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Python Tutorial

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This is one of the chapter in Bioinformatics basic course in Tsinghua University. You may also find it here

The related jupyter file

Python Tutorial

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Install Anaconda Python

URL: (https://www.anaconda.com/download/)

  • Easy install of data science packages (binary distribution)
  • Package management with

conda

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Install Python packages using conda:

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conda install h5py

Update a package to the latest version:

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conda update h5py

Install Python packages using pip:

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pip install h5py

Update a package using pip:

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pip install --upgrade h5py

Python language tips

Compatibility between Python 3.x and Python 2.x

Biggest difference: print is a function rather than statement in Python 3

This does not work in Python 3

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print 1, 2, 3

Solution: use the __future__ module

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from __future__ import print_function
# this works both in Python 2 and Python 3
print(1, 2, 3)

Second biggest difference: some package/function names in the standard library are changed

Python 2 => Python 3

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cStringIO => io.StringIO
Queue => queue
cPickle => pickle
ConfigParser => configparser
HTMLParser => html.parser
SocketServer => socketserver
SimpleHTTPServer => http.server

Solution: use the six module

Get away from IndentationError

Python forces usage of tabs/spaces to indent code

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# use a tab
for i in range(3):
    print(i)
# use 2 spaces
for i in range(3):
  print(i)
# use 4 spaces
for i in range(3):
    print(i)

Best practice: always use 4 spaces. You can set whether to use spaces(soft tabs) or tabs for indentation.

In vim editor, use :set list to inspect incorrect number of spaces/tabs.

Add Shebang and encoding at the beginning of executable scripts

Create a file named welcome.py

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#! /usr/bin/env python
# -*- coding: UTF-8 -*-
print('welcome to python!')

Then set the python script as executable:

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chmod +x welcome.py

Now you can run the script without specifying the Python interpreter:

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./welcome.py

All variables, functions, classes are dynamic objects

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class MyClass():
    def __init__(self, name):
        self.name = name
# assign an integer to a
a = 1
print(type(a))
# assign a string to a
a = 'abc'
print(type(a))
# assign a function to a
a = range
print(type(a))
print(a(10))
# assign a class to a
a = MyClass
print(type(a))
b = a('myclass')
print(b.name)
# assign an instance of a class to a
a = MyClass('myclass')
print(b.name)
# get type of a
print(type(a))

All python variables are pointers/references

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a = [1, 2, 3]
print('a = ', a)
# this add another refrence to the list
b = a
print('b = ', b)
# this will change contents of both a and b
b[2] = 4
print('a = ', a)
print('b = ', b)

Use deepcopy if you really want to COPY a variable

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from copy import deepcopy
a = {'A': [1], 'B': [2], 'C': [3]}
print(a)
# shallow copy
b = dict(a)
# modify elements of b will change contents of a
b['A'].append(2)
print('a = ', a)
print('b = ', b)
# this also does not work
c = {k:v for k, v in a}
c['A'].append(3)
print('a = ', a)
print('c = ', c)
# recurrently copy every object of a
d = deepcopy(a)
# modify elements of c will not change contents of a
d['A'].append(2)
print('a = ', a)
print('d = ', d)

What if I accidentally overwrite my builtin functions?

You can refer to (https://docs.python.org/2/library/functions.html) for builtin functions in the standard library.

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A = [1, 2, 3, 4]
# Ops! the builtin function sum is overwritten by a number
sum = sum(A)
# this will raise an error because sum is not a function now
print(sum(A))
# recover the builtin function into the current environment
from __builtin__ import sum
# this works because sum is a function
print(sum(A))

Note: in Python 3, you should import from builtins rather than __builtin__

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from builtins import sum

int is of arbitrary precision in Python!

