"""
===
ccs
===
Tools for inspecting output of the PacBio ``ccs`` program:
https://github.com/PacificBiosciences/unanimity/blob/develop/doc/PBCCS.md
"""
import collections
import io
import itertools
import math
import os
import re
import tempfile # noqa: F401
import textwrap # noqa: F401
import numpy
import pandas as pd
import pathos
import plotnine as p9
import pysam
import alignparse.utils
from alignparse.constants import CBPALETTE
[docs]class Summary:
"""Summary of a single ``ccs`` run.
Parameters
----------
name : str
Name of the ``ccs`` run.
fastqfile : str
FASTQ file with circular consensus sequences, typically generated by
PacBio ``ccs`` program. Can be gzipped. Number of passes is determined
from the ``np`` tag in the FASTQ comments if present.
reportfile : str or None
Report file generated by ``ccs`` using ``--reportFile`` flag, or
None if no such report available.
Attributes
----------
name : str
Name of ``ccs`` run.
fastqfile : str
FASTQ file with the circular consensus sequences.
reportfile : str or None
``ccs`` report file. Currently handles reports in formats generated
by ``ccs`` versions 3.* and 4.0.
zmw_stats : pandas.DataFrame or None
Stats on ZMW extracted from `reportfile`.
passes : numpy.array or None
Lists number of passes for all circular consensus sequences, or
`None` if this information is not available in a ``np`` tag
in `fastqfile`.
accuracy : numpy.array
Lists accuracies for all circular consensus sequences. This is the
average accuracy across sites computed from Q-values in `fastqfile`.
length : numpy.array
Lists lengths for all circular consensus sequences.
"""
def __init__(self, name, fastqfile, reportfile):
"""See main class docstring."""
self.name = name
self.fastqfile = fastqfile
if not os.path.isfile(fastqfile):
raise IOError(f"cannot find `fastqfile` {fastqfile}")
ccs_stats = get_ccs_stats(self.fastqfile)
self.passes = ccs_stats.passes
self.accuracy = ccs_stats.accuracy
self.length = ccs_stats.length
assert len(self.accuracy) == len(self.length)
assert (self.passes is None) or (len(self.passes) == len(self.length))
if reportfile:
self.reportfile = reportfile
if not os.path.isfile(reportfile):
raise IOError(f"cannot find `reportfile` {reportfile}")
self.zmw_stats = report_to_stats(self.reportfile)
zmw_stats_nccs = self.zmw_stats[
self.zmw_stats["status"].str.match("^Success")
]["number"].sum()
if len(self.length) != zmw_stats_nccs:
raise ValueError(
"`fastqfile`, `reportfile` differ on number "
f"CCSs.\n{fastqfile} has {len(self.passes)}\n"
f"{reportfile} has {zmw_stats_nccs}"
)
else:
self.zmw_stats = None
self.reportfile = None
[docs]class Summaries:
"""Summaries of ``ccs`` runs.
Parameters
----------
df : pandas.DataFrame
Data frame giving information on runs of ``ccs`` being summarized.
name_col : str
Column in `df` with name of ``ccs`` run.
fastq_col : str
Column in `df` with FASTQ file for run, appropriate for passing
to :class:`Summary` as `fastqfile`.
report_col : str or None
Column in `df` with report for run, appropriate for passing to
:class:`Summary` as `reportfile`. Set to `None` if no reports.
If there are no reports, then ZMW stats are not available.
ncpus : int
Number of CPUs to use; -1 means all available. Useful as processing
all the FASTQ files to compute read accuracies can take a while.
Attributes
----------
summaries : list
List of :class:`Summary` objects for each run.
