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Melon-supplementary

Instruction on building the NCBI/GTBD database of Melon.

Prerequisite

Step 1: Install necessary packages

conda install -c bioconda -c conda-forge 'taxonkit>=0.15.1' 'seqkit>=2.6.1' 'hmmer>=3.4' 'mmseqs2>=15.6f452' 'blast>=2.15.0' 'diamond==2.1.8' 'tqdm' 'pandas' 'requests'

Step 2: Download protein sequences from https://ftp.ncbi.nlm.nih.gov/blast/db/

Note

Use ascp from IBM Aspera or update_blastdb.pl from BLAST+ to speed up if necessary.

## download all splits in a loop
for folder in env_nr nr
do
    curl ftp://ftp.ncbi.nlm.nih.gov/blast/db/ \
        | grep '[^ ]*.gz$' -o \
        | grep ^$folder \
        | xargs -P 32 -I {} wget -qN --show-progress https://ftp.ncbi.nlm.nih.gov/blast/db/{} -P protein/$folder

    ## decompress all files
    for file in protein/$folder/*.tar.gz; do tar -xvf $file -C protein/$folder; done
    rm -rf protein/$folder/*.tar.gz
done

Step 3: Download taxonomy files from https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/

## download taxonomy dump and protein accessions
wget -qN --show-progress https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/taxdump.tar.gz -P taxonomy
wget -qN --show-progress https://ftp.ncbi.nlm.nih.gov/pub/taxonomy/accession2taxid/prot.accession2taxid.FULL.gz -P taxonomy
tar -xvf taxonomy/taxdump.tar.gz -C taxonomy

## extract bacteria/archaea taxids using taxonkit
taxonkit list --ids 2 --indent '' --data-dir taxonomy > taxonomy/bacteria.id
taxonkit list --ids 2157 --indent '' --data-dir taxonomy > taxonomy/archaea.id

Step 4: Download profile HMMs from https://www.genome.jp/ftp/db/kofam/

Note

We validated ver. 2023-04-01 using RefSeq proteins. Using other versions, including the latest version is also OK but profiles of specific marker genes may not function well.

## download profiles and ko metadata
wget -qN --show-progress https://www.genome.jp/ftp/db/kofam/archives/2023-04-01/ko_list.gz -P profile
wget -qN --show-progress https://www.genome.jp/ftp/db/kofam/archives/2023-04-01/profiles.tar.gz -P profile
gzip -d profile/ko_list.gz
tar -xvf profile/profiles.tar.gz -C profile

## collect ribosomal protein ko
mkdir -p profile/prokaryote.full profile/prokaryote.subset
python -c "
import requests

## https://www.genome.jp/brite/ko03011
response = requests.get('https://www.genome.jp/kegg-bin/download_htext?htext=ko03011.keg&format=htext&filedir=')

## https://www.genome.jp/kegg/annotation/br01610.html
with open('profile/prokaryote.subset.id', 'w') as w:
    for line in response.text.rstrip().split('\n'):
        if line:
            ls = line.split()
            if ls[0] == 'B':
                if ls[1] in {'Bacteria', 'Archaea'}:
                    kingdom = ls[1].lower()
                else:
                    kingdom = None

            if ls[0] == 'D' and kingdom is not None:
                if ls[1] not in {'K19032', 'K19033'}: # skip these two not in <Ribosomal protein gene clusters in prokaryotes>
                    w.write(ls[1] + '\t' + kingdom + '\n')
"

## copy profiles to a directory
cut profile/prokaryote.subset.id -f 1 | uniq | xargs -P 64 -I {} cp profile/profiles/{}.hmm profile/prokaryote.subset/{}.hmm
cut profile/profiles/prokaryote.hal -f 1 | uniq | xargs -P 64 -I {} cp profile/profiles/{} profile/prokaryote.full/{}

Step 5: Collect NCBI assemblies

Note

If you want to build a GTDB database, please jump to Step 5 (alternative): Collect GTDB assemblies.

## download metadata file
mkdir -p assembly/fna
wget -qN --show-progress https://ftp.ncbi.nlm.nih.gov/genomes/refseq/assembly_summary_refseq.txt -P assembly

## read metadata, split assemblies into chunks according to taxonomy
python -c "
import pandas as pd
import subprocess

def get_taxonomy(taxid, data_dir='taxonomy'):
    output = subprocess.run([
        'taxonkit', 'reformat',
        '--data-dir', data_dir,
        '--taxid-field', '1',
        '--show-lineage-taxids',
        '--fill-miss-rank',
        '--miss-taxid-repl', '0',
        '--miss-rank-repl', 'unclassified',
        '--trim',
        '-f', '{k}\t{p}\t{c}\t{o}\t{f}\t{g}\t{s}'],
        input='\n'.join(taxid)+'\n', text=True, capture_output=True, check=True)
    print(output.stderr)

    taxonomy = {}
    for line in output.stdout.rstrip().split('\n'):
        ls = line.rstrip().split('\t')
        taxonomy[int(ls[0])] = ';'.join([ls[i+7] + '|' + ls[i] for i in range(1, len(ls)-7)])

    return taxonomy

## read assemblies
assembly = pd.read_table('assembly/assembly_summary_refseq.txt', skiprows=1, low_memory=False).rename({'#assembly_accession': 'assembly'}, axis=1)
assembly = assembly[(assembly.group.isin(['archaea', 'bacteria'])) & ((assembly['ftp_path'] != 'na'))]