In Pyhton:

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print(2**10000)

In R:

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print(2^10000)

Easiest way to swap values of two variables

In C/C++:

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int a = 1, b = 2, t;
t = a;
a = b;
b = t;

In Python:

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a = 1
b = 2
b, a = a, b
print(a, b)

List comprehension

Use for-loops:

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a = []
for i in range(10):
    a.append(i + 10)
print(a)

Use list comprehension

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a = [i + 10 for i in range(10)]
print(a)

Dict comprehension

Use for-loops:

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a = {}
for i in range(10):
    a[i] = chr(ord('A') + i) 
print(a)

Use dict comprehension:

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a = {i:chr(ord('A') + i) for i in range(10)}
print(a)

For the one-liners

Use ‘;’ instead of ‘\n’:

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# print the first column of each line
python -c 'import sys; print("\n".join(line.split("\t")[0] for line in sys.stdin))'

For more examples of one-liners, please refer to (https://wiki.python.org/moin/Powerful%20Python%20One-Liners).

Read from standard input

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import sys
# read line by line
for line in sys.stdin:
    print(line)

Order of dict keys are NOT as you expected

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a = {'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5, 'F': 6}
# not in lexicographical order
print([key for key in a])
# now in lexicographical order
print([key for key in sorted(a)])

Use enumerate() to add a number during iteration

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A = ['a', 'b', 'c', 'd']
for i, a in enumerate(A):
    print(i, a)

Reverse a list

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# a = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
a = range(10)
print(a)
print(a[::-1])

Strings are immutable in Python

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a = 'ABCDF'
# will raise an Error
a[4] = 'E'
# convert str to bytearray
b = bytearray(a)
# bytearray are mutable
b[4] = 'E'
# convert bytearray to str
print(str(b))

tuples are hashable while lists are not hashable

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# create dict using tuples as keys
d = {
    ('chr1', 1000, 2000): 'featureA',
    ('chr1', 2000, 3000): 'featureB',
    ('chr1', 3000, 4000): 'featureC',
    ('chr1', 4000, 5000): 'featureD',
    ('chr1', 5000, 6000): 'featureE',
    ('chr1', 6000, 7000): 'featureF'
}
# query the dict using tuples
print(d[('chr1', 3000, 4000)])
print(d[('chr1', 6000, 7000)])
# will raise an error
d = {['chr1', 1000, 2000]: 'featureA'}

Use itertools

Nested loops in a more concise way:

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A = [1, 2, 3]
B = ['a', 'b', 'c']
C = ['i', 'j', 'k']
D = ['x', 'y', 'z']
# Use nested for-loops
for a in A:
    for b in B:
        for c in C:
            for d in D:
                print(a, b, c, d)
# Use itertools.product
import itertools
for a, b, c, d in itertools.product(A, B, C, D):
    print(a, b, c, d)

Get all combinations of a list:

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A = ['A', 'B', 'C', 'D']
# Use itertools.combinations
import itertools
for a, b, c in itertools.combinations(A, 3):
    print(a, b, c)

Convert iterables to lists

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import itertools
A = [1, 2, 3]
B = ['a', 'b', 'c']
a = itertools.product(A, B)
# a is a iterable rather than a list
print(a)
# a is a list now
a = list(a)
print(a)

Use the zip() function to transpose nested lists/tuples/iterables

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records = [
    ('chr1', 1000, 2000),
    ('chr1', 2000, 3000),
    ('chr1', 3000, 4000),
    ('chr1', 4000, 5000),
    ('chr1', 5000, 6000),
    ('chr1', 6000, 7000)
]
# iterate by rows
for chrom, start, end in records:
    print(chrom, start, end)
# extract columns
chroms, starts, ends = zip(*records)
# build records from columns
# now records2 is the same as records
records2 = zip(chroms, starts, ends)
print(records)

Global and local variables

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# a is global
a = 1
def print_local():
    # a is local
    a = 2
    print(a)

def print_global():
    # a is global
    global a
    a = 2
    print(a)

# print global variable
print(a)
# print local variable from function
print_local()
# a is unchanged
print(a)
# change and print global from function
print_global()
# a is changed
print(a)

Use defaultdict

Use dict:

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d = {}
d['a'] = []
d['b'] = []
d['c'] = []
# extend list with new elements
d['a'] += [1, 2]
d['b'] += [3, 4, 5]
d['c'] += [6]
for key, val in d.items():
    print(key, val)

Use defaultdict:

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from collections import defaultdict
# a new list is created automatically when new elements are added
d = defaultdict(list)
# extend list with new elements
d['a'] += [1, 2]
d['b'] += [3, 4, 5]
d['c'] += [6]
for key, val in d.items():
    print(key, val)

Use generators

Example: read a large FASTA file

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def append_extra_line(f):
    """Yield an empty line after the last line in the file
    """
    for line in f:
        yield line
    yield ''

def read_fasta(filename):
    with open(filename, 'r') as f:
        name = None
        seq = ''
        for line in append_extra_line(f):
            if line.startswith('>') or (len(line) == 0):
                if (len(seq) > 0) and (name is not None):
                    yield (name, seq)
                if line.startswith('>'):
                    name = line.strip()[1:].split()[0]
                    seq = ''
            else:
                if name is None:
                    raise ValueError('the first line does not start with ">"')
                seq += line.strip()
# print sequence name and length of each 
for name, seq in read_fasta('test.fa'):
    print(name, len(seq))

Turn off annoying KeyboardInterrupt and BrokenPipe Error

Without exception handling (press Ctrl+C):

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import time
time.sleep(300)

With exception handling (press Ctrl+C):

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import time
import errno

try:
    time.sleep(300)
except KeyboardInterrupt:
    sys.exit(1)
except OSError as e:
    if e.errno == errno.EPIPE:
        sys.exit(-e.errno)

Class and instance variables

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class MyClass():
    name = 'class_name'
    def __init__(self, name):
        self.name = name

    def change_name(self, name):
        self.name = name

# print class variable
print(MyClass.name)
# create an instance from MyClass
a = MyClass('instance_name')
# print instance name
print(a.name)
# change instance name
a.change_name('instance_new_name')
print(a.name)
print(MyClass.name)
# change class name
MyClass.name = 'class_new_name'
print(a.name)
print(MyClass.name)

Useful Python packages for data analysis

Browser-based interactive programming in Python: jupyter

URL: (http://jupyter.org/)

Start jupyter notebook

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jupyter notebook --no-browser

Jupyter notebooks manager

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Jupyter process manager

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Jupyter notebook

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Integrate with matplotlib

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Browser-based text editor

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Browser-based terminal

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Display image

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Display dataframe

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Display audio

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Embedded markdown

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Python packages for scientific computing

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Vector arithmetics: numpy

URL: (http://www.numpy.org/)

Example:

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import numpy as np
# create an empty matrix of shape (5, 4)
X = np.zeros((5, 4), dtype=np.int32)
# create an array of length 5: [0, 1, 2, 3, 4]
y = np.arange(5)
# create an array of length 4: [0, 1, 2, 3]
z = np.arange(4)
# set Row 1 to [0, 1, 2, 3]
X[0] = np.arange(4)
# set Row 2 to [1, 1, 1, 1]
X[1] = 1
# add 1 to all elements
X += 1
# add y to each row of X
X += y.reshape((-1, 1))
# add z to each column of X
X += z.reshape((1, -1))
# get row sums => 
row_sums = X.sum(axis=1)
# get column sums
col_sums = X.sum(axis=0)
# matrix multiplication
A = X.dot(X.T)
# save matrix to text file
np.savetxt('data.txt', A)

Numerical analysis (probability distribution, signal processing, etc.): scipy

URL: (https://www.scipy.org/)

scipy.stats contains a large number probability distributions:
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Unified interface for all probability distributions:
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Just-in-time (JIT) compiler for vector arithmetics

URL: (https://numba.pydata.org/)

Compile python for-loops to native code to achive similar performance to C/C++ code.
Example:

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from numba import jit
from numpy import arange

# jit decorator tells Numba to compile this function.
# The argument types will be inferred by Numba when function is called.
@jit
def sum2d(arr):
    M, N = arr.shape
    result = 0.0
    for i in range(M):
        for j in range(N):
            result += arr[i,j]
    return result

a = arange(9).reshape(3,3)
print(sum2d(a))

Library for symbolic computation: sympy

URL: (http://www.sympy.org/en/index.html)

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Operation on data frames: pandas

URL: (http://pandas.pydata.org/pandas-docs/stable/)

Example:

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import pandas as pd
# read a bed file
genes = pd.read_table('gene.bed', header=None, sep='\t',
                     names=('chrom', 'start', 'end', 'gene_id', 'score', 'strand', 'biotype'))
# get all gene IDs
gene_ids = genes['gene_id']
# set gene_id as index
genes.index = genes['gene_id']
# get row with given gene_id
gene = genes.loc['ENSG00000212325.1']
# get rows with biotype = 'protein_coding'
genes_selected = genes[genes['biotype'] == 'protein_coding']]
# get protein coding genes in chr1
genes_selected = genes.query('(biotype == "protein_coding") and (chrom == "chr1")')
# count genes for each biotype
biotype_counts = genes.groupby('biotype')['gene_id'].count()
# add a column for gene length
genes['length'] = genes['end'] - genes['start']
# calculate average gene length for each chromosome and biotype
length_table = genes.pivot_table(values='length', index='biotype', columns='chrom')
# save DataFrame to Excel file
length_table.to_excel('length_table.xlsx')

Basic graphics and plotting: matplotlib

URL: (https://matplotlib.org/contents.html)

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Statistical data visualization: seaborn

URL: (https://seaborn.pydata.org/)

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Interactive programming in Python: ipython

URL: (http://ipython.org/ipython-doc/stable/index.html)

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Statistical tests: statsmodels

URL: (https://www.statsmodels.org/stable/index.html)

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Machine learning algorithms: scikit-learn

URL: (http://scikit-learn.org/)

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Example:

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from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score, accuracy_score

# generate ramdom data
X, y = make_classification(n_samples=1000, n_classes=2, n_features=10)
# split dataset into training and test dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# create an classifier object
model = LogisticRegression()
# training the classifier
model.fit(X_train, y_train)
# predict outcomes on the test dataset
y_pred = model.predict(X_test)
# evalualte the classification performance
print('roc_auc_score = %f'%roc_auc_score(y_test, y_pred))
print('accuracy_score = %f'%accuracy_score(y_test, y_pred))

Natural language analysis: gensim

URL: (https://radimrehurek.com/gensim/)

HTTP library: requests

URL: (http://docs.python-requests.org/en/master/)

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Lightweight Web framework: flask

URL: (http://flask.pocoo.org/)

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Deep learning framework: tensorflow

URL: (http://tensorflow.org/)

High-level deep learning framework: keras

URL: (https://keras.io/)

Operation on sequence and alignment formats: biopython

URL: (http://biopython.org/)

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from Bio import SeqIO
for record in SeqIO.parse('test.fa', 'fasta'):
    print(record.id, len(record.seq))
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from Bio import SeqIO
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
sequences = [
    SeqRecord(Seq('ACCGGTATCTATATCCCCGAGAGGAATGGGTCAGACATGGACCTAC'), id='A', description=''),
    SeqRecord(Seq('TTACAATGTGGCAGTGAACGCGTGACAATCCTCCCCGTTGGACAT'), id='B', description=''),
    SeqRecord(Seq('CAAAGCTGCATCGAATTGTCGAGACAACACTAGATTTAAGCGCA'), id='C', description=''),
    SeqRecord(Seq('CGCCCGCGAGGGCAATCAGGACGGATTTACGGAT'), id='D', description=''),
    SeqRecord(Seq('CCGCCCACGCTCCCGTTTTCTTCCATACCTGTCC'), id='E', description='')
]
with open('test_out.fa', 'w') as f:
    SeqIO.write(sequences, f, 'fasta')

Operation on genomic formats (BigWig,etc.): bx-python

Operation on HDF5 files: h5py

URL: (https://www.h5py.org/)

