This Lecture Introduction to Python for Biologists Part

This Lecture Introduction to Python for Biologists Part 1

Learning Objectives • Install Python • Data & Variables • String slicing • String methods • List methods & list slicing • Math • Arrays

Why do Biologist need to learn Programming? http: //archive. oreilly. com/pub/a/oreilly//news/perlbio_1001. html http: //www. nature. com/nbt/journal/v 31/n 10/box/nbt. 2721_BX 1. html • Biology is becoming a data-driven field – New technology enables scientists to generate large data sets in semi-automated experiments. – Analysis of your own data is challenging – Automation saves time – Many interesting questions remain unanalyzed in huge amounts of publicly available data – Integration of new experimental results with public data is a challenging computational problem • Scientists who can pursue innovative data analysis methods have an advantage over those limited to existing software (or those who require the assistance of other people with programming and data analysis skills)

Python* • • is a Programming Language Free, open source Runs on all types of computers “User friendly and easy to learn” “clean readable code” Very popular among bioinformaticians Good documentation available https: //wiki. python. org/moin/Beginners. Guide/Overview • Powerful “object oriented” features • Many add-on toolkits (“modules”) available for scientific computing, visualization, statistics, etc. *Python is named after a 1970’s British comedy TV show, not a large snake

Grad School Python Thanks to xkcd: https: //xkcd. com/519/

Python. org

Online Tutorials • You can’t learn an entire programming language from a couple of classroom lectures. • There are many online tutorials for Python, which allow self-learning at your own pace • We recommend: • • • Codecademy. com Try. Python. org Learn. Pythonthe. Hard. Way. org/book Software Carpenty • For Biologists: • Python for Biologists • Rosalind Python Village (learn by solving problems)

Reading For this week: • Python for Biologists, chapter 1 -3 • The anatomy of successful computational biology software. Altschul S, Demchak B, Durbin R, Gentleman R, Krzywinski M, Li H, Nekrutenko A, Robinson J, Rasband W, Taylor J, Trapnell C. Nature Biotechnology 2013 Oct; 31(10): 894 -7. DOI: doi: 10. 1038/nbt. 2721

Install Python • Assignment: Install Python on your computer • Be sure to include the Numpy and Sci. Py modules • One easy way to set up a GUI for Python (on Mac and Windows) is to download the free version of Anaconda: http: //continuum. io/downloads • Or you can run the command line version on Linux or in the Macintosh Terminal (for Mac you will need Xcode, which is a free software developers toolkit from Apple, is not installed by default in OSX)

Anaconda • Your life (in this course) will probably be easier if you install the (free) Anaconda – includes numerical, scientific, statistical, and graphics modules. http: //continuum. io/downloads

Programming Concepts All programming languages are built from the same basic elements: • data • operators • flow control These concepts are expressed in a specific syntax for each programming language

Data types • Basic: • Strings = 'GATCCATGCGAGACCCTTGA‘ • Numbers = 7, 123. 455, 4. 2 e-14 • Boolean = True, False • Every data object has a type – (try these examples on your own) >>> type (1) >>> type (“GATCCT”)

Variables • A Variable is a named container for data (think of it as a box or a shelf that has a name) • In Python, a variable can hold any type of data, does not need to be pre-defined • The data in the variable can be changed at any time (and can change to a different type) • Python variable names must start with a letter, can only contain text letters and numbers and the underscore _ character. • Case sensitive

Comments • Comments are bits of text added by the programmer into the code that explain what is going on. They are not executed by the computer. • Python uses the hash symbol # to mark a comment, anything on a line after the # is ignored • Use lots of clear comments in your code: for a good grade, so others can understand your code, and so you can understand your own code from the past (days, weeks, years… ago).

Examples of Variables A value is assigned to a variable by the = sign. The value to the right of the = is put into the variable name on the left. my_DNA = "ATGCGTA" gene_length = 467 Dog_Text = “my Dog has Fleas” #spaces are part of a string counter = 6 pi_short= 3. 14 my_list = [a, b, c, d] HBB_human=“MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESF GDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLG NVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH” #this string is one line that wraps on the screen

Strings • Strings are text. Must always be in quotes. • Can use single or double quotes, but must be consistent • A string can contain space characters and also newline characters. • Biology data involves a lot of strings: sequences, names (taxonomy, gene names), etc. • A string is usually assigned to a variable >>>my_string = “gi|45478711|ref|NC_005816. 1|Yersinia pestis biovar Microtus”

String Methods • In Python, data objects of type ‘string’ have built in operators called ‘methods” • Methods use a ‘dot’ syntax as follows: >>> my_DNA = "ATGCGTA" >>> my_DNA. count(G) 2 >>> my_DNA. lower() 'atgcgta‘

String Concatenation • Two strings can be joined with the + operator c = 'cat' h = 'hat' print ('cat' + 'hat') ch = c + h print ch print (c + ' in the ' + h) • Numbers must be converted to strings using the str() function before using the string concatentation operator A=5 print (A + c) #note the error message print ('We have' + ' ' + str(A) + ' ' + c + 's')

More String methods • upper() and lower() return a value that changes the case of a string. You usually need to put this value into a variable, otherwise the original string is unchanged. >>> my_DNA = “TATGCGTA" >>> my_DNA. lower() ‘tatgcgta‘ >>> my_DNA 'TATGCGTA‘ • len() gives the length of a string >>> len(my_DNA) 8

Find & Replace • find() is another handy string method. (Note: It only works for exact matches) >>> my_DNA = "ATGCGTA“ >>> my_DNA. find("GC") 2 #returns the position index of the first occurrence of the search string in the target • replace() finds and replaces letters in a string >>> my_DNA. replace('T', 'X' ) 'AXGCGXA'

Lists • Lists contain a group of things, in square brackets, separated by commas List 1 = [a, b, c, d] List 2 = [“XP_008199794”, “PF 03769”, “gi|54037254”] List_mix = [“fish”, “hat”, “box”, 17, 4935. 45, True] • The elements of a list do not all have to be of the same type • Lists are used for many tasks in Python that involve a lot of data.

