def prepare_denovo_input_bed(inputfile, params, outdir):
"""Prepare a BED file for de novo motif prediction.
All regions to same size; split in test and validation set;
converted to FASTA.
Parameters
----------
inputfile : str
BED file with input regions.
params : dict
Dictionary with parameters.
outdir : str
Output directory to save files.
"""
logger.info("preparing input (BED)")
# Create BED file with regions of equal size
width = int(params["width"])
bedfile = os.path.join(outdir, "input.bed")
write_equalwidth_bedfile(inputfile, width, bedfile)
abs_max = int(params["abs_max"])
fraction = float(params["fraction"])
pred_bedfile = os.path.join(outdir, "prediction.bed")
val_bedfile = os.path.join(outdir, "validation.bed")
# Split input into prediction and validation set
logger.debug(
"Splitting %s into prediction set (%s) and validation set (%s)",
bedfile, pred_bedfile, val_bedfile)
divide_file(bedfile, pred_bedfile, val_bedfile, fraction, abs_max)
config = MotifConfig()
genome = Genome(params["genome"])
for infile in [pred_bedfile, val_bedfile]:
genome.track2fasta(
infile,
infile.replace(".bed", ".fa"),
)
# Create file for location plots
lwidth = int(params["lwidth"])
extend = (lwidth - width) // 2
genome.track2fasta(
val_bedfile,
os.path.join(outdir, "localization.fa"),
extend_up=extend,
extend_down=extend,
stranded=params["use_strand"],
)
def prepare_denovo_input_bed(inputfile, params, outdir):
"""Prepare a BED file for de novo motif prediction.
All regions to same size; split in test and validation set;
converted to FASTA.
Parameters
----------
inputfile : str
BED file with input regions.
params : dict
Dictionary with parameters.
outdir : str
Output directory to save files.
"""
logger.info("preparing input (BED)")
# Create BED file with regions of equal size
width = int(params["width"])
bedfile = os.path.join(outdir, "input.bed")
write_equalwidth_bedfile(inputfile, width, bedfile)
abs_max = int(params["abs_max"])
fraction = float(params["fraction"])
pred_bedfile = os.path.join(outdir, "prediction.bed")
val_bedfile = os.path.join(outdir, "validation.bed")
# Split input into prediction and validation set
logger.debug(
"Splitting %s into prediction set (%s) and validation set (%s)",
bedfile, pred_bedfile, val_bedfile)
divide_file(bedfile, pred_bedfile, val_bedfile, fraction, abs_max)
config = MotifConfig()
genome = Genome(params["genome"])
for infile in [pred_bedfile, val_bedfile]:
genome.track2fasta(
infile,
infile.replace(".bed", ".fa"),
)
# Create file for location plots
lwidth = int(params["lwidth"])
extend = (lwidth - width) // 2
genome.track2fasta(
val_bedfile,
os.path.join(outdir, "localization.fa"),
extend_up=extend,
extend_down=extend,
stranded=params["use_strand"],
)
def as_fasta(seqs, genome=None):
ftype = get_seqs_type(seqs)
if ftype == "fasta":
return seqs
elif ftype == "fastafile":
return Fasta(seqs)
else:
if genome is None:
raise ValueError("need genome to convert to FASTA")
tmpfa = NamedTemporaryFile()
if type(genome) == type(""):
genome = Genome(genome)
genome.track2fasta(seqs, tmpfa.name)
return Fasta(tmpfa.name)
def as_fasta(seqs, genome=None):
ftype = get_seqs_type(seqs)
if ftype == "fasta":
return seqs
elif ftype == "fastafile":
return Fasta(seqs)
else:
if genome is None:
raise ValueError("need genome to convert to FASTA")
tmpfa = NamedTemporaryFile()
if type(genome) == type(""):
genome = Genome(genome)
genome.track2fasta(seqs, tmpfa.name)
return Fasta(tmpfa.name)
def _as_seqdict_genome_regions(regions, minsize=None):
"""
Accepts list of regions where the genome is encoded in the region,
using the genome@chrom:start-end format.
"""
genomic_regions = {}
for region in regions:
genome, region = region.split("@")
if genome not in genomic_regions:
Genome(genome)
genomic_regions[genome] = []
genomic_regions[genome].append(region)
tmpfa = NamedTemporaryFile(mode="w", delete=False)
for genome, g_regions in genomic_regions.items():
g = Genome(genome)
fa = g.track2fasta(g_regions)
for seq in fa:
seq.name = f"{genome}@{seq.name}"
print(seq.__repr__(), file=tmpfa)
tmpfa.flush()
# Open tempfile and restore original sequence order
fa = as_seqdict(tmpfa.name)
fa = {region: fa[region] for region in regions}
return _check_minsize(fa, minsize)
def _genomepy_convert(to_convert, genome, minsize=None):
"""
Convert a variety of inputs using track2fasta().
"""
if genome is None:
raise ValueError("input file is not a FASTA file, need a genome!")
if isinstance(genome, Genome):
g = genome
else:
g = Genome(genome)
tmpfile = NamedTemporaryFile()
g.track2fasta(to_convert, tmpfile.name)
fa = as_seqdict(tmpfile.name)
return _check_minsize(fa, minsize)
def as_fasta(seqs, genome=None):
ftype = get_seqs_type(seqs)
if ftype == "fasta":
return seqs
elif ftype == "fastafile":
return Fasta(seqs)
else:
if genome is None:
raise ValueError("need genome to convert to FASTA")
tmpfa = NamedTemporaryFile()
if isinstance(genome, str):
genome = Genome(genome)
if isinstance(seqs, np.ndarray):
seqs = list(seqs)
genome.track2fasta(seqs, tmpfa.name)
return Fasta(tmpfa.name)