Save data to an HDF5 file

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import h5py
import numpy as np
# generate data
chroms = ['chr1', 'chr2', 'chr3']
chrom_sizes = {
    'chr1': 15000,
    'chr2': 12000,
    'chr3': 11000
}
coverage = {}
counts = {}
for chrom, size in chrom_sizes.items():
    coverage[chrom] = np.random.randint(10, 1000, size=size)
    counts[chrom] = np.random.randint(1000, size=size)%coverage[chrom]
# save data to an HDF5 file
with h5py.File('dataset.h5', 'w') as f:
    for chrom in chrom_sizes:
        g = f.create_group(chrom)
        g.create_dataset('coverage', data=coverage[chrom])
        g.create_dataset('counts', data=counts[chrom])
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h5ls -r dataset.h5
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/                        Group
/chr1                    Group
/chr1/counts             Dataset {15000}
/chr1/coverage           Dataset {15000}
/chr2                    Group
/chr2/counts             Dataset {12000}
/chr2/coverage           Dataset {12000}
/chr3                    Group
/chr3/counts             Dataset {11000}
/chr3/coverage           Dataset {11000}

Read data from an HDF file:

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import h5py
# read data from an HDF5 file
with h5py.File('dataset.h5', 'r') as f:
    coverage = {}
    counts = {}
    for chrom in f.keys():
        coverage[chrom] = f[chrom + '/coverage'][:]
        counts[chrom] = f[chrom + '/counts'][:]

Mixed C/C++ and python programming: cython

URL: (http://cython.org/)

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import numpy as np
cimport numpy as np
cimport cython
from cython.parallel import prange
from cython.parallel cimport parallel
cimport openmp

@cython.boundscheck(False) # turn off bounds-checking for entire function
@cython.wraparound(False)  # turn off negative index wrapping for entire function
def compute_mse_grad_linear_ard(np.ndarray[np.float64_t, ndim=1] w,
        np.ndarray[np.float64_t, ndim=2] X1,
        np.ndarray[np.float64_t, ndim=2] X2,
        np.ndarray[np.float64_t, ndim=2] Kinv1,
        np.ndarray[np.float64_t, ndim=2] K2,
        np.ndarray[np.float64_t, ndim=2] a,
        np.ndarray[np.float64_t, ndim=2] err,
        np.ndarray[np.float64_t, ndim=2] mask=None):
    '''Compute the gradients of MSE on the test samples with respect to relevance vector w.
    :param w: 1D array of shape [n_features]
    :return: gradients of MSE wrt. 2, 1D array of shape [n_features]
    '''
    cdef np.int64_t N1, N2, p
    cdef np.int64_t k, i, j, m
    N1 = X1.shape[0]
    N2 = X2.shape[0]
    p = X2.shape[1]

    cdef np.ndarray[np.float64_t, ndim=2] K2Kinv1 = K2.dot(Kinv1)
    cdef np.ndarray[np.float64_t, ndim=1] mse_grad = np.zeros_like(w)

    #cdef np.ndarray[np.float64_t, ndim=3] K1_grad = np.zeros((p, N1, N1), dtype=np.float64)
    #cdef np.ndarray[np.float64_t, ndim=3] K2_grad = np.zeros((p, N2, N1), dtype=np.float64)
    #cdef np.ndarray[np.float64_t, ndim=3] K_grad =  np.zeros((p, N2, N1), dtype=np.float64)
    cdef np.int64_t max_n_threads = openmp.omp_get_max_threads()
    cdef np.ndarray[np.float64_t, ndim=3] K1_grad = np.zeros((max_n_threads, N1, N1), dtype=np.float64)
    cdef np.ndarray[np.float64_t, ndim=3] K2_grad = np.zeros((max_n_threads, N2, N1), dtype=np.float64)
    cdef np.ndarray[np.float64_t, ndim=3] K_grad  = np.zeros((max_n_threads, N1, N1), dtype=np.float64)

