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butcoverage.py
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butcoverage.py
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__author__ = 'julian'
from sys import argv
from Bio.Align.Applications import ClustalOmegaCommandline
from Bio import AlignIO, SeqIO
from Bio.Align import MultipleSeqAlignment
from Bio.Seq import MutableSeq
from Bio.SeqRecord import SeqRecord
from Bio.SeqUtils.CheckSum import seguid
from Bio.Seq import Seq
from Bio.Alphabet import IUPAC
from Bio.Data.IUPACData import ambiguous_dna_values
import pandas as pd
import re
from Bio.Phylo.Applications import FastTreeCommandline
from statistics import mode
from Bio.SeqUtils import MeltingTemp as mt
def length_filter(sequences, minlength=None, maxlength=None):
lengths = []
for seq in sequences:
lengths.append(len(seq.seq))
if minlength is None:
minlength = min(lengths)
if maxlength is None:
maxlength = max(lengths)
goodseqs_min = []
goodseqs_max = []
for seq in sequences:
if len(seq.seq) <= maxlength:
goodseqs_max.append(seq)
else:
print("sequence {} is longer than the maximum length and has been removed".format(seq.id))
for seq in goodseqs_max:
if len(seq.seq) >= minlength:
goodseqs_min.append(seq)
else:
print("sequence {} is shorter than the minimum length and has been removed".format(seq.id))
return goodseqs_min
def codon_align(protein_alignment, dna_sequences):
codon_positions = []
for seq in protein_alignment:
codons = []
codon_count = 0
for position in seq.seq:
if position != "-":
codons.append(codon_count)
codon_count += 3
else:
codon_count += 3
codon_positions.append(codons)
DNA_sequence_index = 0
Codon_list_index = 0
DNA_align_length = len(protein_alignment[1].seq) * 3
Codon_DNA_alignment = []
for x in codon_positions:
blank_alignment = "-" * DNA_align_length
blank_alignment = MutableSeq(blank_alignment)
DNA_align = blank_alignment
DNA_position_index = 0
for y in x:
DNA_align[y:y+3] = dna_sequences[DNA_sequence_index].seq[DNA_position_index:(DNA_position_index+3)]
DNA_position_index += 3
DNA_align = DNA_align.toseq()
DNA_align = SeqRecord(DNA_align)
DNA_align.id = dna_sequences[DNA_sequence_index].id
DNA_align.name = dna_sequences[DNA_sequence_index].name
DNA_align.description = dna_sequences[DNA_sequence_index].description
DNA_align.dbxrefs = dna_sequences[DNA_sequence_index].dbxrefs
DNA_sequence_index += 1
Codon_DNA_alignment.append(DNA_align)
Codon_list_index += 1
return Codon_DNA_alignment
def unique_seqs(sequences):
"""returns a list of SeqRecord objects with redundant sequences removed"""
unique_records = []
checksum_container = []
for seq in sequences:
checksum = seguid(seq.seq)
if checksum not in checksum_container:
checksum_container.append(checksum)
unique_records.append(seq)
return unique_records
def unique_ids(sequences):
"""returns a list of SeqRecord objects with redundant ids renamed"""
unique_records = []
checksum_container = []
redundant_id_count = 0
for seq in sequences:
checksum = seguid(seq.id)
if checksum not in checksum_container:
checksum_container.append(checksum)
unique_records.append(seq)
else:
print("repeated id detected, adding '.{}' suffix".format(redundant_id_count))
seq.id = "{}.{}".format(seq.id, redundant_id_count)
unique_records.append(seq)
redundant_id_count += 1
return unique_records
def make_protein_record(nuc_record):
"""Returns a new SeqRecord with the translated sequence (default table)."""
return SeqRecord(seq=nuc_record.seq.translate(cds=False), id=nuc_record.id, description="translation using def table")
def alignment_screen(protein_alignment, protein_seqs, dna_seqs, cutoff):
aln = protein_alignment
alignment_length = (len(aln[0]))
alignment_iterator = range(0, alignment_length)
iterator_count = 0
list_of_percent_gaps = []
for x in alignment_iterator:
column = aln[:, iterator_count]
number_of_gaps = column.count('-')
percent_gaps = number_of_gaps/len(column)
list_of_percent_gaps.append(percent_gaps)
iterator_count += 1
# print(list_of_percent_gaps)
counter = 0
good_columns = []
bad_columns = []
for entry in list_of_percent_gaps:
if entry < cutoff:
good_columns.append(counter)
counter += 1
else:
bad_columns.append(counter)
counter += 1
prot_DNA_dict = {}
for sequence in protein_alignment:
position_counter = 0
residue_counter = 0
prot_DNA_dict[sequence.id] = {}
for position in sequence.seq:
if position != "-":
prot_DNA_dict[sequence.id][position_counter] = residue_counter
residue_counter += 1
position_counter += 1
residues_removed = {}
for entry in prot_DNA_dict:
# print(entry)
residues_removed[entry] = []
for position in prot_DNA_dict[entry]:
if position in bad_columns:
residues_removed[entry].append(prot_DNA_dict[entry][position])
#print(residues_removed)
nucleotides_removed = {}
for entry in residues_removed:
nucleotides_removed[entry] = []
for y in residues_removed[entry]:
y = [y*3, (y*3) + 1, (y*3) + 2]
nucleotides_removed[entry].append(y)
nucleotides_removed[entry] = sum(nucleotides_removed[entry], []) # flattens the lists of lists into just lists
#for entry in nucleotides_removed:
#print("sequence ID: {}\nNucleotides removed: {}".format(entry, nucleotides_removed[entry]))
prot_dict = {}
dna_dict = {}
for sequence in protein_seqs:
prot_dict[sequence.id] = sequence.seq
for sequence in dna_seqs:
dna_dict[sequence.id] = sequence.seq
edited_prots = []
for seq in protein_seqs:
blank_seq = []
position_counter = 0
# print(len(seq.seq))
#print("id = {}".format(seq.id))
for position in seq.seq:
if position_counter in residues_removed[seq.id]:
#print("RESIDUE #{} REMOVED".format(position_counter))
position_counter += 1
else:
position_counter += 1
blank_seq.append(position)
new_seq = "".join(blank_seq)
seq.seq = Seq(new_seq)
edited_prots.append(seq)
edited_DNAs = []
for seq in dna_seqs:
blank_seq = []
position_counter = 0
for position in seq.seq:
if position_counter in nucleotides_removed[seq.id]:
position_counter += 1
else:
position_counter += 1
blank_seq.append(position)
new_seq = "".join(blank_seq)
seq.seq = Seq(new_seq)
edited_DNAs.append(seq)
return edited_prots, edited_DNAs
def find_hit_regions(primer, alignment): #this one is for all the sequences in the alignment
'''this is currently super inefficient... It basically does the work of primer_coverage() for every single possible
frame in a sliding window for every sequence... If I'm ok with this I should just have this function return the
number of mismatches for the positions which best match... If I do that then I could have the amplicon length be
something that was returned as well.....hmmm very tempting... I think I should do this. what else besides amplicon
length would this allow me to do? I could also have it output potential mispriming sites, and then the amplicon
length for the misprimed sites.... I could include a condition where it would print a warning if mispriming
is likely, output a spreadsheet that tells you what sequences are likely to misprime, how big the amplicon
for the mispriming would be... But this mispriming would only be for these particular sequences that you are
tyring to amplify, A much more liekly source of mispriming would just be other random genomic DNA. A metagenome
might be a good thing to run this, but that would really take a long time.....'''
