# -*- coding: utf-8 -*-
"""
.. currentmodule:: modlamp.datasets
.. moduleauthor:: ETH Zurich Alex Mueller <alex.mueller@pharma.ethz.ch>
This module incorporates functions to load different peptide datasets used for classification.
============================= ============================================================================
Function Data
============================= ============================================================================
:py:func:`load_AMPvsTM` Antimicrobial peptides versus trans-membrane sequences
:py:func:`load_AMPvsUniProt` AMPs from the *APD3* versus other peptides from the *UniProt* database
:py:func:`load_ACPvsTM` Anticancer peptides (*CancerPPD*) versus helical transmembrane sequences
:py:func:`load_ACPvsRandom` Anticancer peptides (*CancerPPD*) versus random scrambled AMP sequences
:py:func:`load_custom` A custom data set provided in ``modlamp/data`` as a ``.csv`` file
============================= ============================================================================
"""
from os.path import dirname, join
import numpy as np
__author__ = "Alex Müller, Gisela Gabernet"
__docformat__ = "restructuredtext en"
[docs]class Bunch(dict):
"""Container object for datasets
Dictionary-like object that exposes its keys as attributes. Taken from the `sklearn <http://scikit-learn.org>`_
package.
:Example:
>>> b = Bunch(a=1, b=2)
>>> b['b']
2
>>> b.b # key can also be called as attribute
2
>>> b.a = 3
>>> b['a']
3
>>> b.c = 6
>>> b['c']
6
"""
[docs] def __init__(self, **kwargs):
dict.__init__(self, kwargs)
def __setattr__(self, key, value):
self[key] = value
def __getattr__(self, key):
try:
return self[key]
except KeyError:
raise AttributeError(key)
def __setstate__(self, state):
pass
[docs]def load_AMPvsTM():
"""Function to load a dataset consisting of **AMP sequences** and **transmembrane regions of proteins** for
classification.
The AMP class consists of an intersection of all activity annotations of the `APD2 <http://aps.unmc.edu/AP/>`_ and
`CAMP <http://camp.bicnirrh.res.in/>`_ databases, where for gram positive, gram negative and antifungal exact
matches were observed. A detailed description of how the dataset was compiled can be found in the following
publication: Schneider, P., Müller, A. T., Gabernet, G., Button, A. L., Posselt, G., Wessler, S., Hiss, J. A. and
Schneider, G. (2016), Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial
Peptides. Mol. Inf.. `doi:10.1002/minf.201600011 <http://onlinelibrary.wiley.com/doi/10.1002/minf.201600011/full>`_
================= ===
Classes 2
Samples per class 206
Samples total 412
Dimensionality 1
================= ===
:return: Bunch, a dictionary-like object, the interesting attributes are: ``sequences``, the sequences, ``target``,
the classification labels, ``target_names``, the meaning of the labels and ``feature_names``, the meaning of the
features.
:Example:
>>> from modlamp.datasets import load_AMPvsTM
>>> data = load_AMPvsTM()
>>> data.sequences
['AAGAATVLLVIVLLAGSYLAVLA','LWIVIACLACVGSAAALTLRA','FYRFYMLREGTAVPAVWFSIELIFGLFA','GTLELGVDYGRAN',...]
>>> list(data.target_names)
['TM', 'AMP']
>>> len(data.sequences)
412
>>> data.target[:10]
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
"""
module_path = dirname(__file__)
with open(join(module_path, 'data', 'AMPvsTMset.csv')) as f:
n_samples, n_features, target_name1, target_name2 = next(f).strip().split(',')
sequences, target = list(), list()
for line in f:
tmp = line.strip().split(',')
sequences.append(tmp[0])
target.append(tmp[1])
return Bunch(sequences=np.array(sequences, dtype='str'), target=np.array(target, dtype='int'),
target_names=[target_name1, target_name2],
feature_names=['Sequence'])
[docs]def load_AMPvsUniProt():
"""Function to load a dataset consisting of the whole **APD3** versus the same number of sequences randomly
extracted from the **UniProt** database, to be used for classification.
The AMP class consists of 2600 AMP sequences from the `APD3 <http://aps.unmc.edu/AP/>`_ (extracted Jan. 2016).
The UniProt class consists of 2600 randomly extracted protein sequences from the `UniProt Database
<http://uniprot.org/>`_ with the search query *length 10 TO 50* filtered for unnatural amino acids.
================= =====
Classes 2
AMP Samples 2600
UniProt Samples 2600
Samples total 5200
Dimensionality 1
================= =====
:return: Bunch, a dictionary-like object, the interesting attributes are: ``sequences``, the sequences, ``target``,
the classification labels, ``target_names``, the meaning of the labels and ``feature_names``, the meaning of the
features.
:Example:
>>> from modlamp.datasets import load_AMPvsUniProt
>>> data = load_AMPvsUniProt()
>>> data.sequences[:10]
['GLWSKIKEVGKEAAKAAAKAAGKAALGAVSEAV', 'YVPLPNVPQPGRRPFPTFPGQGPFNPKIKWPQGY', ... ]
>>> list(data.target_names)
['AMP', 'UniProt']
>>> len(data.sequences)
5200
>>> data.target[:10]
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
"""
module_path = dirname(__file__)
with open(join(module_path, 'data', 'AMPvsUniProt.csv')) as f:
n_samples, n_features, target_name1, target_name2 = next(f).strip().split(',')
sequences, target = list(), list()
for line in f:
tmp = line.strip().split(',')
sequences.append(tmp[0])
target.append(tmp[1])
return Bunch(sequences=np.array(sequences, dtype='str'), target=np.array(target, dtype='int'),
target_names=[target_name1, target_name2],
feature_names=['Sequence'])
[docs]def load_ACPvsTM():
"""Function to load a dataset consisting of ACP sequences from the CancerPPD database and negative peptides
extracted from alpha-helical transmembrane regions of proteins for classification.
