In [7]:

    

% pylab inline
import pybedtools
import pandas as pd
import seaborn as sns
sns.set_context('paper', font_scale=2)
sns.set_style('whitegrid')
from riboraptor.count import get_region_sizes


    

    




Populating the interactive namespace from numpy and matplotlib

In [20]:

    

hg_cds_bed = '/home/cmb-panasas2/skchoudh/genomes/hg38/annotation/gencode.v25.gffutils.cds.bed'
mm_cds_bed = '/home/cmb-panasas2/skchoudh/genomes/mm10/annotation/gencode.vM11.gffutils.cds.bed'
ce_cds_bed = '/home/cmb-panasas2/skchoudh/genomes/WBcel235/annotation/Caenorhabditis_elegans.WBcel235.91.gffutils.cds.bed'
z_cds_bed = '/home/cmb-panasas2/skchoudh/genomes/GRCz10/annotation/Danio_rerio.GRCz10.91.gffutils.cds.bed'
rn_cds_bed = '/home/cmb-panasas2/skchoudh/genomes/rn6/annotation/Rattus_norvegicus.Rnor_6.0.91.gffutils.cds.bed'
dmel_cds_bed = '/home/cmb-panasas2/skchoudh/genomes/drosophila_melanogaster_BDGP6/annotation/Drosophila_melanogaster.BDGP6.91.gffutils.cds.bed'
ecoli_cds_bed = '/home/cmb-panasas2/skchoudh/genomes/escherichia_coli_str_k_12_substr_mg1655/annotation/Escherichia_coli_str_k_12_substr_mg1655.ASM584v2.38.gffutils.cds.bed'

genomes = {'hg38' : hg_cds_bed, 
           'mm10' : mm_cds_bed, 
           'WBcel235' : ce_cds_bed, 
           'GRCz10' : z_cds_bed,
           'rn6' : rn_cds_bed, 
           'BDGP6' : dmel_cds_bed,
           'EColi_MG1655' : ecoli_cds_bed}


    

In [29]:

    

region_sizes = {}
for key, value in genomes.iteritems():
    region_sizes[key] = get_region_sizes(value)


    

In [30]:

    

region_sizes_df = []
for key, value in region_sizes.iteritems():
    region_sizes_df.append((key, np.mean(value.values()), np.median(value.values()), np.std(value.values())))
region_sizes_df = pd.DataFrame(region_sizes_df, columns= ['genome', 'mean_cds_length', 'median_cds_length', 'stddev_cds_length'])
region_sizes_df = region_sizes_df.set_index('genome')


    

In [31]:

    

region_sizes_df


    

    
Out[31]:







  

    

      

      
mean_cds_length
      
median_cds_length
      
stddev_cds_length
    
    

      
genome
      

      

      

    
  
  

    

      
EColi_MG1655
      
950.731884
      
846.0
      
626.723205
    
    

      
BDGP6
      
1641.571921
      
1188.0
      
1965.628589
    
    

      
rn6
      
1556.412989
      
1116.0
      
1497.063878
    
    

      
WBcel235
      
1244.026605
      
993.0
      
1272.831754
    
    

      
hg38
      
1771.304086
      
1331.0
      
2015.284342
    
    

      
mm10
      
1616.977267
      
1164.0
      
1734.850965
    
    

      
GRCz10
      
1671.967030
      
1233.0
      
1780.472524
    
  

In [34]:

    

fig  = plt.figure(figsize=(20,20))
i = 1
for key, value in region_sizes.iteritems():
    ax = plt.subplot(4, 2, i)
    sns.violinplot(value.values(), ax=ax)
    ax.set(xlim=(0, 10000)) 
    ax.set_title(key)
    i+=1
fig.tight_layout()