OSCR

Conservation of Human IgSF Proteins Throughout Eukaryotic Evolution.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Results › CD58 ↔ conservation-IgSFSize.py, lines 203–273 · score 0.88 · egg laying mammals, placental mammals, Dermoptera, Euarchontoglires, Eutheria, Lagomorpha
  2. [2] § Materials and Methods › Clustering of IgSF ↔ otherScripts/brotherhoodMethod.py, lines 80–91 · score 0.64 · squareform, weighted, correlation, linkage, metric, cut

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 532 lines · 18 KB · no license · 1 match

  1. import os
  2. import sys
  3. import argparse
  4. import statistics
  5. import subprocess
  6. import pickle
  7. import numpy as np
  8. import pandas as pd
  9. import matplotlib.pyplot as plt
  10. from labellines import labelLines
  11. import warnings
  12. from datetime import datetime
  14. homeDir = os.getcwd()
  15. colors = plt.get_cmap('tab10').colors
  16. os.makedirs(f'{homeDir}/results', exist_ok=True)
  17. warnings.filterwarnings("ignore", message="Tried to label line")
  18. now = datetime.now()
  19. timestamp = now.strftime("%m-%d-%Y__%H-%M-%S")
  21. ##### Need to update DOI
  22. descr = '''
  23. This script provides two figures:
  24. Gene Conservation:
  25. Genes input by their GeneIDs, GeneIDs, GeneAccession, or Cluster arguments will be shown in shades of gray.
  26. Genes input by their Group will be colored by their Group.
  27. Change in IgSF Cluster Size:
  28. If an indiviudal gene is input, the entire cluster this gene belongs to will be considered.
  29. If multiple genes are input, all clusters including these genes will be considered.
  30. By default each all clusters are combined.
  31. However, each change in cluster size can be seperated with the seperateClusters argument.
  32. Please limit the inputs or else the figure becomes very busy
  33. To investigate the gene accessions considered for this analysis, you can check our work with the CheckAccessions argument.
  34. We recommend setting the seperateClusters argument to yes if you intend to use the CheckAccessions argument as the output is easier to interpret.
  36. You must provide at least one GeneID, GeneName, GeneAccession, Group, or Cluster.
  37. This script can be run across different taxonomic levels (Eukaryotes, Gnathostomata, Mammalia, and Primates)
  38. Multiple entries can be supplied for each above argument by separating them with commas.
  39. '''
  41. parser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter, description = descr)
  43. parser.add_argument("-gi", "--GeneIDs", help = "Please refer to humanIgSFClusters.ods for acceptable GeneIDs.", default = 'NA')
  44. parser.add_argument("-gn", "--GeneNames", help = "Please refer to humanIgSFClusters.ods for acceptable Gene Names.", default = 'NA')
  45. parser.add_argument("-ga", "--GeneAccession", help = "Please refer to humanIgSFClusters.ods for acceptable Gene Accessions", default = 'NA')
  46. parser.add_argument("-gr", "--Groups", help = "Options: Orange, Green, Red, Purple, Brown, Pink. Please refer to Fig xx", default = 'NA') ##### Need to update figure number
