
!pip install seaborn
!pip install biopython==1.76

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Requirement already satisfied: numpy in /usr/local/lib/python2.7/dist-packages (from biopython==1.76)


%pylab inline
import seaborn as sns
import os
import pickle
import pandas
from scipy.stats import linregress
from Bio import SeqIO
from scipy.stats import norm

Populating the interactive namespace from numpy and matplotlib


CCA1_pandas = pandas.read_csv('PBMS/CCA1_8mers.txt', delimiter='\t')
CCA1_pandas = CCA1_pandas.sort_values(['E-score'])


CCA1 = array(pandas.read_csv('PBMS/CCA1_8mers.txt', delimiter='\t'))


CCA1

array([['AAAAAAAA', 'TTTTTTTT', 0.30529, 45366.56, 2.0944],
       ['AAAAAAAC', 'GTTTTTTT', 0.2516, 39653.74, 0.9978],
       ['AAAAAAAG', 'CTTTTTTT', 0.28368000000000004, 47122.61, 2.4038],
       ...,
       ['TTTGAAAA', 'TTTTCAAA', 0.11617999999999999, 36442.57, 0.3097],
       ['TTTGCAAA', 'TTTGCAAA', 0.18292, 37862.76, 0.6212],
       ['TTTTAAAA', 'TTTTAAAA', 0.35291, 53075.71, 3.3732]], dtype=object)


EE = 'AAAATATC'
CBS = 'AAAAAATC'
CCR2_mut = 'AAAATCGA'
for idx,seq in enumerate(CCA1):
    if EE == CCA1[idx][0]:
        pos_EE = idx
    if CBS == CCA1[idx][0]:
        pos_CBS = idx
    if CCR2_mut == CCA1[idx][0]:
        pos_CCR2_mut = idx


array(CCA1[:,2])

array([0.30529, 0.2516, 0.28368000000000004, ..., 0.11617999999999999,
       0.18292, 0.35291], dtype=object)


figure(figsize=(7*2,5*2))
#hist(CCA1[:,2], bins=100)
xlabel('E-score', fontsize=22)
sns.distplot(CCA1[:,2], color="b")
ylabel('Frequency', fontsize=22)
xticks(fontsize=20)
yticks(fontsize=20)
#savefig('images/E_scores_CCA1_dist.png', format='png', dpi=300)

(array([0. , 0.5, 1. , 1.5, 2. , 2.5]), <a list of 6 Text yticklabel objects>)


EE_Kd = 8
CBS_Kd = 16
CCR2_mut_Kd = 235
affinties = log10(array([EE_Kd,CBS_Kd,CCR2_mut_Kd]))
intensities  = array([CCA1[pos_EE][2],CCA1[pos_CBS][2],CCA1[pos_CCR2_mut][2]])


EE_Kd = 2.66
CBS_Kd = 4.91
CCR2_mut_Kd = 235
affinties = log(array([EE_Kd,CBS_Kd,CCR2_mut_Kd]))
intensities  = array([CCA1[pos_EE][2],CCA1[pos_CBS][2],CCA1[pos_CCR2_mut][2]])


figure(figsize=(7,5))
plot(affinties,
     intensities, 'o-')
#ylim(0.2,0.55)
#xlim(1.5,6)
ylabel('E-Score', fontsize=22)
xlabel('ln(Kd)', fontsize=22)
xticks(fontsize=16)
yticks(fontsize=16)
#savefig('images/Kd_vs_Escoe.pdf', dpi=300, format='pdf')

(array([0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 , 0.55]),
 <a list of 8 Text yticklabel objects>)


slope, intercept, r_value, p_value,std_err = linregress(affinties,intensities)


linregress(affinties,intensities)

LinregressResult(slope=-0.05503097538373463, intercept=0.551601304645103, rvalue=-0.9999390585113125, pvalue=0.007028357693400724, stderr=0.0006075731712832275)


figure(figsize=(7*2,5*2))
#subplot(1,2,1)
log_kd = linspace(0,8)
plot(log_kd,slope*log_kd+intercept,'k',lw=5)
plot(affinties,intensities, 'ro', markersize=22)
xlabel('ln(Kd nM)', fontsize=30)
yticks(fontsize=25)
ylabel('E-score', fontsize=30)
xticks(fontsize=25)
#subplot(1,2,2)
#log_kd = linspace(0,10)
#plot(exp(log_kd),exp(slope*log_kd+intercept))
#plot(exp(affinties),exp(intensities), 'ro')
#xlim(0,300)
#xlabel('Kd', fontsize=22)
#yticks(fontsize=16)
#ylabel('exp(E-score)', fontsize=22)
#xticks(fontsize=16)
savefig('images/Kd_vs_Escore_linearregress.png', dpi=300, format='png')
savefig('images/Kd_vs_Escore_linearregress.pdf', dpi=300, format='pdf')
savefig('images/Kd_vs_Escore_linearregress.svg', dpi=300, format='svg')


genome_kds_for = {}
genome_kds_rev = {}
for i in CCA1:
    genome_kds_for[i[0]]=((i[2]-intercept)/slope)
    genome_kds_rev[i[1]]=((i[2]-intercept)/slope)


clockgenes = {'CCA1' : 'AT2G46830', 'LHY': 'AT1G01060', 
              'TOC1' : 'AT5G61380', 'GI':'AT1G22770',
              'PRR7':'AT5G02810', 'PRR9':'AT2G46790',
              'PRR5':'AT5G24470', 'LUX':'AT3G46640',
              'ELF3':'AT2G25930', 'ELF4':'AT2G40080',
              'ZTL':'AT5G57360'}
meristematic = {'AN3':'AT5G28640'}


chromosome_dict = {'AT1':'NC_003070.gbk','AT2':'NC_003071.gbk','AT3':'NC_003074.gbk','AT4':'NC_003075.gbk','AT5':'NC_003076.gbk'}
chromosomes_seq={}
chromosomes_genes={}
chromosome_concatenated = 'A'
for chromosome in os.listdir('ATGenome/'):
    print 'Processing chromosome ', chromosome
    genes={}
    chromosomes_seq[chromosome] = SeqIO.read('ATGenome/'+chromosome, 'genbank')
    chromosome_concatenated = chromosome_concatenated+chromosomes_seq[chromosome].seq
    
    for f in chromosomes_seq[chromosome].features:
        if f.type == 'gene':
            genes[f.qualifiers['locus_tag'][0]] = f
    chromosomes_genes[chromosome] = genes

Processing chromosome  NC_003070.gbk
Processing chromosome  NC_003071.gbk
Processing chromosome  NC_003074.gbk
Processing chromosome  NC_003075.gbk
Processing chromosome  NC_003076.gbk


promoter_length = 400
pos = range(0,len(chromosome_concatenated)-promoter_length)
random_regions = random.choice(pos,1000)


ensemble = load('PBM_matrix_inference/CCA1_ensemble.npy')

seq

array(['TTTTAAAA', 'TTTTAAAA', 0.35291, 53075.71, 3.3732], dtype=object)


def seq_energy(seq, energy_matrix):
    bound = {}
    for j in range(0,len(seq)-7):
            energy = 0
            for i in range(8):
                if seq[j+i] == 'A':
                    energy += energy_matrix[0][0,i]
                elif seq[j+i] == 'T':
                    energy += energy_matrix[0][1,i]
                elif seq[j+i] == 'G':
                    energy += energy_matrix[0][2,i]
                elif seq[j+i] == 'C':
                    energy += energy_matrix[0][3,i]
            if energy < energy_matrix[1] :
                bound[j] = 1
    return bound

def matrix_normalisation(energy_matrix_preturbed):
    temp = copy(energy_matrix_preturbed[0])
    x= 0
    for i in range(8):
        x+=min(temp[:,i]) 
    
    for i in range(8):
        for j in range(4):
            energy_matrix_preturbed[0][j,i] = (energy_matrix_preturbed[0][j,i] - min(temp[:,i]))/(energy_matrix_preturbed[1]-x)
    
    energy_matrix_preturbed[1] = 1.0
    return copy(energy_matrix_preturbed)


norm_mat = matrix_normalisation(ensemble[-20000])
#boundf =  seq_energy(seq,norm_mat)
#boundr =  seq_energy(seq.reverse_complement(),norm_mat)


def seq_energy_eightmere(seq, energy_matrix):
        energy = 0
        for i in range(8):
            if seq[i] == 'A':
                energy += energy_matrix[0][0,i]
            elif seq[i] == 'T':
                energy += energy_matrix[0][1,i]
            elif seq[i] == 'G':
                energy += energy_matrix[0][2,i]
            elif seq[i] == 'C':
                energy += energy_matrix[0][3,i]
        if energy < energy_matrix[1] :
            return 1
        else:
            return 0


bound_bin = []
unbound_bin =[]
for idx, s in enumerate(CCA1_pandas['8-mer']):
    if seq_energy_eightmere(s,norm_mat):
        bound_bin.append(CCA1_pandas.iloc[idx]['E-score'])
    else:
        unbound_bin.append(CCA1_pandas.iloc[idx]['E-score'])


figure(figsize=(7*2,5*2))
hist(unbound_bin, alpha=0.5, label='Unbound', bins=30)
hist(bound_bin, alpha=0.5, color='orange', label='Bound', normed=False, bins=30)
legend(loc='upper right', fontsize=22)
xticks(fontsize=22)
yticks(fontsize=22)
ylabel('Ocurrence', fontsize=22)
xlabel('E-score', fontsize=22)
title('CCA1', fontsize=30)
savefig('images/EMA_separation_CCA1.png', format='png', dpi=600)
savefig('images/EMA_separation_CCA1.svg', format='svg', dpi=600)
savefig('images/EMA_separation_CCA1.pdf', format='pdf', dpi=600)


