2015 bioinformatics python_io_wim_vancriekinge
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This document provides an overview of using Python for bioinformatics. It discusses why Python is useful for bioinformatics due to its built-in libraries and wide scientific use. It also covers Python basics like strings, regular expressions, control structures, lists, dictionaries, reading/writing files, and using GitHub for code sharing. Examples are given for many of these topics. Finally, it poses questions about analyzing sequence data and a protein database using Python.
![Run Install.py (is BioPython installed ?)
import pip
import sys
import platform
import webbrowser
print ("Python " + platform.python_version()+ " installed
packages:")
installed_packages = pip.get_installed_distributions()
installed_packages_list = sorted(["%s==%s" % (i.key, i.version)
for i in installed_packages])
print(*installed_packages_list,sep="n")](https://image.slidesharecdn.com/2015bioinformaticspythoniowimvancriekinge-151019212304-lva1-app6891/85/2015-bioinformatics-python_io_wim_vancriekinge-9-320.jpg)
![Control Structures
if condition:
statements
[elif condition:
statements] ...
else:
statements
while condition:
statements
for var in sequence:
statements
break
continue](https://image.slidesharecdn.com/2015bioinformaticspythoniowimvancriekinge-151019212304-lva1-app6891/85/2015-bioinformatics-python_io_wim_vancriekinge-10-320.jpg)
![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"]](https://image.slidesharecdn.com/2015bioinformaticspythoniowimvancriekinge-151019212304-lva1-app6891/85/2015-bioinformatics-python_io_wim_vancriekinge-11-320.jpg)
![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"}](https://image.slidesharecdn.com/2015bioinformaticspythoniowimvancriekinge-151019212304-lva1-app6891/85/2015-bioinformatics-python_io_wim_vancriekinge-12-320.jpg)
![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))](https://image.slidesharecdn.com/2015bioinformaticspythoniowimvancriekinge-151019212304-lva1-app6891/85/2015-bioinformatics-python_io_wim_vancriekinge-13-320.jpg)
![Hint 2: Translations
Python way:
tab = str.maketrans("ACGU","UGCA")
sequence = sequence.translate(tab)[::-1]](https://image.slidesharecdn.com/2015bioinformaticspythoniowimvancriekinge-151019212304-lva1-app6891/85/2015-bioinformatics-python_io_wim_vancriekinge-16-320.jpg)
2015 bioinformatics python_io_wim_vancriekinge
- 4. Overview What is Python ? Why Python 4 Bioinformatics ? How to Python IDE: Eclipse & PyDev / Athena Code Sharing: Git(hub) Strings Regular expressions
- 5. Python • Programming languages are overrated – If you are going into bioinformatics you probably learn/need multiple – If you know one you know 90% of a second • Choice does matter but it matters far less than people think it does • Why Python? – Lets you start useful programs asap – Build-in libraries – incl BioPython – Free, most platforms, widely (scientifically) used • Versus Perl? – Incredibly similar – Consistent syntax, indentation
- 6. Version 2.7 and 3.4 on athena.ugent.be
- 7. Where is the workspace ?
- 8. GitHub: Hosted GIT • Largest open source git hosting site • Public and private options • User-centric rather than project-centric • http://github.ugent.be (use your Ugent login and password) – Accept invitation from Bioinformatics-I- 2015 URI: – https://github.ugent.be/Bioinformatics-I- 2015/Python.git
- 9. Run Install.py (is BioPython installed ?) import pip import sys import platform import webbrowser print ("Python " + platform.python_version()+ " installed packages:") installed_packages = pip.get_installed_distributions() installed_packages_list = sorted(["%s==%s" % (i.key, i.version) for i in installed_packages]) print(*installed_packages_list,sep="n")
- 10. Control Structures if condition: statements [elif condition: statements] ... else: statements while condition: statements for var in sequence: statements break continue
- 11. 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"]
- 12. 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"}
- 13. 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))
- 14. Find the answer in ultimate-sequence.txt ? >ultimate-sequence ACTCGTTATGATATTTTTTTTGAACGTGAAAATACT TTTCGTGCTATGGAAGGACTCGTTATCGTGAAGT TGAACGTTCTGAATGTATGCCTCTTGAAATGGA AAATACTCATTGTTTATCTGAAATTTGAATGGGA ATTTTATCTACAATGTTTTATTCTTACAGAACAT TAAATTGTGTTATGTTTCATTTCACATTTTAGTA GTTTTTTCAGTGAAAGCTTGAAAACCACCAAGA AGAAAAGCTGGTATGCGTAGCTATGTATATATA AAATTAGATTTTCCACAAAAAATGATCTGATAA ACCTTCTCTGTTGGCTCCAAGTATAAGTACGAAA AGAAATACGTTCCCAAGAATTAGCTTCATGAGT AAGAAGAAAAGCTGGTATGCGTAGCTATGTATA TATAAAATTAGATTTTCCACAAAAAATGATCTG ATAA Question 2
- 16. Hint 2: Translations Python way: tab = str.maketrans("ACGU","UGCA") sequence = sequence.translate(tab)[::-1]
- 17. 17 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'
- 18. 18 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
- 19. 19 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?'
- 20. 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
- 21. Question 3: Getting the database Uniprot_sprot.dat.gz – 528Mb (on Github onder Files) Unzipped 2.92 Gb ! http://www.ebi.ac.uk/uniprot/download-center
- 22. 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
- 23. 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?
- 24. 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