def __print_scaffold_stats(si_dict, s_dict, cg_dict, cov_dict, tax_dict,
agp_obj):
title = "Detailed Scaffold Stats"
headers = [
"Scaffold", "Num Contigs", "Length", "GC", "Coverage(F/J/LR)",
"BLAST Hit", "BLAST Covered", "Circular"
]
data = []
for s in sorted(s_dict.iterkeys()):
tmp = []
tmp.append(s)
tmp.append(len(s_dict[s]))
tmp.append(agp_obj.get_scaffold_length(s))
tmp.append(__get_scaffold_gc(s_dict[s], si_dict, cg_dict))
tmp.append(__get_scaffold_coverage(s_dict[s], cov_dict))
hit, hit_len = __get_scaffold_blast_hits(s_dict[s], tax_dict, si_dict)
tmp.append(hit)
tmp.append(hit_len)
tmp.append("NA")
data.append(tmp)
st = SimpleTable(headers, data, title)
output = None
if options.s_table:
output = options.s_table + ".scaffold_detail"
st.print_output(output, options.html)
def reportStats(list_of_classes, pair_count, output):
title = "Scaffold Accuracy Stats"
headers = ['Stat', 'Total', 'Pct']
data = []
unaligned, multimap, cross_chrom, valid_internal, invalid_ln, valid_circular, invalid_orientation, other = list_of_classes
total_valid = valid_internal + valid_circular
total_invalid = cross_chrom + invalid_ln + invalid_orientation
pct_unaligned = "%.3f" % ((float(unaligned) / pair_count) * 100)
pct_mm = "%.3f" % ((float(multimap) / pair_count) * 100)
pct_cc = "%.3f" % ((float(cross_chrom) / pair_count) * 100)
pct_invalid_ln = "%.3f" % ((float(invalid_ln) / pair_count) * 100)
pct_invalid_o = "%.3f" % ((float(invalid_orientation) / pair_count) * 100)
pct_valid = "%.3f" % ((float(valid_internal) / pair_count) * 100)
pct_valid_circ = "%.3f" % ((float(valid_circular) / pair_count) * 100)
pct_other = "%.3f" % ((float(other) / pair_count) * 100)
total = unaligned + multimap + cross_chrom + valid_internal + invalid_ln + valid_circular + invalid_orientation + other
#print pair_count, unaligned, pct_unaligned, multimap, pct_mm, cross_chrom, pct_cc, invalid_orientation, pct_invalid_o, invalid_ln, pct_invalid_ln, valid_internal, pct_valid, valid_circular, pct_valid_circ, other, pct_other
data.append(["Total Input Pairs", pair_count, "100.0"])
data.append(["Multiply Mapped", multimap, pct_mm])
data.append(["Unaligned", unaligned, pct_unaligned])
data.append(["Cross Chromosome", cross_chrom, pct_cc])
data.append(["Invalid Orientation", invalid_orientation, pct_invalid_o])
data.append(["Invalid Length", invalid_ln, pct_invalid_ln])
data.append(["Valid", valid_internal, pct_valid])
data.append(["Valid Circular", valid_circular, pct_valid_circ])
data.append(["Other", other, pct_other])
st = SimpleTable(headers, data, title)
st.print_output(output, 1)
def __print_contig_stats(si_dict, cov_dict, tax_dict):
#Contig scaffold, length, gc, cov (frg/jump) blast_hit blast_cvg ambig
title = "Detailed Contig Stats"
headers = [
"Contig", "Scaffold", "Length", "GC", "Coverage(F/J/LR)", "BLAST Hit",
"BLAST Covered"
]
data = []
for c in sorted(si_dict.iterkeys()):
tmp = []
tmp.append(c)
if 'scaffold' in si_dict[c]:
tmp.append(si_dict[c]['scaffold'])
else:
tmp.append("NA")
tmp.append(si_dict[c]['length'])
tmp.append("%.2f" % si_dict[c]['gc'])
tmp.append(__get_coverage_string(c, cov_dict))
hit, covered = __get_blast_strings(c, tax_dict, si_dict[c]['length'])
tmp.append(hit)
tmp.append(covered)
data.append(tmp)
st = SimpleTable(headers, data, title)
output = None
if options.c_table:
output = options.c_table + ".contig_detail"
st.print_output(output, options.html)
def main():
data = []
if len(args) != 1:
parser.error("Must supply assembly list file.")