  47. parser.add_argument("-cl", "--Clusters", help = "Options: 0-132. Please refer to humanIgSFClusters.ods", default = 'NA')
  48. parser.add_argument("-tl", "--TaxonomicLevel", help = "Options: Eukaryotes, Gnathostomata, Mammalia, Primates. Default = Gnathostomata", default = 'Gnathostomata')
  49. parser.add_argument("-ca", "--CheckAccessions", help = "Create .tsv file showing queried human and other species clustered IgSF accessions with descriptions. Options: yes or no. Default = no", default = 'no')
  50. parser.add_argument("-sc", "--seperateClusters", help = "Seperates clusters in change in IgSF Cluster Size figure. Options: yes or no. Default = no", default = 'no')
  53. df = pd.read_excel(f"{homeDir}/humanIgSFClusters.ods", engine = "odf",sheet_name='IgSF')
  54. geneIDs = list(map(str,df["GeneID"].values.tolist()))
  55. genes = df["Gene"].values.tolist()
  56. accessions = df["Accession"].values.tolist()
  57. groups = df["Group"].values.tolist()
  58. clusters = list(map(str,df["Cluster"].values.tolist()))
  60. df2 = pd.read_excel(f"{homeDir}/humanIgSFClusters.ods", engine = "odf",sheet_name='Clusters')
  61. clusters2 = df2["Cluster"].values.tolist()
  62. size_u = df2["Size (Unique Genes)"].values.tolist()
  63. subfamily = df2["Subfamily"].values.tolist()
  64. members = df2["Members"].values.tolist()
  66. args = parser.parse_args()
  67. arg1 = args.GeneIDs.split(',')
  68. arg2 = args.GeneNames.split(',')
  69. arg3 = args.GeneAccession.split(',')
  70. arg4 = args.Groups.split(',')
  71. arg5 = args.Clusters.split(',')
  72. arg6 = args.TaxonomicLevel
  73. arg7 = args.CheckAccessions
  74. arg8 = args.seperateClusters
  76. #### Checks for valid inputs
  77. if arg1[0] == 'NA' and arg2[0] == 'NA' and arg3[0] == 'NA' and arg4[0] == 'NA' and arg5[0] == 'NA':
  78. sys.exit(f'Please input at least one GeneID, GeneName, GeneAccession, Group, or Cluster.')
  79. for x in arg1:
  80. if x not in geneIDs and x != 'NA':
  81. sys.exit(f'Invalid GeneID: {x}.')
  82. for x in arg2:
  83. if x not in genes and x != 'NA':
  84. sys.exit(f'Invalid GeneName: {x}.')
  85. for x in arg3:
  86. if x not in accessions and x != 'NA':
  87. sys.exit(f'Invalid GeneAccession: {x}.')
  88. for x in arg4:
  89. if x not in set(groups) and x != 'NA':
  90. sys.exit(f'Invalid Group: {x}. Group must be capatilized')
  91. for x in arg5:
  92. if x not in clusters and x != 'NA':
  93. sys.exit(f'Invalid Cluster: {x}.')
  94. if arg6 not in ['Eukaryotes', 'Gnathostomata', 'Mammalia', 'Primates']:
  95. sys.exit(f'Invalid TaxonomicalLevel: {x}.')
  96. if arg7 != 'yes' and arg7 !='no':
  97. sys.exit(f'Please only input either \"yes\" or \"no\" for the --CheckAccessions argument.')
  98. if arg8 != 'yes' and arg8 !='no':
  99. sys.exit(f'Please only input either \"yes\" or \"no\" for the --seperateClusters argument.')
  101. geneIDs = list(map(int,geneIDs))
  102. clusters = list(map(int,clusters))
  103. if arg1[0] != 'NA':
  104. arg1 = list(map(int,arg1))
  105. if arg5[0] != 'NA':
