In [1]:

    

%pylab inline
%config InlineBackend.figure_format = 'retina'


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

import pandas as pd
import seaborn as sns


    

In [3]:

    

k35_df = pd.read_csv('mmetsp/Asterionellopsis_glacialis/k35/decision_nodes.csv', skipinitialspace=True)
k27_df = pd.read_csv('mmetsp/Asterionellopsis_glacialis/k27/decision_nodes.csv', skipinitialspace=True)


    

In [4]:

    

k35_df.head()


    

    
Out[4]:







  

    

      

      
read_n
      
l_degree
      
r_degree
      
position
      
hash
    
  
  

    

      
0
      
414
      
2
      
1
      
9
      
4552537295491192989
    
    

      
1
      
555
      
2
      
1
      
3
      
10400599629126302337
    
    

      
2
      
917
      
2
      
1
      
5
      
8559623946996443022
    
    

      
3
      
1056
      
1
      
2
      
12
      
9867647372000252236
    
    

      
4
      
1929
      
2
      
1
      
6
      
10446294276324287310
    
  

In [101]:

    

k27_df.hash.nunique(), k35_df.hash.nunique()


    

    
Out[101]:





(1465148, 966286)

In [102]:

    

k35_df['degree'] = k35_df['l_degree'] + k35_df['r_degree']
k27_df['degree'] = k27_df['l_degree'] + k27_df['r_degree']


    

In [103]:

    

figsize(18,10)
fig, ax_mat = subplots(ncols=3, nrows=2)
top = ax_mat[0]
sns.distplot(k35_df.degree, kde=False, ax=top[0], bins=8)
sns.distplot(k35_df.l_degree, kde=False, ax=top[1], bins=5)
sns.distplot(k35_df.r_degree, kde=False, ax=top[2], bins=5)

bottom = ax_mat[1]
sns.distplot(k27_df.degree, kde=False, ax=bottom[0], bins=8)
sns.distplot(k27_df.l_degree, kde=False, ax=bottom[1], bins=5)
sns.distplot(k27_df.r_degree, kde=False, ax=bottom[2], bins=5)


    

    




/home/camille/miniconda/envs/goetia/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "






    
Out[103]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f5f175dccc0>






    

In [6]:

    

figsize(12,8)
sns.distplot(k35_df.position, kde=False, label='K=35')
sns.distplot(k27_df.position, kde=False, label='K=27')
legend()


    

    




/home/camille/miniconda/envs/goetia/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "






    
Out[6]:





<matplotlib.legend.Legend at 0x7f9a5757e5c0>






    

In [118]:

    

k35_melted_df = k35_df.melt(id_vars=['hash', 'position'], value_vars=['l_degree', 'r_degree'], )
k27_melted_df = k27_df.melt(id_vars=['hash', 'position'], value_vars=['l_degree', 'r_degree'], )

k27_melted_df.head()


    

    
Out[118]:







  

    

      

      
hash
      
position
      
variable
      
value
    
  
  

    

      
0
      
9070239862430184705
      
9
      
l_degree
      
2
    
    

      
1
      
13854235864955274342
      
3
      
l_degree
      
2
    
    

      
2
      
6350396296750610583
      
5
      
l_degree
      
2
    
    

      
3
      
18383274751344853795
      
20
      
l_degree
      
1
    
    

      
4
      
18256312570815508169
      
16
      
l_degree
      
2
    
  

In [119]:

    

figsize(18,8)
sns.violinplot('position', 'value', 'variable', k27_melted_df)


    

    
Out[119]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f5f16d4e160>






    

In [7]:

    

k35_dnodes_per_read = k35_df.groupby('read_n').count().\
    reset_index()[['read_n', 'hash']].rename({'hash': 'n_dnodes'}, axis='columns')
    
k27_dnodes_per_read = k27_df.groupby('read_n').count().\
    reset_index()[['read_n', 'hash']].rename({'hash': 'n_dnodes'}, axis='columns')


    

In [9]:

    

ax = k35_dnodes_per_read.rolling(100, min_periods=10, on='read_n').mean().plot(x='read_n', 
                                                                               y='n_dnodes', 
                                                                               label='k = 35')

ax = k27_dnodes_per_read.rolling(100, min_periods=10, on='read_n').mean().plot(x='read_n', 
                                                                               y='n_dnodes', 
                                                                               label='k = 27', 
                                                                               ax=ax)

ax.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter("{x:,}"))


    

In [1]:

    

from goetia.minimizers import WKMinimizer
from goetia.processors import MinimizerProcessor


    

In [6]:

    

M = WKMinimizer(10, 25)
S = "GACAACGGTAAAAGTTCTAATGCTGCCGAGTCACGGGAAGGATAGAGTGAGTCCCACCATATGGCGCACC"


    

In [7]:

    

print(S)
for kmer, pos in M.get_minimizer_kmers(S):
    print(pos * ' ', kmer, sep='')


    

    




GACAACGGTAAAAGTTCTAATGCTGCCGAGTCACGGGAAGGATAGAGTGAGTCCCACCATATGGCGCACC
     CGGTAAAAGTTCTAATGCTGCCGAG
         AAAAGTTCTAATGCTGCCGAGTCAC
                  AATGCTGCCGAGTCACGGGAAGGAT
                      CTGCCGAGTCACGGGAAGGATAGAG
                         CCGAGTCACGGGAAGGATAGAGTGA
                                  GGGAAGGATAGAGTGAGTCCCACCA
                                   GGAAGGATAGAGTGAGTCCCACCAT
                                        GATAGAGTGAGTCCCACCATATGGC

In [2]:

    

%%time 
proc = MinimizerProcessor(25, 25, 'minimizers.csv')
proc.process('/store/biodb/genomes/fugu/Takifugu_rubripes.FUGU5.dna_rm.toplevel.fa', output_interval=1)


    

    




CPU times: user 51.8 s, sys: 1min 10s, total: 2min 1s
Wall time: 2min 6s

In [ ]: