ComGen Course

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	*MIT [http://openwetware.org/wiki/BE.180 BE.180 Biological Engineering Progamming] (Some materials can be used in this course)		*MIT [http://openwetware.org/wiki/BE.180 BE.180 Biological Engineering Progamming] (Some materials can be used in this course)
	**Same Course in 2006 ([http://ocw.mit.edu/OcwWeb/Biological-Engineering/20-180Spring-2006/CourseHome/index.htm OCW.MIT.EDU])		**Same Course in 2006 ([http://ocw.mit.edu/OcwWeb/Biological-Engineering/20-180Spring-2006/CourseHome/index.htm OCW.MIT.EDU])
		+	**[http://openwetware.org/wiki/BE.180:Python Python tutorial in BE.180]
	==Programming==		==Programming==

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Revision as of 06:51, 26 March 2009

Contents 1 Class schedule 2 Chapters 2.1 Chapter 1 2.1.1 Exercise#1 Download a genome sequence & do basic statistical analysis 2.1.2 Exercise#2 Basic Statistical Analysis 2.1.3 Exercise#3 Most frequent word 2.2 Chapter 2 2.2.1 Exercise#1 Finding ORFs 3 A Thinking Chair 4 Links 5 Programming 5.1 Languages 5.2 Packages

Class schedule

Chapter Assign Pages	Presentation    Due date
1	이은혜	21	03/19/09	03/12/09
2	박애경	16	03/26/09	03/21/09
3	고혁진	23	04/02/09	03/26/09
4	장은혁	17	04/07/09	04/02/09
5	이예림	18	04/16/09	04/07/09
6	김소현	14	04/23/09	04/16/09
7	정진아	18	05/14/09	04/23/09
8	김윤식	12	05/21/09	04/30/09
9	김윤식	18	06/04/09	05/07/09
10	김윤식	21	06/11/09	05/14/09

• No Class
  • 4/30 (중간고사)
  • 5/7 (학회참석, SF)
  • 5/28 (학회참석, Cheju)
• 해당 단원은 발표 1주일전에 EKU에 올려 놓을것 (MS-Word 형식으로 제출)
• 발표는 해당 단원의 소개 및 요약
• 각 단원의 연습문제를 풀어서 제출할것 - EKU
• 발표한 내용을 MS-Word의 "Trace Changes" 기능을 이용하여 수정하여 제출

Chapters

Chapter 1

• Reading (download a PDF) by 이은혜
• Installing Python & related Modules (Windows & Linux only)
  • Python(x,y)-2.1.11 - Free scientific and engineering development software download & install (current version is 2.1.1; 3/19/2009) - including very useful scientific modules (Numpy, Scipy...)
  • Download from local deposit (Python(x,y)-2.1.11.exe)
  • Biopython 1.49 download biopython-1.49.win32-py2.5.exe

Exercise#1 Download a genome sequence & do basic statistical analysis

• GC-content?
  • ANS: GC content of NC_01415 is '49.9%'
• Code

>>> from Bio import Entrez, SeqIO
>>> handle = Entrez.efetch(db="nucleotide",id="NC_001416",rettype="fasta")
>>> record = SeqIO.read(handle,"fasta")
>>> print record
ID: gi|9626243|ref|NC_001416.1|
Name: gi|9626243|ref|NC_001416.1|
Description: gi|9626243|ref|NC_001416.1| Enterobacteria phage lambda, complete genome
Number of features: 0
Seq('GGGCGGCGACCTCGCGGGTTTTCGCTATTTATGAAAATTTTCCGGTTTAAGGCG...ACG', SingleLetterAlphabet())
>>> print len(record)
48502
>>> record
SeqRecord(seq=Seq('GGGCGGCGACCTCGCGGGTTTTCGCTATTTATGAAAATTTTCCGGTTTAAGGCG...ACG', SingleLetterAlphabet()), id='gi|9626243|ref|NC_001416.1|', name='gi|9626243|ref|NC_001416.1|', description='gi|9626243|ref|NC_001416.1| Enterobacteria phage lambda, complete genome', dbxrefs=[])
>>> record.seq
Seq('GGGCGGCGACCTCGCGGGTTTTCGCTATTTATGAAAATTTTCCGGTTTAAGGCG...ACG', SingleLetterAlphabet())
>>> from Bio.SeqUtils import GC
>>> GC(record.seq)
49.857737825244321

• GC-content scanning with window size 500 bps?
  • ANS:
    

• Code

>>> x = record.seq
>>> windowsize = 500
>>> gc_values = [ GC(x[i:(i+499)] for i in range(1,len(x)-windowsize+1) ]
>>> import pylab
>>> pylab.plot(gc_values)
>>> pylab.title("GC% 500 bp window size")
>>> pylab.xlabel("Nucleotide positions")
>>> pylab.ylabel("GC%")
>>> pylab.show()

Exercise#2 Basic Statistical Analysis

• Comparing human and chimp complete mitochondiral DNA (NC_001807 and NC_001643)
  • GC% Human: 44.5, Chimp: 43.7

>>> from Bio import Entrez, SeqIO
>>> handle = Entrez.efetch(db="nucleotide",id="NC_001807",rettype="fasta")
>>> record1 = SeqIO.read(handle,"fasta")
>>> handle = Entrez.efetch(db="nucleotide",id="NC_001643",rettype="fasta")
>>> record2 = SeqIO.read(handle,"fasta")
>>> from Bio.SeqUtils import GC
>>> GC(record1.seq)
44.487357431657713
>>> GC(record2.seq)
43.687326325963511
>>> len(record2.seq)
16554
>>> len(record1.seq)
16571

Exercise#3 Most frequent word

• Count frequent dinucleotides in rat Mitochondiral DNA
  • NC_001665

>>> from Bio import Entrez, SeqIO
>>> handle = Entrez.efetch(db="nucleotide",id="NC_001665",rettype="fasta")
>>> ratMT = SeqIO.read(handle,"fasta")
>>> base = [ ratMT.seq[i] for i in range(0,len(ratMT.seq))]
>>> a = base.count('A')
>>> g = base.count('G')
>>> c = base.count('C')
>>> t = base.count('T')
>>> di = [ str(ratMT.seq[i:(i+2)]) for i in range(0,len(ratMT.seq)-1) ]
>>> aa = di.count('AA')
>>> aa
1892
>>> a
5544

Chapter 2

Exercise#1 Finding ORFs

• Human, Chimp and Mouse Mt Genome

>>> han1 = Entrez.efetch(db="nucleotide",id="NC_001807",rettype="fasta")
>>> hum = SeqIO.read(han1,"fasta")
>>> from Bio.Seq import Seq
>>> orf = hum.seq.translate(table="Vertebrate Mitochondrial")
>>> orf.count("*")
326

A Thinking Chair

1. independent and identically distributed (i.i.d.)

Links

• MIT BE.180 Biological Engineering Progamming (Some materials can be used in this course)
  • Same Course in 2006 (OCW.MIT.EDU)
  • Python tutorial in BE.180

Programming

Languages

• Python (Official Site)
• Biopython (Download)
  • Tutorial(follow the instruction)
• Pyplot Tutorial (matplotlib)
• NumPy Tutorial

Packages

• R (R packages)
  • Manuals
  • Contributed Documents