## drop unknown species
assembly['taxonomy'] = assembly['species_taxid'].map(get_taxonomy(assembly['species_taxid'].astype(str).unique()))
assembly = assembly[assembly['taxonomy'].str.split(';').str.get(-1) != '0|unclassified']

## create an index file for tracing
assembly['index'] = pd.Categorical(assembly['taxonomy'], ordered=False).codes + 1
assembly[['assembly', 'taxonomy', 'index']].to_csv('assembly/assembly2species.tsv', sep='\t', index=False, header=None)

## create a list for wget
assembly['fna'] = assembly['ftp_path'] + '/' + assembly['ftp_path'].str.split('/').str.get(-1) + '_genomic.fna.gz'

for kingdom in ['archaea', 'bacteria']:
    fna = assembly[assembly['group'] == kingdom]['fna'].to_list()
    n = 8 if kingdom == 'archaea' else 256 # split into 8 or 256 chunks so that each contains same number of genomes
    chunks = [fna[i::n] for i in range(n)]

    for i, chunk in enumerate(chunks):
        with open('assembly/fna/' + kingdom + '.split.' + str(i) + '.id', 'w') as w:
            w.write('\n'.join(chunk) + '\n')

print(f'#assemblies: {assembly.assembly.nunique()}, #species: {assembly.taxonomy.nunique()}')
"

Step 5 (alternative): Collect GTDB assemblies

Note

Some genomes (including representative genomes) may be deprecated by NCBI at the time of GTDB release. The representative ones can be downloaded from the FTP of GTDB (genomic_files_reps/gtdb_genomes_reps.tar.gz) and manually included. The other non-representative ones cannot be recovered.

## download metadata files
mkdir -p assembly/fna
wget -qN --show-progress https://ftp.ncbi.nlm.nih.gov/genomes/refseq/assembly_summary_refseq.txt -P assembly
wget -qN --show-progress https://ftp.ncbi.nlm.nih.gov/genomes/refseq/assembly_summary_refseq_historical.txt -P assembly
wget -qN --show-progress https://ftp.ncbi.nlm.nih.gov/genomes/genbank/assembly_summary_genbank.txt -P assembly
wget -qN --show-progress https://ftp.ncbi.nlm.nih.gov/genomes/genbank/assembly_summary_genbank_historical.txt -P assembly

wget -qN --show-progress https://data.gtdb.ecogenomic.org/releases/latest/ar53_metadata.tsv.gz -P assembly
wget -qN --show-progress https://data.gtdb.ecogenomic.org/releases/latest/bac120_metadata.tsv.gz -P assembly

gzip -d assembly/ar53_metadata.tsv.gz
gzip -d assembly/bac120_metadata.tsv.gz

## read metadata, split assemblies into chunks according to taxonomy
python -c "
import pandas as pd

## merge gtdb with ncbi ftp link
ncbi = pd.concat([
    pd.read_table('assembly/assembly_summary_refseq.txt', skiprows=1, low_memory=False),
    pd.read_table('assembly/assembly_summary_refseq_historical.txt', skiprows=1, low_memory=False),
    pd.read_table('assembly/assembly_summary_genbank.txt', skiprows=1, low_memory=False),
    pd.read_table('assembly/assembly_summary_genbank_historical.txt', skiprows=1, low_memory=False),
]).rename({'#assembly_accession': 'assembly'}, axis=1)

gtdb = pd.concat([
    pd.read_table('assembly/ar53_metadata.tsv', low_memory=False),
    pd.read_table('assembly/bac120_metadata.tsv', low_memory=False)
])
gtdb['assembly'] = gtdb.accession.str.split('_', n=1).str.get(-1)

## some may no longer be available
assembly = pd.merge(gtdb, ncbi[['assembly', 'ftp_path']], how='left', on='assembly')
assembly[(assembly.ftp_path == 'na') | (assembly.ftp_path.isnull())].to_csv('assembly/deprecated.tsv', index=False, sep='\t')

assembly = assembly[(~(assembly.ftp_path == 'na') | (assembly.ftp_path.isnull())) | (assembly.gtdb_representative == 't')]
assembly['taxonomy'] = assembly['gtdb_taxonomy'].str.replace('[a-z]__', '', regex=True)
assembly['group'] = assembly['taxonomy'].str.split(';').str.get(0).str.lower()