sys.exit(1)
try: # open file or fail
f = open(args[0])
for lines in f.readlines():
data.append(lines.rstrip('\n').split(','))
f.close()
# load assemblies and load stats module
assemblies = __load_assemblies(data)
s = AssemblyStatsUtil(assemblies)
#print s.get_stats()
title = "Basic Assembly Stats"
headers = s.get_assembly_names()
data = s.get_stats()
st = SimpleTable(headers, data, title)
output = None
if options.output:
output = options.output + ".assembly_stats_comparison"
st.print_output(output, options.html)
except IOError as (errno, strerror):
print "I/O Error({0}): {1}".format(errno, strerror)
return -1
def main():
nodes_db, names_db, blast_db = check_dbs(options.nodes_db,options.names_db,options.db)
data = __get_blast_data(args[0])
lengths = __get_lengths(args[1])
tax_obj = Taxonomy(nodes_db=nodes_db, names_db=names_db, blast_db=blast_db)
gi_nums = __get_gis_from_blast_line(data)
blast_hit,hit_lengths = __get_longest_covered_hit(data)
gi_tax_dict = tax_obj.get_gi_tax_lookup(gi_nums)
title = "Taxonomic Classification of BLAST Hits"
headers = ["QueryId","QueryLen","QueryHitLen","PctCovered","TaxonomicString"]
t_data = __get_coverage_string(lengths, blast_hit, tax_obj, gi_tax_dict, gi_nums)
st = SimpleTable(headers,t_data,title)
output = None
if options.output:
output = options.output + ".blast_hit_taxonomy"
st.print_output(output,options.html)
if options.hm_data:
try:
output = open(options.hm_data,'w')
except IOError as (errno,strerror):
print "I/O Error({0}): {1}".format(errno,strerror)
return -1
output.write(__get_heatmap_data(gi_tax_dict, tax_obj, lengths, data, hit_lengths))
output.close()
def __get_details_info(base, extension, title, headers, data_function, coord_obj=None):
output = None
if base:
output = base + extension
if coord_obj:
data = data_function(coord_obj)
else:
data = data_function
st = SimpleTable(headers,data,title)
st.print_output(output, options.html)
def main():
qc_pass = __check_qc_options(options.qc_pass, options.qc_fail)
title, headers, data = __get_table_data(qc_pass)
st = SimpleTable(headers, data, title)
output = None
if options.output:
output = options.output + ".simple_bam_stats"
st.print_output(output, options.html)
return 0
def main():
title, headers, data = __get_cvg_data(options.phys_cvg)
st = SimpleTable(headers, data, title)
output = None
if options.output:
type = "seq_cvg"
if options.phys_cvg:
type = "phys_cvg"
output = options.output + ".bam_" + type + "_stats"
st.print_output(output, options.html)
return 0
def main():
name = "No_Name"
assembler = "None_Given"
if options.name:
name = options.name
if options.assembler:
assembler = options.assembler
a = Assembly(fasta=args[0],
name=name,
assembler=assembler,
minGapSize=options.minGap,
minConSize=options.minContig,
minScaffSize=options.minScaffold,
agp=options.agp,
min_output_gap_size=options.min_output_gap_size)
assembly = []
assembly.append(a)
s = AssemblyStatsUtil(assembly)
title = "Basic Assembly Stats"
headers = s.get_assembly_names()
data = s.get_stats()
st = SimpleTable(headers, data, title)
output = None
if options.output:
output = options.output + ".basic_assembly_stats"
st.print_output(output, options.html)
cumulative_data = {}
num_charts = 0
if options.cumulative_c:
key = "Cumulative Contigs"
cumulative_data[key] = []
cumulative_data[key] = a.get_cumulative_contigs()
num_charts += 1
if options.cumulative_s:
key = "Cumulative Scaffolds"
cumulative_data[key] = []
cumulative_data[key] = a.get_cumulative_scaffolds()
num_charts += 1
if num_charts:
__plot_data(a, num_charts, cumulative_data, options.chart_output)
return 0
def __print_contig_stats(si_dict=None,
cov_dict=None,
tax_dict=None,
most_common_org=None):
#Contig scaffold, length, gc, cov (frg/jump) blast_hit blast_cvg ambig
title = "Detailed Contig Stats"
headers = [
