Fun with Regexes

2. FBW
23-10-2018
Wim Van Criekinge

3. Google Calendar

4. Bioinformatics.be

6. http://course.fast.ai/index.html

7. RECAP: Lists
• Flexible arrays, not Lisp-like linked
lists
• a = [99, "bottles of beer", ["on", "the",
"wall"]]
• Same operators as for strings
• a+b, a*3, a[0], a[-1], a[1:], len(a)
• Item and slice assignment
• a[0] = 98
• a[1:2] = ["bottles", "of", "beer"]
-> [98, "bottles", "of", "beer", ["on", "the", "wall"]]
• del a[-1] # -> [98, "bottles", "of", "beer"]

8. RECAP: Dictionaries
• Hash tables, "associative arrays"
• d = {"duck": "eend", "water": "water"}
• Lookup:
• d["duck"] -> "eend"
• d["back"] # raises KeyError exception
• Delete, insert, overwrite:
• del d["water"] # {"duck": "eend", "back": "rug"}
• d["back"] = "rug" # {"duck": "eend", "back":
"rug"}
• d["duck"] = "duik" # {"duck": "duik", "back":
"rug"}

9. RECAP
if condition:
statements
[elif condition:
statements] ...
else:
statements
while condition:
statements
for var in sequence:
statements
break
continue
Strings
REGULAR EXPRESSIONS

10. Regular Expressions
http://en.wikipedia.org/wiki/Regular_expression
In computing, a regular expression, also
referred to as "regex" or "regexp", provides a
concise and flexible means for matching
strings of text, such as particular characters,
words, or patterns of characters. A regular
expression is written in a formal language that
can be interpreted by a regular expression
processor.
Really clever "wild card" expressions for
matching and parsing strings.

11. Understanding Regular Expressions
• Very powerful and quite cryptic
• Fun once you understand them
• Regular expressions are a language
unto themselves
• A language of "marker characters" -
programming with characters
• It is kind of an "old school"
language - compact

12. Regular Expression Quick Guide
^ Matches the beginning of a line
$ Matches the end of the line
. Matches any character
s Matches whitespace
S Matches any non-whitespace character
* Repeats a character zero or more times
*? Repeats a character zero or more times (non-greedy)
+ Repeats a chracter one or more times
+? Repeats a character one or more times (non-greedy)
[aeiou] Matches a single character in the listed set
[^XYZ] Matches a single character not in the listed set
[a-z0-9] The set of characters can include a range
( Indicates where string extraction is to start
) Indicates where string extraction is to end

13. The Regular Expression Module
• Before you can use regular expressions in
your program, you must import the library
using "import re"
• You can use re.search() to see if a string
matches a regular expression similar to
using the find() method for strings
• You can use re.findall() extract portions of
a string that match your regular expression
similar to a combination of find() and
slicing

14. Wild-Card Characters
• The dot character matches any
character
• If you add the asterisk character,
the character is "any number of
times"
^X.*:
Match the start of the line
Match any character
Many times

15. Matching and Extracting Data
• The re.search() returns a True/False
depending on whether the string matches
the regular expression
• If we actually want the matching strings
to be extracted, we use re.findall()
>>> import re
>>> x = 'My 2 favorite numbers are 19 and 42'
>>> y = re.findall('[0-9]+',x)
>>> print y
['2', '19', '42']

16. Warning: Greedy Matching
• The repeat characters (* and +) push outward in both directions
(greedy) to match the largest possible string
>>> import re
>>> x = 'From: Using the : character'
>>> y = re.findall('^F.+:', x)
>>> print y
['From: Using the :']
^F.+:
One or more
characters
First character in the
match is an F
Last character in the
match is a :

17. Non-Greedy Matching
• Not all regular expression repeat codes are
greedy! If you add a ? character - the + and *
chill out a bit...
>>> import re
>>> x = 'From: Using the : character'
>>> y = re.findall('^F.+?:', x)
>>> print y
['From:']
^F.+?:
One or more
characters but
not greedily
First character in the
match is an F
Last character in the
match is a :

18. Fine Tuning String Extraction
• Parenthesis are not part of the match -
but they tell where to start and stop what
string to extract
From stephen.marquard@uct.ac.za Sat Jan 5 09:14:16
2008
>>> y = re.findall('S+@S+',x)
>>> print y
['stephen.marquard@uct.ac.za']
>>> y = re.findall('^From (S+@S+)',x)
>>> print y
['stephen.marquard@uct.ac.za']
^From (S+@S+)

19. The Double Split Version
• Sometimes we split a line one way and then grab
one of the pieces of the line and split that piece
again
From stephen.marquard@uct.ac.za Sat Jan 5 09:14:16
2008
words = line.split()
email = words[1]
pieces = email.split('@')
print pieces[1]
stephen.marquard@uct.ac.za
['stephen.marquard', 'uct.ac.za']
'uct.ac.za'

