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Roadmap

Roadmap. The topics: basic concepts of molecular biology Elements of Python Python ’ s data types Python functions Python control of flow Python OOP Python regex overview of the field biological databases and database searching sequence alignments phylogenetics structure prediction

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Roadmap

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  1. Roadmap The topics: • basic concepts of molecular biology • Elements of Python • Python’s data types • Python functions • Python control of flow • Python OOP • Python regex • overview of the field • biological databases and database searching • sequence alignments • phylogenetics • structure prediction • microarray & next gen

  2. Where to get Python? If you want to run your python programs on your own machine, download Python interpreter from different places: http://www.activestate.com/activepython/ or http://www.python.org/download/ You can find the links at the class website.

  3. Running Python Python is one of the best scripting languages. It is often being used in text-based command shell.

  4. Download IEP as your IDE There are many good Python IDEs. I have the links to some of them at the class website. I’ll use IEP in class: Download IEP at: http://www.iep-project.org/downloads.html I downloaded : “iep-3.2.win32.exe - Windows installer”

  5. Use IEP • You’ll see an icon like the following on your desktop. Start your IEP. Ctrl S: save Ctrl E: execution Drawback: Won’t be able to pass arguments to script

  6. Programming Pythonfor BioinformaticsPart I

  7. A Taste of Python: at prompt • Type statements or expressions at prompt: >>> print "Hello, world" Hello, world >>> x = 12**2 >>> x/2 72 >>> # this is a comment

  8. A Taste of Python: print a message • demo1.py: Greet the entire world. #!/usr/bin/python #greet the entire world x = 7e9; print “Hello world!”; print “All”, x, “of you!”; • command interpretation header • (where to find python) - a comment - variable assignment statement } - function calls (output statements)

  9. A Taste of Python: scripting • demo2.py: parsing email addresses

  10. Overview • Assignment & Names • Data types • Sequences types: Lists, Tuples, and Strings • Mutability • Understanding Reference Semantics in Python

  11. Assignment • Assignment uses = and comparison uses == • The first assignment to a variable creates it • Dynamic typing: no declarations, names don’t have types, objects do • For numbers + - * / %are as expected. • Use of + for string concatenation. • Use of % for string formatting (like printf in C) • Logical operators are words (and,or,not) not symbols • The basic printing command is print • Indentation matters to meaning the code • Block structure indicated by indentation

  12. Assignment • Binding a variable in Python means setting a name to hold a referenceto some object • Assignment creates references, not copies • Names in Python don’t have an intrinsic type, objects have types Python determines type of the reference auto-matically based on what data is assigned to it • You create a name the first time it appears on the left side of an assignment expression: x = 3 • A reference is deleted via garbage collection after any names bound to it have passed out of scope • Python uses reference semantics

  13. Naming Rules • Names are case sensitive and cannot start with a number. They can contain letters, numbers, and underscores. bob Bob _bob _2_bob_ bob_2 BoB • There are some reserved words: and, assert, break, class, continue, def, del, elif, else, except, exec, finally, for, from, global, if, import, in, is, lambda, not, or, pass, print, raise, return, try, while

  14. Naming conventions The Python community has these recommended naming conventions • joined_lower for functions, methods and, attributes • joined_lower or ALL_CAPS for constants • StudlyCaps for classes • camelCase only to conform to pre-existing conventions • Attributes: interface, _internal, __private

  15. Accessing Non-Existent Name Accessing a name before it’s been properly created (by placing it on the left side of an assignment), raises an error >>> y Traceback (most recent call last): File "<pyshell#16>", line 1, in -toplevel- y NameError: name ‘y' is not defined >>> y = 3 >>> y 3

  16. Whitespace Whitespace is meaningful in Python, especially indentation and placement of newlines • Use a newline to end a line of code Use \when must go to next line prematurely • No braces { } to mark blocks of code, use consistentindentation instead • First line with less indentation is outside of the block • First line with more indentation starts a nested block • Colons start of a new block in many constructs, e.g. function definitions, then clauses

  17. Comments • Start comments with #,rest of line is ignored • Can include a “documentation string” as the first line of a new function or class you define • Development environments, debugger, and other tools use it: it’s good style to include one def fact(n): “““fact(n) assumes n is a positive integer and returns factorial of n.”””assert(n>=0) return 1 if n==0 else n*fact(n-1)

  18. Python’s data types

  19. Everything is an object • Python data is represented by objects or by relations between objects • Every object has an identity, a type and a value • Identity never changes once created Location or address in memory • Type (e.g., integer, list) is unchangeable and determines the possible values it could have and operations that can be applied • Value of some objects is fixed (e.g., an integer) and can change for others (e.g., list)

  20. Python’s built-in type hierarchy

  21. Basic Datatypes • Integers (default for numbers) z = 5 / 2 # Answer 2, integer division • Floats x = 3.456 • Strings • Can use "…" or '…' to specify, "foo" == 'foo' • Unmatched can occur within the string “John’s”or ‘John said “foo!”.’ • Use triple double-quotes for multi-line strings or strings than contain both ‘ and “ inside of them: “““a‘b“c”””

  22. Numbers • Numbers • Normal Integers –represent whole numbers Ex: 3, -7, 123, 76 • Long Integers – unlimited size Ex: 9999999999999999999999L • Floating-point – represent numbers with decimal places Ex: 1.2, 3.14159,3.14e-10 • Octal and hexadecimal numbers Ex: O177, 0x9ff, Oxff • Complex numbers Ex: 3+4j, 3.0+4.0j, 3J

  23. Python Basics – arithmetic operations + add - subract * multiply / divide % modulus/remainder OperatorsExample y=5; z=3 x = y + z x = y – z x = y * z x = y / z x = y % z x = 8 x = 2 x = 15 x = 1 x = 2

  24. Python Basics – arithmetic operations << shift left >> shift right | bitwise or ^ bitwise exclusive or & bitwise and ** raise to power OperatorsExample y=5; z=3 x = y << 1 x = y >> 2 x = y | z x = y ^ z x = y & z x = y ** z x = 10, y=5 x = 1 x = 7 x=6 x=1 x = 125

  25. Relational operators == equal !=, <> not equal > greater than >= greater than or equal < less than <= less than or equal Logical operators andand oror not not Python Basics – Relational and Logical Operators

  26. Python Basics – Relational Operators • Assume x = 1, y = 4, z = 14

  27. Python Basics – Logical Operators • Assume x = 1, y = 4, z = 14

  28. Three sequence types: Tuples, Lists, and Strings

  29. Sequence Types • Sequences are containers that hold objects • Finite, ordered, indexed by integers • Tuple: (1, “a”, [100], “foo”) • An immutableordered sequence of items • Items can be of mixed types, including collection types • String: “foo bar” • An immutable ordered sequence of chars • Conceptually very much like a tuple • List: [“one”, “two”, 3] • A Mutableordered sequence of items of mixed types

  30. Similar Syntax • All three sequence types (tuples, strings, and lists) share much of the same syntax and functionality. • Key difference: • Tuples and strings are immutable • Lists are mutable • The operations shown in this section can be applied to all sequence types • most examples will just show the operation performed on one

  31. Sequence Types - 1 • Define tuples using parentheses and commas >>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’) • Define lists are using square brackets and commas >>> li = [“abc”, 34, 4.34, 23] • Define strings using quotes (", ', or """). >>> st = "Hello World" >>> st = 'Hello World' >>> st = """This is a multi-line string that uses triple quotes. """

  32. Sequence Types - 2 • Access individual members of a tuple, list, or string using square bracket “array” notation • Note that all are 0 based… >>> tu = (23, ‘abc’, 4.56, (2,3), ‘def’) >>> tu[1] # Second item in the tuple. ‘abc’ >>> li = [“abc”, 34, 4.34, 23] >>> li[1] # Second item in the list. 34 >>> st = “Hello World” >>> st[1] # Second character in string. ‘e’

  33. Positive and negative indices >>> t = (23, ‘abc’, 4.56, (2,3), ‘def’) Positive index: count from the left, starting with 0 >>> t[1] ‘abc’ Negative index: count from right, starting with –1 >>> t[-3] 4.56

  34. Slicing: Return Copy of a Subset >>> t = (23, 'abc', 4.56, (2,3), 'def') Returns copy of container with subset of original members. Start copying at first index, and stop copying before the second index >>> t[1:4] ('abc', 4.56, (2,3)) You can also use negative indices (counting backward) >>> t[1:-1] ('abc', 4.56, (2,3))

  35. Slicing: Return Copy of a Subset >>> t = (23, 'abc', 4.56, (2,3), 'def') Omit first index to make a copy starting from the beginning of container >>> t[:2] (23, ‘abc’) Omit second index to make a copy starting at 1st index and going to end of the container >>> t[2:] (4.56, (2,3), ‘def’)

  36. Copying the Whole Sequence • [ : ] makes a copy of an entire sequence >>> t[:] (23, ‘abc’, 4.56, (2,3), ‘def’) • Note the difference between these two lines for mutable sequences >>> l2 = l1 # Both refer to same ref, # changing one affects both >>> l2 = l1[:] # Independent copies, 2 refs

  37. The ‘in’ Operator • Boolean test whether a value is inside a container: >>> t = [1, 2, 4, 5] >>> 3 in t False >>> 4 in t True >>> 4 not in t False • For strings, tests for substrings >>> 'TATA' in 'TATATATATATATATATATATATA' True >>> 'ATG' in 'TATATATATATATATATATATATA' False >>> 'AA' not in 'TATATATATATATATATATATATA' True • Careful: the in keyword is also used in the syntax of forloopsand list comprehensions