alignment_len = len(alignment[0])
primer_length = len(primer)
number_of_frames = (alignment_len - primer_length) + 1
range_of_frames = range(0, number_of_frames)
list_of_indexes = []
first_indexes = []
last_indexes = []
frame_indexes = {}
for frame in range_of_frames:
frame_indexes[frame] = {}
frame_indexes[frame]["first"] = frame
frame_indexes[frame]["last"] = frame + primer_length
hit_regions = {}
for seq in alignment:
sequences = {}
for frame in frame_indexes:
sequence = seq[frame_indexes[frame]["first"]:frame_indexes[frame]["last"]]
#print(sequence)
sequences[frame] = sequence
number_mismatches = {}
for key in sequences:
number_mismatches[key] = 0
for count, position in enumerate(sequences[key].upper()):
#print(count, position)
if position not in ambiguous_dna_values[primer[count]]:
number_mismatches[key] += 1
indexes = frame_indexes[min(number_mismatches, key=number_mismatches.get)]
hit_regions[seq.id] = indexes
#print("number of sequences checked: {}".format(len(hit_regions)))
#print("Percent complete: {}".format(len(hit_regions)/len(alignment)))
#hit_regions = set(hit_regions)
#print(hit_regions)
starting = []
ending = []
for key in hit_regions:
#print(key)
starting.append(hit_regions[key]["first"])
ending.append(hit_regions[key]["last"])
#print(starting)
#print(ending)
starting = mode(starting)
ending = mode(ending)
return starting, ending
def primer_coverage(FWDprimer, REVprimer, FWDregion, REVregion):
""" Returns a pandas dataframe with the number of mismatches for each primer in their corresponding region.
It also attempts to pull metadata from the sequences (organism, and gene definition) this works if the sequences
are from fungene. Row names are sequence IDs. """
number_mismatches_FWD = {}
melt_temp_FWD = {}
for seq in FWDregion:
number_mismatches_FWD[seq.id] = 0
melt_temp_FWD[seq.id] = mt.Tm_NN(seq.seq)
for count, position in enumerate(seq.seq.upper()):
if position not in ambiguous_dna_values[FWDprimer[count]]:
number_mismatches_FWD[seq.id] += 1
REVprimer = REVprimer.reverse_complement()
number_mismatches_REV = {}
melt_temp_REV = {}
for seq in REVregion:
number_mismatches_REV[seq.id] = 0
melt_temp_REV[seq.id] = mt.Tm_NN(seq.seq)
for count, position in enumerate(seq.seq.upper()):
if position not in ambiguous_dna_values[REVprimer[count]]:
number_mismatches_REV[seq.id] += 1
mismatches_fwd_rev = {} # there is probably a better way to name this dict....
for key in number_mismatches_FWD:
mismatches_fwd_rev[key] = {}
mismatches_fwd_rev[key]["FWD_mismatch"] = number_mismatches_FWD[key]
mismatches_fwd_rev[key]["REV_mismatch"] = number_mismatches_REV[key]
mismatches_fwd_rev[key]["FWD_Melt_temp"] = melt_temp_FWD[key]
mismatches_fwd_rev[key]["REV_Melt_temp"] = melt_temp_REV[key]
mismatches_df = pd.DataFrame.from_dict(mismatches_fwd_rev, 'index')
# making the metadata whatnot
metadata = {}
org_regex = re.compile(r"organism=.*,")
def_regex = re.compile(r"definition=.*,")
fun_regex = re.compile(r"function=.*")
for seq in FWDregion:
metadata[seq.id] = {}
organism = re.search(org_regex, seq.description).group(0)
organism = (organism[9:(len(organism)-1)]) #gets rid of the "organism=" and "," that my regex pulled out
definition = re.search(def_regex, seq.description).group(0)
definition = (definition[11:(len(definition)-1)]) #gets rid of the "definition=" that my regex pulled out
function = re.search(fun_regex, seq.description).group(0)
function = function[9:(len(function))]
metadata[seq.id]["organism"] = organism
metadata[seq.id]["definition"] = definition
metadata[seq.id]["function"] = function
metadata_df = pd.DataFrame.from_dict(metadata, 'index')
final_df = pd.merge(mismatches_df, metadata_df, left_index=True, right_index=True)
return final_df
gap_cutoff = 0.95
dna = SeqIO.parse(argv[1], "fasta")
dna = list(dna)
print("you input {} DNA sequences".format(len(dna)))
print("filtering sequences based on length, maxlength = 1600, minlength = 1200...")