The ACP class consists of a collection of 413 ACPs from the `CancerPPD
<http://crdd.osdd.net/raghava/cancerppd/index.php>`_ database with length between 7 and 30 aa and without cysteines
to facilitate peptide synthesis.
The Negative peptide set contains a random selection of 413 transmembrane alpha-helices (extracted from
the `PDBTM <http://pdbtm.enzim.hu/>`_ ) isolated directly from the proteins crystal structure.
================= ===
Classes 2
ACP peptides 413
Negative peptides 413
Total peptides 826
Dimensionality 1
================= ===
:return: Bunch, a dictionary-like object, the interesting attributes are: ``sequences``, the sequences, ``target``,
the classification labels, ``target_names``, the meaning of the labels and ``feature_names``, the meaning of the
features.
:Example:
>>> from modlamp.datasets import load_ACPvsTM
>>> data = load_ACPvsTM()
>>> data.sequences[:4]
['AAKKWAKAKWAKAKKWAKAA', 'AAVPIVNLKDELLFPSWEALFSGSE', 'AAWKWAWAKKWAKAKKWAKAA', 'AFGMALKLLKKVL']
>>> list(data.target_names)
['TM', 'ACP']
>>> len(data.sequences)
826
"""
module_path = dirname(__file__)
with open(join(module_path, 'data', 'ACP_CancPPD_vs_TM.csv')) as f:
n_samples, n_features, target_name1, target_name2 = next(f).strip().split(',')
sequences, target = list(), list()
for line in f:
tmp = line.strip().split(',')
sequences.append(tmp[0])
target.append(tmp[1])
return Bunch(sequences=np.array(sequences, dtype='str'), target=np.array(target, dtype='int'),
target_names=[target_name1, target_name2],
feature_names=['Sequence'])
[docs]def load_ACPvsRandom():
"""Function to load a dataset consisting of ACP sequences from the CancerPPD database and negative peptides generated
randomly with the amino acid composition of AMPs.
The ACP class consists of a collection of 413 ACPs from the `CancerPPD
<http://crdd.osdd.net/raghava/cancerppd/index.php>`_ database with length between 7 and 30 aa and without cysteines
to facilitate peptide synthesis.
The Negative peptide set contains a random selection of 413 randomly generated peptides with the amino acid
composition of AMPs in the APD2 database.
================= ===
Classes 2
ACP peptides 413
Negative peptides 413
Total peptides 826
Dimensionality 1
================= ===
:return: Bunch, a dictionary-like object, the interesting attributes are: ``sequences``, the sequences, ``target``,
the classification labels, ``target_names``, the meaning of the labels and ``feature_names``, the meaning of the
features.
:Example:
>>> from modlamp.datasets import load_ACPvsRandom
>>> data = load_ACPvsRandom()
>>> data.sequences[:3]
['AAKKWAKAKWAKAKKWAKAA', 'AAVPIVNLKDELLFPSWEALFSGSE', 'AAWKWAWAKKWAKAKKWAKAA']
>>> list(data.target_names)
['Random', 'ACP']
>>> len(data.sequences)
826
"""
module_path = dirname(__file__)
with open(join(module_path, 'data', 'ACP_CancPPD_vs_Random.csv')) as f:
n_samples, n_features, target_name1, target_name2 = next(f).strip().split(',')
sequences, target = list(), list()
for line in f:
tmp = line.strip().split(',')
sequences.append(tmp[0])
target.append(tmp[1])
return Bunch(sequences=np.array(sequences, dtype='str'), target=np.array(target, dtype='int'),
target_names=[target_name1, target_name2],
feature_names=['Sequence'])
[docs]def load_custom(filename):
"""Function to load a custom dataset saved in ``modlamp/data/`` as a ``.csv`` file.
The following header needs to be included: *Nr. of sequences*, *Nr. of columns - 1*, *Class name for 0*,
*Class name for 1*
Example ``.csv`` file structure::
4, 1, TM, AMP
GTLEFDVTIGRAN, 0
GSNVHLASNLLA, 0
GLFDIVKKVVGALGSL, 0
GLFDIIKKIAESF, 0
:param filename: {str} filename of the data file to be loaded; the file must be located in ``modlamp/data/``
:return: Bunch, a dictionary-like object, the interesting attributes are: ``sequences``, the sequences, ``target``,
the classification labels, ``target_names``, the meaning of the labels and ``feature_names``, the meaning of the
features.
:Example:
>>> from modlamp.datasets import load_custom
>>> data = load_custom('custom_data.csv')
"""
module_path = dirname(__file__)
with open(join(module_path, 'data', filename)) as f:
n_samples, n_features, target_name1, target_name2 = next(f).strip().split(',')
sequences, target = list(), list()
for line in f:
tmp = line.strip().split(',')
sequences.append(tmp[0])
target.append(tmp[1])
return Bunch(sequences=np.array(sequences, dtype='str'), target=np.array(target, dtype='int'),
target_names=[target_name1, target_name2],
feature_names=['Sequence'])