"""
def __init__(
self, df, *, name_col="name", fastq_col="fastq", report_col="report", ncpus=-1
):
"""See main class docstring."""
cols = [name_col, fastq_col]
if report_col:
cols.append(report_col)
if len(cols) != len(set(cols)):
raise ValueError("repeated column names in `df`")
for col in cols:
if col not in df.columns:
raise ValueError(f"`df` lacks column {col}")
if len(df[name_col]) != len(df[name_col].unique()):
raise ValueError(f"the run names in {name_col} are not unique")
# get Summary for each run in a multiprocessing pool
if ncpus == -1:
ncpus = pathos.multiprocessing.cpu_count()
else:
ncpus = min(pathos.multiprocessing.cpu_count(), ncpus)
if ncpus < 1:
raise ValueError("`ncpus` must be >= 1")
elif ncpus > 1:
pool = pathos.pools.ProcessPool(ncpus)
map_func = pool.map
else:
def map_func(f, *args):
return [f(*argtup) for argtup in zip(*args)]
self.summaries = map_func(
Summary,
df[name_col],
df[fastq_col],
(df[report_col] if report_col else itertools.repeat(None)),
)
# close, clear pool: https://github.com/uqfoundation/pathos/issues/111
if ncpus > 1:
pool.close()
pool.join()
pool.clear()
[docs] def plot_ccs_stats(
self,
variable,
*,
trim_frac=0.005,
bins=25,
histogram_stat="count",
maxcol=None,
panelsize=1.75,
):
"""Plot histograms of CCS stats for all runs.
Parameters
----------
variable : {'length', 'passes', 'accuracy'}
Variable for which we plot stats. You will get an error
if :meth:`Summaries.has_stat` is not true for `variable`.
trim_frac : float
Trim this amount of the bottom and top fraction from the
data before plotting. Useful if outliers greatly extend scale.
bins : int
Number of histogram binds
histogram_stat : {'count', 'density'}
Plot the count of CCSs or their density normalized for each run.
maxcol : None or int
Max number of columns in faceted plot.
panelsize : float
Size of each plot panel.
Returns
-------
plotnine.ggplot.ggplot
A panel of histograms.
"""
df = (
self.ccs_stats(variable)
.assign(
lower=lambda x: x[variable].quantile(trim_frac),
upper=lambda x: x[variable].quantile(1 - trim_frac),
trim=lambda x: (
(x[variable] > x["upper"]) | (x[variable] < x["lower"])
),
)
.query("not trim")
)
npanels = len(df["name"].unique())
if maxcol is None:
ncol = npanels
else:
ncol = min(maxcol, npanels)
nrow = math.ceil(npanels / ncol)
p = (
p9.ggplot(df, p9.aes(variable, y=f"..{histogram_stat}.."))
+ p9.geom_histogram(bins=bins)
+ p9.facet_wrap("~ name", ncol=ncol)
+ p9.theme(
figure_size=(panelsize * ncol, panelsize * nrow),
axis_text_x=p9.element_text(angle=90, vjust=1, hjust=0.5),
)
+ p9.ylab("number of CCSs")
)
return p
[docs] def ccs_stats(self, variable):
"""Get CCS stats for all runs.
Parameters
----------
variable : {'length', 'passes', 'accuracy'}
Variable for which we get stats. You will get an error
if :meth:`Summaries.has_stat` is not true for `variable`.
Returns
-------
pandas.DataFrame
Data frame with columns of 'name' (holding run name) and the value
of `variable` giving variable value for all CCSs.
"""
if not self.has_stat(variable):
raise ValueError(f"no stats for `variable` {variable}")
df_list = []
for summary in self.summaries:
df_list.append(
pd.DataFrame(
{"name": summary.name, variable: getattr(summary, variable)}
)
)
return pd.concat(df_list, sort=False, ignore_index=True).assign(
name=lambda x: pd.Categorical(x["name"], x["name"].unique(), ordered=True)
)
[docs] def has_stat(self, variable):
"""Do the summaries contain a statistic?
Parameters
----------
variable : {'length', 'passes', 'accuracy'}
Variable for which we want statistics.
Returns
-------
bool
`True` if all runs have statistics for `variable`,
`False` otherwise.
"""
valid_variables = {"length", "passes", "accuracy"}
if variable not in valid_variables:
raise ValueError(
f"Invalid `variable` {variable}. Must be one " f"of: {valid_variables}"
)
return all(getattr(summary, variable) is not None for summary in self.summaries)
[docs] def plot_zmw_stats(self, **kwargs):
"""Plot of ZMW stats for all runs.
Note
----
Raises an error if :meth:`Summaries.has_zmw_stats` is not `True`.
Parameters
----------
``**kwargs`` : dict
Keyword arguments passed to :meth:`Summaries.zmw_stats`.
Returns
-------
plotnine.ggplot.ggplot
Stacked bar graph of ZMW stats for each run.
"""
df = self.zmw_stats(**kwargs)
p = (
p9.ggplot(df, p9.aes(x="name", y="number", fill="status"))
+ p9.geom_col(position=p9.position_stack(reverse=True), width=0.8)
+ p9.theme(
axis_text_x=p9.element_text(angle=90, vjust=1, hjust=0.5),
figure_size=(0.4 * len(df["name"].unique()), 2.5),
)
+ p9.ylab("number of ZMWs")
+ p9.xlab("")
)
if len(df["status"].unique()) < len(CBPALETTE):
p = p + p9.scale_fill_manual(CBPALETTE[1:])
return p
[docs] def zmw_stats(self, *, minfailfrac=0.01, groupsuccess=True):
"""Get ZMW stats for all runs.
Note
----
Raises an error if :meth:`Summaries.has_zmw_stats` is not `True`.
Parameters
----------
minfailfrac : float
Group failure categories with <= this fraction ZMWs for all runs.
groupsuccess : bool
Group all success categories into 'Success -- CCS generated'.
Returns
-------
pandas.DataFrame
Data frame with stats on ZMW status for all runs.
"""
if not self.has_zmw_stats():
raise ValueError("ZMW stats not available")
df = pd.concat(
[summary.zmw_stats.assign(name=summary.name) for summary in self.summaries],
sort=False,
ignore_index=True,
)[["name", "status", "number", "fraction"]].assign(
max_fraction=lambda x: (x.groupby("status")["fraction"].transform(max)),
failed=lambda x: x["status"].str.contains("Failed"),
other=lambda x: ((x["max_fraction"] < minfailfrac) & x["failed"]),
)
other_df = (
df.query("other")
.groupby("name")
.aggregate({"number": sum, "fraction": sum, "failed": any})
.assign(status="Failed -- Other reason")
.reset_index()
.assign(
max_fraction=lambda x: (x.groupby("status")["fraction"].transform(max))
)
)
df = df.query("not other").drop(columns="other").merge(other_df, how="outer")
if groupsuccess:
success_df = (
df[df["status"].str.match("^Success")]
.groupby("name")
.aggregate(
{"number": sum, "fraction": sum, "failed": any, "max_fraction": max}
)
.reset_index()
.assign(status="Success -- CCS generated")
)
df = df[~df["status"].str.match("^Success")].merge(success_df, how="outer")
# order columns
names = [summary.name for summary in self.summaries]
statuses = df.sort_values(["failed", "max_fraction"], ascending=[True, False])[
"status"
].unique()
df = (
df.assign(
name=lambda x: pd.Categorical(x["name"], names, ordered=True),
status=lambda x: pd.Categorical(x["status"], statuses, ordered=True),
)
.sort_values(["name", "status"])
.drop(columns=["max_fraction", "failed"])
.reset_index(drop=True)
)
return df
[docs] def has_zmw_stats(self):
"""Are ZMW stats available?
Returns
-------
bool
`True` if all runs have ZMW stats, `False` otherwise.
"""
return all(summary.zmw_stats is not None for summary in self.summaries)
[docs]def report_to_stats(reportfile):
"""Parse ZMW statistics from report file.
Parameters
----------
reportfile : str
Report file generated by ``ccs`` using ``--reportFile`` flag
using ``ccs`` version 3.* and 4.0, or the ``--report-file``
flag using ``ccs`` version 5.0.
Returns
-------
pandas.DataFrame
A data frame with the statistics.
Example
-------
An example of the ``ccs`` version 6.0.0 output:
>>> pd.set_option('display.max_columns', 10) # show many columns
>>> reportfile = tempfile.NamedTemporaryFile(mode='w')
>>> _ = reportfile.write(textwrap.dedent('''
... ZMWs input : 3739007
...
... ZMWs pass filters : 1669857 (44.66%)
... ZMWs fail filters : 2069150 (55.34%)
... ZMWs shortcut filters : 0 (0.000%)
...
... ZMWs with tandem repeats : 1096 (0.053%)
...