"Contig", "Scaffold", "Length", "GC", "Coverage(F/J/LR)", "BLAST Hit",
"BLAST Covered", "Best BLAST Score", "Best Covered"
]
if most_common_org:
headers += [
"Most Common (" + most_common_org + ")", "SequenceAnnotations"
]
else:
headers += ["Most Common ( None )", "SequenceAnnotations"]
data = []
for c in sorted(si_dict.iterkeys()):
tmp = []
tmp.append(c)
if 'scaffold' in si_dict[c]:
tmp.append(si_dict[c]['scaffold'])
else:
tmp.append("NA")
tmp.append(si_dict[c]['length'])
tmp.append("%.2f" % si_dict[c]['gc'])
tmp.append(__get_coverage_string(c, cov_dict))
name, len = __get_blast_strings(c, tax_dict, si_dict[c]['length'],
'genus', 'hit len')
tmp += [name, len]
name, len = __get_blast_strings(c, tax_dict, si_dict[c]['length'],
'best', 'best len')
tmp += [name, len]
if most_common_org and tax_dict:
tmp += [tax_dict[c]['common'], tax_dict[c]['tags']]
elif tax_dict:
tmp += ["NA", tax_dict[c]['tags']]
else:
tmp += ["NA", "NA"]
data.append(tmp)
st = SimpleTable(headers, data, title)
output = None
if options.c_table:
output = options.c_table + ".contig_detail"
st.print_output(output, options.html)
def print_metrics(data,labels,m_output):
headers = __get_header_line(data,labels)
title = 'Insert Size Metrics'
table_data = []
metrics = data[0].get_metrics_headers() if len(data) > 0 else []
ordered_metrics = ['PAIR_ORIENTATION','READ_PAIRS','MEAN_INSERT_SIZE','STANDARD_DEVIATION',
'MEDIAN_INSERT_SIZE','MEDIAN_ABSOLUTE_DEVIATION','MIN_INSERT_SIZE',
'MAX_INSERT_SIZE','WIDTH_OF_50_PERCENT', 'WIDTH_OF_90_PERCENT',
'SAMPLE','LIBRARY','READ_GROUP']
if len(metrics):
for i in ordered_metrics:
tmp = [i]
for j in data:
for k in data[j].get_metric_value(i):
tmp.append(k)
table_data.append(tmp)
st = SimpleTable(headers,table_data,title)
st.print_output(m_output,options.html)
def main():
nodes_db, names_db, blast_db = check_dbs(options.nodes_db,
options.names_db, options.db)
blast_data = _get_blast_data(args[0])
# dictionary of list of BlastLines
gi_list = list(_get_gi_set(blast_data))
lengths = _get_lengths(args[1])
# lengths will be a list of ContigLength objects
tax_obj = Taxonomy(nodes_db=nodes_db,
names_db=names_db,
blast_db=blast_db,
gi_list=gi_list)
if not options.no_annotate:
_make_annotated_blast_file(args[0], lengths, blast_data, tax_obj)
contig_gi_hit_lengths_dict = _get_gi_hits_length(blast_data)
gi_tax_dict = tax_obj.get_gi_tax_lookup(gi_list)
gi_name_lookup = tax_obj.get_gi_tax_name_lookup_by_level(
gi_tax_dict, 'genus')
#print contig_gi_hit_lengths_dict
most_common_genus = _get_most_common_genus(blast_data, gi_name_lookup)
#print most_common_genus
blast_hit, hit_lengths = _get_longest_covered_hit(
blast_data, contig_gi_hit_lengths_dict)
#print blast_hit, hit_lengths
blast_score = _get_best_hit_by_score(blast_data)
#print blast_score
most_common_genus_hits = _get_most_common_genus_hits(
blast_data, gi_name_lookup, most_common_genus[0],
contig_gi_hit_lengths_dict)
#print most_common_genus_hits
non_genomic_hits = _find_non_genomic_types(blast_data, tax_obj)
title = "Taxonomic Classification of BLAST Hits"
headers = [
"QueryId", "QueryLen", "LongestHitLen", "LongestPctCovered",
"LongestHitTaxonomy", "BestScoringHitLen", "BestScoringPctCovered",
"BestScoringTaxonomy", "MostCommonOrg (" + most_common_genus[0] + ")",
"SequenceAnnotations"
]
t_data = _get_coverage_string(lengths, blast_hit, tax_obj, gi_tax_dict,
blast_score, most_common_genus_hits,
non_genomic_hits)
st = SimpleTable(headers, t_data, title)
output = None
if options.output:
output = options.output + ".blast_hit_taxonomy"
st.print_output(output, options.html)
if options.hm_data:
try:
output = open(options.hm_data, 'w')
except IOError as (errno, strerror):
print "I/O Error({0}): {1}".format(errno, strerror)
return -1
output.write(
_get_heatmap_data(gi_tax_dict, tax_obj, lengths, blast_data,
hit_lengths))
output.close()
distinct = 0
by_count = {}
for n in counts.itervalues():
by_count[n] = by_count.get(n, 0) + 1
distinct += 1
total += n
cn = by_count.keys()
cn.sort()
mers = "%d-mers" % options.k
print '#', total, 'total', mers
print '#', distinct, 'distinct', mers
table = SimpleTable(
['CopyNumber', 'Count', 'BasePct', 'BaseTotal', 'KmerPct', 'KmerTotal'],
[], 'Kmer Copy Number (k=' + str(options.k) + ')')
btotal = 0.0
ktotal = 0.0
for n in cn:
c = by_count[n]
bpct = n * c * 100.0 / total
btotal += bpct
kpct = c * 100.0 / distinct
ktotal += kpct
table.add_row([
n, c,
"%.2f%%" % bpct,
"%.2f%%" % btotal,
"%.2f%%" % kpct,
if a:
distinct_covered += 1
if a > n:
over += a - n
def format_row(label, n, d):
return [label, n, d, "%.2f%%" % (n * 100.0 / d)]
headers = ['Category', 'Count', 'RefCount', 'Pct']
data = []
data.append(format_row('Covered', covered, total))
data.append(format_row('DistinctCovered', distinct_covered, distinct))
data.append(format_row('OverCovered', over, total))
for k, n in assembly_counts.iteritems():
if k not in reference_counts:
novel += n
novel_distinct += 1
data.append(format_row('Novel', novel, total))
data.append(format_row('NovelDistinct', novel_distinct, distinct))
table = SimpleTable(headers, data, 'Kmer Coverage (k=' + str(options.k) + ')')
if options.o:
table.print_output(options.o + ".kmer_coverage", options.html)
else:
print table.to_table()
def __print_rna_analysis(molecules):
# print out the info
title = 'rRNA Analysis'
rna_table = []
rna_table_headers = ['Gene','Genus','Query ID','Query Start','Query End','Hit Direction','Length']
if options.classify:
rna_table_headers.append('Taxonomy')
rna_count = 0
summary_dict = {}
gene_count = {}
for m in sorted(molecules.iterkeys()):
for i in molecules[m]:
rna_table.append([])
gene = i.get_molecule()
lineage = i.get_genus()
if options.gene == "all":
rna_table[rna_count] += [gene, i.get_genus(), i.get_contig(), i.get_start(),
i.get_end(), i.get_direction(), i.get_length()]
if options.classify:
if gene not in summary_dict:
summary_dict[gene] = {}
gene_count[gene] = 0
if lineage not in summary_dict[gene]:
summary_dict[gene][lineage] = 0
summary_dict[gene][lineage] += 1
gene_count[gene] += 1
else:
if m == options.gene:
rna_table[rna_count] += [i.get_molecule(), i.get_genus(), i.get_contig(), i.get_start(),
i.get_end(), i.get_direction(), i.get_length()]
if options.classify:
if gene not in summary_dict:
summary_dict[gene] = {}
gene_count[gene] = 0
if lineage not in summary_dict[gene]:
summary_dict[gene][lineage] = 0
summary_dict[gene][lineage] += 1
gene_count[gene] += 1
if options.classify:
rna_table[rna_count] += [i.get_taxonomy()]
rna_count += 1
st = SimpleTable(rna_table_headers,rna_table,title)
output = None
if options.output:
output = options.output + ".rna_analysis_details"
st.print_output(output,options.html)
if options.classify:
headers, data, title = __summarize_results(summary_dict,gene_count)
output = None
if options.output:
output = options.output + ".rna_analysis_summary"
st = SimpleTable(headers,data,title)
st.print_output(output,options.html)
def __write_output_data(agp_file=None,
flank_kmer=None,
asm_kmer=None,
contig_seqs=None,
asm_counts=None,
cg_ss_counts=None,
ue_ss_counts=None):
#captured gap stats
cg_seqs = []
cg_sizes = []
cg_dist = []
cg_cn = []
cg_gc = []
#uncaptured end stats
ue_seqs = []
ue_dist = []
ue_cn = []
ue_gc = []
print "Pulling gap end sequence..."
agp = AgpFile(agp_file)
scaffolds = agp.get_agp_scaffolds()
for scaffold in scaffolds:
print "Scaffold:", scaffold
for record in agp.get_agp_file_record(scaffold):
if (not agp.is_gap(scaffold, record)):
ctg_seq = contig_seqs[agp.get_contig_id(scaffold, record)]
if (len(ctg_seq) < options.e):
print "WARNING:", agp.get_contig_id(
scaffold,
record), " is less than ", options.e, ". Ignoring."
continue
if (record == 1):
#print "\tFirst contig..."