## create an index file for tracing
assembly['index'] = pd.Categorical(assembly['taxonomy'], ordered=False).codes + 1
assembly[['assembly', 'taxonomy', 'index']].to_csv('assembly/assembly2species.tsv', sep='\t', index=False, header=None)

## create a list for wget
assembly['fna'] = assembly['ftp_path'] + '/' + assembly['ftp_path'].str.split('/').str.get(-1) + '_genomic.fna.gz'

for kingdom in ['archaea', 'bacteria']:
    fna = assembly[assembly['group'] == kingdom]['fna'].to_list()
    n = 8 if kingdom == 'archaea' else 256 # split into 8 or 256 chunks so that each contains same number of genomes
    chunks = [fna[i::n] for i in range(n)]

    for i, chunk in enumerate(chunks):
        with open('assembly/fna/' + kingdom + '.split.' + str(i) + '.id', 'w') as w:
            w.write('\n'.join(chunk) + '\n')

deprecated = (assembly.ftp_path == 'na') | (assembly.ftp_path.isnull())
print(f'#species: {assembly.taxonomy.nunique()}')
print(f'#assemblies: {assembly.assembly.nunique()}')
print(f'#deprecated_reps: {len(assembly[deprecated])}')
"

# ## grab representative genomes if necessary
# wget -qN --show-progress https://data.gtdb.ecogenomic.org/releases/latest/genomic_files_reps/gtdb_genomes_reps.tar.gz
# tar -xvf gtdb_genomes_reps.tar.gz

# python -c "
# import pandas as pd
# import glob
# import os
# import shutil
# dset = set(pd.read_table('assembly/deprecated.tsv').assembly)
# files = glob.glob('gtdb_genomes_reps/**/*.fna.gz', recursive=True)
# files = [file for file in files if os.path.basename(file).rsplit('_genomic')[0] in dset]
# os.makedirs('assembly/fna/deprecated_reps', exist_ok=True)
# for file in files:
#     shutil.copy(file, f'assembly/fna/deprecated_reps/{os.path.basename(file)}')
# "
# 
# cat assembly/fna/deprecated_reps/*.fna.gz > assembly/fna/deprecated_reps.fna.gz

Step 6: Download assemblies and generate an accession2assembly mapping

## download assemblies then cat
find assembly/fna -maxdepth 1 -name '*.id' | xargs -P 32 -I {} bash -c '
    wget -i ${1} -qN --show-progress -P ${1%.id}; \
    find ${1%.id} -maxdepth 1 -name "*.fna.gz" | xargs cat > ${1%.id}.fna.gz' - {}

## create a dictionary that maps sequence to assembly
python -c "
import glob
import os
import gzip
import pandas as pd
from tqdm.contrib.concurrent import process_map

def parse_file(file):
    lines = []
    with gzip.open(file, 'rt') as f:
        for line in f:
            if line[0] == '>':
                lines.append([line[1:].split()[0], '_'.join(os.path.basename(file).split('_')[:2])])
    return lines

pd.DataFrame([
    x for y in process_map(parse_file, glob.glob('assembly/fna/**/*.fna.gz'), max_workers=64, chunksize=1) for x in y
], columns=['accession', 'assembly']).to_csv('assembly/accession2assembly.tsv', sep='\t', index=False, header=None)
"

Construction of the protein database

Step 1: Extract protein sequences from BLAST databases

The detailed taxonomic information of env_nr is not available, we therefore create a diamond database using the full nr, and later assign bacteria/archaea labels to env_nr using LCA.

Note

Extracting sequences and building diamond database can take a while. To speed up, consider running each blastdbcmd block in parallel.

## env_nr
blastdbcmd -db protein/env_nr/env_nr -entry all > protein/env_nr.full.fa

## make a diamond database using full nr
blastdbcmd -db protein/nr/nr -entry all > protein/nr.full.fa
diamond makedb --in protein/nr.full.fa --db protein/nr.full --taxonmap taxonomy/prot.accession2taxid.FULL.gz --taxonnodes taxonomy/nodes.dmp --taxonnames taxonomy/names.dmp
rm -rf protein/nr.full.fa

## extract bacteria/archaea sequences from nr
for kingdom in archaea bacteria
do
    blastdbcmd -db protein/nr/nr -target_only -taxidlist taxonomy/${kingdom}.id > protein/nr.${kingdom}.fa
    blastdbcmd -db protein/nr/nr -target_only -taxidlist taxonomy/${kingdom}.id -outfmt '%a@%o' > protein/nr.${kingdom}.id # accession@oid
done