CCA1_peaks = asanyarray(pandas.read_csv('CCA1peaks_Kamioka2016', delimiter='\t', header=None))


CCA1_peaks

array([[1, 107656, 107910, 107783, 'AT1G01250'],
       [1, 107656, 107910, 107783, 'AT1G01260'],
       [1, 120415, 120931, 120673, 'AT1G01305'],
       ...,
       [5, 26932953, 26933344, 26933149, 'AT5G67488'],
       [5, 26949586, 26950106, 26949846, 'AT5G67550'],
       [5, 26949586, 26950106, 26949846, 'AT5G67560']], dtype=object)


def affinity_calc_energy_mat(seq, genome_kds_for, genome_kds_rev, boundf,boundr):
    affinities = 0
    k = boundf.keys()
    for pos in k:
        try:
            affinities+=(1/exp(genome_kds_for[seq[pos:pos+8]]))
        except:
            pass
    k = boundr.keys()
    len_seq = len(seq)
    for pos in k:
        try:
            affinities+=(1/exp(genome_kds_rev[seq[abs(pos-len_seq):abs(pos-len_seq)+8]]))
        except:
            pass
    return affinities


def affinity_calc(seq, genome_kds_for, genome_kds_rev):
    affinities = 0
    for pos in range(len(seq)-8):
        try:
            affinities+=(1/exp(genome_kds_for[seq[pos:pos+8]]))
        except:
            pass
        try:
            affinities+=(1/exp(genome_kds_rev[seq[pos:pos+8]]))
        except:
            pass
    return affinities


CCA1_peaks = asanyarray(pandas.read_csv('CCA1peaks_Kamioka2016', delimiter='\t', header=None))


CCA1_peaks

array([[1, 107656, 107910, 107783, 'AT1G01250'],
       [1, 107656, 107910, 107783, 'AT1G01260'],
       [1, 120415, 120931, 120673, 'AT1G01305'],
       ...,
       [5, 26932953, 26933344, 26933149, 'AT5G67488'],
       [5, 26949586, 26950106, 26949846, 'AT5G67550'],
       [5, 26949586, 26950106, 26949846, 'AT5G67560']], dtype=object)


affinities = {}
background = {}
gene_num = 863
for idx, gene in enumerate(CCA1_peaks[:,4]):
    print gene, float(idx)/gene_num 
    peak = chromosomes_seq[chromosome_dict[gene[0:3]]].seq[CCA1_peaks[idx,1]:CCA1_peaks[idx,2]]
    affinities[gene]=affinity_calc(peak,genome_kds_for,genome_kds_rev)
    print gene, float(idx)/gene_num 
    promoter_length = CCA1_peaks[idx,2]-CCA1_peaks[idx,1]
    pos = range(0,len(chromosome_concatenated)-promoter_length)
    random_regions = random.choice(pos,1000)
    affinities_background = []
    for randome_region in random_regions:
        affinities_background.append(affinity_calc(chromosome_concatenated[randome_region:(randome_region+promoter_length)], 
                      genome_kds_for, genome_kds_rev, ))
    background[gene] = affinities_background

AT1G01250 0.0
AT1G01250 0.0


KeyboardInterruptTraceback (most recent call last)
<ipython-input-46-c17ccf8a8ddf> in <module>()
      9     promoter_length = CCA1_peaks[idx,2]-CCA1_peaks[idx,1]
     10     pos = range(0,len(chromosome_concatenated)-promoter_length)
---> 11     random_regions = random.choice(pos,1000)
     12     affinities_background = []
     13     for randome_region in random_regions:

mtrand.pyx in mtrand.RandomState.choice()

/usr/local/lib/python2.7/dist-packages/numpy/core/fromnumeric.pyc in prod(a, axis, dtype, out, keepdims, initial)
   2662 
   2663 
-> 2664 @array_function_dispatch(_prod_dispatcher)
   2665 def prod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue, initial=np._NoValue):
   2666     """

KeyboardInterrupt:


#with open('background_with_matrix.pickle', 'wb') as handle:
#    pickle.dump(background, handle)
#with open('affinities_with_matrix.pickle', 'wb') as handle:
#    pickle.dump(affinities, handle)


with open('background_with_matrix.pickle', 'rb') as handle:
    background_matrix = pickle.load(handle)
with open('affinities_with_matrix.pickle', 'rb') as handle:
    affinities_matrix = pickle.load(handle)


#with open('background.pickle', 'wb') as handle:
#    pickle.dump(background, handle)