  106. arg5 = list(map(int,arg5))
  108. ### Create file tag describing inputs
  109. ### If there are many inputs, the tag will instead be the date and time
  110. inputts = []
  111. inputts_black = []
  112. inputts_black2 = []
  113. s1 = ''
  114. s2 = ''
  115. s3 = ''
  116. s4 = ''
  117. s5 = ''
  118. hold = []
  119. for a,b,c,d,e in zip(geneIDs,genes,accessions,groups,clusters):
  120. if a in arg1:
  121. inputts.append(b)
  122. inputts_black.append(b)
  123. if a not in hold:
  124. s1 += str(a)+'-'
  125. hold.append(a)
  126. if b in arg2:
  127. inputts.append(b)
  128. inputts_black.append(b)
  129. if b not in hold:
  130. s2 += b+'-'
  131. hold.append(b)
  132. if c in arg3:
  133. inputts.append(b)
  134. inputts_black.append(b)
  135. if c not in hold:
  136. s3 += c+'-'
  137. hold.append(c)
  138. if d in arg4:
  139. inputts.append(b)
  140. if d not in hold:
  141. s4 += d+'-'
  142. hold.append(d)
  143. if e in arg5:
  144. inputts.append(b)
  145. inputts_black2.append(b)
  146. if e not in hold:
  147. s5 += str(e)+'-'
  148. hold.append(e)
  150. inputts_black2 = list(set(inputts_black2))
  151. if inputts_black == []:
  152. inputts_black = inputts_black2
  153. else:
  154. inputts_black = list(set(inputts_black))
  155. inputts = list(set(inputts))
  158. inputTag = arg6+'_'
  159. if s1 != '':
  160. inputTag += (s1[0:-1]+'_')
  161. if s2 != '':
  162. inputTag += (s2[0:-1]+'_')
  163. if s3 != '':
  164. inputTag += (s3[0:-1]+'_')
  165. if s4 != '':
  166. inputTag += (s4[0:-1]+'_')
  167. if s5 != '':
  168. inputTag += (s5[0:-1]+'_')
  169. inputTag = inputTag[0:-1]
  170. if len(inputTag) > 50:
  171. inputTag = timestamp
  173. geneToGroupDict = {}
  174. group_colors = ['Orange', 'Green', 'Red', 'Purple', 'Brown', 'Pink']
  175. for ge,gr in zip(genes,groups):
  176. geneToGroupDict[ge] = group_colors.index(gr)+1
  178. with open(f'{homeDir}/accessionDescriptionDict.pkl', 'rb') as f:
  179. accessionDescriptionDict = pickle.load(f)
  181. with open(f'{homeDir}/accessionToGeneDict_2.pkl', 'rb') as f:
  182. accessionToGeneDict = pickle.load(f)
  185. exclude = ['GCF_000001405.40']
  186. ### exclude assemblies whose accessions cannot be assigned geneIDs (https://ftp.ncbi.nih.gov/gene/DATA/gene2accession.gz)
  187. file = f'{homeDir}/exclude.txt'
  188. fh = open(file)
  189. for f in fh:
  190. exclude.append(f.strip())
  191. fh.close()
  193. assemblies = []
  194. file = f'{homeDir}/assembliesWithIgSF.txt'
  195. fh = open(file)
  196. for f in fh:
  197. ### exclude taxa with only one assembly
  198. if f.strip() not in ['GCF_019279795.1','GCF_000003605.2','GCF_037176945.1','GCA_003344405.1'] and f.strip() not in exclude:
  199. assemblies.append(f.strip())
  200. fh.close()
  203. #################
  204. if arg6 == 'Eukaryotes':
  205. taxa = ['Amoebozoa', 'Viridiplantae', 'Sar', 'Fungi',
  206. 'Porifera', 'Cnidaria', 'Protostomia', 'Tunicata', 'Echinodermata', 'Cephalochordata', 'Cyclostomata',
  207. 'Chondrichthyes','Actinopteri', 'Cladistia', 'Amphibia', 'Aves', 'Lepidosauria', 'Crocodylia','Testudines', 'Mammalia']