20. The Regex Version
From stephen.marquard@uct.ac.za Sat Jan 5 09:14:16
2008
import re
lin = 'From stephen.marquard@uct.ac.za Sat Jan 5 09:14:1
y = re.findall('@([^ ]*)',lin)
print y['uct.ac.za']
'@([^ ]*)'
Look through the string until you find an at-sign
Match non-blank character
Match many of them

21. Escape Character
• If you want a special regular expression
character to just behave normally (most
of the time) you prefix it with ''
>>> import re
>>> x = 'We just received $10.00 for cookies.'
>>> y = re.findall('$[0-9.]+',x)
>>> print y
['$10.00']
$[0-9.]+
A digit or periodA real dollar sign
At least one
or more

22. Real world problems
• Match IP Addresses, email addresses,
URLs
• Match balanced sets of parenthesis
• Substitute words
• Tokenize
• Validate
• Count
• Delete duplicates
• Natural Language processing

25. RE in Python
• Unleash the power - built-in re module
• Functions
– to compile patterns
• compile
– to perform matches
• match, search, findall, finditer
– to perform operations on match object
• group, start, end, span
– to substitute
• sub, subn
• - Metacharacters

26. Regex.py
text = 'abbaaabbbbaaaaa'
pattern = 'ab'
for match in re.finditer(pattern, text):
s = match.start()
e = match.end()
print ('Found "%s" at %d:%d' % (text[s:e], s, e))

27. 27
Reading Files
name = open("filename")
– opens the given file for reading, and returns a file object
name.read() - file's entire contents as a string
name.readline() - next line from file as a string
name.readlines() - file's contents as a list of lines
– the lines from a file object can also be read using a for loop
>>> f = open("hours.txt")
>>> f.read()
'123 Susan 12.5 8.1 7.6 3.2n
456 Brad 4.0 11.6 6.5 2.7 12n
789 Jenn 8.0 8.0 8.0 8.0 7.5n'

28. 28
File Input Template
• A template for reading files in Python:
name = open("filename")
for line in name:
statements
>>> input = open("hours.txt")
>>> for line in input:
... print(line.strip()) # strip() removes n
123 Susan 12.5 8.1 7.6 3.2
456 Brad 4.0 11.6 6.5 2.7 12
789 Jenn 8.0 8.0 8.0 8.0 7.5

29. 29
Writing Files
name = open("filename", "w")
name = open("filename", "a")
– opens file for write (deletes previous contents), or
– opens file for append (new data goes after previous data)
name.write(str) - writes the given string to the file
name.close() - saves file once writing is done
>>> out = open("output.txt", "w")
>>> out.write("Hello, world!n")
>>> out.write("How are you?")
>>> out.close()
>>> open("output.txt").read()
'Hello, world!nHow are you?'

30. Question 4
• Program your own prosite parser !
• Download prosite pattern database
(prosite.dat)
• Automatically generate >2000 search
patterns, and search in sequence set
from question 1