  38. + Operator is Concatenation • The + operator produces a new tuple, list, or string whose value is the concatenationof its arguments. >>> (1, 2, 3) + (4, 5, 6) (1, 2, 3, 4, 5, 6) >>> [1, 2, 3] + [4, 5, 6] [1, 2, 3, 4, 5, 6] >>> 'ACCTGAGAGCT' + 8*'A' 'ACCTGAGAGCTAAAAAAAA'

  39. Build-in functions vs. methods len() is a function on collections that returns the number of things they contain >>> len(['a', 'b', 'c']) 3 >>> len(('a','b','c')) 3 >>> len("abc") 3 index() is a method on collections that returns the index of the 1st occurrence of its arg >>> ['a’,'b’,'c'].index('a') 0 >>> ('a','b','c').index('b') 1 >>> "abc".index('c') 2 • Operations can be functions or methods • Remember that (almost) everything is an object • You just have to learn (and remember or lookup) which operations are functions, which are methods

  40. Other String operations • Many useful built-in functions • >>> mystring = 'ACCTGAGAGCT' • mystring.upper() • 'ACCTGAGAGCT' • >>> mystring.replace('GC', 'CG') • 'ACCTGAGACGT' • >>> set(mystring) • set(['A', 'C', 'T', 'G'])

  41. “blanks” Values to put in blanks Strings • “%” operator:sort of “fill in the blanks” operation:mystring="%s has %d marbles" % ("John",35) • mystring-> “John has 35 marbles” • %s replace with string • %d,%i replace with integer • %f replace with float

  42. Exercise • count.py: dna ="ATGaCGgaTCAGCCGcAAtACataCACTgttca" GC content? • dna = "ATGaCGgaTCAGCCGcAAtACataCACTgttca" • dna1 = dna.upper() • (dna1.count('G') + dna1.count('C'))*1.0 / len(dna1)

  43. Exercise • transcribe.py: dna =“ATGaCGgaTCAGCCGcAAGcGGaattGGCGACataa" rna = ???; • dna = "ATGaCGgaTCAGCCGcAAtACataCACTgttca" • rna = dna.upper() • rna1 = rna.replace('A', 'a') • rna = rna1.replace('T', 'A') • rna1 = rna.replace('C', 'c') • rna = rna1.replace('G', 'C') • rna1 = rna.replace('a', 'U') • rna= rna1.replace('c', 'G') • rna[::-1] # reverse rna

  44. Exercise A valid DNA sequence? dna ="ATGaCGgaTDCUAGCCPGcAAtACataCACTngttca"

  45. Mutability:Tuples vs. Lists

  46. Lists are mutable >>> li = [‘abc’, 23, 4.34, 23] >>> li[1] = 45 >>> li[‘abc’, 45, 4.34, 23] • We can change lists in place. • Nameli still points to the same memory reference when we’re done.

  47. Tuples are immutable >>> t = (23, ‘abc’, 4.56, (2,3), ‘def’) >>> t[2] = 3.14 File "C:\Users\duan\Desktop\CS445\demos\Ch0\tmp.py", line 1 t = (23, ‘abc’, 4.56, (2,3), ‘def’) ^ SyntaxError: invalid syntax • You can’t change a tuple. • You can make a fresh tuple and assign its reference to a previously used name. >>> t = (23, ‘abc’, 3.14, (2,3), ‘def’) • Immutability of tuples they are faster than lists

  48. Tuple details • The comma is the tuple creation operator, not parens >>> 1, (1,) • Python shows parens for clarity (best practice) >>> (1,) (1,) • Don't forget the comma for singletons! >>> (1) 1 • Empty tuples have a special syntactic form >>> () () >>> tuple() ()

  49. Tuples vs. Lists • Lists slower but more powerful than tuples • Lists can be modified and they have many handy operations and methods • Tuples are immutable & have fewer features • Sometimes an immutable collection is required (e.g., as a hash key) • Tuples used for multiple return values and parallel assignments x,y,z = 100,200,300 old,new = new,old • Convert tuples and lists using list() and tuple(): mylst = list(mytup); mytup = tuple(mylst)

  50. Lists methods • Lists have many methods, including index, count, append, remove, reverse, sort, etc. • Many of these modify the list >>> l = [1,3,4] >>> l.append(0) # adds a new element to the end of the list >>> l [1, 3, 4, 0] >>> l.insert(1,200) # insert 200 just before index position 1 >>> l [1, 200, 3, 4, 0] >>> l.reverse() # reverse the list in place >>> l [0, 4, 3, 200, 1] >>> l.sort() # sort the elements. Optional arguments can give >>> l # the sorting function and direction [0, 1, 3, 4, 200] >>> l.remove(3) # remove first occurence of element from list >>> l [0, 1, 4, 200]

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