... Exclusive failed counts
... Below SNR threshold : 53110 (2.567%)
... Median length filter : 0 (0.000%)
... Lacking full passes : 1382995 (66.84%)
... Heteroduplex insertions : 30137 (1.456%)
... Coverage drops : 2193 (0.106%)
... Insufficient draft cov : 9055 (0.438%)
... Draft too different : 71 (0.003%)
... Draft generation error : 22575 (1.091%)
... Draft above --max-length : 235 (0.011%)
... Draft below --min-length : 227 (0.011%)
... Reads failed polishing : 0 (0.000%)
... Empty coverage windows : 264 (0.013%)
... CCS did not converge : 525 (0.025%)
... CCS below minimum RQ : 567763 (27.44%)
... Unknown error : 0 (0.000%)
...
... Additional passing metrics
... ZMWs missing adapters : 403623 (24.17%)
... ''').lstrip())
>>> reportfile.flush()
>>> report_to_stats(reportfile.name) # doctest: +NORMALIZE_WHITESPACE
status number fraction
0 Success -- CCS generated 1669857 0.446604
1 Failed -- Below SNR threshold 53110 0.014204
2 Failed -- Median length filter 0 0.000000
3 Failed -- Lacking full passes 1382995 0.369883
4 Failed -- Heteroduplex insertions 30137 0.008060
5 Failed -- Coverage drops 2193 0.000587
6 Failed -- Insufficient draft cov 9055 0.002422
7 Failed -- Draft too different 71 0.000019
8 Failed -- Draft generation error 22575 0.006038
9 Failed -- Draft above --max-length 235 0.000063
10 Failed -- Draft below --min-length 227 0.000061
11 Failed -- Reads failed polishing 0 0.000000
12 Failed -- Empty coverage windows 264 0.000071
13 Failed -- CCS did not converge 525 0.000140
14 Failed -- CCS below minimum RQ 567763 0.151849
15 Failed -- Unknown error 0 0.000000
16 Failed -- shortcut filter 0 0.000000
>>> reportfile.close()
An example of the ``ccs`` version 5.0.0 output:
>>> pd.set_option('display.max_columns', 10) # show many columns
>>> reportfile = tempfile.NamedTemporaryFile(mode='w')
>>> _ = reportfile.write(textwrap.dedent('''
... ZMWs input : 535101
...
... ZMWs pass filters : 325574 (60.84%)
... ZMWs fail filters : 209527 (39.16%)
... ZMWs shortcut filters : 0 (0.00%)
...
... ZMWs with tandem repeats : 98 (0.02%)
...
... Exclusive counts for ZMWs failing filters:
... Below SNR threshold : 0 (0.00%)
... Median length filter : 0 (0.00%)
... Lacking full passes : 94869 (45.28%)
... Heteroduplex insertions : 4373 (2.09%)
... Coverage drops : 526 (0.25%)
... Insufficient draft cov : 2783 (1.33%)
... Draft too different : 1436 (0.69%)
... Draft generation error : 5426 (2.59%)
... Draft above --max-length : 469 (0.22%)
... Draft below --min-length : 152 (0.07%)
... Reads failed polishing : 0 (0.00%)
... Empty coverage windows : 136 (0.06%)
... CCS did not converge : 44 (0.02%)
... CCS below minimum RQ : 99782 (47.62%)
... Unknown error : 0 (0.00%)
... ''').lstrip())
>>> reportfile.flush()
>>> report_to_stats(reportfile.name)
status number fraction
0 Success -- CCS generated 325574 0.607902
1 Failed -- Below SNR threshold 0 0.000000
2 Failed -- Median length filter 0 0.000000
3 Failed -- Lacking full passes 94869 0.177137
4 Failed -- Heteroduplex insertions 4373 0.008165
5 Failed -- Coverage drops 526 0.000982
6 Failed -- Insufficient draft cov 2783 0.005196
7 Failed -- Draft too different 1436 0.002681
8 Failed -- Draft generation error 5426 0.010131
9 Failed -- Draft above --max-length 469 0.000876
10 Failed -- Draft below --min-length 152 0.000284
11 Failed -- Reads failed polishing 0 0.000000
12 Failed -- Empty coverage windows 136 0.000254
13 Failed -- CCS did not converge 44 0.000082
14 Failed -- CCS below minimum RQ 99782 0.186310
15 Failed -- Unknown error 0 0.000000
16 Failed -- shortcut filter 0 0.000000
>>> reportfile.close()
An example of the ``ccs`` version 4.0.0 output:
>>> pd.set_option('display.max_columns', 10) # show many columns
>>> reportfile = tempfile.NamedTemporaryFile(mode='w')
>>> _ = reportfile.write(textwrap.dedent('''
... ZMWs input (A) : 686919
... ZMWs generating CCS (B) : 182500 (26.57%)
... ZMWs filtered (C) : 504419 (73.43%)
...