#with open('affinities.pickle', 'wb') as handle:
#    pickle.dump(affinities, handle)


with open('background.pickle', 'rb') as handle:
    background = pickle.load(handle)
with open('affinities.pickle', 'rb') as handle:
    affinities = pickle.load(handle)


background[clockgenes['TOC1']]

[1.8344671185606463,
 0.5581885675753785,
 1.6666260996447475,
 2.9631302790305827,
 0.8573496464806986,
 1.2839613795817875,
 0.6676937073951321,
 1.2722133658388959,
 0.8862839630077786,
 0.6280772286263727,
 2.2634420641586392,
 0.15331004126379708,
 0.359699122303179,
 0.8209949599075179,
 1.2987760109081201,
 3.658462906410868,
 1.310714779359457,
 0.7108775019423363,
 0.570284156900204,
 1.2206739975154122,
 2.3699734335697697,
 0.8783783519025448,
 2.2751364663556757,
 0.6778623843486707,
 1.7342371460087027,
 1.5433241348362228,
 1.7940045459214824,
 1.7920277977545442,
 1.628918826244327,
 0.8918074565850429,
 1.4511866862269174,
 0.8481687548175212,
 1.3862989023444077,
 1.0713848890694222,
 1.5275990023017596,
 2.4102298035103953,
 1.8382837789971387,
 1.5155863656461352,
 0.5599169824366934,
 1.7300741055403541,
 1.1319675440705532,
 1.2305319042599574,
 1.7493274443812805,
 1.7779092154478942,
 0.7712527986924155,
 1.1289557247397062,
 0.49471197738765665,
 1.6858865806565722,
 1.0257009482860995,
 1.61073416132463,
 1.1165992887453715,
 1.488331295183724,
 0.3737272606050346,
 3.1490945011105493,
 0.9747344430994359,
 1.1803827418822501,
 1.1515768306456615,
 1.1184115820894345,
 1.3666939411027872,
 2.0589379867848288,
 1.306263646507218,
 0.9407865898687695,
 3.1694604860190116,
 1.711066867956474,
 0.9980875935629131,
 3.8177349185636618,
 1.3788766108705584,
 1.7820452274601735,
 0.49301124605016616,
 1.8584766430884958,
 1.3158557562582054,
 0.8748038648004073,
 1.379600562612426,
 1.7876236774415228,
 1.6258121266418504,
 2.0630156096055425,
 2.2727787593094537,
 1.6370173654599023,
 1.6280590323812254,
 1.756849041411956,
 0.78154844563768,
 1.853390389550714,
 1.3141358981787556,
 0.6607650110854766,
 1.234143647446182,
 2.7707860193519127,
 1.2963116988290637,
 1.5290417224767419,
 1.9430776449737348,
 3.0619643497202653,
 1.1067695003789253,
 2.3249846417039866,
 2.3067519009930146,
 0.21334181467179106,
 1.6408004495164072,
 2.1708228396539226,
 3.872032474569528,
 0.5841653250155974,
 1.2670965554364793,
 0.39448329011753797,
 0.4226206779208499,
 2.9249763154403827,
 1.3507592735141736,
 1.1312300568701101,
 1.4272165823362748,
 1.0406770754828383,
 2.581813634617987,
 0.7312900958359524,
 1.5268063310333118,
 1.1327883298774348,
 1.3390150552638111,
 1.729811341964295,
 1.2755424049036832,
 2.5939922857355824,
 0.7963378489157146,
 0.6178955679166092,
 0.722382871884315,
 0.6499144917935413,
 1.6457151765383515,
 1.3795070198570676,
 1.3027171099981876,
 1.3980069437278757,
 0.5739701000774289,
 1.8432519782185273,
 1.228557466249194,
 0.5737028130869316,
 2.5290383836026717,
 1.4562323433477102,
 1.1115547546463118,
 0.3895736210552276,
 2.5956221319476063,
 1.3772738940160827,
 1.0605931411579574,
 1.4264257015922563,
 1.5389988901021874,
 0.6037374700709102,
 0.8840519093258482,
 0.7699866924683155,
 2.808871285389614,
 0.9242213291408938,
 1.308762881414728,
 2.3169037406266817,
 2.823930282399112,
 0.6974349049318779,
 1.2206457929077728,
