def test_exception(self):
with self.assertRaises(TypeError):
pyfastx.Fasta(flat_fasta, key_func=1)
with self.assertRaises(FileExistsError):
pyfastx.Fasta('a_file_not_exists')
with self.assertRaises(ValueError):
self.fastx.fetch('seq1', {'a': 1})
with self.assertRaises(NameError):
self.fastx.fetch('seq1', (1, 10))
with self.assertRaises(ValueError):
self.fastx.fetch(self.fastx[0].name, (1, 10, 20))
with self.assertRaises(ValueError):
self.fastx.fetch(self.fastx[0].name, (20, 10))
with self.assertRaises(ValueError):
self.fastx.fetch(self.fastx[0].name, [20, 10])
with self.assertRaises(IndexError):
_ = self.fastx[self.count]
with self.assertRaises(KeyError):
_ = self.fastx[list()]
with self.assertRaises(ValueError):
self.fastx.nl(101)
def setUp(self):
self.fastx = pyfastx.Fasta(gzip_fasta)
self.fasta = pyfastx.Fasta(flat_fasta)
self.faidx = pyfaidx.Fasta(flat_fasta, sequence_always_upper=True)
self.count = len(self.fastx)
def test_seq_type(self):
#test dna format
self.assertEqual(self.fastx.type, 'DNA')
#test rna format
rna = pyfastx.Fasta(rna_fasta)
self.assertEqual(rna.type, "RNA")
#test protein format
prot = pyfastx.Fasta(protein_fasta)
self.assertEqual(prot.type, "protein")
def load_seqfile(infile):
fxifile = infile + ".fxi"
if os.path.exists(fxifile) and infile.endswith(FASTA_SUFFIX):
seqfile = pyfastx.Fasta(infile, build_index=False)
elif not os.path.exists(fxifile) and infile.endswith(FASTA_SUFFIX):
seqfile = pyfastx.Fasta(infile, build_index=True)
elif os.path.exists(fxifile) and infile.endswith(FASTQ_SUFFIX):
seqfile = pyfastx.Fastq(infile, build_index=False)
elif not os.path.exists(fxifile) and infile.endswith(FASTQ_SUFFIX):
seqfile = pyfastx.Fastq(infile, build_index=True)
return seqfile
def setUp(self): self.fastx = pyfastx.Fasta(gzip_fasta, build_index=False) self.fastx.build_index() self.fastx.rebuild_index() #reload index self.fastx = pyfastx.Fasta(gzip_fasta) self.fasta = pyfastx.Fasta(flat_fasta) self.faidx = pyfaidx.Fasta(flat_fasta, sequence_always_upper=True) self.count = len(self.fastx)
def fasta_sample(args):
fa = pyfastx.Fasta(args.fastx)
if args.num is not None and args.num > 0:
seq_num = args.num
if seq_num > len(fa):
seq_num = len(fa)
elif args.prop is not None and 0 < args.prop <= 1:
seq_num = round(len(fa)*args.prop)
if seq_num == 0:
raise RuntimeError("the proportion is too small")
else:
raise RuntimeError("specify a right number for seq number or proportion")
selected = random.sample(range(len(fa)), k=seq_num)
if args.outfile is None:
fw = sys.stdout
else:
fw = open(args.outfile, 'w')
for idx in selected:
s = fa[idx]
fw.write(">{}\n{}\n".format(s.name, s.seq))
if args.outfile is None:
fw.flush()
else:
fw.close()
def fastx_subseq(args):
fa = pyfastx.Fasta(args.fastx)
if args.chr is not None:
if args.chr not in fa:
raise RuntimeError("no sequence named {} in fasta file".format(args.chr))
subseq = fa[args.chr]
else:
if args.id <= 0:
raise RuntimeError("sequence id must be a integer between 1 and {}".format(len(fa)))
subseq = fa[args.id]
if args.region:
start, end = args.region.split(':')
if start:
start = int(start) - 1
else:
start = 0
if end:
end = int(end)
else:
end = len(s)
sys.stdout.write("{}\n".format(subseq[start:end].seq))
else:
sys.stdout.write("{}\n".format(subseq.seq))
sys.stdout.flush()
def get_output_handle(fpath: str, fastx: bool = False, out: bool = True):
if fpath == "-":
if out:
handle = sys.stdout
else:
handle = sys.stdin
else:
p = Path(fpath)
if not p.parent.is_dir():
raise NotADirectoryError(
"Directory specified for output file does not exist: {}".format(
p.parent
)
)
if fastx:
if fpath.endswith("a"):
handle = pyfastx.Fasta(p)
else:
handle = pyfastx.Fastq(p)
else:
handle = p.open("w")
return handle
def main():
# Configure argparser
argparser = get_argparser()
# Parse the arguments
args = argparser.parse_args()
# Input files: json input file to be used as template and
in_fasta = args.in_fasta
out_fasta = args.out_fasta
# Configure logging appropriate for verbosity
configure_logging(args.verbosity_level)
# Count total length of bases in fasta
logging.info("Reading input fasta...")
fa = pyfastx.Fasta(in_fasta)
total_contigs = len(fa)
total_bases = fa.size
logging.info("Total input contigs: {0}".format(total_contigs))
logging.info("Total input bases: {0}".format(total_bases))
# Merge contigs
logging.info("Cleaning fastq records")
filter_fasta(fa, out_fasta)
def test_build(self):
self.fastx = pyfastx.Fasta(gzip_fasta, build_index=False)
if os.path.exists('{}.fxi'.format(gzip_fasta)):
os.remove('{}.fxi'.format(gzip_fasta))
self.fastx.build_index()
def generateHtml(self):
sql = "SELECT * FROM primer,primer_meta WHERE id=pid AND id=%s" % self.id
primer = self.db.get_row(sql)
table = primer.category
tid = primer.target
#table, tid = primer.target.split('-')
sql = "SELECT path FROM fasta LIMIT 1"
fasta_file = self.db.get_one(sql.format(table, tid))
self.fasta = pyfastx.Fasta(fasta_file)
sql = "SELECT * FROM %s WHERE id=%s" % (table, tid)
ssr = self.db.get_row(sql)
seq, left, right = self.getSequence(ssr.sequence, ssr.start, ssr.end)
tandem = "%s%s%s" % (
self.formatPrimer(left, primer.start1, primer.length1),
self.formatTarget(seq),
self.formatPrimer(right, primer.start2-primer.length2-len(seq)-len(left)+1, primer.length2)
)
return template_render("sequence.html", tandem=tandem, ssr=ssr, table=self.table)
def parse_fasta(fasta_file):
busco_seqs = pyfastx.Fasta(fasta_file)
ids = busco_seqs.keys()
busco_names = pd.DataFrame(data=list(ids), columns=['seqNames'])
busco_names = pd.DataFrame(busco_names.seqNames.str.split("_", 1).tolist(),
columns=['buscoId', 'sampleId'])
return busco_names, busco_seqs
def fastx_info(args):
fastx_type = fastx_format_check(args.fastx)
if fastx_type == 'fasta':
fa = pyfastx.Fasta(args.fastx)
comp = fa.composition
print("Sequence counts: {}".format(len(fa)))
print("Total bases: {}".format(fa.size))
print("GC content: {:.2f}%".format(fa.gc_content))
for b in comp:
print("{} counts: {}".format(b, comp[b]))
print("Mean length: {:.2f}".format(fa.mean))
print("Median length: {:.2f}".format(fa.median))
print("Max length: {}".format(len(fa.longest)))
print("Min length: {}".format(len(fa.shortest)))
print("N50, L50: {}, {}".format(*fa.nl()))
print("length >= 1000: {}".format(fa.count(1000)))
elif fastx_type == 'fastq':
fq = pyfastx.Fastq(args.fastx)
comp = fq.composition
print("Read counts: {}".format(len(fq)))
print("Total bases: {}".format(fq.size))
print("GC content: {:.2f}%".format(fq.gc_content))
for b in comp:
print("{} counts: {}".format(b, comp[b]))
print("Quality encoding system maybe: {}".format(", ".join(fq.encoding_type)))
def create_fastx_index(fastx):
if is_fasta(fastx):
return pyfastx.Fasta(str(fastx), build_index=True), build_read_fasta
elif is_fastq(fastx):
return pyfastx.Fastq(str(fastx), build_index=True), build_read_fastq
else:
raise ValueError(f'Could not determine input file format: {fastx}')
def stat_query_mismatch(alnfile: str, reffile: str, sitesfile: str):
"""
统计指定位点的错配情况
alnfile为bam/cram文件
reffile为对应的参考基因组文件
sitesfile为待检测的位点，两列，gzip或bgzip压缩，第一列为染色体，第二列为坐标位置（1-based），vcf.gz文件符合这一格式，可以直接用vcf.gz作为输入
"""
print(f'stat_query_mismatch: {alnfile}, {sitesfile}')
# 打开reffile
reffa = pyfastx.Fasta(reffile, uppercase=True) # always output uppercase sequence, 为了方便下面比较
# 读取位点信息
sites = []
with gzip.open(sitesfile, 'rb') as f:
for line in f:
tline = line.decode().strip().split()
if tline[0][0] != '#':
sites.append([tline[0], int(tline[1])-1]) # 后续pysam输入的是0-base索引，在这儿提前转换了
# 打开bam文件
if alnfile.endswith('bam'):
alnfile = pysam.AlignmentFile(alnfile, 'rb', threads=10)
elif alnfile.endswith('.cram'):
alnfile = pysam.AlignmentFile(alnfile, 'rc', reference_filename=reffile, threads=10)
# 开始遍历位点进行判断
reads2QMis = defaultdict(int) # 每对reads在目标位点(query sites)上与参考基因组(reffile)之间的错配数量。统计错配的话，后续缺失值用0填充就比较合理。
reads2nQuery = defaultdict(int) # 记录每个reads cover到了几个目标位点，用来区分没有错配还是没有cover到query位点
for chrom, pos in sites:
refbase = reffa[chrom][pos: pos+1].seq
# stepper='all': skip reads in which any of the following flags are set: BAM_FUNMAP, BAM_FSECONDARY, BAM_FQCFAIL, BAM_FDUP
for ncolumn, pileupcolumn in enumerate(alnfile.pileup(chrom, pos, pos+1,
truncate=True, stepper='all', ignore_orphans=False,
min_base_quality=0, min_mapping_quality=0)):
assert pileupcolumn.reference_pos == pos
for pileupread in pileupcolumn.pileups:
readname = pileupread.alignment.query_name
reads2nQuery[readname] += 1
if not pileupread.is_del:
# query position is None if is_del or is_refskip is set. 统一大小写进行比较，fasta前面已经转过了
if pileupread.alignment.query_sequence[pileupread.query_position].upper() != refbase:
reads2QMis[readname] += 1
else:
reads2QMis[readname] += 1
alnfile.close()
maxnQuery = max(reads2nQuery.values())
nQuery_dtype = select_min_dtype_uint(maxnQuery)
reads2nQuery = pd.Series(reads2nQuery, dtype=nQuery_dtype)
print(f'max(nQuery): {maxnQuery}, select dtype: {nQuery_dtype}')
maxQMis = max(reads2QMis.values())
QMis_dtype = select_min_dtype_uint(maxQMis)
reads2QMis = pd.Series(reads2QMis, dtype=QMis_dtype)
print(f'max(QMis): {maxQMis}, select dtype: {QMis_dtype}')
return reads2nQuery, reads2QMis
def build_fasta_index(self, fasta_id, fasta_path):
'''
build index for fasta file and write fasta sequence to database
@para fasta_id int, the fasta file id in database
@para fasta_path str, the file path of fasta
@return Fasta object
'''
#seqs = fasta.GzipFasta(fasta_path)
self.emit_message("Building fasta index for %s" % fasta_path)
with multiprocessing.Pool() as pool:
pool.apply_async(build_full_index, (fasta_path, ))
pool.close()
pool.join()
seqs = pyfastx.Fasta(fasta_path)
#get sequence detail information
#sql = "SELECT * FROM seq INNER JOIN fasta ON (seq.fid=fasta.id) WHERE fasta.path='{}' LIMIT 1".format(fasta_path)
#if not self.db.get_one(sql):
# rows = []
# for seq in seqs:
# compos = seq.composition
# ns = sum(compos[b] for b in compos if b not in ['A', 'T', 'G', 'C'])
# row = (None, seq.name, fasta_id, len(seq), compos.get('G',0)+compos.get('C',0), ns)
# rows.append(row)
# self.db.insert("INSERT INTO seq VALUES (?,?,?,?,?,?)", rows)
sql = "SELECT * FROM option WHERE name='gc_content'"
if not self.db.get_one(sql):
gc = seqs.gc_content
compos = seqs.composition
ns = sum(compos[b] for b in compos
if b not in ['A', 'T', 'G', 'C'])
self.db.insert("INSERT INTO option (name, value) VALUES (?,?)",
[('total_base', str(seqs.size)),
('total_seqs', str(len(seqs))),
('gc_content', str(gc)), ('unkown_base', str(ns))])
self.total_bases = seqs.size
seqs = pyfastx.Fasta(fasta_path, build_index=False)
return seqs
def randomizeMatchingPositions():
########################
#command line arguments#
########################
parser = argparse.ArgumentParser()
#MANDATORY PARAMETERS
parser.add_argument("outfile",help="Output fasta-file name.",type=str)
#OPTIONAL PARAMETERS
parser.add_argument("--wt",help="Full path to a fasta-file containing the wild type sequence.",type=str)
parser.add_argument("--seqs",help="Full path to the fasta-file containing the sequences where we want to randomize the positions matching to wild type.",type=str)
parser.add_argument("--N",help="Exact number of mismatches in seqs needed for including to output.",type=int,default=2)
parser.add_argument("--addToReadName",help="String added to read names to distinguish them from input reads (default=:randomized).",type=str,default=":randomized")
parser.add_argument("--alphabet",help="Alphabet used as a string containing each possible character (case sensitive, default=ACGT).",type=str,default='ACGT')
args = parser.parse_args()
#read in the wild type sequence
for name,seq in pyfastx.Fasta(args.wt):
wtseq = seq
#read in rest of the sequences, save to outfile those that have N mismatches to wtseq
#and randomize other positions from them
with open(args.outfile,'wt') as outfile:
w = csv.writer(outfile,delimiter='\t')
for name,seq in pyfastx.Fasta(args.seqs):
rands = np.random.randint(0,high=len(args.alphabet),size=len(seq)) #draw the random sequence
newseq = ""
N_mismatch = 0 #mismatch counter
for i in range(0,len(seq)):
if seq[i]!=wtseq[i]:
N_mismatch += 1
newseq += seq[i]
else: newseq += args.alphabet[rands[i]]
if N_mismatch>args.N: break
if N_mismatch==args.N:
#save the sequence
w.writerow(['>'+name+args.addToReadName])
w.writerow([newseq])
def get_total_seq_len(fasta_file):
"""Simple function that uses pyfastx to quickly read in a fasta,
and then the sum of sequence lengths is returned
"""
try:
x = [
len(seq) for h, seq in pyfastx.Fasta(fasta_file, build_index=False)
]
except RuntimeError:
x = [0]
return sum(x)
def test_key_func(self):
del self.fastx
#remove previously created index file
if os.path.exists("{}.fxi".format(gzip_fasta)):
os.remove("{}.fxi".format(gzip_fasta))
self.fastx = pyfastx.Fasta(gzip_fasta, key_func=lambda x: x.split()[1])
idx = self.get_random_index()
self.assertEqual(self.fastx[idx].name,
self.fastx[idx].description.split()[1])
def build_index(infile):
fxifile = infile + ".fxi"
if os.path.exists(fxifile):
print("fxi index is present")
else:
print("buliding fxi index for {}".format(infile))
if infile.endswith((".fa", ".fa.gz", ".fasta", ".fasta.gz")):
pyfastx.Fasta(infile)
else:
pyfastx.Fastq(infile)
print("fxi index has been created for {}".format(infile))
def main():
if len(sys.argv) < 3:
print(f"USAGE: {sys.argv[0]} <a.fa> <b.fa>")
sys.exit(1)
fp1 = sys.argv[1]
fp2 = sys.argv[2]
if CLEAN_IDX:
for fp in (fp1, fp2):
Path(fp + ".fxi").unlink(missing_ok=True)
fa1 = pyfastx.Fasta(fp1)
fa2 = pyfastx.Fasta(fp2)
n_seqs = len(fa1)
if n_seqs != len(fa2):
raise ValueError("Different number of sequences in the two files")
ids = sorted(fa1.keys())
all_seq_ids_same = all(x == y for x, y in zip(ids, sorted(fa2.keys())))
if not all_seq_ids_same:
raise ValueError(
"Sequence IDs are not identical between the two files")
mtx = np.zeros(shape=(n_seqs, n_seqs), dtype=np.int)
for i, j in tqdm(product(range(n_seqs), range(n_seqs)),
total=n_seqs * n_seqs):
dist = hamming(fa1[ids[i]].seq, fa2[ids[j]].seq)
mtx[i][j] = dist
print(DELIM.join(["sample", *ids]))
for i, sample in enumerate(ids):
row = DELIM.join(map(str, mtx[i]))
print(f"{sample}{DELIM}{row}")
if CLEAN_IDX:
for fp in (fp1, fp2):
Path(fp + ".fxi").unlink(missing_ok=True)
def generateHtml(self):
sql = "SELECT path FROM fasta LIMIT 1"
fasta_file = self.db.get_one(sql.format(self.table, self.id))
self.fasta = pyfastx.Fasta(fasta_file)
sql = "SELECT * FROM %s WHERE id=%s" % (self.table, self.id)
ssr = self.db.get_row(sql)
seq, left, right = self.getSequence(ssr.sequence, ssr.start, ssr.end)
tandem = "%s%s%s" % (self.formatFlank(left), self.formatTarget(seq), self.formatFlank(right))
return template_render("sequence.html", tandem=tandem, ssr=ssr, table=self.table)
def chunkPolishSeq(chunkBarcodeWithReadIter, nanoporeReadPath, tempDirPath,
finalDirPath, penaltyPath, i, minimapPath, poaPath,
raconPath):
nanoporeRead = pyfastx.Fasta(nanoporeReadPath)
commandExecuted = list(
map(polishSeq, chunkBarcodeWithReadIter, repeat(nanoporeRead),
repeat(tempDirPath), repeat(finalDirPath), repeat(penaltyPath),
repeat(minimapPath), repeat(poaPath), repeat(raconPath)))
commandExecuted = ' ;\\\n'.join(commandExecuted)
os.system(commandExecuted)
if i % 100 == 0:
logger.info(f'{i*100} reads processed')
def generate_coverage(read1, read2, mapping, ref, pwid=0.95, ncpu=1, chunk_size=500000, quiet=False):
if not quiet: print("Building index and data structures...")
seq_cov = {}
for name, seq in pyfastx.Fasta(ref, build_index=False):
seq_cov[name] = np.zeros(len(seq), dtype=int)
nreads = 0
read_len = 0
for r in mp.fastx_read(read1):
nreads+=1
read_len += len(r[1])
read_len /= nreads
min_chain_score = int(0.9*read_len)
min_mis_match = int(read_len-pwid*read_len)
a = mp.Aligner(ref, preset='sr', n_threads=ncpu, best_n=1000, min_chain_score=min_chain_score) # load or build index
if not a: raise Exception("ERROR: failed to load/build index")
def mpile(seqs):
if seqs is None: return([])
thrbuf = mp.ThreadBuffer()
hits = []
chrom=None
for hit in a.map(seqs[1], buf=thrbuf):
if (hit.NM<=min_mis_match) and ('S' not in hit.cigar_str) and ('H' not in hit.cigar_str):
if chrom is None:
chrom=mapping[hit.ctg]
hits.append((hit.ctg, hit.r_st-1, hit.r_en))
elif mapping[hit.ctg] == chrom:
hits.append((hit.ctg, hit.r_st-1, hit.r_en))
else:
break
return(hits)
if not quiet: print("Aligning reads...")
pool = ThreadPool(ncpu)
for reads in tqdm(grouper(chain(
mp.fastx_read(read1),
mp.fastx_read(read2)), chunk_size),
total=int(1+2*nreads/chunk_size), disable=quiet):
hits = pool.map(mpile, reads)
for hit in chain.from_iterable(hits):
if hit is None: continue
seq_cov[hit[0]][hit[1]:hit[2]] += 1
#close the pool and wait for the work to finish
pool.close()
pool.join()
return(seq_cov)
def fasta_split(args):
fa = pyfastx.Fasta(args.fastx)
if args.seq_count:
parts_num = math.ceil(len(fa)/args.seq_count)
else:
parts_num = args.file_num
name, suffix1 = os.path.splitext(os.path.basename(args.fastx))
if fa.is_gzip:
name, suffix2 = os.path.splitext(name)
digit = len(str(parts_num))
lens = [0] * parts_num
if args.seq_count:
seqs = [0] * parts_num
fhs = []
for i in range(1, parts_num+1):
if fa.is_gzip:
subfile = "{}.{}{}{}".format(name, str(i).zfill(digit), suffix2, suffix1)
else:
subfile = "{}.{}{}".format(name, str(i).zfill(digit), suffix1)
if args.outdir is not None:
subfile = os.path.join(args.outdir, subfile)
if fa.is_gzip:
fh = gzip.open(subfile, 'wt')
else:
fh = open(subfile, 'w')
fhs.append(fh)
ids = fa.keys()
for chrom in ids.sort('length', reverse=True):
idx = min_index(lens)
fhs[idx].write(">%s\n%s\n" % (chrom, fa[chrom].seq))
lens[idx] += len(fa[chrom])
if args.seq_count:
seqs[idx] += 1
if seqs[idx] == args.seq_count:
lens[idx] = fa.size
for fh in fhs:
fh.close()
def main(args):
with open(args.prefix + ".fasta", "w") as O:
with open(args.prefix + ".meta.csv", "w") as M:
writer = csv.DictWriter(M,
fieldnames=[
"id", "iso_a3", "country", "continent",
"date", "seqlen", "missing_fraction"
])
writer.writeheader()
for entry in tqdm(pyfastx.Fasta(args.fasta, full_name=True)):
seqname = entry.name
# print(seqname)
if check_for_disallowed_countries(seqname): continue
meta = seqname.split("|")
if len(meta) != 3: continue
if len(meta[0].split("/")) == 1: continue
country = meta[0].split("/")[1]
country = country2country.get(country, country)
iso_a3 = country2iso_a3[country2country.get(country, country)]
if country == "": continue
continent = country2continent[country]
date = meta[2]
if date_qc(date) == False: continue
if entry.end < args.seqlen: continue
missing_chars = sum([
n for d, n in entry.composition.items()
if d.upper() not in acgt
])
if missing_chars / entry.end > args.missing: continue
seqid = meta[1]
writer.writerow({
"id": seqid,
"country": country,
"continent": continent,
"iso_a3": iso_a3,
"date": date,
"seqlen": entry.end,
"missing_fraction": missing_chars / entry.end,
})
seq = list(entry.seq)
for pos in [i for i, n in enumerate(seq) if n not in acgt]:
seq[pos] = "N"
seq = "".join(seq)
O.write(">%s\n%s\n" % (seqid, seq))
def main():
args = get_options()
print("Calling genes from reads is still under active development and may change frequently!")
# make sure trailing forward slash is present
args.output_dir = os.path.join(args.output_dir, "")
# create temporary directory
temp_dir = os.path.join(tempfile.mkdtemp(dir=args.output_dir), "")
# check files exist
# clean database and remove sequences shorter than 300bp.
temp_db = temp_dir + "temp_db.fasta"
mapping = {}
mapping_clust = {}
with open(temp_db, 'w') as outfile:
index = 0
for name, seq in pyfastx.Fasta(args.db, build_index=False):
if len(seq)<=100: continue
outfile.write('>' + str(index) + '\n' + seq + '\n')
mapping[str(index)] = name
mapping_clust[str(index)] = name.split("__")[0]
index += 1
# align reads
coverage = generate_coverage(
read1=args.r1,
read2=args.r2,
ref=temp_db,
mapping=mapping_clust,
pwid=args.pwid,
ncpu=args.n_cpu,
chunk_size=500000,
quiet=args.quiet)
# call genes and write output
prefix = os.path.basename(args.r1).split('.')[0].strip('1').strip('_')
prefix += '_' + os.path.basename(args.db).split('.')[0]
find_genes(coverage, mapping,
cov_threshold=args.min_cov,
prefix=prefix,
outdir=args.output_dir,
fold_threshold=args.min_fold,
quiet=args.quiet)
# clean up
shutil.rmtree(temp_dir)
return
def main():
# Parse arguments: Requires n_words and outfile destination
parser = argparse.ArgumentParser(
description='Produce Byte Pair Encoder trained from .FASTA file.')