## find shared sequences by original id (oid)
python -c "
from collections import defaultdict

oid = defaultdict(set)
with open('protein/nr.archaea.id') as f:
    for line in f:
        ls = line.rstrip().split('@')
        oid[ls[-1]].add(ls[0])

accession = set()
with open('protein/nr.bacteria.id') as f:
    for line in f:
        ls = line.rstrip().split('@')
        if ls[-1] in oid:
            accession.add(ls[0])
            accession.update(oid.get(ls[-1]))

with open('protein/nr.shared.id', 'w') as w:
    w.write('\n'.join(accession) + '\n')
"

Step 2: Re-annotate protein sequences

Annotating against the full KEGG profile HMMs will take days, so we first use a subset of prokaryotic profile HMMs (91 ribosomal protein profile HMMs) to get a candidate subset of sequences, then use the full profile HMMs to get the complete annotations.

mkdir -p prot/out/prokaryote.subset

for file in protein/*.fa
do
    filename=${file%.fa}
    filename=${filename##*/}

    ls profile/prokaryote.subset \
    | xargs -P 8 -I {} hmmsearch \
        --domtblout prot/out/prokaryote.subset/$filename.{} \
        -E 2147483647 --domE 2147483647 \
        --noali \
        --cpu 8 \
        profile/prokaryote.subset/{} $file > /dev/null
done

Extract candidate sequences from BLAST databases with seqkit. Discard also cross-kingdom sequences.

python -c "
import glob
import os
import subprocess
from tqdm import tqdm
from collections import defaultdict

## kick out cross-kingdom sequences shared by bacteria and archaea
shared_accession = set()
with open('protein/nr.shared.id') as f:
    for line in f:
        shared_accession.add(line.rstrip())

## read pre-defined threshold score for each ko
ko = defaultdict(dict)
with open('profile/ko_list') as f:
    next(f)
    for line in f:
        ls = line.rstrip().split('\t')
        ko[ls[0]]['threshold'], ko[ls[0]]['score_type'] = ls[1:3]

## parse domain output files, record accession of sequences
accession = defaultdict(set)
for file in tqdm(glob.glob('prot/out/prokaryote.subset/*.hmm')):
    filename = os.path.basename(file).rsplit('.', 2)[0]
    with open(file) as f:
        for line in f:
            if line[0] != '#':
                ls = line.rstrip().split(maxsplit=22)
                if ls[0] not in shared_accession:
                    ks = ko.get(ls[3])
                    if (
                        ks['score_type'] == 'full' and float(ls[7]) > float(ks['threshold']) * 0.75 or
                        ks['score_type'] == 'domain' and float(ls[13]) > float(ks['threshold']) * 0.75
                    ):
                        accession[filename].add(ls[0])

for key, val in accession.items():
    with open('prot/out/{}.fa'.format(key), 'w') as w:
        subprocess.run([
            'seqkit', 'grep', '-f', '-', 'protein/{}.fa'.format(key)
        ], check=True, text=True, input='\n'.join(val) + '\n', stdout=w)
"

Run diamond LCA to env_nr then extract sequences with matched kingdom.

diamond blastp \
    --db protein/nr.full.dmnd \
    --query prot/out/env_nr.full.fa \
    --outfmt 102 \
    --top 0 \
    --threads 64 \
    | taxonkit reformat \
        --data-dir taxonomy \
        --taxid-field 2 \
        --format "{k}" > prot/out/env_nr.full.id

python -c "
import subprocess
from collections import defaultdict

accession = defaultdict(set)
with open('prot/out/env_nr.full.id') as f:
    for line in f:
        ls = line.split()
        if len(ls) == 4 and ls[-1] in {'Bacteria', 'Archaea'}:
            accession['env_nr.' + ls[-1].lower()].add(ls[0])

for key, val in accession.items():
    with open('prot/out/{}.fa'.format(key), 'w') as w:
        subprocess.run([
            'seqkit', 'grep', '-f', '-', 'prot/out/env_nr.full.fa'
        ], check=True, text=True, input='\n'.join(val) + '\n', stdout=w)
"

rm -rf prot/out/env_nr.full.*

Rerun hmmsearch but against the full set of prokaryotic profile HMMs.

mkdir -p prot/out/prokaryote.full

for file in prot/out/*.fa
do
    filename=${file%.fa}
    filename=${filename##*/}

    ls profile/prokaryote.full \
    | xargs -P 16 -I {} hmmsearch \
        --domtblout prot/out/prokaryote.full/$filename.{} \
        -E 2147483647 --domE 2147483647 \
        --noali \
        --cpu 4 \
        profile/prokaryote.full/{} $file > /dev/null
done

Parse domain output files of hmmsearch, select only sequences that pass the pre-defined threshold, remove partial sequences.