 1.1564399870318305,
 0.9932588037622054,
 0.7459649921918311,
 0.6407361120254438,
 1.0538894520239923,
 3.0185029131726053,
 0.8274033155286387,
 0.7547129173420422,
 1.037387129121822,
 2.0255894474249345,
 2.9420895900026105,
 0.41875963638270103,
 1.450995763936315,
 0.6323362898366329,
 0.5715985137933254,
 2.1869668263838733,
 1.934714853723143,
 1.1678803462873066,
 1.569036396428576,
 0.8461246244012719,
 1.7980771179092023,
 1.1702881417804327,
 0.9143078551908002,
 0.633818578213963,
 2.0414252882236172,
 0.5827999216734266,
 0.6199397306952568,
 2.782918824931988,
 1.7933030300162534,
 2.0950210188901446,
 0.9498539568365775,
 1.8346539266003148,
 1.2532642144787998,
 1.2561417934319195,
 1.1763630924344746,
 1.2190439435908673,
 0.6395772209718067,
 1.9261465191006075,
 0.7091086182553801,
 4.1044523607800585,
 1.8553821003587123,
 1.0527500232504974,
 0.6227504186255158,
 1.1120702300245102,
 1.3675297206965586,
 1.3887562818614234,
 0.7062992591628383,
 2.839519757651368,
 1.8717495941406128,
 2.1093526454757234,
 0.28813956013218045,
 0.9549669262104108,
 0.4801884238707022,
 0.7469760557036084,
 0.5389283847147212,
 1.0783329694252548,
 2.233248883329926,
 1.2681790265408335,
 0.7762471766127289,
 1.219895280261644,
 0.9407811558023226,
 1.4766331767075145,
 1.8226732713515423,
 1.2371150126948565,
 1.5086728102771731,
 1.2669608853238223,
 1.0142787658986925,
 0.5555741546340411,
 2.1180203924106,
 1.181378299684626,
 0.9817283585234224,
 1.0833961715672062,
 0.5147695280483782,
 1.4439115808386627,
 1.0620915692169446,
 2.091654107751902,
 1.6188217789412462,
 2.62962724411249,
 1.876318163438182,
 0.6717420099997343,
 2.3138918311587897,
 1.019846012393044,
 0.7617597102722453,
 2.0000402948280924,
 0.9580876436193031,
 3.099972420071439,
 1.3240414826963,
 0.6730649818695763,
 3.166135750839546,
 1.4622587607695514,
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 2.8039245001103335,
 1.7566276439366295,
 0.562409528006622,
 1.5089634616841947,
 0.9280276205495114,
 1.385684774775358,
 0.9505942597268305,
 0.8219483620258132,
 2.032211996426403,
 1.5119710128784658,
 0.46851791954170924,
 1.469222256350784,
 1.6640782019831704,
 1.002860138848225,
 1.2166387364155622,
 2.3155160043568452,
 1.4883468918976746,
 1.2816254918459644,
 3.0827506575445325,
 1.392639823248613,
 0.6840456433802147,
 2.171201889946028,
 0.7928731539448367,
 1.6055548126162447,
 0.9387334097736886,
 1.9863490550111356,
 0.742861936141805,
 2.1219107561955806,
 0.9926734488651351,
 1.023122573954412,
 1.2830302103295086,
 1.1620732966990628,
 0.9655207573941689,
 1.5369457992853237,
 0.9908083785654317,
 2.600672418514531,
 1.0445089189750953,
 1.8351355430233172,
 0.9204585613319385,
 1.63288068112249,
 1.5493478085796888,
 1.4377672091378213,
 1.646423670894179,
 0.71676403348772,
 1.119488948925118,
 1.0801996224344657,
 0.8788218741290419,
 1.0462183135136272,
 0.848836439950894,
 3.203103034058676,
 3.0621928321918483,
 0.8129494882312861,
 0.5786654715999358,
 0.8019187647230215,
 1.1397332121703203,
 1.6462109007171568,
 1.0900802186572627,
 1.7201256924006327,
 0.6861831182927246,
 1.8221887722029408,
 0.5059151184874964,
 1.294210410904231,
 1.3841492856877151,
 2.860851531655892,
 0.38700479429059736,
 1.385767535413621,
 0.669784637868556,
 0.43618271203091546]


