Description

Sequence Identity

Method

Each chromosome was mashmapped using mashmap v3.1.1 against every other chromosome one-by-one with options mashmap -t $cpus -r $chrA.fa -q $chrB.fa -M --pi 80 -s 10000. This maps each 10kb window of the reference chromosome against the target, does not combine consecutive matches, retains only the best match, and only matches with a percent identity greater than 80%. The percent identity of each of these mappings was converted to an RGB value on a scale from 80-100 using a custom python script, such that anything below 80% identity is colored a lighter yellow outside the heatmap range, and anything with no mapping is left blank. Then all percent identities from 80 to 100 are on a scale from yellow to deep red, with deep red/maroon being the highest percent identity and yellow being the lowest.

Code for converting identity to RGB color:
import matplotlib.pyplot as plt import matplotlib.colors as mcolors
def percent_identity_to_rgb(percent_identity, min_percent_identity=80): if percent_identity == 0: return "255,255,255"
if percent_identity < min_percent_identity: return "255,255,220"
cmap = plt.get_cmap('YlOrRd')

norm = mcolors.Normalize(vmin=min_percent_identity, vmax=100)

rgba = cmap(norm(percent_identity))

rgb = tuple(int(255 * x) for x in rgba[:3])
return str(rgb[0]) + "," + str(rgb[1]) + "," + str(rgb[2])
mashmap_fp = sys.argv[1] mashmap_f = open(mashmap_fp)
for line in mashmap_f: line_split = line.strip().split('\t') chrom, start, end = line_split[0], int(line_split[2]), int(line_split[3]) idy = float(line_split[12].split(':')[2]) if idy<=1: idy*=100 q_chr, q_start, q_end = line_split[5], int(line_split[7]), int(line_split[8]) score_str = str(round(idy,1)) name = f"{q_chr}:{q_start}-{q_end}_{score_str}%" strand="." thick_start = start thick_end = end print('\t'.join([chrom, str(start), str(end), name, str(int(round(10*idy,0))), strand, str(thick_start), str(thick_end), percent_identity_to_rgb(idy)]))

Contacts