def get_rate_correlation_windowed(dns, dss, aligned_prots, cdna_dicts,
spec_orf_list, xfold_only, xfold_degeneracy):
window_size = 1
ns = []
ss = []
# Examine only codons of a certain degeneracy?
xfold_ending = 'CTAG'
xfold_wrong_aas = ''
aligned_cdnas = []
for xi in range(len(spec_orf_list)):
(spec, orf) = spec_orf_list[xi]
try:
aligned_cdnas.append(
muscle.align_gene_from_protein(cdna_dicts[spec][orf],
aligned_prots[xi]))
except KeyError:
aligned_cdnas.append(
muscle.align_gene_from_protein(cdna_dicts[spec + '-mit'][orf],
aligned_prots[xi]))
assert len(aligned_prots[xi]) == len(dns)
for site in range(0, len(dns), window_size):
if xfold_only and (window_size == 1):
codons = [
aligned_cdna[3 * site:3 * site + 3]
for aligned_cdna in aligned_cdnas
]
wrong_degeneracy = False
wrong_ending = False
wrong_aa = False
degs = [codon_degeneracy[codon] for codon in codons]
for codon in codons:
if codon_degeneracy[codon] != xfold_degeneracy:
wrong_degeneracy = True
if not codon[2] in xfold_ending:
wrong_ending = True
if not codon == '---' and translate._genetic_code[
codon] in xfold_wrong_aas:
wrong_aa = True
if wrong_degeneracy or wrong_ending or wrong_aa:
continue
# Add up substitutions in window
syn = 0
nsyn = 0
valid_sites = False
for nsite in range(site, min(len(dns), site + window_size)):
(s, n) = (dss[nsite], dns[nsite])
# don't consider cases with missing counts
if not (s is None) and not (n is None):
syn += s
nsyn += n
valid_sites = True
if valid_sites:
ns.append(nsyn)
ss.append(syn)
return stats.PearsonCorrelation(ns, ss), ns, ss
def get_rate_correlation_windowed(dns, dss, aligned_prots, cdna_dicts, spec_orf_list, xfold_only, xfold_degeneracy): window_size = 1 ns = [] ss = [] # Examine only codons of a certain degeneracy? xfold_ending = 'CTAG' xfold_wrong_aas = '' aligned_cdnas = [] for xi in range(len(spec_orf_list)): (spec,orf) = spec_orf_list[xi] try: aligned_cdnas.append(muscle.align_gene_from_protein(cdna_dicts[spec][orf], aligned_prots[xi])) except KeyError: aligned_cdnas.append(muscle.align_gene_from_protein(cdna_dicts[spec+'-mit'][orf], aligned_prots[xi])) assert len(aligned_prots[xi]) == len(dns) for site in range(0,len(dns),window_size): if xfold_only and (window_size == 1): codons = [aligned_cdna[3*site:3*site+3] for aligned_cdna in aligned_cdnas] wrong_degeneracy = False wrong_ending = False wrong_aa = False degs = [codon_degeneracy[codon] for codon in codons] for codon in codons: if codon_degeneracy[codon] != xfold_degeneracy: wrong_degeneracy = True if not codon[2] in xfold_ending: wrong_ending = True if not codon=='---' and translate._genetic_code[codon] in xfold_wrong_aas: wrong_aa = True if wrong_degeneracy or wrong_ending or wrong_aa: continue # Add up substitutions in window syn = 0 nsyn = 0 valid_sites = False for nsite in range(site, min(len(dns),site+window_size)): (s,n) = (dss[nsite], dns[nsite]) # don't consider cases with missing counts if not (s is None) and not (n is None): syn += s nsyn += n valid_sites = True if valid_sites: ns.append(nsyn) ss.append(syn) return stats.PearsonCorrelation(ns,ss), ns, ss
def getShortestDistanceHits(hits, queryGeneDict, targetGeneDict, queryProtDict, targetProtDict, distanceCache, alignmentCache):
queryToTargetSDHits = {}
totalHits = len(hits.keys())
nHits = 0
for (queryGene, qHits) in hits.items():
minDist = 100.0
minHitGene = None
for targetGene in qHits:
key = cacheKey(queryGene, targetGene)
dNML = 0.0
try:
(dDML, dSML, dNML, dDNG, dSNG, dNNG, numSynonymousSites, numNonsynonymousSites, fracAligned, seqIdentity) = distanceCache[key]
except KeyError:
queryGeneSeq = queryGeneDict[queryGene]
targetGeneSeq = targetGeneDict[targetGene]
[alignedQueryProt, alignedTargetProt] = muscle.align_sequences([queryProtDict[queryGene], targetProtDict[targetGene]])
alignedQueryGene = muscle.align_gene_from_protein(queryGeneSeq, alignedQueryProt)
alignedTargetGene = muscle.align_gene_from_protein(targetGeneSeq, alignedTargetProt)