mkdir -p prot/seq

python -c "
import glob
import pandas as pd
from collections import defaultdict
from tqdm.contrib.concurrent import process_map

def parse_file(file):
    lines = []
    with open(file) as f:
        for line in f:
            if line[0] != '#':
                ls = line.rstrip().split(maxsplit=22)
                if (ks := ko.get(ls[3])) is not None:
                    if (
                        ks['score_type'] == 'full' and float(ls[7]) > float(ks['threshold']) * 0.75 or
                        ks['score_type'] == 'domain' and float(ls[13]) > float(ks['threshold']) * 0.75
                    ):
                        lines.append([ls[0], ls[3], ks['definition'], int(ls[17]), int(ls[18]), int(ls[2]), float(ls[21])])
    return lines

## read pre-defined threshold score for each ko
ko_subset = set()
with open('profile/prokaryote.subset.id') as f:
    for line in f:
        ko_subset.add(line.split()[0])

ko = defaultdict(dict)
with open('profile/ko_list') as f:
    next(f)
    for line in f:
        ls = line.rstrip().split('\t')
        if ls[2] != '-':
            ko[ls[0]]['threshold'], ko[ls[0]]['score_type'] = ls[1:3]
            ko[ls[0]]['definition'] = ls[-1].split('protein ')[-1].lower() if ls[0] in ko_subset else 'others'

for source in ['env_nr', 'nr']:
    for kingdom in ['bacteria', 'archaea']:
        domain = pd.DataFrame([
            x for y in process_map(parse_file, glob.glob('prot/out/prokaryote.full/{}.{}.*'.format(source, kingdom)), max_workers=64, chunksize=1) for x in y
        ], columns=['accession', 'knum', 'definition', 'tstart', 'tend', 'tlen', 'acc'])
        domain['tcov'] = (domain['tend'] - domain['tstart'] + 1) / domain['tlen']

        ## remove low quality and invalid sequences
        domain = domain[
            (domain.groupby('accession')['knum'].transform('nunique') == 1) &
            (domain['definition'] != 'others') &
            (domain['tcov'] > 0.75)
        ].sort_values(['accession', 'tcov'], ascending=False).groupby('accession', as_index=False).first()

        ## create a accession2description mapping
        domain['description'] = domain['definition'] + '-tcov:' + domain['tcov'].map('{:.2f}'.format) + '-acc:' + domain['acc'].map('{:.2f}'.format) + '-' + source + '-' + kingdom
        accession2description = domain.set_index('accession')['description'].to_dict()

        ## save filtered sequences
        with open('prot/seq/{}.{}.fa'.format(source, kingdom), 'w') as w, open('prot/out/{}.{}.fa'.format(source, kingdom)) as f:
            for line in f:
                if line[0] == '>':
                    accession = line[1:].split()[0]
                    if (description := accession2description.get(accession)) is not None and ', partial' not in line:
                        w.write('>' + description + '-' + accession + '\n')
                        save = True
                        continue
                    else:
                        save = False
                if save:
                    w.write(line)
"

Step 3: Cluster to reduce redundancy

Combine all extracted sequences, shuffle, then split for each combination of kingdom and ribosomal protein gene.

mkdir -p prot/raw
find prot/seq -maxdepth 1 -name '*.fa' | sort | xargs cat > prot/raw.fa

python -c "
from collections import defaultdict

sequence = defaultdict(list)
with open('prot/raw.fa') as f:
    for line in f:
        if line[0] == '>':
            ls = line[1:].rstrip().split('-')
            subset = ls[4] + '.' + ls[0].replace('/', '_')
        sequence[subset].append(line)

for key, val in sequence.items():
    with open('prot/raw/{}.fa'.format(key), 'w') as w:
        w.write(''.join(val))
"

Note

If your are working on HPC, please consider submitting a SLURM job for each mmseqs to make them run in parallel, and increase --threads to reduce the computational time.

mkdir -p prot/clustered

ls prot/raw/*.fa | sort | xargs -P 8 -I {} bash -c '
    filename=${1%.fa*};
    filename=${filename##*/};
    FILE_SIZE=$(stat -c "%s" prot/raw/$filename.fa)
    if [ "$FILE_SIZE" -gt 10000000 ]; then THREADS=8; else THREADS=1; fi;
    echo $filename $FILE_SIZE $THREADS;
    mmseqs easy-cluster \
        $1 prot/clustered/$filename prot/clustered/$filename \
        -c 0.95 --min-seq-id 0.95 --cov-mode 0 \
        -s 7.5 --cluster-reassign --threads $THREADS -v 0 > /dev/null' - {}

Merge all clustered files to get the final protein database.

cat prot/clustered/*rep_seq.fasta | seqkit sort | seqkit shuffle -s 0 > prot/clustered.fa

python -c "
import subprocess
from collections import defaultdict

archaea = {'l2', 'l11', 'l10e', 'l15e', 'l18e', 's3ae', 's19e', 's28e'}
bacteria = {'l2', 'l11', 'l20', 'l27', 's2', 's7', 's9', 's16'}

with open('prot/prot.fa', 'w') as w, open('prot/clustered.fa') as f:
    for line in f:
        if line[0] == '>':
            if line[1:].split('-')[0] in archaea | bacteria:
                save = True
            else:
                save = False
        if save:
            w.write(line)
"

