAGTTAGCTAAAATCATTAGCTACAAATCTCAAACCATTCGTAAATGGCTTTGTCAGGACAAATTGCCTGAGGGGCTACCTCGCCCAAAACAAATCAATGGCCGCCATTACTGGTTACGTAAAGATGTCCTCGATTTTATAGATACATTTTCTGTACGAGAAAGTCTGTAATAAATTACAGATTTAATTTTATTGATTTATAGCGATGTTGCCCCGAGAAAAATGGGGCAACACTGAGAAATTTCAGATAGTAGTTTTATATTGAGATAACAAAGAGGTTTCCTTAAAAATGTCTAATAGTGTTACTAATTTTGAGATGAGCAGCGTTCTACCAGGAAAAAAACCTTGTCAAGGCAAAAACAATGAGTCACAGGTAGTACAGACTACTCCCATAAAAAAACACTCAGTCACGTTCAAAAATCAATCTTCATTAGGAGTAATTGATCATTATGCCAGACTAACAAATAAATCTCACTCTTCCGTAATAGCGGAAGTTGTGGATTTGGCTATCCCTATATTAGAAAAATGCAATCGTCATAACTGGTCAATAAATGAAATAAAAAATGACCTGTTAAAGTTCTCTATAAAAGAAAGCATCAATCGAAGCCGAGGTAAAACAGAAGTAACTCTGGAAGAGTACTGTTCGTTAATCTGGAAAACGAACATCATGAGTCCATTAAAAATCCCCATTGCAG
+>k141_50
+CGCTGGCCCTGCTTATTACAGGATGTGCTCAACAGACATTTACTGTTCAAAACAAACAGACAGCAGTAGCACCAAAGGAAACCATCACCCATCATTTCTTCGTTTCTGGAATTGGGCAGAAGAAAACTGTCGATGCAGCTAAAATTTGTGGCGGCGCAGAAAATGTTGTTAAAACAGAAACCCAGCAAACATTCGTAAATGGATTGCTCGGTTTTATTACTTTAGGCATTTATACTCCGCTGGAAGCGCGTGTGTATTGCTCAAAATAATTGCATGAGTTGCCCATCGATATGGTCAGCTCTATCTGCACTGCTCATTAATATACTTCTGGGTTCCTTCCAGTTGTTTTTGCATAGTGATCAGCCTCTCTCTGAGGGTGAAATAATCCCGTTCAGCGGTGTCTGCCAGTCGGGGGGAGGCTGCATTATCCACGCCGGAGGCCGTGGTGGCTTCACGCACTGACTGACAGACTGCTTTGATGTGCAACCGACGACGACCAGCGGCAACATCATCACGCAGAGCATCATTTTCAGCTTTCGCATCAGCTAACTCCTTCGTGTATTTTGCATCGAGCGCAGCAACATCACGCTGACGCATCTGCATGTCAGTAATTGCCGCGTTCGCTAGCTTCAGTTCTCTGGCATTTTTGTCGCGCTGGACTTTGTAGGCGATTGCGTTATCACGGTAATGATTGACCGCCCATGACAGGCTGACGATGATGCAGATAATCAGAGCGGATATAATCGCGGTTACTCTGCTCACTGTTGCCCCCACAAACAGACTTCACGCTCAATCTCACGACGAGTCATCAGGCCTTTCCATTGCTTACCGCCAGCGTATGTCCAGCGACGCAGCTGATCACATGCGCCTTTGATATCGCCCTGGTTTATTTTGCGAAGAAGCGTCGATGTTCTAAAATTGCCAGCACCCACGTTGTAAACGAATGAGTAAAGAGCGCCGCGCGTTGTTTCCGGTATATCGACTTTGATATACGGGTTAATTTGTCTGGCGACAGTGGCAAGGTCTTTATTCAAGAGTGCTTTGCATTCTGCTTTGGTATACGTTTTACCGAGCATGATGTCTTTTCCGGTGTGTCCGTGACATACAGTCCATACACCAACAATATCTTTGTATGGTATGTAGCTGACACCTTCCAGACCATCGTTACCACTTGGGCCAGTGATTAACACTGATGCTATAGCAATTGCTCCGCCACCAATAGCAGCAGCAACGGCTTTTCGTAATGATGGAGGCATTATTCACCTCTCGCAGCCTTGCGCTTATCTTCTTTAATCTTGAAATAAAGGTTTGTCAGGTACGTCAGCAGGCCAAATACCAGGCTACCCAGCACACCTATTGCTGCCCACTGTGAGGGAGTGACTTTATCTAGCAGCTGTAAAAACCAGTACCCGGCACTACCTGCTGAGGTGCCATAGGCGACACCCGTTGTTAACTTATCCATGGATTTCATAACCCCACCTCGCAGACAAAGCGGGTGTAAATTGAGGGAATACTACGAAACGTAACAGACTCGGAGTCAGTGAATAACTCAGGTATTGGGTTATCAGCTAATATCGAGACTCAAAAAATGGAAAAACCCGCTCGACGGCGGGTTTAAGCTGTGTGACGAAGTAACCACTCTTAACAGCATAACCAATTTTTTACGTACGTAAACCACTAAATGATATTTGAGAGAATGCTACCGAGTATTGAAAACACCACTACAAATACATAAGCAAATCTCAACAAATAACCAAAAAATAATTTCCAGTGTTATTTTTAGCCGGTTTAAATTGAACCTTCAAATTATAGAGCACTTATAAATAACAGCCGTTAATATAAATTGGCTAATAGATTTATTTTTATTCAGCCAAGAGCCATGAATAGGATTCGATAGAAAAAAGTTCAGATAAAAATAGAGATCTACTTCACAAATCAAACGAGAAACCAAAACTTACATCTTGAATAATCACATTGATTAGATGAATATTTATCGCGCAGTGACATCATTTTTTAATAATAGTTCAAAAAAAGGGCTCACGATGAAAAAATTAACAGTGGCAATTTCTGCTGTAGCTGCATCAGTACTAATGGCGATGTCTGCTCAGGCAGCTGAAATTTATAATAAAGACAGTAACAAGCTGGATCTGTACGGGAAAGTTAATGCTAAGCACTACTTCTCCTCTAATGATGCAGATGATGGTGATACTACTTATGCCCGTCTTGGCTTCAAAGGTGAAACCCAAATCAACGATCAACTGACTGGTTTCGGTCAGTGGGAATATGAATTCAAAGGCAACCGCGCTGAATCTCAAGGTTCCTCCAAAGATAAAACCCGTCTTGCCTTCGCTGGCCTGAAATTCGGTGACTACGGCTCCATCGATTACGGCCGTAACTACGGTGTAGCATACGACATCGGTGCGTGGACTGACGTCCTGCCAGAATTCGGTGGTGACACTTGGACTCAAACCGACGTGTTCATGACTCAACGTGCAACTGGTGTTGCAACCTATCGTAACAACGACTTCTTTGGTCTGGTTGATGGTCTGAACTTTGCTGCTCAGTACCAAGGCAAAAACGATCGTAGCGATTTCGATAACTACACTGAAGGTAACGGTGATGGCTTCGGTTTCTCTGCTACCTATGAATACGAAGGATTCGGTATCGGTGCAACTTATGCGAAATCTGATCGTACCGACACTCAAGTTAATGCAGGGAAAGTTCTTCCTGAAGTATTTGCTTCCGGTAAAAATGCAGAAGTTTGGGCCGCAGGTCTGAAATATGACGCTAACAACATTTACCTGGCCACTACCTATTCTGAAACCCAGAATATGACTGTATTTGCTGATCACTTCGTTGCTAATAAAGCCCAAAACTTCGAAGCTGTTGCACAATATCAGTTCGATTTCGGTCTGCGTCCGTCCGTTGCTTACCTGCAATCTAAAGGTAAGGATCTTGGAGTATGGGGCGATCAGGACTTAGTCAAATATGTTGATGTAGGTGCAACCTATTACTTCAACAAAAATATGTCTACTTTCGTTGATTACAAAATCAACCTGCTTGACAAAAATGACTTCACTAAGGAAGGTGCGAACAAGTCCCTGATATGAGATCATGTTTGTCATCTGGAGCCATAGAACAGGGTTCATCATGAGTCATCAACTTACCTTCGCCGACAGTGAATTCAGCAGTAAGCGCCGTCAGACCAGAAAAGAGATT
+>k141_203
+GGGCTTCACTGCGAAATTCAGGCGAATGCTGTTTACGGGGTTTTTTACTGGTTGATACTGTTTTTGTCATGTGAGTCACCTCTGACTGAGAGTTTACTCACTTAGCCGCGTGTCCACTATTGCTGGGTAAGATCAGATTACGGTTGCGCCTGTTACCGCGGCAACGTCCTGTGCACAGAAGCTCTTATGCGTCCCCAGGTAATGAATAATTGCCTCTTTGCCCGTCATACACTTGCTCCTTTCAGTCCGAACTTAGCTTTAATTTCTGCGATCTTCGCCAGAGCCTGTGCACGATTTAGAGGTCTACCGCCCATAACAGGAAGTTGTTTTACTGGTTCAGGTATCGTCTCACCACGGTTAATTCGCGCTGTCATACAGGTCAGTTCATCGGCAGCCTTGCGCCGTAATTCCGCGTCAGCCAGCGCATTGGCCCGCATGTTCTGGTACAAGTTGGTAACCAACCAGTAATGCGCGTTCGATTTCCACGGATAAGACTCTGCATCCGGATACAGGCCACGCTTCCGGCAATACTCGTACCTCCCGGGATTTCATGAAATTCCGGCTCGGTGGTTTCGAGGCAATAAAATCGGCTTACATGGCCCAGGTGCAGTACAGCATGTGGGTGACGCGAAAAGATGCCTGGTACTTTGCCAACTATGACCCGCGCATGAAGCGTGAAGGCCTGCATTATGTCGTGATTGAGCGGAATGAAAAGTACATGGCGAGTTTTGACGAGATGGTGCCGGAGTTCATCGAAAAAATGGACGAGGCACTGGCTGAAATTGGTTTTGTATTTGGGGAGCAATGGCGATGACGCATCCTCACGATAATATCCGGGTACCT