parser.add_argument('-i',
metavar='INFILE',
type=str,
help='Path of FASTA file for training model.')
args = parser.parse_args()
assert args.i
OUTFILE = "{}mer_compare_bpe_dna_wordsize_256.model"
# Load all seqs into memory. This may be an issue depending on the machine.
fa = pyfastx.Fasta(args.i)
#Make everything upper case DNA
#seqs = ['{}'.format(record.seq).upper().replace('U', 'T') for record in fa]
seqs = ['{}'.format(record.seq).upper() for record in fa]
k_compares = [6, 8]
#Calculate target vocabulary sizes, in descending order
k_compares = sorted(k_compares, reverse=True)
vocab_sizes = [4**k for k in k_compares]
#Calculation loop
# SentencePiece does not appear to have a natural way of continuing
# training from a checkpoint.
# Therefore, approach here is train -> encode -> train etc...
for i, vocab_size in enumerate(vocab_sizes):
#Create iterable for model input
s_iter = iter(seqs)
#Create bytes stream for model output
model = BytesIO()
#Train encoder.
spm.SentencePieceTrainer.train(sentence_iterator=s_iter,
model_writer=model,
vocab_size=vocab_size,
hard_vocab_limit="False",
max_sentencepiece_length=256)
#Save the model
with open(OUTFILE.format(k_compares[i]), 'wb') as f:
f.write(model.getvalue())
#Encode the corpus to the reduced vocabulary
sp = spm.SentencePieceProcessor(model_proto=model.getvalue())
print("DONE.")
def create_fastx_index(fastx: Path) -> (pyfastx.Fasta, Path):
if is_fasta(fastx):
return pyfastx.Fasta(
str(fastx), build_index=True
), Path(str(fastx) + '.fxi')
elif is_fastq(fastx):
return pyfastx.Fastq(
str(fastx), build_index=True
), Path(str(fastx) + '.fxi')
else:
raise ValueError(
f'Could not determine input file format: {fastx}'
)
def process(self):
self.emit_message("Exporting fasta sequence to %s" % self.outfile)
table_name = self.model.tableName()
whole_ssrs = self.db.get_one("SELECT COUNT(1) FROM %s" % table_name)
total_ssrs = whole_ssrs
if self.selected == 'whole' or len(self.model.selected) == whole_ssrs:
sql = "SELECT * FROM {}".format(table_name)
else:
ids = sorted(self.model.selected)
total_ssrs = len(ids)
sql = "SELECT * FROM {} WHERE id IN ({})".format(
table_name, ",".join(map(str, ids)))
current = 0
progress = 0
prev_progress = 0
current_seq = None
current_name = None
with open(self.outfile, 'wt') as fp:
for item in self.db.query(sql):
if item.sequence != current_name:
sql = "SELECT path FROM fasta LIMIT 1"
seqfile = self.db.get_one(sql)
seqs = pyfastx.Fasta(seqfile)
current_seq = seqs[item.sequence].seq
current_name = item.sequence
start = item.start - self.flank
if start < 1:
start = 1
end = item.end + self.flank
#ssr = seqs.fetch(item.sequence, (start, end))
ssr = current_seq[start - 1:end]
name = ">{}{} {}:{}-{}|motif:{}".format(
table_name.upper(), item.id, item.sequence, item.start,
item.end, item.motif)
fp.write("{}\n{}".format(name, format_fasta_sequence(ssr, 70)))
current += 1
progress = int(current / total_ssrs * 100)
if progress > prev_progress:
self.emit_progress(progress)
prev_progress = progress
self.emit_finish("Successfully exported to fasta %s" % self.outfile)
