motif_filename = "Gamma_collection.txt"

genome_filename = "NC_000913.fna" # Escherichia coli str. K-12 substr. MG1655

top_sites = ["TACTGTATAAATAAACAGTA", "TACTGTATATAAAAACAGTA", "TACTGTATATAAAAACAGTA", "AACTGTATATAAATACAGTT", "TACTGTATATAAAACCAGTT", "TACTGGATAAAAAAACAGTT", "TACTGTATAAAATCACAGTT", "AACTGGATAATCATACAGTA", "CACTGTATAAATAAACAGCT", "TACTGTACATCCATACAGTA", "ACCTGTATAAATAACCAGTA", "TACTGTATGAGCATACAGTA", "TACTGGATATTTAAACAGGT", "AACTGTATATACACCCAGGG", "TGCTGTATATACTCACAGCA", "AACTGGATAAAATTACAGGG", "TACTGTATATTCATTCAGGT", "TACTGTACACAATAACAGTA", "AGCTGAATAAATATACAGCA", "ATCTGTATATATACCCAGCT", "CACTGGATAGATAACCAGCA", "TACTGCATATACAACCAGAA", "CACTGTATACTTTACCAGTG", "AACTGTCGATACGTACAGTA", "TGCTGTACAAACGTCCAGTT", "CGCTGGATATCTATCCAGCA", "TGCTGTTTATTAAACCAGAA", "CATTGTTTATAAAAACAGCA", "TACTGGAGACAAATACAGCT", "ACCTGTATATATCATCAGTA", "TACTGTATAAACAGCCAATA", "TACTGTTTATCTTCCCAGCG", "GACTGTATAAAACCACAGCC", "TATTGTATATATTCACATTA", "AAGTGTATTTACACACAGCG", "AACTGGATAATCATACCGTT", "TGCTGTATGGATTAACAGGA", "TCCTGTATGAAAAACCATTA", "GCCTGTCTGAACAAACAGTA", "AAGTGTATATATATCCATCG", "AATTGTTTAAAAAACCAGAA", "TATTGTATTTATAAACATTA", "AACTGATTAAAAACCCAGCG", "GAGTGTATATAAAGCCAGAA", "TTCTGGATAAGCATCCAGAA", "ACCTGAATATTCAAACAGCG", "TACTGTCTACCAAAACAGAG", "TCCTGTATAAATTAACCGTT", "TAATGAATATAAAACCAGGA", "AACTGTAAATAATTACATGA", "TACTGAATAAAAAAGCAGAA", "TGCTGTACAATCAGCCAGCA", "TGCTGGATTTACGACCAGAA", "TACTGTTTATAAACCGAGCG", "TCTTGTATATCCAACCAGTT", "CACTGTATTAAAAACCATTC", "AACTGTATTACCTTCCAGCC", "TACTGTTTCCATTTACAGCC", "TAGTGGATGTAAAAACATTT", "CACTGTCTATACTTACATGT", "TACTGTTTGTGCAAATAGTA", "AACTGGTTATCAACCCAGAC", "CTCTGTATCTAATTACAGGT", "CACTGAATGCTAAAACAGCA", "TGCTGGATGTGAAACCAGCG", "TGCTGATTAAAAAACCAGCG", "AACTGGATATCTATCCGGAA", "CACTGTCTGATACAACAGTT", "AATTGTTAATATATCCAGAA", "AACTTTATGTACAGCCAGTG", "AACTGGTTATTCCACCAGAA", "GAATGGATAAAAAAACAGCC", "AGCTGTATAAAAATCCTGAG", "TCCTGGCTATTTTGCCAGTA", "AGCTGGCTATCTGAACAGTT", "CACTGGATATTCCTTCAGGT", "GGCTGGATAAAGAACCAGAA", "AACTGAATAAAAACAAAGGA", "TGCTGTACATCAGCACAGAT", "CAGTGGATTAACTTCCAGTT", "TACTGGATACAAAAACGGAT", "AACTATATAAATAAACATAA", "TGCTGCATGAAGAAACAGTA", "GTCTGAATGAATACCCAGTA", "TACTTTATTTACTCCCAGTG", "TACTGGAAACTAACACAGGC", "TACTGGATATCAAACCTGAA", "TACTGTCGGAAAAATCAGTG", "AACTGTATATGTCGCCAGGC", "TACTCTTCATTAAAACAGTG", "GACTGGCTTAATACACAGCC", "CAGTGTAAGAATAGACAGTG", "CGCTGGATGAGCGTACAGCA", "AACGGGATAATAAAACAGCC", "TACCGTTTGTATTTCCAGCT", "CTCTGGACATTAAACCAGGA", "TAGTTTATATAAATTCAGTC", "TACTGTATATTCCTCAAGCG", "AACTGGATATATAAATAATG", "AACGGTTTAAACACCCAGCG", "GCCTGGTTAAATGACCAGCA", "TACTGTTTAGCCAGCCAGTC", "TGCTGAACATTCTTCCAGCA", "TACTGGAAATAAGATCAGCC", "AACTGGATCTTAAAAAAGTA", "GGCTGGACAATTTTACAGCT", "AACTGTCTCTTATGACAGTT", "AACTGGATACAGAAACAATA", "TAGTGTTCAGTTTTACAGTA", "TAGTGTTTGTTCATCCATTA", "CAGTGTTGAAAAGTCCAGTA", "ATCTGGCTGAAATTACAGAA", "GATTGAATGAATATACAGGG", "CACTGGTTTTCCACCCAGCA", "AACTGGTTGAAAAACCATCA", "AACTGGTTTAACTCCCAGGG", "AGCTGGATAAACAAAAAGCG", "AACTGGATAAATTACCGGAT", "AACTGGATTTAAATCCTGGT", "AACTGTATTTACAATCATCT", "TACTCTCTGTTCATCCAGCA", "TACTCTATGCAATAACAGAA", "TGCTGAATGCATTAACAGCG", "CACTGGTTATCTTTACCGTA", "TGGTGGATATTTTTTCAGGA", "AACTGGATATCAATCCGGAT", "AAGTGTAAAATTCTACAGAA", "AACCGGACATAAAGCCAGTA", "CACTGTATAAAAATCCTATA", "AACTCTATATTACCCCAGTT", "ACCAGAATAAACATCCAGTA", "TACTGGATGCATTACCGGTA", "CACTCTTTATAAAACCAGGC", "CAGTGAAAATAAAAACAGGA", "AACGGTTTATCTAGCCAGTA", "TCATGTATGATCATACAGAC", "TGCTGAATATATAAAAAGAG", "ACCTGGATGTCACCACAGTT", "TACTAAATGAAAAAACAGCG", "AACTGGATGAACAACCGGCG", "TTCTTTATACATATCCAGCG", "AAGTGTTTGCTAACACAGCA", "AAGTGTAAAAAATCCCAGCG", "AACTGGATAAAGACACCGCT", "TGCTGTATGGTAAATCAGAA", "AACTGTATGATTTAAAAGAT", "TACGGTATAAAAAGACCGTA", "TGCTGGATATTATCCCATCA", "TACTGTCTGAAGAAGCAGTG", "AACTGAATAAATACCCCGGT", "TGCTGTATGAGTAACCGGTA", "CACTGGAAAAATGCGCAGTA", "AACTGGATAGCTATGCAGAA", "AATTGTAAAAAACAACAGCA", "AACTGTTTATCAACACCGCT", "ACCTGTACCTTAAACCAGGA", "TACTTTATAGTTTCCCAGTT", "ATCTGCATAAAGAACCAGTA", "CCCTCTTTATATTTCCAGTG", "TACTGTTTATTAATGTAGCA", "ATGTGAATGAATATCCAGTT", "AACTGGTTAAAATTAGAGAT", "TACTGTAAGAAAAACCCGCA", "ACCTGGAGAAAGAAACAGCG", "CACTGTTTACCCTGACAGTC", "AACTGGCTCATAACCCAGAA"]

top_seqs = [nt2int(seq) for seq in top_sites]

top_seqs = top_seqs[0:3] # Truncate to first three for the sake of less output spam

# Load genome (validated)

genome, __ = load_scaffolds(genome_filename)

_genome, __ = metagenomics.load_scaffolds(genome_filename)

print "Genomes loaded are the same:", np.array_equal(genome, _genome)

print

# Ensure the sites are in the genome (validated)

print "Finding the sites in the genome:"

positions = []

sites = []

strands = []

for seq in top_seqs:

matches = np.where(np.all(sliding_window(genome, seq.size) == seq, axis=1))

matches_r = np.where(np.all(sliding_window(genome, seq.size) == wc(seq), axis=1))

# forward strand

for match in matches:

if match.size > 0 and match[0] not in positions:

pos = match[0]

seq = genome[pos:pos + 20]

print "Found:", int2nt(seq), "->", match[0]

positions.append(pos)

sites.append(seq)

strands.append(0) # forward

# reverse strand

for match in matches_r:

if match.size > 0 and match[0] not in positions:

pos = match[0]

seq = wc(genome[match[0]:match[0] + 20])

print "Found:", int2nt(seq), "<-", match[0]

positions.append(pos)

sites.append(seq)

strands.append(1) # reverse

print

# Load PSSM (validated)

genome_frequencies = np.bincount(genome).astype(np.float) / genome.size

_pssm = create_pssm(motif_filename, genome_frequencies=genome_frequencies)