    cdef np.int64_t thread_id
    with nogil, parallel():
        for k in prange(p):
            thread_id = openmp.omp_get_thread_num()
            # compute K1_grad
            for i in range(N1):
                for j in range(N1):
                    K1_grad[thread_id, i, j] = 2.0*w[k]*X1[i, k]*X1[j, k]
            # compute K2_grad
            for i in range(N2):
                for j in range(N1):
                    K2_grad[thread_id, i, j] = 2.0*w[k]*X2[i, k]*X1[j, k]
            # compute K_grad
            for i in range(N2):
                for j in range(N1):
                    K_grad[thread_id, i, j] = K2_grad[thread_id, i, j]
                    for m in range(N1):
                        K_grad[thread_id, i, j] += K2Kinv1[i, m]*K1_grad[thread_id, m, j]
            # compute mse_grad
            for i in range(N2):
                for j in range(N1):
                    mse_grad[k] += err[i, 0]*K_grad[thread_id, i, j]*a[j, 0]
    return mse_grad, K_grad

Progress bar: tqdm

URL: (https://pypi.python.org/pypi/tqdm)

Markdown

Markdown

Example Python scripts

View a table in a pretty way

The original table is ugly:

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head -n 15 metadata.tsv

Output:

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File accession	File format	Output type	Experiment accession	Assay	Biosample term id
ENCFF983DFB	fastq	reads	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF590TBW	fastq	reads	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF258RWG	bam	unfiltered alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF468LRV	bam	unfiltered alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF216EBS	bam	alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF232QFN	bam	unfiltered alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF682NGE	bam	alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF328UKA	bam	unfiltered alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF165COO	bam	alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF466OLG	bam	alignments	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF595HIY	bigBed narrowPeak	peaks	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF494CKB	bigWig	fold change over control	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF308BXW	bigWig	fold change over control	ENCSR429XTR	ChIP-seq	EFO:0002067
ENCFF368IHM	bed narrowPeak	peaks	ENCSR429XTR	ChIP-seq	EFO:0002067

Now display the table more clearly:

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head -n 15 metadata.tsv | tvi -d $'\t' -j center

Output:

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File accession    File format          Output type        Experiment accession  Assay   Biosample term id
 ENCFF983DFB         fastq                reads               ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF590TBW         fastq                reads               ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF258RWG          bam         unfiltered alignments       ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF468LRV          bam         unfiltered alignments       ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF216EBS          bam               alignments            ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF232QFN          bam         unfiltered alignments       ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF682NGE          bam               alignments            ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF328UKA          bam         unfiltered alignments       ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF165COO          bam               alignments            ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF466OLG          bam               alignments            ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF595HIY   bigBed narrowPeak          peaks               ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF494CKB        bigWig       fold change over control     ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF308BXW        bigWig       fold change over control     ENCSR429XTR      ChIP-seq    EFO:0002067
 ENCFF368IHM    bed narrowPeak            peaks               ENCSR429XTR      ChIP-seq    EFO:0002067

You can also get some help by typing tvi -h:

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usage: tvi [-h] [-d DELIMITER] [-j {left,right,center}] [-s SEPARATOR]
           [infile]

Print tables pretty

positional arguments:
  infile                input file, default is stdin

optional arguments:
  -h, --help            show this help message and exit
  -d DELIMITER          delimiter of fields of input. Default is white space.
  -j {left,right,center}
                        justification, either left, right or center. Default
                        is left
  -s SEPARATOR          separator of fields in output

tvi.py

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#! /usr/bin/env python

import sys
import argparse
import os
from cStringIO import StringIO

def main():
	parser = argparse.ArgumentParser(description='Print tables pretty')
	parser.add_argument('infile', type=str, nargs='?',
					 help='input file, default is stdin')
	parser.add_argument('-d', dest='delimiter', type=str,
					 required=False,
					 help='delimiter of fields of input. Default is white space.')
	parser.add_argument('-j', dest='justify', type=str,
					 required=False, default='left',
					 choices=['left', 'right', 'center'],
					 help='justification, either left, right or center. Default is left')
	parser.add_argument('-s', dest='separator', type=str,
					 required=False, default=' ',
					 help='separator of fields in output')
	args = parser.parse_args()

	table = []
	maxwidth = []

