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Blaise LI
libcodonusage
Commits
705a8246
Commit
705a8246
authored
3 years ago
by
Blaise Li
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Code to run diagnostics about genes.
parent
f43bdd71
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libcodonusage/__init__.py
+3
-1
3 additions, 1 deletion
libcodonusage/__init__.py
libcodonusage/libcodonusage.py
+140
-1
140 additions, 1 deletion
libcodonusage/libcodonusage.py
with
143 additions
and
2 deletions
libcodonusage/__init__.py
+
3
−
1
View file @
705a8246
__copyright__
=
"
Copyright (C) 2022 Blaise Li
"
__licence__
=
"
GNU GPLv3
"
__version__
=
0.
1
__version__
=
0.
3
from
.libcodonusage
import
(
aa2colour
,
columns_by_aa
,
detect_fishy_genes
,
load_bias_table
,
load_counts_table
,
render_md
,
violin_usage
,
violin_usage_vertical
,
...
...
This diff is collapsed.
Click to expand it.
libcodonusage/libcodonusage.py
+
140
−
1
View file @
705a8246
...
...
@@ -42,6 +42,12 @@ import numpy as np
import
pandas
as
pd
# python3 -m pip install cytoolz
from
cytoolz
import
groupby
# Python module that facilitates the exploration of tbular data
# python3 -m pip install plydata
# from plydata import define, pull, query
# Python module to vizualize set intersections
# python3 -m pip install upsetplot
from
upsetplot
import
from_indicators
,
UpSet
def
render_md
(
md_str
):
...
...
@@ -76,8 +82,141 @@ with Path(bgraphs.colorschemes._scheme_dir).joinpath(
# value: hexadecimal html colour code
aa2colour
=
{
**
colscheme
[
"
colors
"
],
"
*
"
:
'
#000000
'
}
def
load_counts_table
(
table_path
,
index_col
=
"
old_locus_tag
"
):
"""
Load a table or pre-computed codon counts at *table_path*.
The lines correspond to genes.
Besides the columns containing the counts for each codon,
there are other columns containing various pieces of information
regarding those genes.
"""
render_md
(
f
"
Loading data from
{
table_path
}
...
\n
"
)
codon_counts
=
pd
.
read_table
(
table_path
,
index_col
=
index_col
)
nb_genes
=
len
(
codon_counts
)
render_md
(
f
"""
There are
{
nb_genes
}
genes in the table.
The table looks as follows (first 3 lines):
"""
)
display
(
codon_counts
.
head
(
3
))
return
codon_counts
def
detect_fishy_genes
(
codon_counts
):
"""
Run diagnostics about genes included in table *codon_counts*.
These should help decide whether to exclude some of the genes.
A table of boolean criteria is returned, with one line per gene.
"""
render_md
(
"
### Searching for mis-annotated genes
"
)
wrong_start_col
=
codon_counts
[
"
expected_start_aa
"
]
==
"
-
"
start_stop_col
=
codon_counts
[
"
expected_start_aa
"
]
==
"
*
"
no_met_start_col
=
codon_counts
[
"
expected_start_aa
"
]
!=
"
M
"
start_upstream_col
=
codon_counts
[
"
start_upstream
"
]
has_stops_col
=
codon_counts
[
"
nb_stops
"
]
>
0
start_upstream_met_start_col
=
start_upstream_col
&
~
no_met_start_col
start_upstream_met_start_nostops_col
=
(
start_upstream_met_start_col
&
~
has_stops_col
)
has_stops_good_met_start_col
=
(
has_stops_col
&
~
no_met_start_col
&
~
start_upstream_met_start_col
)
no_stop_good_met_start_col
=
(
~
has_stops_col
&
~
no_met_start_col
&
~
start_upstream_met_start_col
)
criteria
=
pd
.