_pssm2 = gpu_pssm.create_pssm(motif_filename, genome_frequencies=genome_frequencies)

print "PSSMs are the same:", np.array_equal(_pssm, _pssm2)

print

# Score sites (validated!!)

print "Scoring sites (method score seq strand pos):"

for n, site in enumerate(sites):

print "True:", score_site(site, _pssm), int2nt(site), strands[n], positions[n]

print "CPU: ", cpu_score_site(site, _pssm), int2nt(site), strands[n], positions[n]

print "GPU: ", gpu_score_site(site, _pssm), int2nt(site), strands[n], positions[n]

print

# motif_seqs = load_fasta(motif_filename)

# pscm = count_matrix([nt2int(seq) for seq in motif_seqs])

# psfm = freq_matrix(pscm)

# print psfm

# genome_frequencies = [0.25, 0.25, 0.25, 0.25]

# _pssm2 = pssm(psfm, genome_frequencies)

# #print _pssm2

with open(filename) as f:

lines = f.readlines()

lines = [line.strip() for line in lines if ">" not in line]

scaffolds = []

with open(filename, "r+") as f:

# Pre-allocate sequence to size of file

f.seek(0, os.SEEK_END)

size = f.tell()

f.seek(0, os.SEEK_SET)

sequence = np.tile(-1, size)

# Process the lines in the file

pos = 0

next_line_starts_scaffold = False

for line in f:

if ">" in line:

next_line_starts_scaffold = True

else:

# Keep sequence as the charcode integers

scaffold_seq = np.array(line.strip(), "c").view(np.uint8)

# Replace the sequence chunk in memory

sequence[pos:pos + len(scaffold_seq)] = scaffold_seq

if next_line_starts_scaffold:

# Keep track of the beginning of scaffolds

scaffolds.append(pos)

next_line_starts_scaffold = False

# Update position in the sequence array

pos += len(scaffold_seq)

# Truncate the sequence array to fit the data

sequence = sequence[0:np.where(sequence == -1)[0][0]]

# Replace character codes with 0-3 int representation of the nucleotides

for value, base in enumerate(bases):

sequence[np.where(sequence == ord(base))] = value

# Initialize language and counts

bases = "ACGT"

counts = np.array([])

# Read in file "without slurping"

with open(motif_filename, "r+") as f:

for line in f:

if ">" not in line:

# Initialize counts array

if counts.size is 0:

counts = np.zeros(len(line.strip()) * 4)

# Add to nucleotide count per position

for pos, base in enumerate(line.strip()):

counts[pos * 4 + bases.index(base)] += 1

# Intialize PSSM array

pssm = np.zeros(counts.size)

# Calculate PSSM

for pos in range(counts.size / 4):

total_count = sum(counts[pos * 4:pos * 4 + 4])

genomic_entropy = -sum([p_i * math.log(p_i, 2) for p_i in genome_frequencies])

for b in range(4):

# p = frequency of this base in the genome

p = genome_frequencies[b]

### Laplacian

# pseudo_freq = frequency of this base at this position using a

# Laplacian pseudocount

pseudo_freq = (counts[pos * 4 + b] + p) / (total_count + 1)

# Each entry in the PSSM is the log-likelihood of that base at that

# position.

pssm[pos * 4 + b] = math.log(pseudo_freq / p, 2)

### Epsilon pseudocount

# f = frequency of this base at this position

#f = counts[pos * 4 + b] / total_count

# We add a very small epsilon to avoid log(0) situations, which

# should yield -Inf, but is not supported by Python. This is known

# as a computational pseudocount

#pssm[pos * 4 + b] = math.log(f / p + epsilon, 2)

### Information score

#f = counts[pos * 4 + b] / total_count

#pssm[pos * 4 + b] = math.log(f + epsilon, 2) + genomic_entropy

complements = [np.array([])] * len(bases)

for b in range(len(bases)):

complements[b] = np.where(int_seq == b)

_int_seq = np.copy(int_seq)

for b, indices in enumerate(complements):

_int_seq[indices] = len(bases) - b - 1

sequence = np.array(nt_seq, "c").view(np.uint8)

for value, base in enumerate(bases):

sequence[np.where(sequence == ord(base))] = value