(dDML, dSML, dNML, dDNG, dSNG, dNNG, numSynonymousSites, numNonsynonymousSites) = my_paml.Get_Distance_NS(alignedQueryGene, alignedTargetGene, 'codon')
(seqIdentity, numIdentical, numAligned) = sequenceIdentity(alignedQueryProt, alignedTargetProt)
fracAligned = numAligned/float(len(queryProtDict[queryGene]))
if fracAligned == 1.0 and seqIdentity == 1.0:
dNML = 0.0 # Obviously no nonsyn. changes if proteins are identical
elif 1.0/numNonsynonymousSites > dNML:
dNML = 1.0/numNonsynonymousSites # Minimum possible change
(ntseqIdentity, ntnumIdentical, ntnumAligned) = sequenceIdentity(alignedQueryGene, alignedTargetGene)
ntfracAligned = ntnumAligned/float(len(queryGeneSeq))
if ntfracAligned == 1.0 and ntseqIdentity == 1.0:
dSML = 0.0 # Obviously no syn. changes if genes are identical
elif 1.0/numSynonymousSites > dSML:
dSML = 1.0/numSynonymousSites # Minimum possible change
# Cache this distance and alignment
distanceCache[key] = (dDML, dSML, dNML, dDNG, dSNG, dNNG, numSynonymousSites, numNonsynonymousSites, fracAligned, seqIdentity)
alignmentCache[key] = ((queryGene, alignedQueryGene, alignedQueryProt), (targetGene, alignedTargetGene, alignedTargetProt))
# Check to see if this is a shorter distance
if dNML < minDist and targetGene != queryGene:
minDist = dNML
minHitGene = targetGene
nHits += 1
(dDML, dSML, dNML, dDNG, dSNG, dNNG, numSynonymousSites, numNonsynonymousSites, fracAligned, seqIdentity) = distanceCache[cacheKey(queryGene, minHitGene)]
print "# %d of %d: %s was %s, dN = %1.6f, fracAlign = %1.6f" % (nHits, totalHits, queryGene, minHitGene, minDist, fracAligned)
queryToTargetSDHits[queryGene] = minHitGene
return queryToTargetSDHits
xprot for ((xgenome, xorf), xprot) in zip(corr_keys, aligned_prots)
if xgenome in tree_species
]
all_genes = []
for (xgenome, xorf) in corr_keys:
if xgenome in tree_species:
try:
seq = cdna_dicts[xgenome][xorf]
except KeyError:
seq = cdna_dicts[xgenome + '-mit'][xorf]
all_genes.append(seq)
all_species = tree_species #[xgenome for (xgenome, xorf) in corr_keys]
assert len(all_genes) == len(all_prots)
assert len(all_species) == len(all_genes)
all_aligned_genes = [
muscle.align_gene_from_protein(xgene, xprot)
for (xgene, xprot) in zip(all_genes, all_prots)
]
# Put the genes in the right order
sub_gene_dict = dict(zip(all_species, all_aligned_genes))
recon_gene_list = [sub_gene_dict[spec] for spec in tree_species]
try:
#(rates,ancestor) = (1,2)
(dns, dss) = my_paml.Get_dNdS_Per_Codon(recon_gene_list,
tree_species, tree, 100)
#(rates, ancestor) = my_paml.Get_Site_Rates_And_Ancestor(recon_gene_list, tree_species, tree)
(r, p, n) = stats.PearsonCorrelation(dns, dss)
print "# %d %s %1.3f" % (n_genes, gene, r)
except my_paml.PAMLError, pe:
print "# rejected orf (mfal,msid) %s (%1.2f,%1.2f)" % (gene, mfal, msid) continue all_prots = [xprot for ((xgenome,xorf), xprot) in zip(corr_keys,aligned_prots) if xgenome in tree_species] all_genes = [] for (xgenome, xorf) in corr_keys: if xgenome in tree_species: try: seq = cdna_dicts[xgenome][xorf] except KeyError: seq = cdna_dicts[xgenome+'-mit'][xorf] all_genes.append(seq) all_species = tree_species #[xgenome for (xgenome, xorf) in corr_keys] assert len(all_genes) == len(all_prots) assert len(all_species) == len(all_genes) all_aligned_genes = [muscle.align_gene_from_protein(xgene,xprot) for (xgene,xprot) in zip(all_genes,all_prots)] # Put the genes in the right order sub_gene_dict = dict(zip(all_species, all_aligned_genes)) recon_gene_list = [sub_gene_dict[spec] for spec in tree_species] try: #(rates,ancestor) = (1,2) (dns, dss) = my_paml.Get_dNdS_Per_Codon(recon_gene_list, tree_species, tree, 100) #(rates, ancestor) = my_paml.Get_Site_Rates_And_Ancestor(recon_gene_list, tree_species, tree) (r, p, n) = stats.PearsonCorrelation(dns, dss) print "# %d %s %1.3f" % (n_genes, gene, r) except my_paml.PAMLError, pe: print "#",pe continue #print len(rates), len(ancestor)