sns.set()
b_dist = array(background[clockgenes['TOC1']])[array(background[clockgenes['TOC1']]) > 0]
figure(figsize=(10,7))
hist((log(b_dist)-mean(log(b_dist)))/std(log(b_dist)), bins=100, normed=True, label='Full E-Score')
#savefig('images/toc1_background_raw_matrix.pdf', format='pdf', dpi=300)
b_dist = array(background_matrix[clockgenes['TOC1']])[array(background_matrix[clockgenes['TOC1']]) > 0]
hist((log(b_dist)-mean(log(b_dist)))/std(log(b_dist)), bins=100, normed=True, alpha=0.6, color='orange', label='EMA selected')
xlabel('Affinity ($1/K_d$)', fontsize=22)
xticks(fontsize=16)
yticks(fontsize=16)
ylabel('Frequency', fontsize=22)
legend(loc='upper right', fontsize=22)
#savefig('images/toc1_background_raw.png', format='png', dpi=300)

<matplotlib.legend.Legend at 0x43352c6050>


g = "CCA1"
figure(figsize=(10,7))
b_dist = array(background_matrix[clockgenes[g]])[array(background_matrix[clockgenes[g]]) > 0]
hist((log(b_dist)-mean(log(b_dist)))/std(log(b_dist)), bins=100, normed=True, label='Normed Ln(affinites)')
plot(linspace(-5,5,100),norm.pdf(linspace(-5,5,100),loc=0), lw=3, color='r', label='$N(0,1)$')
axvline((log(affinities_matrix[clockgenes[g]])-mean(log(b_dist)))/std(log(b_dist)), color='g', lw=3, label=g+' z-score')
xticks(fontsize=16)
yticks(fontsize=16)
xlabel('z-score', fontsize=22)
ylabel('Frequency', fontsize=22)
legend(loc='upper left', fontsize=16)
savefig('images/CCA1_background_matrix.pdf',format='pdf',dpi=300)
savefig('images/CCA1_background_matrix.png',format='png',dpi=600)
savefig('images/CCA1_background_matrix.svg',format='svg',dpi=600)


g = "PRR9"
figure(figsize=(10,7))
b_dist = array(background_matrix[clockgenes[g]])[array(background_matrix[clockgenes[g]]) > 0]
hist((log(b_dist)-mean(log(b_dist)))/std(log(b_dist)), bins=100, normed=True, label='Normed Ln(affinites)')
plot(linspace(-5,5,100),norm.pdf(linspace(-5,5,100),loc=0), lw=3, color='r', label='$N(0,1)$')
axvline((log(affinities_matrix[clockgenes[g]])-mean(log(b_dist)))/std(log(b_dist)), color='g', lw=3, label=g+' z-score')
xticks(fontsize=16)
yticks(fontsize=16)
xlabel('z-score', fontsize=22)
ylabel('Frequency', fontsize=22)
legend(loc='upper left', fontsize=16)
savefig('images/PRR9_background_matrix.pdf',format='pdf',dpi=300)
savefig('images/PRR9_background_matrix.png',format='png',dpi=600)
savefig('images/PRR9_background_matrix.svg',format='svg',dpi=600)


g = "PRR7"
figure(figsize=(10,7))
b_dist = array(background_matrix[clockgenes[g]])[array(background_matrix[clockgenes[g]]) > 0]
hist((log(b_dist)-mean(log(b_dist)))/std(log(b_dist)), bins=100, normed=True, label='Normed Ln(affinites)')
plot(linspace(-5,5,100),norm.pdf(linspace(-5,5,100),loc=0), lw=3, color='r', label='$N(0,1)$')
axvline((log(affinities_matrix[clockgenes[g]])-mean(log(b_dist)))/std(log(b_dist)), color='g', lw=3, label=g+' z-score')
xticks(fontsize=16)
yticks(fontsize=16)
xlabel('z-score', fontsize=22)
ylabel('Frequency', fontsize=22)
legend(loc='upper left', fontsize=16)
savefig('images/PRR7_background_matrix.pdf',format='pdf',dpi=300)
savefig('images/PRR7_background_matrix.png',format='png',dpi=600)
savefig('images/PRR7_background_matrix.svg',format='svg',dpi=600)


g = "GI"
figure(figsize=(10,7))
b_dist = array(background_matrix[clockgenes[g]])[array(background_matrix[clockgenes[g]]) > 0]
hist((log(b_dist)-mean(log(b_dist)))/std(log(b_dist)), bins=100, normed=True, label='Normed Ln(affinites)')
plot(linspace(-5,5,100),norm.pdf(linspace(-5,5,100),loc=0), lw=3, color='r', label='$N(0,1)$')
axvline((log(affinities_matrix[clockgenes[g]])-mean(log(b_dist)))/std(log(b_dist)), color='g', lw=3, label=g+' z-score')
xticks(fontsize=16)
yticks(fontsize=16)
xlabel('z-score', fontsize=22)
ylabel('Frequency', fontsize=22)
legend(loc='upper left', fontsize=16)
savefig('images/GI_background_matrix.pdf',format='pdf',dpi=300)
savefig('images/GI_background_matrix.png',format='png',dpi=600)
savefig('images/GI_background_matrix.svg',format='svg',dpi=600)


g = "PRR5"
figure(figsize=(10,7))
b_dist = array(background_matrix[clockgenes[g]])[array(background_matrix[clockgenes[g]]) > 0]
hist((log(b_dist)-mean(log(b_dist)))/std(log(b_dist)), bins=100, normed=True, label='Normed Ln(affinites)')
plot(linspace(-5,5,100),norm.pdf(linspace(-5,5,100),loc=0), lw=3, color='r', label='$N(0,1)$')
axvline((log(affinities_matrix[clockgenes[g]])-mean(log(b_dist)))/std(log(b_dist)), color='g', lw=3, label=g+' z-score')
xticks(fontsize=16)
yticks(fontsize=16)
xlabel('z-score', fontsize=22)
ylabel('Frequency', fontsize=22)
legend(loc='upper left', fontsize=16)
savefig('images/PRR5_background_matrix.pdf',format='pdf',dpi=300)
savefig('images/PRR5_background_matrix.png',format='png',dpi=600)
savefig('images/PRR5_background_matrix.svg',format='svg',dpi=600)


