ComGen Course
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===Chapter 3=== | ===Chapter 3=== | ||
| - | ==== | + | ====Excersize#1==== |
| + | *Local & Global alignment of [http://www.ncbi.nlm.nih.gov/nuccore/641809 X79493] and [http://www.ncbi.nlm.nih.gov/nuccore/AY707088 AY707088] | ||
| + | **Needlman-Wunsch | ||
| + | **Smith-Waterman | ||
| + | **[http://www.interactive-biosoftware.com/embosswin/embosswin.html EMBOSS package] - packing various sequence analysis programs | ||
| + | |||
===Chapter 4=== | ===Chapter 4=== | ||
====Exercise#2==== | ====Exercise#2==== | ||
Revision as of 02:24, 2 April 2009
Contents |
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:
- 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
Chapter 3
Excersize#1
- Local & Global alignment of X79493 and AY707088
- Needlman-Wunsch
- Smith-Waterman
- EMBOSS package - packing various sequence analysis programs
Chapter 4
Exercise#2
Chapter 5
Chapter 6
wikipedia:sodium metawikipedia:Main Page
Chapter 7
Chapter 8
Chapter 9
A Thinking Chair
- 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