DataFrame
({
"
wrong_start
"
:
wrong_start_col
,
"
start_stop
"
:
start_stop_col
,
"
no_met_start
"
:
no_met_start_col
,
"
start_upstream
"
:
start_upstream_col
,
"
has_stops
"
:
has_stops_col
,
"
start_upstream_met_start
"
:
start_upstream_met_start_col
,
"
start_upstream_met_start_nostops
"
:
(
start_upstream_met_start_nostops_col
),
"
has_stops_good_met_start
"
:
has_stops_good_met_start_col
})
render_md
(
"
Number of genes in potentially fishy categories:
\n\n
"
)
display
(
criteria
.
agg
(
sum
))
render_md
(
"
Upset plot of the non-empty categories:
\n\n
"
)
fig
=
plt
.
figure
()
UpSet
(
from_indicators
(
*
[
criteria
.
columns
],
data
=
criteria
),
show_counts
=
True
).
plot
(
fig
=
fig
)
display
(
fig
)
plt
.
close
(
fig
)
wrong_start
=
set
(
criteria
[
wrong_start_col
].
index
)
start_stop
=
set
(
criteria
[
start_stop_col
].
index
)
no_met_start
=
set
(
criteria
[
no_met_start_col
].
index
)
start_upstream
=
set
(
criteria
[
start_upstream_col
].
index
)
# Not used
# has_stops = set(criteria[has_stops_col].index)
start_upstream_met_start
=
set
(
criteria
[
start_upstream_met_start_col
].
index
)
start_upstream_met_start_nostops
=
set
(
criteria
[
start_upstream_met_start_nostops_col
].
index
)
has_stops_good_met_start
=
set
(
criteria
[
has_stops_good_met_start_col
].
index
)
no_stop_good_met_start
=
set
(
criteria
[
no_stop_good_met_start_col
].
index
)
render_md
(
"""
We should avoid genes that are not in-frame. We can likely exclude
those that do not start with a valid start codon.
Here, this will be seen by looking at the translation of the first
codon.
If it is
"
*
"
(`start_stop`) or
"
-
"
(`wrong_start`),
the codon is not a valid start codon.
"""
)
render_md
(
f
"
There are
{
len
(
wrong_start
)
}
genes that start with a `-`:
"
)
display
(
wrong_start
)
render_md
(
f
"
There are
{
len
(
start_stop
)
}
genes that start with a stop codon:
"
)
display
(
start_stop
)
if
no_met_start
==
wrong_start
|
start_stop
:
render_md
(
"""
Genes not belonging to the `start_stop` or `wrong_start`
category all start with a methionine.
"""
)
else
:
raise
NotImplementedError
(
"
There are genes that start with something else than
'
M
'
,
"
"'
-
'
or
'
*
'
.
"
)
render_md
(
f
"
{
len
(
start_upstream
)
}
genes have a possibly ill-defined
"
"
start position:
"
)
display
(
start_upstream
)
if
start_upstream_met_start
:
render_md
(
f
"
{
len
(
start_upstream_met_start
)
}
of them have a valid
'
M
'"
"
start:
"
)
display
(
start_upstream_met_start
)
if
start_upstream_met_start_nostops
:
render_md
(
f
"
{
len
(
start_upstream_met_start_nostops
)
}
of them
"
"
also do not contain any stop:
"
)
display
(
start_upstream_met_start_nostops
)
render_md
(
"
Those genes could probably be kept.
"
)
render_md
(
f
"
{
len
(
has_stops_good_met_start
)
}
genes contain stops
"
"
but have a well defined start position with
'
M
'
.
"
)
if
len
(
has_stops_good_met_start
)
<=
10
:
display
(
has_stops_good_met_start
)
render_md
(
"""
If genes that have stop readthrough are a known phenomenon,
only those among them that do not have a valid start codon
might be excluded.
"""
)
render_md
(
f
"
{
len
(
no_stop_good_met_start
)
}
genes have a well defined
"
"
start position with
'
M
'
and contain no stop codon.
"
)
if
len
(
no_stop_good_met_start
)
<=
10
:
display
(
no_stop_good_met_start
)
return
criteria
def
load_bias_table
(
table_path
,
nb_cluster_series
=
2
):
"""
...
...
@@ -109,7 +248,7 @@ def load_bias_table(table_path, nb_cluster_series=2):
# * `cluster_{aa}_full_bias` for each amino-acid
# having more than one codon
index_col
=
list
(
range
(
9
+
nb_cluster_series
*
(
len
(
aa2colour
)
-
2
))),
header
=
[
0
,
1
])
header
=
[
0
,
1
])
def
boxplot_usage
(
usage_table
,
ylabel
,
whiskers
=
"
1.5 IQR
"
):
...
...
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