  208. if arg6 == 'Gnathostomata':
  209. taxa = ['Chondrichthyes','Actinopteri', 'Cladistia', 'Amphibia', 'Aves', 'Lepidosauria', 'Crocodylia','Testudines', 'Mammalia']
  210. if arg6 == 'Mammalia':
  211. taxa = [
  212. ## Monotremata (egg-laying mammals)
  213. "Monotremata",
  215. ## Marsupials (pouched mammals)
  216. "Didelphimorphia", # opossums
  217. #"Microbiotheria", # monito del monte
  218. "Dasyuromorphia", # quolls, dunnarts, Tasmanian devil
  219. "Diprotodontia", # kangaroos, koalas, wombats, possums
  221. ### Eutheria (placental mammals)
  222. ## Afrotheria
  223. "Afrosoricida", # tenrecs, golden moles
  224. #"Macroscelidea", # elephant shrews
  225. #"Tubulidentata", # aardvark
  226. "Proboscidea", # elephants
  227. #"Sirenia", # manatees, dugongs
  229. ## Xenarthra
  230. #"Cingulata", # armadillos
  231. #"Pilosa", # sloths and anteaters
  233. ## Laurasiatheria
  234. "Eulipotyphla", # shrews, moles, hedgehogs
  235. "Chiroptera", # bats
  236. "Pholidota", # pangolins
  237. "Carnivora", # cats, dogs, seals, etc.
  238. "Perissodactyla", # horses, rhinos, tapirs
  239. "Cetacea", # whales, dolphins, porpoises
  240. "Artiodactyla", # pigs, deer, cows, etc.
  242. ## Euarchontoglires
  243. "Lagomorpha", # rabbits, hares, pikas
  244. "Rodentia", # rodents
  245. #"Scandentia", # treeshrews
  246. "Dermoptera", # colugos
  247. "Primates" # monkeys, apes, humans
  248. ]
  249. if arg6 == 'Primates':
  250. taxa = [
  251. ## Platyrrhines (New World monkeys)
  252. "Cebidae",
  254. ## Catarrhines (Old World monkeys & apes)
  255. "Cercopithecidae", # Old World monkeys
  256. "Hylobatidae", # gibbons
  257. "Hominidae" # great apes (including humans)
  258. ]
  260. newAssemblies = []
  261. taxDict = {}
  262. file = f'{homeDir}/eukaryotaTaxonomy.tsv'
  263. fh = open(file)
  264. for f in fh:
  265. f = f.split('\t')
  266. if f[0] in assemblies:
  267. for t in taxa:
  268. if t+',' in f[1]:
  269. taxDict[f[0]] = t
  270. newAssemblies.append(f[0])
  271. if t == 'Cetacea' and arg6 == 'Mammalia':
  272. break
  273. fh.close()
  275. ########## Create Gene Conservation Figure
  276. print('Creating Gene Conservation Figure')
  277. df3 = pd.read_excel(f"{homeDir}/gene_conservation.ods", engine = "odf",sheet_name=arg6)
  278. geneMatrix = []
  279. for col in taxa:
  280. col_values = (df3[col].astype(float)).tolist()
  281. holdG = df3['Gene'].tolist()
  282. geneMatrix.append(col_values)
  284. # plt.plot([1,2],[-1,-1],colors[0])
  285. plt.figure(figsize=(8, 4))
  286. for c,g in enumerate(holdG):
  287. if g in inputts:
  288. plt.plot(taxa,list(np.array(geneMatrix).T)[c],c=colors[geneToGroupDict[g]])
  289. grays = ['0','0.35','0.175']
  290. cc = 0
  291. for c,g in enumerate(holdG):
  292. if g in inputts_black:
  293. if arg4[0] == 'NA':
  294. if len(inputts_black) > 10:
  295. plt.plot(taxa,list(np.array(geneMatrix).T)[c],c=colors[geneToGroupDict[g]])
  296. elif inputts_black == inputts_black2:
  297. if cc == 10:
  298. cc -= 10