... Exclusive ZMW counts for (C):
... No usable subreads : 0 (0.00%)
... Below SNR threshold : 0 (0.00%)
... Lacking full passes : 344375 (68.27%)
... Heteroduplexes : 1056 (0.21%)
... Min coverage violation : 2035 (0.40%)
... Draft generation error : 7636 (1.51%)
... Draft above --max-length : 49 (0.01%)
... Draft below --min-length : 2 (0.00%)
... Lacking usable subreads : 0 (0.00%)
... CCS did not converge : 0 (0.00%)
... CCS below minimum RQ : 149315 (29.60%)
... Unknown error : 0 (0.00%)
... ''').lstrip())
>>> reportfile.flush()
>>> report_to_stats(reportfile.name)
status number fraction
0 Success -- CCS generated 182500 0.265660
1 Failed -- No usable subreads 0 0.000000
2 Failed -- Below SNR threshold 0 0.000000
3 Failed -- Lacking full passes 344375 0.501297
4 Failed -- Heteroduplexes 1056 0.001537
5 Failed -- Min coverage violation 2035 0.002962
6 Failed -- Draft generation error 7636 0.011116
7 Failed -- Draft above --max-length 49 0.000071
8 Failed -- Draft below --min-length 2 0.000003
9 Failed -- Lacking usable subreads 0 0.000000
10 Failed -- CCS did not converge 0 0.000000
11 Failed -- CCS below minimum RQ 149315 0.217354
12 Failed -- Unknown error 0 0.000000
>>> reportfile.close()
An example of the ``ccs`` version 3.1.0 output:
>>> reportfile = tempfile.NamedTemporaryFile(mode='w')
>>> _ = reportfile.write(textwrap.dedent('''
... ZMW Yield
... Success -- CCS generated,242220,45.57%
... Failed -- Below SNR threshold,0,0.00%
... Failed -- No usable subreads,4877,0.92%
... Failed -- Insert size too long,35,0.00%
... Failed -- Insert size too small,0,0.00%
... Failed -- Not enough full passes,180620,33.98%
... Failed -- Too many unusable subreads,1,0.00%
... Failed -- CCS did not converge,23,0.00%
... Failed -- CCS below minimum predicted accuracy,103801,19.53%
... Failed -- Unknown error during processing,0,0.00%
...
...
... Subread Yield
... Success - Used for CCS,10972010,89.06%
... Failed -- Below SNR threshold,0,0.00%
... Failed -- Alpha/Beta mismatch,171,0.00%
... Failed -- Below minimum quality,0,0.00%
... Failed -- Filtered by size,144209,1.17%
... Failed -- Identity too low,2745750,22.29%
... Failed -- Z-Score too low,0,0.00%
... Failed -- From ZMW with too few passes,274296,2.23%
... Failed -- Other,928871,7.54%
... ''').lstrip())
>>> reportfile.flush()
>>> report_to_stats(reportfile.name)
status number fraction
0 Success -- CCS generated 242220 0.4557
1 Failed -- Below SNR threshold 0 0.0000
2 Failed -- No usable subreads 4877 0.0092
3 Failed -- Insert size too long 35 0.0000
4 Failed -- Insert size too small 0 0.0000
5 Failed -- Not enough full passes 180620 0.3398
6 Failed -- Too many unusable subreads 1 0.0000
7 Failed -- CCS did not converge 23 0.0000
8 Failed -- CCS below minimum predicted accuracy 103801 0.1953
9 Failed -- Unknown error during processing 0 0.0000
>>> reportfile.close()
An example of the ``ccs`` version 3.4.1 output:
>>> reportfile = tempfile.NamedTemporaryFile(mode='w')
>>> _ = reportfile.write(textwrap.dedent('''
... ZMW Yield
... Success (without retry) -- CCS generated,202033,29.44%
... Success (with retry) -- CCS generated,2,0.00%
... Failed -- Below SNR threshold,0,0.00%
... Failed -- No usable subreads,2093,0.31%
... Failed -- Insert size too long,10,0.01%
... Failed -- Insert size too small,79,0.01%
... Failed -- Not enough full passes,343876,50.12%
... Failed -- Too many unusable subreads,0,0.00%
... Failed -- CCS did not converge,0,0.00%
... Failed -- CCS below minimum predicted accuracy,138083,20.12%
... Failed -- Unknown error during processing,0,0.00%
...
...
... ''').lstrip())
>>> reportfile.flush()
>>> report_to_stats(reportfile.name) # doctest: +NORMALIZE_WHITESPACE
status number fraction
0 Success (without retry) -- CCS generated 202033 0.2944
1 Success (with retry) -- CCS generated 2 0.0000
2 Failed -- Below SNR threshold 0 0.0000
3 Failed -- No usable subreads 2093 0.0031
4 Failed -- Insert size too long 10 0.0001
5 Failed -- Insert size too small 79 0.0001
6 Failed -- Not enough full passes 343876 0.5012
7 Failed -- Too many unusable subreads 0 0.0000
8 Failed -- CCS did not converge 0 0.0000
9 Failed -- CCS below minimum predicted accuracy 138083 0.2012
10 Failed -- Unknown error during processing 0 0.0000
>>> reportfile.close()
"""
for func in [
_report_to_stats_v6,
_report_to_stats_v5,
_report_to_stats_v4,
_report_to_stats_v3,
]:
df = func(reportfile)
if df is not None:
return df
raise IOError(f"Cannot match report in {reportfile}")
def _reportfile_version_check(reportfile, pattern):
"""
Check reportfile against known version specific patterns
Parameters
----------
reportfile : str
Report file generated by ``ccs``
pattern : re.Pattern
Pattern specific to a version of ``ccs``
Returns
-------
bool
Returns True is pattern is found, False otherwise
"""
with open(reportfile) as f:
matches = re.findall(pattern, f.read())
if len(matches) == 0:
return False
return True
def _reportfile_to_dict(reportfile):
"""
Read in reportfile and convert to dictionary. Assumes colon delimited entries.
Parameters
----------
reportfile : str
Report file generated by ``ccs``
Returns
-------
dict
Returns a dictionary where the keys are names of the ccs statistics (to the
left of the colon in the reportfile) and the values are the statistic
integers (to the right of the colon in the reportfile)
"""
report_dict = {}
with open(reportfile) as f:
lines = [line.strip() for line in f]
for line in lines:
ls = line.split(":", 1)
key = ls[0].strip()
if (len(ls)) == 2:
val = ls[1].split("(")[0].strip()
report_dict[key] = val
return report_dict
def _reportdict_to_df(report_dict, success_key, failure_categories):
"""
Convert the report dictionary (generated by ``_reportfile_to_dict`` to a
pandas DataFrame
Parameters
----------
report_dict : str
Python dictionary generated by ``_reportfile_to_dict``
success_key : str
String specifying value to pull for CCS generated (name can be version specific)
failure_categories : list
List of failure category names to display in the resulting DataFrame
Returns
-------
pandas.DataFrame
Returns a pandas DataFrame with ccs statistics
"""
failed_records = [
("Failed -- " + fc, int(report_dict[fc])) for fc in failure_categories
]
return (
pd.concat(
[
pd.DataFrame(
{
"status": ["Success -- CCS generated"],
"number": [int(report_dict[success_key])],
}
),
pd.DataFrame(failed_records, columns=["status", "number"]),
]
)
.reset_index(drop=True)
.assign(fraction=lambda x: x["number"] / x["number"].sum())
)
def _report_to_stats_v6(reportfile):
"""Implement :func:`report_to_stats` for ``ccs`` 6.* output.
Returns
-------
pandas.DataFrame or None
Returns `None` if cannot match `reportfile`.
"""
failure_categories = [
"Below SNR threshold",
"Median length filter",
"Lacking full passes",
"Heteroduplex insertions",
"Coverage drops",
"Insufficient draft cov",
"Draft too different",
"Draft generation error",
"Draft above --max-length",
"Draft below --min-length",
"Reads failed polishing",
"Empty coverage windows",
"CCS did not converge",
"CCS below minimum RQ",
"Unknown error",
"shortcut filter",
]
v6_pattern = re.compile(r"Exclusive failed counts\n")
if _reportfile_version_check(reportfile, v6_pattern):
report_dict = _reportfile_to_dict(reportfile)
report_dict["shortcut filter"] = report_dict.pop("ZMWs shortcut filters")
return _reportdict_to_df(report_dict, "ZMWs pass filters", failure_categories)
return None
def _report_to_stats_v5(reportfile):
"""Implement :func:`report_to_stats` for ``ccs`` 5.* output.
Returns
-------
pandas.DataFrame or None
Returns `None` if cannot match `reportfile`.
"""
failure_categories = [
"Below SNR threshold",
"Median length filter",
"Lacking full passes",
"Heteroduplex insertions",
"Coverage drops",
"Insufficient draft cov",
"Draft too different",
"Draft generation error",
"Draft above --max-length",
"Draft below --min-length",
"Reads failed polishing",
"Empty coverage windows",
"CCS did not converge",
"CCS below minimum RQ",
"Unknown error",
"shortcut filter",
]
v5_pattern = re.compile(r"Exclusive counts for ZMWs failing filters:\n")
if _reportfile_version_check(reportfile, v5_pattern):
report_dict = _reportfile_to_dict(reportfile)
report_dict["shortcut filter"] = report_dict.pop("ZMWs shortcut filters")
return _reportdict_to_df(report_dict, "ZMWs pass filters", failure_categories)
return None
def _report_to_stats_v4(reportfile):
"""Implement :func:`report_to_stats` for ``ccs`` 4.* output.
Returns
-------
pandas.DataFrame or None
Returns `None` if cannot match `reportfile`.
"""
failure_categories = [
"No usable subreads",
"Below SNR threshold",
"Lacking full passes",
"Heteroduplexes",
"Min coverage violation",
"Draft generation error",
"Draft above --max-length",
"Draft below --min-length",
"Lacking usable subreads",
"CCS did not converge",
"CCS below minimum RQ",
"Unknown error",
]
v4_pattern = re.compile(r"Exclusive ZMW counts for \(C\):\n")
if _reportfile_version_check(reportfile, v4_pattern):
report_dict = _reportfile_to_dict(reportfile)
return _reportdict_to_df(
report_dict, "ZMWs generating CCS (B)", failure_categories
)
return None
def _report_to_stats_v3(reportfile, *, stat_type="zmw"):
"""Implement :func:`report_to_stats` for ``ccs`` 3.* output.
Returns
-------
pandas.DataFrame or None
Returns `None` if cannot match `reportfile`.
"""
reportmatch = re.compile(
"^ZMW Yield\n(?P<zmw>(.+\n)+)\n\n" "(?:Subread Yield\n(?P<subread>(.+\n)+))?$"
)
with open(reportfile) as f:
report = f.read()
m = reportmatch.search(report)
if m:
return pd.read_csv(
io.StringIO(m.group(stat_type)), names=["status", "number", "percent"]
).assign(
fraction=lambda x: (x.percent.str.slice(None, -1).astype(float) / 100)
)[
["status", "number", "fraction"]
]
else:
return None
[docs]def get_ccs_stats(fastqfile, *, pass_tag="np"):
"""Get basic statistics about circular consensus sequences.
Parameters
----------
fastqfile : str
FASTQ file with circular consensus sequences, generated from BAM output
of ``ccs``.
pass_tag : str
Tag in FASTQ file header giving number of passes (if present).
Returns
-------
collections.namedtuple
The 3-tuple `(passes, accuracy, length)` contains numpy arrays
giving number of passes, accuracies, and lengths for sequences in
`fastqfile`. The accuracy (average across sequence) and length
are computed from the quality scores and sequence in `fastqfile`;
the number of passes must be provided as `pass_tag` and are returned
as `None` if that tag is missing from any read in `fastqfile`.
