Overview

As we begin our exploration of the Python programming language, our first goal will be to gain a high-level overview of programming concepts and a comfort level in reading Python code and understanding its instructions (as opposed to writing new Python code). I'd equate this similar to natural languages, in which it is easier to learn to read/hear existing samples of the language before learning to express yourself by writing/speaking the language. Therefore, this document provides an initial overview of the language and programming concepts. We will add further links to more specific documentation on aspects of the language at a later point.

Data Types

It is important to understand that there are various types of data that programmers will want to store and manipulate, and that each of these has a different internal representation. In Python, a concept known as a class is used to define a type of data. The primary classes that we will use include:

• int -- represents an integer value
  (e.g. 53, -10000)

• float -- a more general numeric representation that can handle integral or non-integral values, albeit with inherent limits on precision
  (e.g. 3.14159, 53.0, 6.022e23)

• str -- a string of characters. Strings in Python most often represent language/text such as 'hello' or 'How are you?', though in bioinformatics we will routinely use character strings to represent omics sequences such as dna 'ACCTACGT' or protein sequences 'MQRSPLEKASVV'.

• bool -- named for George Boole a "bool" or "boolean" value is a logial value that is either True or False. We will see boolean values a great deal implicity when testing conditions such as if x > y, but we can also explicitly store boolean values, perhaps as a variable such as isMutant.

• list -- a list is used to maintain a general sequence of values. That is, while a string is specifically a sequence of characters, a list can be a sequence of characters, sequence of numbers, sequence of strings, sequence of other lists.

• dict -- a "dictionary" is a more advanced data structure that is optimized to efficient map from a set of unique "keys" to their associated "values". For example, we could use a dictionary to represent the mapping from codons to amino acids, for example mapping 'ATG' → 'M', 'CTT' → 'L' and so forth. Keys of a dictionary must be unique (but two keys might map to the same value, such as with 'TTA' → 'L').

Identifiers (a.k.a Names, Variables)

When programming in Python we can give internal names to any values that we compute as a way to store and later identify such a value. For example, we might set

  samples = 357

  dna = 'ACCTAAGA'

Control Structures

The order in which commands are executed by a computer is called the "flow of control." The default flow is that commands are executed in a Python script in the order in which they are expressed.

firstCommand
secondCommand
thirdCommand
...

• conditionals -- An "if statement" is the simplest form of a conditional, as it allows you to specify a block of code that may or may not be executed depending on the evaluation of a boolean condition, such as

        if raining:
            openTheUmbrella
        

  The indented block of code will be executed if the defining condition is true, but otherwise that block of code is skipped.

  Conditionals may also use an "if/else" form in which there is a second block that should be executed with the condition is false. It is also possible to chain the conditions for more multiway branching statements.

• loops -- One of the most powerful aspects of computing is the ability to repeat blocks of code. Loops are the most common control structure used to identify a block of code that are to be repeated. In fact, we will see three different styles of loops. A for loop will be used to repeat a block of code once for each element of a sequence (such as each basepair in a dna strand). A range-based for loop will be used to repeat a block of code over a numeric range. Finally, a while loop will give us the most flexible form that allows for a block of code to be repeated so long as some boolean condition is satisfied.

• functions -- Functions provide a means for abstraction in that they allow for a more complex behavior to defined a named and subsequently used in other portions of code. For example, we can think of higher-level tasks such as transcribing DNA and translating RNA to an amino acid sequence. In Python, we might implement these behaviors and define a method named translate allowing us to write code such as

        aminoSeq = translate(dna)
        

  There are some functions that are already automatically supported by Python, such as max which computes the maximum value among a sequence of values. Many more functions have been implemented and included in various libraries known as modules in Python, which can be imported into a script. Finally, we can define new custom functions that suit our own purposes.

Examples

Cleaning Input

The primary task of the SMS Filter tool is to produce a result that is equivalent to the original piece of text, except omitting all characters that are not alphabetic. We offer our own function, named clean which accomplishes this task. Our implementation appears as follows.

def clean(original):
    result = ''
    for c in original:
        if c.isalpha():
            result = result + c
    return result

To step through this function running on an example, click the "Forward" button below.

Reverse Complement

A primary task of the SMS Reverse Complement tool is to compute the reverse complement of an original strand of dna. We offer our own function, named complement which accomplishes this task. Our implementation appears as follows.

# convert a given DNA sequence to its reverse complementary strand
dna2dna = {'A':'T', 'T':'A', 'C':'G', 'G':'C'}
def reverse_complement(dna):
    other = ''
    for base in dna:
        other = other + dna2dna[base]
    return other[::-1]   # python trick to reverse a string

Finding Pairwise Differences

The SMS Color Align Conservation tool, though more advanced than the following, was used to try to graphically highlight locations within two equal-length sequences that differe from each other. We compute a (non-graphical) summary of the pairwise differences of two sequences as follows.

# compute pairwise difference between two equal-length strings
# list of difference returned using notation such as 'T35G'
# to reflect that at location 35 a T in first sequence is G in second
def difference(first, second):
    diff = []
    for k in range(len(first)):
        if first[k] != second[k]:
            diff.append( first[k] + str(1+k) + second[k])
    return diff