  299. plt.plot(taxa,list(np.array(geneMatrix).T)[c],c=colors[cc],label=g)
  300. else:
  301. if cc == 3:
  302. cc -= 3
  303. plt.plot(taxa,list(np.array(geneMatrix).T)[c],c=grays[cc],label=g)
  304. cc += 1
  305. else:
  306. if cc == 3:
  307. cc -= 3
  308. if len(inputts_black) > 10:
  309. plt.plot(taxa,list(np.array(geneMatrix).T)[c],c=colors[geneToGroupDict[g]])
  310. else:
  311. plt.plot(taxa,list(np.array(geneMatrix).T)[c],c=grays[cc],label=g)
  312. cc += 1
  313. labelLines(plt.gca().get_lines(), align=True, fontsize=10)
  314. plt.ylim([-0.08,1.08])
  315. plt.ylabel('Average Normalized Bit Score',fontsize=12)
  316. plt.xticks(range(0,len(taxa)),taxa,rotation=90, fontsize=10)
  317. plt.savefig(f'{homeDir}/results/gene_conservation_{inputTag}.png', bbox_inches='tight',dpi=600) #################
  318. # plt.show()
  319. plt.close()
  321. ##################### create change in IgSF size figure
  322. sizeDiff = []
  323. clusterAccessions = []
  324. otherClusterAccessions = []
  325. for a in newAssemblies:
  326. s = int(subprocess.check_output(f"cut -f2 {homeDir}/clusteredIgSF_withHuman/{a}_clusters | sort -g | uniq | wc -l", shell=True))
  327. holdHuman = s*[0]
  328. holdOther = s*[0]
  329. holdClusterAccessions = []
  330. holdOtherClusterAccessions = []
  331. for hh in holdOther:
  332. holdClusterAccessions.append([])
  333. holdOtherClusterAccessions.append([])
  334. holdGenes = []
  335. file = f'{homeDir}/clusteredIgSF_withHuman/{a}_clusters'
  336. fh = open(file)
  337. for f in fh:
  338. f = f.strip()
  339. f = f.split()
  340. if accessionToGeneDict[f[0]] not in holdGenes:
  341. holdGenes.append(accessionToGeneDict[f[0]])
  342. if f[0] in accessions:
  343. holdHuman[int(f[1])] += 1
  344. holdClusterAccessions[int(f[1])].append(f[0])
  345. else:
  346. holdOther[int(f[1])] += 1
  347. holdOtherClusterAccessions[int(f[1])].append(f[0])
  348. fh.close()
  349. clusterAccessions.append(holdClusterAccessions)
  350. otherClusterAccessions.append(holdOtherClusterAccessions)
  351. sizeDiff.append(list(np.array(holdHuman)-np.array(holdOther)))
  353. print('Creating Change in IgSF Cluster Size Figure')
  356. if arg8 == 'no':
  357. queryClusts = []
  358. # queryClustsLabels = []
  359. for i in inputts:
  360. if int(clusters[genes.index(i)]) not in queryClusts:
  361. queryClusts.append(int(clusters[genes.index(i)]))
  362. # queryClustsLabels.append(members[int(clusters[gene.index(i)])])
  363. print(f'Cluster {clusters[genes.index(i)]} | {subfamily[int(clusters[genes.index(i)])]} | {members[int(clusters[genes.index(i)])]}')
  365. queryAccessions = []
  366. for a,b in zip(clusters,accessions):
  367. if int(a) in queryClusts:
  368. queryAccessions.append(b)
  370. ### Adds all clusters in the combined set where at least one human query gene appears
  371. assemblyEquivClusts = []
  372. for ca in clusterAccessions:
  373. equivClusts = set()
  374. for qa in queryAccessions:
  375. for c,a in enumerate(ca):
  376. if qa in a:
  377. equivClusts.add(c)