	# default is to read from stdin
	fin = sys.stdin
	if args.infile:
		try:
			fin = open(args.infile, 'rt')
		except IOError as e:
			sys.stderr.write('Error: %s: %s\n'%(e.strerror, args.infile))
			sys.exit(e.errno)

	for line in fin:
		fields = None
		# split line by delimiter
		if args.delimiter:
			fields = line.strip().split(args.delimiter)
		else:
			fields = line.strip().split()
		for i in xrange(len(fields)):
			width = len(fields[i])
			if (i+1) > len(maxwidth):
				maxwidth.append(width)
			else:
				if width > maxwidth[i]:
					maxwidth[i] = width
		table.append(fields)
	fin.close()

	try:
		for fields in table:
			line = StringIO()
			for i in xrange(len(fields)):
				# format field with different justification
				nSpace = maxwidth[i] - len(fields[i])
				if args.justify == 'left':
					line.write(fields[i])
					for j in xrange(nSpace):
						line.write(' ')
				elif args.justify == 'right':
					for j in xrange(nSpace):
						line.write(' ')
					line.write(fields[i])
				elif args.justify == 'center':
					for j in xrange(nSpace/2):
						line.write(' ')
					line.write(fields[i])
					for j in xrange(nSpace - nSpace/2):
						line.write(' ')

				line.write(args.separator)
			print line.getvalue()
			line.close()
	except IOError:
		sys.exit(-1)

if __name__ == '__main__':
	main()

Generate a random FASTA file

seqgen.py

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#! /usr/bin/env python

import sys
import argparse
import textwrap
import random

def write_fasta(fout, seq, name='seq', description=None):
	if description:
		fout.write('>' + name + ' ' + description + '\n')
	else:
		fout.write('>' + name + '\n')
	fout.write(textwrap.fill(seq) + '\n')

def main():
	parser = argparse.ArgumentParser(description='Generate sequences and output in various formats')
	parser.add_argument('-n', '--number', dest='number', type=int, required=False,
					 default=10, help='Number of sequences to generate')
	parser.add_argument('--min-length', dest='min_length', type=int, required=False,
					 default=30, help='Minimal length')
	parser.add_argument('--max-length', dest='max_length', type=int, required=False,
					 default=50, help='Maximal length')
	parser.add_argument('-l', '--length', type=int, required=False,
					 help='Fixed length. If specified, --min-length and --max-length will be ignored.')
	parser.add_argument('-a', '--alphabet', type=str, required=False,
					 default='ATGC', help='Letters to used in the sequences')
	parser.add_argument('-f', '--format', type=str, required=False,
					 choices=['fasta', 'text'], default='fasta', help='Output formats')
	parser.add_argument('-o', '--outfile', type=argparse.FileType('w'), required=False,
					 default=sys.stdout, help='Output file name')
	parser.add_argument('-p', '--prefix', type=str, required=False,
					 default='RN_', help='Prefix of sequence names for fasta format')
	args = parser.parse_args()

	rand = random.Random()
	for i in xrange(args.number):
		if args.length:
			length = args.length
		else:
			length = rand.randint(args.min_length, args.max_length)
		seq = bytearray(length)
		for j in xrange(length):
			seq[j] = rand.choice(args.alphabet)
		if args.format == 'fasta':
			write_fasta(args.outfile, str(seq), args.prefix + '%08d'%i)
		else:
			args.outfile.write(seq + '\n')
	args.outfile.close()

if __name__ == '__main__':
	main()

Weekly tasks

All files you need for completing the tasks can be found at: weekly_tasks.zip

Task 1: run examples (Python tips, numpy, pandas) in this tutorial

Install Anaconda on your PC. Try to understand example code and run in Jupyter or IPython.