g = "TOC1"
figure(figsize=(10,7))
b_dist = array(background_matrix[clockgenes[g]])[array(background_matrix[clockgenes[g]]) > 0]
hist((log(b_dist)-mean(log(b_dist)))/std(log(b_dist)), bins=100, normed=True, label='Normed Ln(affinites)')
plot(linspace(-5,5,100),norm.pdf(linspace(-5,5,100),loc=0), lw=3, color='r', label='$N(0,1)$')
axvline((log(affinities_matrix[clockgenes[g]])-mean(log(b_dist)))/std(log(b_dist)), color='g', lw=3, label=g+' z-score')
xticks(fontsize=16)
yticks(fontsize=16)
xlabel('z-score', fontsize=22)
ylabel('Frequency', fontsize=22)
legend(loc='upper left', fontsize=16)
savefig('images/TOC1_background_matrix.pdf',format='pdf',dpi=300)
savefig('images/TOC1_background_matrix.png',format='png',dpi=600)
savefig('images/TOC1_background_matrix.svg',format='svg',dpi=600)


z_dist = {}
for b in background.keys():
    abackground = array(background[b])
    abackground = abackground[abackground>0]
    z_dist[b]=(log(affinities[b])-mean(log(abackground)))/std(log(abackground))

    
z_dist_matrix = {}
for b in background.keys():
    abackground = array(background_matrix[b])
    abackground = abackground[abackground>0]
    z_dist_matrix[b]=(log(affinities_matrix[b])-mean(log(abackground)))/std(log(abackground))


figure(figsize=(10,7))
hist(z_dist.values(),bins=50, normed=True, label='Full E-scores')
for c in clockgenes.keys():
    try:
        #axvline(z_dist[clockgenes[c]],color='blue', lw=2)
        if z_dist[clockgenes[c]] < 1:
            print 'Less ',c,z_dist[clockgenes[c]]
        else:
            print 'More ',c,z_dist[clockgenes[c]]
    except:
        print 'Not in list ',c
        pass
xticks(fontsize=16)
yticks(fontsize=16)
xlabel('z-score', fontsize=22)
ylabel('Frequency', fontsize=22)

hist(z_dist_matrix.values(),bins=50, normed=True, color='orange', alpha=0.6, label = 'EMA selected')
for c in clockgenes.keys():
    try:
        #axvline(z_dist_matrix[clockgenes[c]],color='orange', lw=2)
        if z_dist_matrix[clockgenes[c]] < 1:
            print 'Less ',c,z_dist_matrix[clockgenes[c]]
        else:
            print 'More ',c,z_dist_matrix[clockgenes[c]]
    except:
        print 'Not in list ',c
        pass
xticks(fontsize=16)
yticks(fontsize=16)
xlabel('z-score', fontsize=22)
ylabel('Frequency', fontsize=22)

legend(loc='upper left', fontsize=22)
savefig('images/z_scores_across_genome.pdf', format='pdf', dpi=300)
savefig('images/z_scores_across_genome.png', format='png', dpi=600)
savefig('images/z_scores_across_genome.svg', format='svg', dpi=600)

Not in list  ZTL
More  LUX 1.3675763889618644
Not in list  ELF3
More  ELF4 2.169719290651254
Less  GI 0.998965250737051
More  PRR9 1.4288326116755836
Less  CCA1 0.11522348684192192
More  PRR5 2.208151979517305
More  PRR7 1.6299056367106595
Not in list  LHY
More  TOC1 1.5567804475849913
Not in list  ZTL
More  LUX 2.0263284863458964
Not in list  ELF3
More  ELF4 2.7021271353025513
More  GI 1.2700458345468633
More  PRR9 2.1300995719695046
Less  CCA1 0.7537500478166206
More  PRR5 2.0604308816327457
More  PRR7 2.2371650409077137
Not in list  LHY
More  TOC1 2.14672848448777