Construction of the nucleotide database

Step 1: Map assemblies to the protein databases

Note

This step will take a while to finish. If you are working on HPC, please submit a SLURM job for each *.fna.gz file and increase --threads to reduce computational time. If not, combining all *.fna.gz files into a single one then running diamond with tuned -b -c may help to speed up.

mkdir -p nucl/out

find assembly/fna -maxdepth 1 -name '*.fna.gz' | sort | xargs -P 8 -I {} bash -c '
    filename=${1%.fna*};
    filename=${filename##*/};
    echo $filename;
    diamond blastx \
        --db prot/prot.fa \
        --query $1 \
        --out nucl/out/${filename}.txt \
        --outfmt 6 qseqid sseqid pident length qlen qstart qend slen sstart send evalue bitscore \
        --evalue 1e-15 --subject-cover 75 \
        --range-culling --frameshift 15 --range-cover 25 \
        --max-hsps 0 --max-target-seqs 25 \
        --threads 8 --quiet' - {}

Step 2: Parse output files and extract sequences

Parse output files of diamond by ensuring at most 25% overlap of HSPs, keep only a single sequence per qseqid-gene combination, discard also sequences close to the boundaries.

mkdir -p nucl/seq

python -c "
import os
import glob
import subprocess
import pandas as pd
from math import floor, ceil
from collections import defaultdict
from tqdm.contrib.concurrent import process_map

def sort_coordinate(start, end):
    return (start - 1, end, '+') if start < end else (end - 1, start, '-')

def compute_overlap(coordinates):
    qstart, qend, sstart, send = coordinates
    overlap = min(qend, send) - max(qstart, sstart)
    return max(overlap / (qend - qstart), overlap / (send - sstart))

def extract_sequence(file):
    filename = os.path.basename(file).split('.fna.gz')[0]
    qrange = defaultdict(set)
    lines = []

    with open('nucl/out/' + filename + '.txt') as f:
        for line in f:
            ls = line.split()
            qseqid, sseqid = ls[0], ls[1]
            qstart, qend, strand = sort_coordinate(int(ls[5]), int(ls[6]))
            if (
                qseqid not in qrange or
                all([compute_overlap((qstart, qend, *x)) < 0.25 for x in qrange.get(qseqid)])
            ):
                qrange[qseqid].add((qstart, qend))
                pident = float(ls[2])
                qlen, slen = int(ls[4]), int(ls[7]) * 3
                qcoord = floor((qstart + qend) / 2) if strand == '+' else ceil((qstart + qend) / 2)

                ## make sure not too close to the boundary
                if qend + 2500 < qlen and qstart - 2500 > 0:
                    lines.append([qseqid, qcoord - 5000, qcoord + 5000, sseqid, pident, strand, sseqid.split('-')[0]])

    ## keep only one sequence per qseqid + gene
    lines = pd.DataFrame(lines, columns=['qseqid', 'qstart', 'qend', 'sseqid', 'pident', 'strand', 'gene'])
    lines = lines.sort_values(['qseqid', 'gene', 'pident', 'strand'], ascending=False).groupby(['qseqid', 'gene'], as_index=False).first()
    lines.drop('gene', axis=1).to_csv('nucl/seq/' + filename + '.bed', sep='\t', header=None, index=False)

    ## extract sequneces from original files
    with open('nucl/seq/' + filename + '.fa', 'w') as f:
        subprocess.run([
            'seqkit', 'subseq', file,
            '--bed', 'nucl/seq/' + filename + '.bed'
        ], stdout=f, stderr=subprocess.DEVNULL, check=True)

process_map(extract_sequence, glob.glob('assembly/fna/*.fna.gz'), max_workers=64, chunksize=1)
"

Step 3: Cluster to remove duplicated sequences

Create a file for each species-gene combo.

mkdir -p nucl/raw
find nucl/seq -maxdepth 1 -name '*.fa' | sort | xargs cat > nucl/raw.fa

python -c "
import pandas as pd
from collections import defaultdict

archaea = {'l2', 'l11', 'l10e', 'l15e', 'l18e', 's3ae', 's19e', 's28e'}
bacteria = {'l2', 'l11', 'l20', 'l27', 's2', 's7', 's9', 's16'}

assembly2species = {}
with open('assembly/assembly2species.tsv') as f:
    for line in f:
        ls = line.rstrip().split('\t')
        assembly2species[ls[0]] = ls[1:]

accession2assembly = {}
with open('assembly/accession2assembly.tsv') as f:
    for line in f:
        ls = line.rstrip().split('\t')
        accession2assembly[ls[0]] = ls[1]

sequence = defaultdict(list)
accession = set()
with open('nucl/raw.fa') as f:
    for line in f:
        if line[0] == '>':
            ls = line.split()
            gs = ls[1].split('-')
            ac = ls[0][1:].rsplit('_', 1)[0]
            species = assembly2species.get(accession2assembly.get(ac))
            phylum = species[0].split(';')[0].split('|')[-1]
            if (
                phylum == 'Archaea' and gs[4] == 'archaea' and gs[0] in archaea or
                phylum == 'Bacteria' and gs[4] == 'bacteria' and gs[0] in bacteria
            ):
                save = True
                accession.add(ac)
                subset = gs[0].replace('/', '_') + '.' + species[-1]
            else:
                save = False
        if save:
            sequence[subset].append(line)