#print "SEQ: ", contig_seqs[agp.get_contig_id(scaffold,record)][:options.e]
(gc, dist, copy) = analyze_seq(
contig_seqs[agp.get_contig_id(scaffold,
record)][:options.e],
flank_kmer, asm_kmer, asm_counts)
ue_dist.append(dist)
ue_cn.append(copy)
ue_gc.append(gc)
ue_ss_counts = get_simple_sequences(
ue_ss_counts,
contig_seqs[agp.get_contig_id(scaffold,
record)][:options.e])
#print "UE", ue_ss_counts
if (record == len(agp.get_agp_file_record(scaffold))):
#print "\tLast contig..."
#print "SEQ: ", contig_seqs[agp.get_contig_id(scaffold,record)][-options.e:]
(gc, dist, copy) = analyze_seq(
contig_seqs[agp.get_contig_id(scaffold,
record)][-options.e:],
flank_kmer, asm_kmer, asm_counts)
ue_dist.append(dist)
ue_cn.append(copy)
ue_gc.append(gc)
ue_ss_counts = get_simple_sequences(
ue_ss_counts,
contig_seqs[agp.get_contig_id(scaffold,
record)][-options.e:])
#print "UE", ue_ss_counts
if (agp.is_gap(scaffold, record)):
#print "Gap..."
left_ctg_record = record - 1
right_ctg_record = record + 1
if (len(contig_seqs[agp.get_contig_id(
scaffold, left_ctg_record)]) < options.e) or (len(
contig_seqs[agp.get_contig_id(
scaffold, right_ctg_record)]) < options.e):
print "Warning: This gap is flanked by a contig less than", options.e, "long. Skipping analysis."
continue
left_seq = contig_seqs[agp.get_contig_id(
scaffold, left_ctg_record)][-options.e:]
#left_seq = contig_seqs[agp.get_contig_id(scaffold,left_ctg_record)][:options.e]
#print "LEFT: ", left_seq
(left_gc, left_dist,
left_copy) = analyze_seq(left_seq, flank_kmer, asm_kmer,
asm_counts)
cg_ss_counts = get_simple_sequences(cg_ss_counts, left_seq)
#print "CG", cg_ss_counts
#print left_seq
#print left_gc,left_dist,left_copy
right_seq = contig_seqs[agp.get_contig_id(
scaffold, right_ctg_record)][:options.e]
#right_seq = contig_seqs[agp.get_contig_id(scaffold,right_ctg_record)][-options.e:]
#print "RIGHT SEQ: ", right_seq
(right_gc, right_dist,
right_copy) = analyze_seq(right_seq, flank_kmer, asm_kmer,
asm_counts)
cg_ss_counts = get_simple_sequences(cg_ss_counts, right_seq)
#print "CG", cg_ss_counts
#print right_gc,right_dist,right_copy
cg_sizes.append(agp.get_feature_length(scaffold, record))
cg_dist.append((left_dist + right_dist) / 2)
cg_gc.append((left_gc + right_gc) / 2)
cg_cn.append((left_copy + right_copy) / 2)
headers = ["Metric", "Uncaptured Ends", "Captured Gaps"]
table = SimpleTable(headers, [], "Gap Analysis Metrics")
ss_table = SimpleTable([
"Sequence", "Uncaptured Ends Bases", "Uncaptured Ends Percent",
"Captured Gap Bases", "Captured Gap Percent"
], [], "Gap Simple Sequence Analysis")
if (len(cg_sizes) > 0):
chart_tools.gen_histogram(cg_sizes, "Gap Sizes", "Number of Gaps",
"Histogram of Captured Gap Sizes",
options.header + ".cg_sizes")
chart_tools.gen_histogram(cg_dist, "Gap Flank Complexity",
"Number of Gaps",
"Histogram of Gap Flank Complexity",
options.header + ".cg_distinctness")
chart_tools.gen_histogram(cg_cn, "Gap Flank Copy Number",
"Number of Gaps",
"Histogram of Gap Flank Copy Number",
options.header + ".cg_copy_number")
chart_tools.gen_histogram(cg_gc, "Gap Flank GC", "Number of Gaps",