31. >SEQ1
MGNLFENCTHRYSFEYIYENCTNTTNQCGLIRNVASSIDVFHWLDVYISTTIFVISGILNFYCLFIALYT
YYFLDNETRKHYVFVLSRFLSSILVIISLLVLESTLFSESLSPTFAYYAVAFSIYDFSMDTLFFSYIMIS
LITYFGVVHYNFYRRHVSLRSLYIILISMWTFSLAIAIPLGLYEAASNSQGPIKCDLSYCGKVVEWITCS
LQGCDSFYNANELLVQSIISSVETLVGSLVFLTDPLINIFFDKNISKMVKLQLTLGKWFIALYRFLFQMT
NIFENCSTHYSFEKNLQKCVNASNPCQLLQKMNTAHSLMIWMGFYIPSAMCFLAVLVDTYCLLVTISILK
SLKKQSRKQYIFGRANIIGEHNDYVVVRLSAAILIALCIIIIQSTYFIDIPFRDTFAFFAVLFIIYDFSILSLLGSFTGVA
M MTYFGVMRPLVYRDKFTLKTIYIIAFAIVLFSVCVAIPFGLFQAADEIDGPIKCDSESCELIVKWLLFCI
ACLILMGCTGTLLFVTVSLHWHSYKSKKMGNVSSSAFNHGKSRLTWTTTILVILCCVELIPTGLLAAFGK
SESISDDCYDFYNANSLIFPAIVSSLETFLGSITFLLDPIINFSFDKRISKVFSSQVSMFSIFFCGKR
>SEQ2
MLDDRARMEA AKKEKVEQIL AEFQLQEEDL KKVMRRMQKE MDRGLRLETH EEASVKMLPT YVRSTPEGSE
VGDFLSLDLG GTNFRVMLVK VGEGEEGQWS VKTKHQMYSI PEDAMTGTAE MLFDYISECI SDFLDKHQMK
HKKLPLGFTF SFPVRHEDID KGILLNWTKG FKASGAEGNN VVGLLRDAIK RRGDFEMDVV AMVNDTVATM
ISCYYEDHQC EVGMIVGTGC NACYMEEMQN VELVEGDEGR MCVNTEWGAF GDSGELDEFL LEYDRLVDES
SANPGQQLYE KLIGGKYMGE LVRLVLLRLV DENLLFHGEA SEQLRTRGAF ETRFVSQVES DTGDRKQIYN
ILSTLGLRPS TTDCDIVRRA CESVSTRAAH MCSAGLAGVI NRMRESRSED VMRITVGVDG SVYKLHPSFK
ERFHASVRRL TPSCEITFIE SEEGSGRGAA LVSAVACKKA CMLGQ
>SEQ3
MESDSFEDFLKGEDFSNYSYSSDLPPFLLDAAPCEPESLEINKYFVVIIYVLVFLLSLLGNSLVMLVILY
SRVGRSGRDNVIGDHVDYVTDVYLLNLALADLLFALTLPIWAASKVTGWIFGTFLCKVVSLLKEVNFYSGILLLA
CISVDRY
LAIVHATRTLTQKRYLVKFICLSIWGLSLLLALPVLIFRKTIYPPYVSPVCYEDMGNNTANWRMLLRILP
QSFGFIVPLLIMLFCYGFTLRTLFKAHMGQKHRAMRVIFAVVLIFLLCWLPYNLVLLADTLMRTWVIQET
CERRNDIDRALEATEILGILGRVNLIGEHWDYHSCLNPLIYAFIGQKFRHGLLKILAIHGLISKDSLPKDSRPSFVGS
SSGH TSTTL
>SEQ4
MEANFQQAVK KLVNDFEYPT ESLREAVKEF DELRQKGLQK NGEVLAMAPA FISTLPTGAE TGDFLALDFG
GTNLRVCWIQ LLGDGKYEMK HSKSVLPREC VRNESVKPII DFMSDHVELF IKEHFPSKFG CPEEEYLPMG
FTFSYPANQV SITESYLLRW TKGLNIPEAI NKDFAQFLTE GFKARNLPIR IEAVINDTVG TLVTRAYTSK
ESDTFMGIIF GTGTNGAYVE QMNQIPKLAG KCTGDHMLIN MEWGATDFSC LHSTRYDLLL DHDTPNAGRQ
IFEKRVGGMY LGELFRRALF HLIKVYNFNE GIFPPSITDA WSLETSVLSR MMVERSAENV RNVLSTFKFR
FRSDEEALYL WDAAHAIGRR AARMSAVPIA SLYLSTGRAG KKSDVGVDGS LVEHYPHFVD MLREALRELI
GDNEKLISIG IAKDGSGIGA ALCALQAVKE KKGLA MEANFQQAVK KLVNDFEYPT ESLREAVKEF
DELRQKGLQK NGEVLAMAPA FISTLPTGAE TGDFLALDFG GTNLRVCWIQ LLGDGKYEMK HSKSVLPREC
VRNESVKPII DFMSDHVELF IKEHFPSKFG CPEEEYLPMG FTFSYPANQV SITESYLLRW TKGLNIPEAI
NKDFAQFLTE GFKARNLPIR IEAVINDTVG TLVTRAYTSK ESDTFMGIIF GTGTNGAYVE QMNQIPKLAG
KCTGDHMLIN MEWGATDFSC LHSTRYDLLL DHDTPNAGRQ IFEKRVGGMY LGELFRRALF HLIKVYNFNE
GIFPPSITDA WSLETSVLSR MMVERSAENV RNVLSTFKFR FRSDEEALYL WDAAHAIGRR AARMSAVPIA
SLYLSTGRAG KKSDVGVDGS LVEHYPHFVD MLREALRELI GDNEKLISIG IAKDGSGIGA ALCALQAVKE
KKGLA
Oefening 1

32. Question 3. Swiss-Knife.py
• Using a database as input ! Parse
the entire Swiss Prot collection
– How many entries are there ?
– Average Protein Length (in aa and
MW)
– Relative frequency of amino acids
• Compare to the ones used to construct
the PAM scoring matrixes from 1978 –
1991

33. Question 3: Getting the database
Uniprot_sprot.dat.gz – 528Mb
(on Github onder Files)
https://github.ugent.be/wvcrieki/Bioinformatics
blob/script_branch/Files/swiss-prot.dat
http://www.ebi.ac.uk/uniprot/download-center

34. Amino acid frequencies
1978 1991
L 0.085 0.091
A 0.087 0.077
G 0.089 0.074
S 0.070 0.069
V 0.065 0.066
E 0.050 0.062
T 0.058 0.059
K 0.081 0.059
I 0.037 0.053
D 0.047 0.052
R 0.041 0.051
P 0.051 0.051
N 0.040 0.043
Q 0.038 0.041
F 0.040 0.040
Y 0.030 0.032
M 0.015 0.024
H 0.034 0.023
C 0.033 0.020
W 0.010 0.014
Second step: Frequencies of Occurence

35. Extra Questions
• How many records have a sequence of length 260?
• What are the first 20 residues of 143X_MAIZE?
• What is the identifier for the record with the
shortest sequence? Is there more than one record
with that length?
• What is the identifier for the record with the
longest sequence? Is there more than one record
with that length?
• How many contain the subsequence "ARRA"?
• How many contain the substring "KCIP-1" in the
description?