	8-mer	8-mer.1	E-score	Median	Z-score
27981	GCTCCCCC	GGGGGAGC	-0.48828	25687.96	-2.5398
25953	GAGGGGCC	GGCCCCTC	-0.46804	25757.92	-2.5176
27394	GCCTGCCC	GGGCAGGC	-0.45769	27481.59	-1.9898
5949	ACGACCGA	TCGGTCGT	-0.45199	29127.70	-1.5158
9049	AGCGACGG	CCGTCGCT	-0.45145	29360.71	-1.4509
26822	GCAGCACC	GGTGCTGC	-0.44968	28347.12	-1.7372
29517	GGTGCACC	GGTGCACC	-0.44605	30882.95	-1.0391
29188	GGGGCCTA	TAGGCCCC	-0.44496	26950.85	-2.1487
26801	GCAGACCC	GGGTCTGC	-0.44250	28495.80	-1.6945
29099	GGGCCCCC	GGGGGCCC	-0.44223	28889.13	-1.5828
18273	CCCCCTGC	GCAGGGGG	-0.44188	27396.30	-2.0152
6269	ACGCGTGC	GCACGCGT	-0.44097	26891.55	-2.1667
6700	ACGTGCGC	GCGCACGT	-0.44065	28821.50	-1.6019
29033	GGGAGCCC	GGGCTCCC	-0.43975	28944.52	-1.5672
20718	CGCACGTC	GACGTGCG	-0.43467	29395.04	-1.4414
9754	AGGCCCGG	CCGGGCCT	-0.43335	26597.53	-2.2563
28872	GGCGTACC	GGTACGCC	-0.43291	28223.05	-1.7729
29122	GGGCGCGA	TCGCGCCC	-0.43138	29019.16	-1.5463
18058	CCCAATGC	GCATTGGG	-0.43128	29110.97	-1.5205
28675	GGCAGCTA	TAGCTGCC	-0.42901	29017.56	-1.5467
9130	AGCGCTGC	GCAGCGCT	-0.42242	29291.61	-1.4701
1688	AACGGGGC	GCCCCGTT	-0.42039	28421.62	-1.7158
9924	AGGGAGCC	GGCTCCCT	-0.42014	28947.24	-1.5665
18256	CCCCCGAC	GTCGGGGG	-0.41950	26842.06	-2.1817
13225	ATGGAGCC	GGCTCCAT	-0.41903	29595.17	-1.3861
17614	CCACGGGG	CCCCGTGG	-0.41775	28963.92	-1.5618
29097	GGGCCCAC	GTGGGCCC	-0.41556	29072.65	-1.5313
18303	CCCCGGCG	CGCCGGGG	-0.41452	28757.47	-1.6201
2351	AAGCCCCC	GGGGGCTT	-0.41420	28141.49	-1.7965
9749	AGGCCCCC	GGGGGCCT	-0.41417	26884.50	-2.1688
...	...	...	...	...	...
25733	GAGATATC	GATATCTC	0.47773	95080.10	8.1234
11876	ATATCTCC	GGAGATAT	0.47804	86375.12	7.3411
3205	AATATCAA	TTGATATT	0.48000	96129.09	8.2128
8410	AGATATAT	ATATATCT	0.48000	104584.56	8.8997
8420	AGATATTG	CAATATCT	0.48159	101836.03	8.6827
11883	ATATCTTG	CAAGATAT	0.48196	95941.59	8.1969
2815	AAGTATCT	AGATACTT	0.48370	108555.24	9.2033
2219	AAGATATC	GATATCTT	0.48449	110205.64	9.3263
11882	ATATCTTC	GAAGATAT	0.48521	100548.56	8.5791
55	AAAAATCT	AGATTTTT	0.48575	113259.50	9.5490
3212	AATATCCT	AGGATATT	0.48582	97310.50	8.3124
11872	ATATCTAA	TTAGATAT	0.48690	115308.85	9.6951
11881	ATATCTTA	TAAGATAT	0.48751	110205.64	9.3263
562	AAAGATAT	ATATCTTT	0.48833	114539.50	9.6406
8419	AGATATTC	GAATATCT	0.48885	124776.51	10.3381
11327	ATAATATC	GATATTAT	0.49176	115367.77	9.6993
819	AAATATCG	CGATATTT	0.49198	111871.07	9.4485
3218	AATATCTC	GAGATATT	0.49298	122324.11	10.1763
8414	AGATATCT	AGATATCT	0.49332	149562.21	11.8144
817	AAATATCA	TGATATTT	0.49348	140105.47	11.2822
3219	AATATCTG	CAGATATT	0.49376	131118.93	10.7421
14522	CAAATATC	GATATTTG	0.49481	130830.38	10.7241
3217	AATATCTA	TAGATATT	0.49520	140469.23	11.3033
8418	AGATATTA	TAATATCT	0.49588	159459.88	12.3365
24743	GAAATATC	GATATTTC	0.49619	149768.90	11.8256
2221	AAGATATT	AATATCTT	0.49719	177068.59	13.1900
26225	GATATTTA	TAAATATC	0.49745	181082.77	13.3726
818	AAATATCC	GGATATTT	0.49795	160868.87	12.4082
205	AAAATATC	GATATTTT	0.49912	253196.75	16.1039
820	AAATATCT	AGATATTT	0.49942	270175.71	16.6327

Genomic regulation of CCA1/LHY¶