## split the sequences into two parts
with open('nucl/nucl_a.fa', 'w') as w:
    for key, val in sequence.items():
        record = ''.join(val)
        if record.count('>') == 1:
            w.write(record)
        else:
            with open('nucl/raw/{}.fa'.format(key), 'w') as ww:
                ww.write(record)

## save the metadata file for later usage
pd.DataFrame([
    [x] + assembly2species.get(accession2assembly.get(x))[0].split(';') for x in accession
], columns=['accession', 'superkingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']).sort_values('accession').to_csv('nucl/raw.tsv', index=False, sep='\t')
"

Note

If your are working on HPC, please consider submitting a SLURM job for each mmseqs to make them run in parallel, and increase --threads to reduce the computational time. Lower -P if you encounter memory issues.

mkdir -p nucl/clustered

find nucl/raw -maxdepth 1 -name '*.fa' | sort | xargs -P 16 -I {} bash -c '
    filename=${1%.fa*};
    filename=${filename##*/};
    FILE_SIZE=$(stat -c "%s" nucl/raw/$filename.fa)
    if [ "$FILE_SIZE" -gt 10000000 ]; then THREADS=4; else THREADS=1; fi;
    echo $filename $FILE_SIZE $THREADS;
    mmseqs easy-cluster \
        $1 nucl/clustered/$filename nucl/clustered/$filename \
        -c 0.9995 --min-seq-id 0.9995 --cov-mode 1 \
        -s 7.5 --cluster-reassign --threads $THREADS -v 0 > /dev/null' - {}

Combine all clustered files to get the nucleotide database.

find nucl/clustered -maxdepth 1 -name '*rep_seq.fasta' -exec cat {} \; > nucl/nucl_b.fa
cat nucl/nucl_a.fa nucl/nucl_b.fa | seqkit sort | seqkit shuffle -s 0 > nucl/nucl.fa

python -c "
import pandas as pd
from collections import defaultdict

sequence = defaultdict(list)
accession = set()
with open('nucl/nucl.fa') as f:
    for line in f:
        if line[0] == '>':
            ls = line.split()
            accession.add(ls[0][1:].rsplit('_', 1)[0])
            subset = ls[1].split('-')[-2] + '.' + ls[1].split('-')[0].replace('/', '_')
        sequence[subset].append(line)

for key, val in sequence.items():
    with open('nucl/nucl.{}.fa'.format(key), 'w') as w:
        w.write(''.join(val))

metadata = pd.read_table('nucl/raw.tsv')
metadata[metadata.accession.isin(accession)].to_csv('nucl/metadata.tsv', index=False, sep='\t')
"

Add non-prokaryotic sequences as decoys

Step 1: Download RefSeq non-prokaryotic protein sequences

Download RefSeq fungi, protozoa, viral, plant, and human GRCh38/hg38 (Kraken2's PlusPFP equivalent).

mkdir -p plus/faa
python -c "
import pandas as pd

assembly = pd.read_table('assembly/assembly_summary_refseq.txt', skiprows=1, low_memory=False).rename({'#assembly_accession': 'assembly'}, axis=1)
assembly = assembly[assembly['ftp_path'] != 'na']

## plusPFP setup
assembly.loc[(assembly.organism_name.str.contains('Homo sapiens')) & (assembly.refseq_category == 'reference genome'), 'group'] = 'human'
assembly = assembly[assembly.group.isin({'fungi', 'protozoa', 'viral', 'plant', 'human'})]
assembly = assembly[assembly.assembly_level.isin({'Chromosome', 'Complete Genome'})]

## download protein sequences
assembly['fna'] = assembly['ftp_path'] + '/' + assembly['ftp_path'].str.split('/').str.get(-1) + '_protein.faa.gz'
for taxonomy in assembly.group.unique():
    fna = assembly[assembly['group'] == taxonomy]['fna'].to_list()
    n = 32 if taxonomy == 'viral' else 8
    chunks = [fna[i::n] for i in range(n)]

    for i, chunk in enumerate(chunks):
        if chunk:
            with open('plus/faa/' + taxonomy + '.split.' + str(i) + '.id', 'w') as w:
                w.write('\n'.join(chunk) + '\n')
"

find plus/faa -maxdepth 1 -name '*.id' | xargs -P 32 -I {} bash -c '
    wget -i ${1} -qN --show-progress -P ${1%.id}; \
    find ${1%.id} -maxdepth 1 -name "*.faa.gz" | xargs cat | gzip -d > ${1%.id}.faa' - {}