  378. assemblyEquivClusts.append(list(sorted(equivClusts)))
  380. ### Creates .tsv file showing queried human and other species clustered IgSF accessions with descriptions
  381. if arg7 == 'yes':
  382. newFile = open(f'{homeDir}/results/clusteredIgSF_{inputTag}.tsv','w')
  383. newFile.write('Taxa\tAssembly\tGeneID\tSpecies\tIntendedGene\tAccessionDescription\n')
  384. c = 0
  385. for a1,a2,b in zip(clusterAccessions,otherClusterAccessions,assemblyEquivClusts):
  386. for bb in b:
  387. holdClusters = []
  388. s = ''
  389. for x in a1[bb]:
  390. if clusters[accessions.index(x)] not in holdClusters:
  391. holdClusters.append(clusters[accessions.index(x)])
  392. s += f'Cluster {str(clusters[accessions.index(x)])} | {members[clusters2.index(clusters[accessions.index(x)])]}, '
  393. newFile.write(f'{s[0:-2]}\n')
  394. for x in a1[bb]:
  395. newFile.write(f'{taxDict[newAssemblies[c]]}\t{newAssemblies[c]}\t{accessionToGeneDict[x]}\tHuman\t{accessionDescriptionDict[x]}\n')
  396. newFile.write('-\t-\t-\t-\t-\n')
  397. for x in a2[bb]:
  398. newFile.write(f'{taxDict[newAssemblies[c]]}\t{newAssemblies[c]}\t{accessionToGeneDict[x]}\tOther\t{accessionDescriptionDict[x]}\n')
  399. newFile.write('-\t-\t-\t-\t-\n')
  400. newFile.write(f'-\t-\t-\t-\t-\n')
  401. newFile.write(f'-\t-\t-\t-\t-\n')
  402. c += 1
  403. newFile.close()
  406. taxaSizeDiff = []
  407. for t in taxa:
  408. taxaSizeDiff.append([])
  410. c = 0
  411. for aec,sd in zip(assemblyEquivClusts,sizeDiff):
  412. hold = 0
  413. for ii in aec:
  414. hold += sd[ii]
  415. taxaSizeDiff[taxa.index(taxDict[newAssemblies[c]])].append(int(hold))
  416. c += 1
  418. r = []
  419. rstd = []
  420. for tsd in taxaSizeDiff:
  421. r.append(statistics.mean(tsd))
  422. if len(tsd) < 2:
  423. rstd.append(0)
  424. else:
  425. rstd.append(statistics.stdev(tsd))
  427. alpha = 0.8
  428. cap_size = 0.3
  429. plt.figure(figsize=(8, 4))
  430. plt.plot(range(len(taxa)),r,c=colors[0])
  431. for xi, yi, err in zip(range(0,len(taxa)),r,rstd):
  432. if err != 0:
  433. plt.vlines(xi, yi - err, yi + err,colors=colors[0],linestyles='dashed',alpha=alpha,linewidth=1)
  434. plt.hlines([yi - err, yi + err],xi - cap_size, xi + cap_size,colors=colors[0],alpha=alpha,linewidth=1)
  435. plt.axhline(y=0, color='k', linestyle='--')
  436. plt.xticks(range(0,len(taxa)),taxa,rotation=90, fontsize=10)
  437. plt.ylabel('Δ (Human – Avg. Taxa) Cluster Size',fontsize=12)
  438. plt.savefig(f'{homeDir}/results/IgSFSize_{inputTag}.png', bbox_inches='tight',dpi=600)
  439. # plt.show()
  440. plt.close()
  441. else:
  442. queryClusts = []
  443. color_count = 0
  444. plt.figure(figsize=(8, 4))
  445. for i in inputts:
  446. if color_count == 10:
  447. color_count -= 10
  448. if int(clusters[genes.index(i)]) not in queryClusts:
  449. queryClusts.append(int(clusters[genes.index(i)]))
  450. print(f'Cluster {clusters[genes.index(i)]} | {subfamily[int(clusters[genes.index(i)])]} | {members[int(clusters[genes.index(i)])]}')
  452. queryAccessions = []
  453. for a,b in zip(clusters,accessions):