"Histogram of Gap Flank GC",
options.header + ".cg_gc")
table.add_row(["Number", len(ue_dist), len(cg_dist)])
table.add_row([
"Average Complexity",
"%.0f" % (sum(ue_dist) / len(ue_dist)),
"%.0f" % (sum(cg_dist) / len(cg_dist))
])
table.add_row([
"Less than " + str(options.l) + "% Complex",
len(filter(lambda x: x < options.l, ue_dist)),
len(filter(lambda x: x < options.l, cg_dist))
])
table.add_row([
"Average GC",
"%.0f" % (sum(ue_gc) / len(ue_gc)),
"%.0f" % (sum(cg_gc) / len(cg_gc))
])
table.add_row([
"Less than 30% GC",
len(filter(lambda x: x < 30, ue_gc)),
len(filter(lambda x: x < 30, cg_gc))
])
table.add_row([
"Greater than 70% GC",
len(filter(lambda x: x > 70, ue_gc)),
len(filter(lambda x: x > 70, cg_gc))
])
table.add_row([
"Average Copy Number",
"%.0f" % (sum(ue_cn) / len(ue_cn)),
"%.0f" % (sum(cg_cn) / len(cg_cn))
])
ss_table.add_row([
"End Bases",
len(ue_dist) * options.e, "",
len(cg_dist) * options.e, ""
])
ss_table.add_row([
"Total SS",
sum(ue_ss_counts.itervalues()),
"%.2f%%" % (float(sum(ue_ss_counts.itervalues()) * 100) /
(len(ue_dist) * options.e)),
sum(cg_ss_counts.itervalues()),
"%.2f%%" % (float(sum(cg_ss_counts.itervalues()) * 100) /
(len(cg_dist) * options.e))
])
for n in cg_ss_counts:
ss_table.add_row([
n, ue_ss_counts[n],
"%.2f%%" % (float(ue_ss_counts[n] * 100) /
(len(ue_dist) * options.e)), cg_ss_counts[n],
"%.2f%%" % (float(cg_ss_counts[n] * 100) /
(len(cg_dist) * options.e))
])
else:
print "No captured gaps."
table.add_row(["Number", len(ue_dist), len(cg_dist)])
table.add_row([
"Average Uniqueness",
"%.0f" % (sum(ue_dist) / len(ue_dist)), "N/A"
])
table.add_row([
"Less than " + str(options.l) + "% distinct",
"%.2f" % (len(filter(lambda x: x < options.l, ue_dist))),
"%.2f" % (len(filter(lambda x: x < options.l, cg_dist)))
])
table.add_row(
["Average GC",
"%.0f" % (sum(ue_gc) / len(ue_gc)), "N/A"])
table.add_row([
"Less than 30% GC",
len(filter(lambda x: x < 30, ue_gc)),
len(filter(lambda x: x < 30, cg_gc))
])
table.add_row([
"Greater than 70% GC",
len(filter(lambda x: x > 70, ue_gc)),
len(filter(lambda x: x > 70, cg_gc))
])
table.add_row(
["Average Copy Number",
"%.0f" % (sum(ue_cn) / len(ue_cn)), "N/A"])
ss_table.add_row(["End Bases", len(ue_dist) * options.e, "", "NA", ""])
ss_table.add_row([
"Total SS",
sum(ue_ss_counts.itervalues()),
"%.2f%%" % (float(sum(ue_ss_counts.itervalues()) * 100) /
(len(ue_dist) * options.e)), "NA", "NA"
])
for n in cg_ss_counts:
ss_table.add_row([
n, ue_ss_counts[n],
"%.2f%%" % (float(ue_ss_counts[n] * 100) /
(len(ue_dist) * options.e)), "NA", "NA"
])
chart_tools.gen_histogram(ue_dist, "End Distinctness",
"Number of Uncaptured Ends",
"Histogram of End Complexity",
options.header + ".ue_distinctness")
chart_tools.gen_histogram(ue_cn, "End Copy Number",
"Number of Uncaptured Ends",
"Histogram of End Copy Number",
options.header + ".ue_copy_number")
chart_tools.gen_histogram(ue_gc, "End GC", "Number of Uncaptured Ends",
"Histogram of End GC", options.header + ".ue_gc")
table.print_output(options.t_header + ".gap_analysis", options.html)
ss_table.print_output(options.t_header + ".gap_ss_analysis", options.html)