Step 2: Annotate sequences with a subset of HMM profiles

Annotate sequences using the selected marker HMMs.

mkdir -p plus/out
python -c "
from collections import defaultdict
import os
import shutil
os.makedirs('plus/profile', exist_ok=True)
archaea = {'l2', 'l11', 'l10e', 'l15e', 'l18e', 's3ae', 's19e', 's28e'}
bacteria = {'l2', 'l11', 'l20', 'l27', 's2', 's7', 's9', 's16'}

ko = []
with open('profile/ko_list') as f:
    next(f)
    for line in f:
        ls = line.rstrip().split('\t')
        gene = ls[-1].split('ribosomal protein ')[-1].lower()
        if ls[2] != '-' and gene in archaea | bacteria:
            ko.append(ls[0])

for i in ko:
    shutil.copy(f'profile/profiles/{i}.hmm', f'plus/profile/{i}.hmm')
"

for file in plus/faa/*.faa
do
    filename=${file%.faa}
    filename=${filename##*/}

    ls plus/profile \
    | xargs -P 8 -I {} hmmsearch \
        --domtblout plus/out/$filename.{} \
        -E 2147483647 --domE 2147483647 \
        --noali \
        --cpu 8 \
        plus/profile/{} $file > /dev/null
done

Extract valid sequences.

python -c "
import glob
import pandas as pd
from collections import defaultdict
from tqdm.contrib.concurrent import process_map
from tqdm import tqdm

def parse_file(file):
    lines = []
    with open(file) as f:
        for line in f:
            if line[0] != '#':
                ls = line.rstrip().split(maxsplit=22)
                if (ks := ko.get(ls[3])) is not None:
                    if (
                        ks['score_type'] == 'full' and float(ls[7]) > float(ks['threshold']) * .75 or
                        ks['score_type'] == 'domain' and float(ls[13]) > float(ks['threshold']) * .75
                    ):
                        lines.append([ls[0], ls[3], ks['definition'], int(ls[17]), int(ls[18]), int(ls[2]), float(ls[21])] + [file.split('/')[-1].split('.')[0]])
    return lines

## read pre-defined threshold score for each ko
ko = defaultdict(dict)
with open('profile/ko_list') as f:
    next(f)
    for line in f:
        ls = line.rstrip().split('\t')
        if ls[2] != '-':
            ko[ls[0]]['threshold'], ko[ls[0]]['score_type'] = ls[1:3]
            ko[ls[0]]['definition'] = ls[-1].split('protein ')[-1].lower()

domain = pd.DataFrame([
    x for y in process_map(parse_file, glob.glob(f'plus/out/*.hmm'), max_workers=64, chunksize=1) for x in y
], columns=['accession', 'knum', 'definition', 'tstart', 'tend', 'tlen', 'acc', 'taxonomy'])
domain['tcov'] = (domain['tend'] - domain['tstart'] + 1) / domain['tlen']

## use permissive tcov cutoff for filtering since eukaryotic RPGs may be more complicated than prokaryotic RPGs
domain = domain[domain['tcov'] > 0.25].sort_values(['accession', 'tcov'], ascending=False).groupby('accession', as_index=False).first()

## create a accession2description mapping
domain['description'] = domain['definition'] + '-tcov:' + domain['tcov'].map('{:.2f}'.format) + '-acc:' + domain['acc'].map('{:.2f}'.format) + '-' + 'refseq' + '-' + domain['taxonomy']
accession2description = domain.set_index('accession')['description'].to_dict()

## save filtered sequences
with open('plus/plus.fa', 'w') as w:
    for file in tqdm(sorted(glob.glob('plus/faa/*.faa'))):
        with open(file) as f:
            for line in f:
                if line[0] == '>':
                    accession = line[1:].split()[0]
                    if (description := accession2description.get(accession)) is not None and ', partial' not in line:
                        w.write('>' + description + '-' + accession + '\n')
                        save = True
                        continue
                    else:
                        save = False
                if save:
                    w.write(line)
"

Step 3: Cluster for deduplication

Cluster to remove redundant sequences.

mmseqs easy-cluster \
        plus/plus.fa plus/plus plus/plus \
        -c 0.95 --min-seq-id 0.95 --cov-mode 0 \
        -s 7.5 --cluster-reassign --threads 64 -v 0

Compress

Get all necessary files into the database.

mkdir -p database
cp nucl/metadata.tsv nucl/nucl.bacteria*.fa nucl/nucl.archaea*.fa database
cat plus/plus_rep_seq.fasta prot/prot.fa | seqkit sort | seqkit shuffle -s 0 > database/prot.fa
tar --sort=name -zcvf database.tar.gz database

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