assets/test-datasets/b_fragilis_provirus_coords.tsv

@@ -0,0 +1,5 @@
+scaffold	fragment	start	stop
+k141_126	k141_126|provirus_1_1000	1	1000
+k141_126	k141_126|provirus_2501_3500	2501	3500
+k141_50	k141_50|provirus_1_1000	1	1000
+k141_203	k141_203|provirus_1_500	1	500

bin/derep_coordinates.py

@@ -0,0 +1,50 @@
+#!/usr/bin/env python
+
+from Bio import SeqIO
+import argparse
+import sys
+import gzip
+import pandas as pd
+import math
+
+
+def parse_args(args=None):
+    Description = "Dereplicate provirus coordinates to match dereplicated provirus assemblies"
+    Epilog = "Example usage: python derep_coordinates.py --coordinates <COORDS_TSV> --clusters <CLUSTERS_TSV> --output <DEREP_COORDS_TSV>"
+
+    parser = argparse.ArgumentParser(description=Description, epilog=Epilog)
+    parser.add_argument(
+        "-r",
+        "--coordinates",
+        help="Path to TSV file containing provirus coordinates for each assembly.",
+    )
+    parser.add_argument(
+        "-c",
+        "--clusters",
+        help="Path to the TSV file containing cluster information from dereplicating assemblies.",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        help="Output TSV file containing provirus coordinates for dereplicated assembly.",
+    )
+    return parser.parse_args(args)
+
+def derep_coordinates(coords_tsv, clusters_tsv, output):
+    # open coordinates file
+    coords = pd.read_csv(coords_tsv, sep='\t')
+    # open cluster results
+    clusters = pd.read_csv(clusters_tsv, sep='\t', header=None)
+    # identify coords contained in derep clusters
+    derep_coords = coords[coords['scaffold'].isin(set(clusters[0]))]
+
+    # save coords file
+    derep_coords.to_csv(output, sep='\t', index=False)
+
+def main(args=None):
+    args = parse_args(args)
+    derep_coordinates(args.coordinates, args.clusters, args.output )
+
+
+if __name__ == "__main__":
+    sys.exit(main())

bin/extract_proviruses.py

@@ -0,0 +1,130 @@
+#!/usr/bin/env python
+
+from Bio import SeqIO
+import argparse
+import sys
+import gzip
+import pandas as pd
+import math
+
+
+def parse_args(args=None):
+    Description = "Extract proviruses present in assemblies based on geNomad and CheckV outputs."