Example
-------
Test with ``np`` tag in format from ``ccs`` version 5.0.
>>> fastqfile = tempfile.NamedTemporaryFile(mode='w')
>>> _ = fastqfile.write(textwrap.dedent('''
... @m54228_190118_102822/4194373/ccs np=18
... GGTACCACACTCTTTCCCTACACGACGCTCTGCCGATCTCGGCCATTACGTGTTTTATCTA
... +
... ~~~~{~~~~~~~c~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~i~~~~~~~(
... @m54228_190118_102822/4194374/ccs np=51
... GCACGGCGTCACACTTTGCTATGCCATAGCATGTTTATCCATAAGATTAGCGGATCCTACCT
... +
... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
... ''').lstrip())
>>> fastqfile.flush()
>>> get_ccs_stats(fastqfile.name) # doctest: +NORMALIZE_WHITESPACE
CCS_Stats(passes=array([18, 51]),
accuracy=array([0.99672907, 1. ]),
length=array([61, 62]))
>>> fastqfile.close()
Test with ``np`` tag in format from ``ccs`` version 4.0.
>>> fastqfile = tempfile.NamedTemporaryFile(mode='w')
>>> _ = fastqfile.write(textwrap.dedent('''
... @m54228_190118_102822/4194373/ccs np:i:18
... GGTACCACACTCTTTCCCTACACGACGCTCTGCCGATCTCGGCCATTACGTGTTTTATCTA
... +
... ~~~~{~~~~~~~c~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~i~~~~~~~(
... @m54228_190118_102822/4194374/ccs np:i:51
... GCACGGCGTCACACTTTGCTATGCCATAGCATGTTTATCCATAAGATTAGCGGATCCTACCT
... +
... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
... ''').lstrip())
>>> fastqfile.flush()
>>> get_ccs_stats(fastqfile.name) # doctest: +NORMALIZE_WHITESPACE
CCS_Stats(passes=array([18, 51]),
accuracy=array([0.99672907, 1. ]),
length=array([61, 62]))
>>> fastqfile.close()
Similar test without ``np`` tag in FASTQ file:
>>> fastqfile = tempfile.NamedTemporaryFile(mode='w')
>>> _ = fastqfile.write(textwrap.dedent('''
... @m54228_190118_102822/4194373/ccs
... GGTACCACACTCTTTCCCTACACGACGCTCTGCCGATCTCGGCCATTACGTGTTTTATCTA
... +
... ~~~~{~~~~~~~c~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~i~~~~~~~(
... @m54228_190118_102822/4194374/ccs
... GCACGGCGTCACACTTTGCTATGCCATAGCATGTTTATCCATAAGATTAGCGGATCCTACCT
... +
... ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
... ''').lstrip())
>>> fastqfile.flush()
>>> get_ccs_stats(fastqfile.name) # doctest: +NORMALIZE_WHITESPACE
CCS_Stats(passes=None,
accuracy=array([0.99672907, 1. ]),
length=array([61, 62]))
>>> fastqfile.close()
"""
if pass_tag:
passmatch = re.compile(
r"(?:^|\s)" # start of str or space
rf"{pass_tag}(?::i:|=)(?P<pass>\d+)"
r"(?:\s|$)" # end of str or space
)
passes = []
else:
passes = None
length = []
accuracy = []
for rec in pysam.FastxFile(fastqfile):
length.append(len(rec.sequence))
accuracy.append(
alignparse.utils.qvals_to_accuracy(numpy.array(rec.get_quality_array()))
)
if passes is not None:
if not rec.comment:
passes = None
else:
m = passmatch.search(rec.comment)
if not m:
passes = None
else:
passes.append(m.group("pass"))
if passes is not None:
passes = numpy.array(passes, dtype="int")
CCS_Stats = collections.namedtuple("CCS_Stats", "passes accuracy length")
return CCS_Stats(
passes=passes,
accuracy=numpy.array(accuracy, dtype="float"),
length=numpy.array(length, dtype="int"),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