dna = length_filter(dna, 1200, 1700)
print("There are {} DNA sequences after length filter".format(len(dna)))
dna = unique_seqs(dna)
print("there are {} unique DNA sequences".format(len(dna)))
dna = unique_ids(dna)
prot = []
for seq in dna:
seq = make_protein_record(seq)
prot.append(seq)
unique_dna_handle = "uniqueDNA.{}".format(argv[1])
protein_handle = "protein.{}".format(argv[1])
protein_align_handle = "align.{}".format(protein_handle)
unique_prot_dna_handle = "unique.prot.DNA.{}".format(argv[1])
unique_proteins = unique_seqs(prot) # this little block removes redundant protein sequences
unique_protein_ids = []
for seq in unique_proteins:
unique_protein_ids.append(seq.id)
unique_prots_dna = []
# generates the final DNA seqs list based on the the protein seqs that made the cut
for seq in dna:
if seq.id in unique_protein_ids:
unique_prots_dna.append(seq)
SeqIO.write(unique_proteins, protein_handle, "fasta")
print("protein sequences output to {}".format(protein_handle))
SeqIO.write(unique_prots_dna, unique_prot_dna_handle, "fasta")
print("final DNA sequences output to {}".format(unique_prot_dna_handle))
print("there are {} sequences in the analysis".format(len(unique_prots_dna)))
print("aligning sequences with Clustal Omega...")
cline = ClustalOmegaCommandline(infile=protein_handle, outfile=protein_align_handle, auto=True)
cline()
print("Done! Alignment output to {}".format(protein_align_handle))
prot_align = AlignIO.read(protein_align_handle, "fasta")
dna_codon_align = codon_align(prot_align, unique_prots_dna)
dna_codon_align_handle = "codon.align.{}".format(argv[1])
print("generating DNA codon alignment...")
SeqIO.write(dna_codon_align, dna_codon_align_handle, "fasta")
print("Done! DNA codon alignment output to {}".format(dna_codon_align_handle))
print("screening protein alignment, removing residues in columns that have >95% gaps")
screened_prot, screened_dna = alignment_screen(prot_align, unique_proteins, unique_prots_dna, gap_cutoff)
screened_dna_handle = "screened.DNA.{}".format(argv[1])
screened_prot_handle = "screened.prots.{}".format(argv[1])
SeqIO.write(screened_prot, screened_prot_handle, "fasta")
SeqIO.write(screened_dna, screened_dna_handle, "fasta")
print("realigning screened protein residues...")
screened_protein_aligned_handle = "screened.prot.align.{}".format(argv[1])
cline1 = ClustalOmegaCommandline(infile=screened_prot_handle, outfile=screened_protein_aligned_handle, auto=True)
cline1()
print("Done! Screened protein alignment output to {}".format(screened_protein_aligned_handle))
screened_prot_align = AlignIO.read(screened_protein_aligned_handle, "fasta")
print("regenerating DNA codon alignment with screened sequences")
screened_codon_align = codon_align(screened_prot_align, screened_dna)
screened_codon_align_handle = "screened.codon.align.{}".format(argv[1])
SeqIO.write(screened_codon_align, screened_codon_align_handle, "fasta")
print("DONE!!!! Screened DNA codon alignment output to {}".format(screened_codon_align_handle))
screened_codon_align = MultipleSeqAlignment(screened_codon_align) # do I need this??