Task 2: write a Python program to convert a GTF file to BED12 format 

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chr1	HAVANA	gene	29554	31109	.	+	.	gene_id "ENSG00000243485.5"; gene_type "lincRNA"; gene_name "MIR1302-2HG"; level 2; tag "ncRNA_host"; havana_gene "OTTHUMG00000000959.2";
chr1	HAVANA	transcript	29554	31097	.	+	.	gene_id "ENSG00000243485.5"; transcript_id "ENST00000473358.1"; gene_type "lincRNA"; gene_name "MIR1302-2HG"; transcript_type "lincRNA"; transcript_name "MIR1302-2HG-202"; level 2; transcript_support_level "5"; tag "not_best_in_genome_evidence"; tag "dotter_confirmed"; tag "basic"; havana_gene "OTTHUMG00000000959.2"; havana_transcript "OTTHUMT00000002840.1";
chr1	HAVANA	exon	29554	30039	.	+	.	gene_id "ENSG00000243485.5"; transcript_id "ENST00000473358.1"; gene_type "lincRNA"; gene_name "MIR1302-2HG"; transcript_type "lincRNA"; transcript_name "MIR1302-2HG-202"; exon_number 1; exon_id "ENSE00001947070.1"; level 2; transcript_support_level "5"; tag "not_best_in_genome_evidence"; tag "dotter_confirmed"; tag "basic"; havana_gene "OTTHUMG00000000959.2"; havana_transcript "OTTHUMT00000002840.1";

BED12 example:

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chr1	67522353	67532326	ENST00000230113	0	+	0	0	0	5	45,60,97,64,221,	0,5024,7299,7961,9752,
chr1	39249837	39257649	ENST00000289890	0	-	0	0	0	3	365,78,115,	0,4304,7697,
chr1	144245237	144250279	ENST00000294715	0	-	0	0	0	3	78,135,55,	0,448,4987,
chr1	15111814	15152464	ENST00000310916	0	-	0	0	0	6	5993,578,121,88,146,174,	0,6512,8762,9157,12413,40476,
chr1	34975698	34978706	ENST00000311990	0	-	0	0	0	3	1704,154,29,	0,2232,2979,
  • The GTF file is weekly_tasks/gencode.v27.long_noncoding_RNAs.gtf.
  • Each line in the output file is a transcript with the 4th columns as transcript ID
  • The version number of the transcript ID should be stripped (e.g. ENST00000473358.1 => ENST00000473358).
  • The output file is sorted first by transcript IDs and then by chromosome in lexicographical order.
  • Column 5, 7, 8, 9 in the BED12 file should be set to 0.
  • Please do NOT use any external tools (e.g. sort, awk, etc.) in your program other than Python.
  • An example output can be found in weekly_tasks/transcripts.bed.

Hint: use dict, list, tuple, str.split, re.match, sorted.

Task 3: write a Python program to add a prefix to all directories

  • Each prefix is a two-digit number starting from 00 and ‘-‘. If the number is less than 10, a single ‘0’ letter should be filled.
  • The files/directories should be numbered according to the lexicographical order.
    For example, if the original directory structure is:
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.
├── A
│   ├── A
│   │   ├── A
│   │   ├── B
│   │   └── C
│   ├── B
│   │   └── A
│   └── C
│       └── A
├── B
│   ├── A
│   └── B
└── C
    ├── A
    └── B
        └── A

then you should get the following directory structure after renaming:

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.
├── 00-A
│   ├── 00-A
│   │   ├── 00-A
│   │   ├── 01-B
│   │   └── 02-C
│   ├── 01-B
│   │   └── 00-A
│   └── 02-C
│       └── 00-A
├── 01-B
│   ├── 00-A
│   └── 01-B
└── 02-C
    ├── 00-A
    └── 01-B
        └── 00-A
  • The original directories can be found in weekly_tasks/original_dirs.
  • The root directory (i.e. original_dirs) should not be renamed.
  • You can use tree command to display the directory structure as shown above.
  • An example result can be found in weekly_tasks/renamed_dirs.
    Hint: use os.listdir, os.rename, str.format, sorted, yield.
-----The ---- end ------- Thanks --- for --- Reading----