  454. if int(a) == int(clusters[genes.index(i)]):
  455. queryAccessions.append(b)
  457. ### Adds all clusters in the combined set where at least one human query gene appears
  458. assemblyEquivClusts = []
  459. for ca in clusterAccessions:
  460. equivClusts = set()
  461. for qa in queryAccessions:
  462. for c,a in enumerate(ca):
  463. if qa in a:
  464. equivClusts.add(c)
  465. assemblyEquivClusts.append(list(sorted(equivClusts)))
  467. ### Creates .tsv file showing queried human and other species clustered IgSF accessions with descriptions
  468. if arg7 == 'yes':
  469. newFile = open(f'{homeDir}/results/clusteredIgSF_Cluster-{int(clusters[genes.index(i)])}.tsv','w')
  470. newFile.write('Taxa\tAssembly\tGeneID\tSpecies\tIntendedGene\tAccessionDescription\n')
  471. c = 0
  472. for a1,a2,b in zip(clusterAccessions,otherClusterAccessions,assemblyEquivClusts):
  473. for bb in b:
  474. holdClusters = []
  475. s = ''
  476. for x in a1[bb]:
  477. if clusters[accessions.index(x)] not in holdClusters:
  478. holdClusters.append(clusters[accessions.index(x)])
  479. s += f'Cluster {str(clusters[accessions.index(x)])} | {members[clusters2.index(clusters[accessions.index(x)])]}, '
  480. newFile.write(f'{s[0:-2]}\n')
  481. for x in a1[bb]:
  482. newFile.write(f'{taxDict[newAssemblies[c]]}\t{newAssemblies[c]}\t{accessionToGeneDict[x]}\tHuman\t{accessionDescriptionDict[x]}\n')
  483. newFile.write('-\t-\t-\t-\t-\n')
  484. for x in a2[bb]:
  485. newFile.write(f'{taxDict[newAssemblies[c]]}\t{newAssemblies[c]}\t{accessionToGeneDict[x]}\tOther\t{accessionDescriptionDict[x]}\n')
  486. newFile.write('-\t-\t-\t-\t-\n')
  487. newFile.write(f'-\t-\t-\t-\t-\n')
  488. newFile.write(f'-\t-\t-\t-\t-\n')
  489. c += 1
  490. newFile.close()
  492. taxaSizeDiff = []
  493. for t in taxa:
  494. taxaSizeDiff.append([])
  496. c = 0
  497. for aec,sd in zip(assemblyEquivClusts,sizeDiff):
  498. hold = 0
  499. for ii in aec:
  500. hold += sd[ii]
  501. taxaSizeDiff[taxa.index(taxDict[newAssemblies[c]])].append(int(hold))
  502. c += 1
  504. r = []
  505. rstd = []
  506. for tsd in taxaSizeDiff:
  507. r.append(statistics.mean(tsd))
  508. if len(tsd) < 2:
  509. rstd.append(0)
  510. else:
  511. rstd.append(statistics.stdev(tsd))
  513. alpha = 0.8
  514. cap_size = 0.3
  515. plt.plot(range(len(taxa)),r,c=colors[color_count],label=subfamily[int(clusters[genes.index(i)])])
  516. for xi, yi, err in zip(range(0,len(taxa)),r,rstd):
  517. plt.vlines(xi, yi - err, yi + err,colors=colors[color_count],linestyles='dashed',alpha=alpha,linewidth=1)
  518. plt.hlines([yi - err, yi + err],xi - cap_size, xi + cap_size,colors=colors[color_count],alpha=alpha,linewidth=1)
  519. color_count += 1
  520. labelLines(plt.gca().get_lines(), align=True, fontsize=10)
  521. plt.axhline(y=0, color='k', linestyle='--')
  522. plt.xticks(range(0,len(taxa)),taxa,rotation=90, fontsize=10)
  523. plt.ylabel('Δ (Human – Avg. Taxa) Cluster Size',fontsize=12)
  524. plt.savefig(f'{homeDir}/results/IgSFSize_{inputTag}.png', bbox_inches='tight',dpi=600)
  525. # plt.show()