vitalF1 = Seq("CAGCTNGGYATYGGNGS", IUPAC.ambiguous_dna)
vitalF2 = Seq("GGWATWGGMGSYATGCC", IUPAC.ambiguous_dna)
vitalF3 = Seq("GHATYGGNGSTATGCC", IUPAC.ambiguous_dna)
vitalR1 = Seq("AARTCCANYTGNCCVCC", IUPAC.ambiguous_dna)
vitalR2 = Seq("AARTCCANYTGNCCVCC", IUPAC.ambiguous_dna)
vitalR3 = Seq("AAGTCWAAYTGWCCRCC", IUPAC.ambiguous_dna)
vitalR1rc = vitalR1.reverse_complement()
vitalR2rc = vitalR2.reverse_complement()
vitalR3rc = vitalR3.reverse_complement()
BCoATscrF = Seq("GCNGANCATTTCACNTGGAAYWSNTGGCAYATG", IUPAC.ambiguous_dna)
BCoATscrR = Seq("CCTGCCTTTGCAATRTCNACRAANGC", IUPAC.ambiguous_dna)
BCoATscrRrc = BCoATscrR.reverse_complement()
MybutFWD = Seq("CARYTNGGNATYGGNGGNATSCC", IUPAC.ambiguous_dna)
MybutREV = Seq("TGTCCGCCNGYNCCRSWRAT", IUPAC.ambiguous_dna)
MybutREVrc = MybutREV.reverse_complement()
print("Finding optimal binding regions for each primer. This will take a very long time because my code is lazy")
BCoATscrF_indexes = find_hit_regions(BCoATscrF, screened_codon_align)
print('one primer done, out of 10 total')
BCoATscrR_indexes = find_hit_regions(BCoATscrRrc, screened_codon_align)
print('two...done with the flint primers')
FlintFWDregion = (screened_codon_align[:, BCoATscrF_indexes[0]:BCoATscrF_indexes[1]])
print(FlintFWDregion)
FlintREVregion = (screened_codon_align[:, BCoATscrR_indexes[0]:BCoATscrR_indexes[1]])
print(FlintREVregion)
vitalF1_indexes = find_hit_regions(vitalF1, screened_codon_align)
print('three...')
vitalF2_indexes = find_hit_regions(vitalF2, screened_codon_align)
print('four')
vitalF3_indexes = find_hit_regions(vitalF3, screened_codon_align)
print('five')
vitalR1_indexes = find_hit_regions(vitalR1rc, screened_codon_align)
print('six')
vitalR2_indexes = find_hit_regions(vitalR2rc, screened_codon_align)
print('seven')
vitalR3_indexes = find_hit_regions(vitalR3rc, screened_codon_align)
print('eight....done with the Vital primers')
vitalF1region = (screened_codon_align[:, vitalF1_indexes[0]:vitalF1_indexes[1]])
vitalF2region = (screened_codon_align[:, vitalF2_indexes[0]:vitalF2_indexes[1]])
vitalF3region = (screened_codon_align[:, vitalF3_indexes[0]:vitalF3_indexes[1]])
vitalR1region = (screened_codon_align[:, vitalR1_indexes[0]:vitalR1_indexes[1]])
vitalR2region = (screened_codon_align[:, vitalR2_indexes[0]:vitalR2_indexes[1]])
vitalR3region = (screened_codon_align[:, vitalR3_indexes[0]:vitalR3_indexes[1]])
print(vitalF3region)
print(vitalR3region)
MyFWDindexes = find_hit_regions(MybutFWD, screened_codon_align)
print('nine')
MyREVindexes = find_hit_regions(MybutREVrc, screened_codon_align)
print('ten!')
MyFWDregion = (screened_codon_align[:, MyFWDindexes[0]:MyFWDindexes[1]])
MyREVregion = (screened_codon_align[:, MyREVindexes[0]:MyREVindexes[1]])
print(MyFWDregion)
print(MyREVregion)
print('running primer_coverage()')
vitalF1_R1_df = primer_coverage(vitalF1, vitalR1, vitalF1region, vitalR1region)
vitalF1_R1_df = vitalF1_R1_df[["FWD_mismatch", "REV_mismatch", "FWD_Melt_temp", "REV_Melt_temp", "organism", "definition", "function"]]
vitalF1_R1_df.columns = ["vitalF1_MM", "vitalR1_MM", "vitalF1_Melt", "vitalR1_Melt", "organism", "definition", "function"]
vitalF1_R1_df.to_csv('vitalF1_R1_df.primermismatch.csv', sep="\t")
vitalF1_R1_df = vitalF1_R1_df.drop(["definition", "function", "organism"], 1)
vitalF2_R2_df = primer_coverage(vitalF2, vitalR2, vitalF2region, vitalR2region)
vitalF2_R2_df = vitalF2_R2_df[["FWD_mismatch", "REV_mismatch", "FWD_Melt_temp", "REV_Melt_temp", "organism", "definition", "function"]]
vitalF2_R2_df.columns = ["vitalF2_MM", "vitalR2_MM", "vitalF2_Melt", "vitalR2_Melt", "organism", "definition", "function"]
vitalF2_R2_df.to_csv('vitalF2_R2_df.primermismatch.csv', sep="\t")
vitalF2_R2_df = vitalF2_R2_df.drop(['definition', 'function', 'organism'], 1)
vitalF3_R3_df = primer_coverage(vitalF3, vitalR3, vitalF3region, vitalR3region)
vitalF3_R3_df = vitalF3_R3_df[["FWD_mismatch", "REV_mismatch", "FWD_Melt_temp", "REV_Melt_temp", "organism", "definition", "function"]]
vitalF3_R3_df.columns = ["vitalF3_MM", "vitalR3_MM", "vitalF3_Melt", "vitalR3_Melt", "organism", "definition", "function"]
vitalF3_R3_df.to_csv('vitalF3_R3_df.primermismatch.csv', sep="\t")
vitalF3_R3_df = vitalF3_R3_df.drop(['definition', 'function', 'organism'], 1)
Myprimers_df = primer_coverage(MybutFWD, MybutREV, MyFWDregion, MyREVregion)
Myprimers_df = Myprimers_df[["FWD_mismatch", "REV_mismatch", "FWD_Melt_temp", "REV_Melt_temp", "organism", "definition", "function"]]
Myprimers_df.columns = ["but672FWD_MM", "but1031REV_MM", "but672FWD_Melt", "but1031REV_Melt", "organism", "definition", "function"]
Myprimers_df.to_csv('Myprimers_df.primermismatch.csv', sep="\t")
Myprimers_df = Myprimers_df.drop(['definition', 'function', 'organism'], 1)
Flint_df = primer_coverage(BCoATscrF, BCoATscrR, FlintFWDregion, FlintREVregion)
Flint_df = Flint_df[["FWD_mismatch", "REV_mismatch", "FWD_Melt_temp", "REV_Melt_temp", "organism", "definition", "function"]]
Flint_df.columns = ["BCoATscrF_MM", "BCoATscrR_MM", "BCoATscrF_Melt", "BCoATscrR_Melt", "organism", "definition", "function"]
Flint_df.to_csv('Flint_df.primermismatch.csv', sep="\t")
combined_df = pd.merge(vitalF1_R1_df, vitalF2_R2_df, left_index=True, right_index=True, how='outer')
combined_df = pd.merge(combined_df, vitalF3_R3_df, left_index=True, right_index=True, how='outer')
combined_df = pd.merge(combined_df, Myprimers_df, left_index=True, right_index=True, how='outer')
combined_df = pd.merge(combined_df, Flint_df, left_index=True, right_index=True, how='outer')
combined_df.to_csv("finalbutcoverage.csv", sep="\t")
fasttree_cline = FastTreeCommandline("fasttreeMP", input=protein_align_handle, out="Tree1.nwk")
print("building tree.....")
print('using FastTree with the command: {}'.format(fasttree_cline))
fasttree_cline = FastTreeCommandline("fasttreeMP", input=protein_align_handle, out="Tree1.nwk")
fasttree_cline()
print('done! Thanks!!')