List Elements • The elements in a list are ordered. They can be accessed by their index number in the list. • Python starts counting list elements at zero • The list index is indicated by a number in square brackets following the name of the list • List slicing uses this format: [begin: end: step] • You can do fancy things with list slicing, but intervals are counted with strange rules. You need to study this. >>> my_list=['G', 'A', 'hat', 'cat'] >>> my_list[1] 'A' >>> my_list[1: 3] ['A', 'hat'] >>> my_list[: -2] ['G', 'A']

List Methods • You can assign a value to a specific position in a list: >>> my_list=['G', 'A', 'hat', 'cat'] >>> my_list[1] = “X” >>> my_list ['G', ‘X', 'hat', 'cat'] • List methods are functions built into the list data type. They use the ‘dot’ syntax just like string methods. my_list. count(‘G’) 1 • list. append() is a commonly used list method. It adds its argument to the end of a list. It is frequently used to collect results as a program steps through a loop my_list. append(‘T’) >>> my_list ['G', 'X', 'hat', 'cat', 'T' ]

String Slicing • Strings can be treated as a list of letters, and sliced with the exact same methods as lists >>> my_DNA = "ATGCGTA" >>> my_DNA[1] 'T' >>> my_DNA[1: 4] 'TGC'

Split a string into a List • Sometimes it is helpful to turn a string into a list of words or numbers. The split() method does this. • By default, it splits on whitespace, but any character specified in the parentheses can be used as delimiter. • This is useful when working with tab delimited or comma delimited (csv) data. >>> names = "melanogaster, simulans, yakuba, ananassae" >>> species = names. split(", ") >>> print(names[1] + ' ' + species[2]) e yakuba

The list() function • The list() function splits a string into a list of characters >>> hi = "Hello world" >>> list(hi) ['H', 'e', 'l', 'o', 'w', 'o', 'r', 'l', 'd']

Join • join() turns a list of strings into a single string. You can add a spacer such as a comma or space character. It has a backwards syntax, where the spacer is the thing being acted upon by the method: >>> my_list=['G', 'A', 'hat', 'cat'] >>> spacer = ': ' >>> newstring = spacer. join(my_list) >>> newstring 'G: A: hat: cat' >>> '#'. join(my_list) 'G#A#hat#cat'

String Slicing: Exercises (do these yourself in Python shell) >>> dna = 'CGGTTAATAGGGACTCTC' >>> dna[0] >>> dna[0: 3] >>> dna[-1: -3] >>> dna[-3: -1] >>> dna[0: 5: 2] >>> dna[0: 5][: : -1] >>> dna[0: 5][: : -2] >>> dna

Math • Python can do simple math like a calculator. • Type the following expressions into an interactive Python session (or the IDE editor), hit the enter/return key (or Run button) and observe the results: 2+2 6– 3 8 / 3. 0 9*3 6 ** 2

Math module • Python does not activate all of its built-in functions when you start it up • You use the “import” command to add modules. • Type “import math” to get more advanced mathematics functions. math. sqrt() is a function in the math module. Try this: import math. sqrt(36) 6. 0

Simple Navigation • Doing some simple file system navigation in Python is unreasonably difficult (uses a module called os) • Where am I? >>> import os >>> os. getcwd() 'C: \Python 27‘ • What files are in this directory (folder)? >>> os. listdir('. ') ['at. py', 'hello. py', 'JASPAR-pfm_all. txt', 'Jaspar. Client. py', 'MA 0024. 1. pfm', 'my_blast. xml', 'ros 4. py', 'rosalind_ini 5. txt', 'SRR 020192. fastq', 'Test_100. fasta‘] • Change directory >>> os. chdir('/Users/stu/Python')

Num. Py and Arrays • Arrays are like lists, but they contain only numbers, and they have dimensions. • Num. Py is a Python module that enables array operations. Here is a simple one dimensional array of integers (just like a list): >>> import numpy as np >>> x = np. array([42, 47, 11], int) >>> x >>> array([42, 47, 11]) Software Carpentry has a nice introduction to Num. Py arrays: http: //swcarpentry. github. io/python-novice-inflammation/01 -numpy. html

2 -Dimensional Array • A two dimensional array is like a list of lists, but each row must have the same number of elements. >>> x = np. array( ((11, 12, 13), (21, 22, 23), (31, 32, 33)) ) >>> print x [ [11 12 13] [21 22 23] [31 32 33] ] • Note the nested square brackets • Num. Py has no problem with 3, 4, or more dimensions, but it is annoying to represent as text.

Matrix Math • Matrices are 2 -dimensional arrays. • Num. Py has linear algebra methods for operations on matrices. These operations require that two matrices be of the same size. • • • Vector addition Matrix subtraction Matrix multiplication Scalar product (dot product) Cross product >>> x = np. array([3, 2]) >>> y = np. array([5, 1]) >>> z = x + y >>> z array([8, 3])

Assignment: • Rosalind Python Village – All 6 problems (should take you 1 -2 hours) Rosalind Python Village: http: //rosalind. info/problems/list-view/? location=python-village

Summary • Install Python • Data & Variables • String slicing • String methods • List methods & list slicing • Math • Arrays