conservation-IgSFSize.py at commit af03288, no license · at the source

Overview

Authors: Steven Grudman1,2, Andras Fiser1,2
ORCID iDs: Steven Grudman, Andras Fiser
  1. Department of Systems & Computational Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
  2. Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY 10461, USA
Institutions: Albert Einstein College of Medicine (United States)
Journal: Genome biology and evolution, volume 18, issue 6, article evag133
Dates: accepted 25 May 2026; published online 2 June 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC
Identifiers: DOI 10.1093/gbe/evag133 · PMID 42230317 · PMCID PMC13262535 · OpenAlex W7163206163
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), cellular / molecular (subfield)
Methods: Machine learning
Keywords: extracellular IgSF, gnathostomes, evolution of adaptive immune system, evolution of co-stimulatory immune receptors
MeSH: Evolution, Molecular*, Immunoglobulins*, Animals, Conserved Sequence, Humans, Phylogeny (* major topic)
Topic: T-cell and B-cell Immunology (Immunology, Immunology and Microbiology), according to OpenAlex
Funding: NIH (GM136357)
Citations: not cited yet (Europe PMC); 107 references in the paper

Abstract

The human immunoglobulin superfamily (IgSF) encompasses hundreds of proteins involved in cell–cell adhesion, neural connectivity, junctional organization, and immune regulation, with many serving as key checkpoint proteins. To elucidate the evolutionary history of human IgSF members, we systematically analyzed all available eukaryotic reference genomes to determine when each IgSF subfamily first appeared. The human IgSF was partitioned into six major evolutionary timeframes: Metazoa, Vertebrata, Gnathostomata, Tetrapoda, Amniota, and Mammalia. Although proteins were grouped solely by their conservation across eukaryotes, their biological functions clustered naturally, reflecting how new physiological systems create selective pressures that drive the appearance, retention, and diversification of protein architectures suited to those functions. Conservation and functional analyses indicate that human IgSF members arising in tetrapods and amniotes primary regulate the strength and duration of immune responses and form many of the critical components of the immune synapse, while IgSF genes that appear first in mammals have evolved to fine-tune immune activation thresholds to support maternal–fetal tolerance. Case studies are provided to illustrate three key evolutionary themes: (i) highly conserved yet catalytically inactive proteins retain essential regulatory functions, (ii) functional convergence among evolutionarily distinct IgSF families, and (iii) compensatory evolution within adaptive immunity following a lineage-specific loss of an IgSF member. Together, these findings establish an evolutionary framework for organizing the human IgSF by both ancestry and function, providing a foundation for assessing IgSF importance. Notably, this study facilitates the identification of conserved but understudied proteins that emerged alongside the development of the adaptive immune system, highlighting them as promising candidates for future experimental investigation.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

StevenGrudman/IgSF-Conservation

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: af032888ebd9488e70430d119a5f9e2cb71bdd7c, 25 November 2025
Languages: Python (4), Shell (2)
Size: 1,172 files, 6 scripts
Software Heritage: not archived
Found in: the text, “Discussion”
Holds: README
Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: Matplotlib (2 files), NumPy (2 files), pandas (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/StevenGrudman/IgSF-Conservation

The paper's code and data availability statement is in the Data section.

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 6 scripts, each with its path and the digest of its content;
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Data

No dataset and no data link were found in the paper.

Data Availability

The data underlying this article can be found in the online Supplementary material. In addition, all data and a program that can facilitate browsing results are publicly accessible at https://github.com/StevenGrudman/IgSF-Conservation.

Reproduced under the paper's license (CC BY-NC), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 4 keywords, 6 MeSH terms, 1 funder, 105 references.

Cite

This paper

Grudman, S., & Fiser, A. (2026). Conservation of Human IgSF Proteins Throughout Eukaryotic Evolution. Genome biology and evolution, 18(6), evag133. https://doi.org/10.1093/gbe/evag133

BibTeX

@article{grudman2026conservation,
author = {Grudman, Steven and Fiser, Andras},
title = {{Conservation of Human IgSF Proteins Throughout Eukaryotic Evolution}},
journal = {Genome biology and evolution},
year = {2026},
month = jun,
volume = {18},
number = {6},
pages = {evag133},
publisher = {Oxford University Press},
issn = {1759-6653},
doi = {10.1093/gbe/evag133},
url = {https://doi.org/10.1093/gbe/evag133},
pmid = {42230317},
pmcid = {PMC13262535}
}

RIS

TY - JOUR
AU - Grudman, Steven
AU - Fiser, Andras
TI - Conservation of Human IgSF Proteins Throughout Eukaryotic Evolution
T2 - Genome biology and evolution
J2 - Genome Biol Evol
PY - 2026
DA - 2026/06/01
VL - 18
IS - 6
SP - evag133
SN - 1759-6653
PB - Oxford University Press
DO - 10.1093/gbe/evag133
UR - https://doi.org/10.1093/gbe/evag133
LA - en
ER -

CSL-JSON

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"id": "10.1093/gbe/evag133",
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"title": "Conservation of Human IgSF Proteins Throughout Eukaryotic Evolution",
"container-title": "Genome biology and evolution",
"author": [
{
"family": "Grudman",
"given": "Steven"
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],
"container-title-short": "Genome Biol Evol",
"volume": "18",
"issue": "6",
"page": "evag133",
"DOI": "10.1093/gbe/evag133",
"PMID": "42230317",
"PMCID": "PMC13262535",
"ISSN": "1759-6653",
"publisher": "Oxford University Press",
"URL": "https://doi.org/10.1093/gbe/evag133",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
1
]
]
}
}

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