+    Epilog = "Example usage: python extractproviruses.py --fasta <FASTA> --genomad <virus_summary> --checkv <quality_summary> --output <proviruses.fasta.gz> --tsv <provirus_coordinates.tsv>"
+
+    parser = argparse.ArgumentParser(description=Description, epilog=Epilog)
+    parser.add_argument(
+        "-f",
+        "--fasta",
+        help="Path to FASTA file (gzipped) that contains assemblies.",
+    )
+    parser.add_argument(
+        "-g",
+        "--genomad",
+        help="Path to the TSV file containing geNomad's virus summary.",
+    )
+    parser.add_argument(
+        "-c",
+        "--checkv",
+        help="Path to the TSV file containing CheckV's contamination summary.",
+    )
+    parser.add_argument(
+        "-o",
+        "--output",
+        help="Output FASTA file with assemblies containing proviruses.",
+    )
+    parser.add_argument(
+        "-t",
+        "--tsv",
+        help="Output TSV file containing provirus coordinates.",
+    )
+    return parser.parse_args(args)
+
+def extract_proviruses(fasta, genomad, checkv, out_fasta, out_tsv):
+    # open genomad results
+    genomad = pd.read_csv(genomad, sep='\t')
+    # identify genomad proviruses
+    genomad_proviruses = genomad[genomad['topology'] == 'Provirus']
+
+    # open checkv results
+    checkv = pd.read_csv(checkv, sep='\t')
+    # filter to checkv proviruses
+    checkv_proviruses = checkv[checkv['provirus'] == 'Yes']
+
+    # identify checkv provirus coordinates
+    checkv_coords = pd.DataFrame()
+    for index, row in checkv_proviruses.iterrows():
+        contig_id = row['contig_id']
+        region_types = row['region_types'].split(',')
+        provirus_count = 0
+        # parse though regions for each contig
+        for i in range(len(region_types)):
+            if region_types[i] == 'viral':
+                # if a region is viral, extract contig name, assign a provirus id, and add checkv start/end coords
+                provirus_info = pd.DataFrame()
+                provirus_count += 1
+                provirus_info['seq_name'] = [contig_id]
+                provirus_info['provirus_id'] = [contig_id + '|checkv_provirus_' + str(provirus_count)]
+                provirus_info[['provirus_start', 'provirus_stop']] = [row['region_coords_bp'].split(',')[i].split('-')]
+                checkv_coords = pd.concat([checkv_coords, provirus_info], axis=0)
+
+    # merge genomad and checkv
+    genomad_checkv = genomad_proviruses.merge(checkv_coords, on='seq_name', how='outer')
+
+    # if checkv provirus exists, add it to df and skip associated genomad provirus. If not, use geNomad provirus information
+    provirus_combined_coords = pd.DataFrame()
+    already_added = {}
+    for index, row in genomad_checkv.iterrows():
+        if str(row['seq_name']) + '_' + str(row['provirus_id']) in already_added:
+            continue
+        else:
+            provirus_coords = pd.DataFrame()
+            # remove prefix added above or by geNomad
+            provirus_coords['scaffold'] = [row['seq_name'].split('|provirus')[0]]
+            # if checkv provirus only; use checkv coordinates
+            if float(row['provirus_start']) > 0 and '-' not in str(row['coordinates']):
+                provirus_coords['start'] = [row['provirus_start']]
+                provirus_coords['stop'] = [row['provirus_stop'] ]
+            # if checkv and genomad provirus; add genomad start and checkv start (-1 to set checkv start to 0)
+            # find stop by adding length (checkv_stop - checkv_start) to start coord found above
+            elif float(row['provirus_start']) > 0 and '-' in row['coordinates']:
+                provirus_coords['start'] = [int(row['coordinates'].split('-')[0]) + (int(row['provirus_start']) - 1)]
+                provirus_coords['stop'] = [provirus_coords['start'][0] + int(row['provirus_stop']) - int(row['provirus_start'])]
+            # if genomad provirus only; use genomad coordinates
+            elif math.isnan(row['provirus_start']) and '-' in row['coordinates']:
+                provirus_coords['start'] = [int(row['coordinates'].split('-')[0])]
+                provirus_coords['stop'] = [int(row['coordinates'].split('-')[1])]
+            # rename provirus fragment based on final start/end
+            if '|provirus_' in row['seq_name']:
+                provirus_coords['fragment'] = row['seq_name'].split('|provirus_')[0] + '|provirus_' + str(provirus_coords['start'][0]) + '_' + str(provirus_coords['stop'][0])
+            else:
+                provirus_coords['fragment'] = row['seq_name'] + '|provirus_' + str(provirus_coords['start'][0]) + '_' + str(provirus_coords['stop'][0])
+            # concatenate all provirus coordinates
+            provirus_combined_coords = pd.concat([provirus_combined_coords, provirus_coords], axis=0)
+
+    # reorder columns to match propagate input
+    provirus_combined_coords_reorg = provirus_combined_coords[['scaffold', 'fragment', 'start', 'stop']]
+    # save coords file
+    provirus_combined_coords_reorg.to_csv(out_tsv, sep='\t', index=False)
+    # identify scaffolds to extract
+    provirus_scaffolds = set(provirus_combined_coords_reorg['scaffold'])
+
+    # extract provirus scaffolds (full length) from fasta
+    exracted_scaffolds = []
+    fasta_gunzipped = gzip.open(fasta, "rt")
+    for record in SeqIO.parse(fasta_gunzipped, "fasta"):
+        if record.id in provirus_scaffolds:
+            record.description = ''
+            record.name = ''
+            exracted_scaffolds.append(record)
+    # save all extracted provirus scaffolds to specified file
+    SeqIO.write(exracted_scaffolds, out_fasta, "fasta")
+
+
+def main(args=None):
+    args = parse_args(args)
+    extract_proviruses(args.fasta, args.genomad, args.checkv, args.output, args.tsv)
+
+
+if __name__ == "__main__":
+    sys.exit(main())