In [1]:

    

%run ~/relmapping/annot/notebooks/__init__.ipynb


    

    




/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.
  from pandas.core import datetools






    




os.getcwd(): /mnt/b2/scratch/ahringer/jj374/lab/relmapping

In [2]:

    

df_regl = regl_cv()
print(len(df_regl))

df_regl['atac_flank_rev'] = df_regl['start'] - 50 
df_regl['atac_flank_fwd'] = df_regl['end'] + 50

COLS_FLANK = ['chrom', 'atac_flank_rev', 'atac_flank_fwd']
df_regl.head()


    

    




/mnt/home3/jj374/anaconda36/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: Mean of empty slice
  






    




13086 of 42245 sites with CV values via promoter annotation
42245






    
Out[2]:







  

    

      

      
chrom
      
start
      
end
      
annot
      
annot_fwd
      
annot_rev
      
promoter_gene_id_fwd
      
promoter_locus_id_fwd
      
promoter_gene_biotype_fwd
      
promoter_gene_id_rev
      
...
      
lcap_incr_glp1_d7_fwd
      
lcap_incr_glp1_d7_rev
      
lcap_incr_glp1_d10_fwd
      
lcap_incr_glp1_d10_rev
      
lcap_incr_glp1_d14_fwd
      
lcap_incr_glp1_d14_rev
      
CV
      
CV_rank
      
atac_flank_rev
      
atac_flank_fwd
    
  
  

    

      
0
      
chrI
      
1870
      
2092
      
putative_enhancer
      
transcription_initiation
      
transcription_initiation
      
.
      
.
      
.
      
.
      
...
      
False
      
False
      
False
      
False
      
False
      
False
      
NaN
      
NaN
      
1820
      
2142
    
    

      
1
      
chrI
      
3809
      
3994
      
non-coding_RNA
      
unknown_promoter
      
non-coding_RNA
      
.
      
.
      
.
      
WBGene00023193
      
...
      
False
      
False
      
False
      
True
      
False
      
True
      
NaN
      
NaN
      
3759
      
4044
    
    

      
2
      
chrI
      
4302
      
4410
      
putative_enhancer
      
transcription_initiation
      
transcription_initiation
      
.
      
.
      
.
      
.
      
...
      
False
      
False
      
False
      
False
      
False
      
False
      
NaN
      
NaN
      
4252
      
4460
    
    

      
3
      
chrI
      
11245
      
11465
      
coding_promoter
      
coding_promoter
      
transcription_initiation
      
WBGene00022276
      
nlp-40
      
protein_coding
      
.
      
...
      
False
      
False
      
False
      
False
      
False
      
False
      
0.517
      
0.618562
      
11195
      
11515
    
    

      
4
      
chrI
      
13087
      
13207
      
putative_enhancer
      
transcription_initiation
      
transcription_initiation
      
.
      
.
      
.
      
.
      
...
      
False
      
False
      
False
      
False
      
False
      
False
      
NaN
      
NaN
      
13037
      
13257
    
  

5 rows × 76 columns

In [3]:

    

#fp_ = 'http://genome.cshlp.org/content/suppl/2014/04/24/gr.161992.113.DC1/Supplemental_Information_S1.zip'
#!cd ~/relmapping/wget; wget -m --no-parent {fp_}
fp_ = 'wget/genome.cshlp.org/content/suppl/2014/04/24/gr.161992.113.DC1/S01 Information of TF overlap worm_ce10.csv'
df_ = pd.read_csv(fp_)
print('%d records in S1' % (len(df_),))
df_.head(5)


    

    




35062 records in S1






    
Out[3]:







  

    

      

      
Unnamed: 0
      
N_of_factors_bound
      
N_of_factors_in_core
      
chromosome
      
core_start
      
core_stop
      
core_mid
      
extended_start
      
extended_stop
      
ChIPseq_data_core_summary
      
ChIPseq_data_ext_summary
    
  
  

    

      
0
      
7079
      
87
      
86
      
chrII
      
5844805
      
5844808
      
5844806
      
5844301
      
5845398
      
EGL-5;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;NHR-67...
      
EGL-5;EOR-1;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;...
    
    

      
1
      
23326
      
86
      
77
      
chrV
      
6224694
      
6224698
      
6224696
      
6220642
      
6230087
      
EGL-5;EOR-1;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;...
      
EGL-5;EOR-1;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;...
    
    

      
2
      
30454
      
86
      
86
      
chrX
      
6296505
      
6296509
      
6296507
      
6296008
      
6297099
      
EGL-5;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;NHR-67...
      
EGL-5;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;NHR-67...
    
    

      
3
      
32390
      
86
      
86
      
chrX
      
11094147
      
11094171
      
11094159
      
11093630
      
11094622
      
EGL-5;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;NHR-67...
      
EGL-5;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;NHR-67...
    
    

      
4
      
33766
      
86
      
86
      
chrX
      
14525863
      
14525881
      
14525872
      
14525230
      
14526451
      
EGL-5;EOR-1;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;...
      
EGL-5;EOR-1;W03F9.2;PAX-1;CEH-39;NHR-2;FKH-10;...
    
  

In [4]:

    

df_core = df_[['chromosome', 'core_start', 'core_stop']]
df_core.columns = ['chrom', 'start', 'end']
df_core.head(5)


    

    
Out[4]:







  

    

      

      
chrom
      
start
      
end
    
  
  

    

      
0
      
chrII
      
5844805
      
5844808
    
    

      
1
      
chrV
      
6224694
      
6224698
    
    

      
2
      
chrX
      
6296505
      
6296509
    
    

      
3
      
chrX
      
11094147
      
11094171
    
    

      
4
      
chrX
      
14525863
      
14525881
    
  

In [5]:

    

df_extd = df_[['chromosome', 'extended_start', 'extended_stop']]
df_extd.columns = ['chrom', 'start', 'end']
df_extd.head(5)


    

    
Out[5]:







  

    

      

      
chrom
      
start
      
end
    
  
  

    

      
0
      
chrII
      
5844301
      
5845398
    
    

      
1
      
chrV
      
6220642
      
6230087
    
    

      
2
      
chrX
      
6296008
      
6297099
    
    

      
3
      
chrX
      
11093630
      
11094622
    
    

      
4
      
chrX
      
14525230
      
14526451
    
  

In [8]:

    

gv = yp.GenomicVenn2(
    BedTool.from_dataframe(df_regl[COLS_FLANK + ['annot']]),
    BedTool.from_dataframe(df_core),
    label_a='Accessible sites',
    label_b='(Chen et al., 2014)\ncore regions',
)

plt.figure(figsize=(18,8))
plt.subplot(1,2,1)
gv.plot()

plt.subplot(1,2,2)
gv = yp.GenomicVenn2(
    BedTool.from_dataframe(df_regl[COLS_FLANK + ['annot']]),
    BedTool.from_dataframe(df_extd),
    label_a='Accessible sites',
    label_b='(Chen et al., 2014)\nextended regions',
)
gv.plot()
plt.savefig('annot/FigA_overlaps/overlaps_Chen2014_TFs.pdf', bbox_inches='tight')


    

In [ ]: