ComGen Course
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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
- What is dynamic programming? by Sean Eddy
Excersize#1
- Local & Global alignment of X79493 and AY707088
- Needlman-Wunsch
- Smith-Waterman
- EMBOSS package - packing various sequence analysis programs
Chapter 4
- What is Hidden Markov Model? by Sean Eddy
- What is Bayesian statistics? by Sean Eddy
Exercise#1
- Segmenting with 4-state model
- 2-state='AT' and 'GC', 4-state = A,G,C,T
Chapter 5
Excercise#1
- Which of the modern elephants seems to be more closely related to mammoths? Hint: make a global alignment and calculate the genetic distance between them
- 12S rRNA sequence of Saber-Tooth Tiger can tell which of modern felines is most closest one.
- Genetic distance between blue-whale, hippo and cow
- Download sequences & write into a file
8 : from Bio import Entrez, SeqIO 9 : h1 = Entrez.efetch(db="nucleotide",id="NC_001601",rettype="fasta") 10: h2 = Entrez.efetch(db="nucleotide",id="NC_000889",rettype="fasta") 11: h3 = Entrez.efetch(db="nucleotide",id="NC_006853",rettype="fasta") 12: blue = SeqIO.read(h1,"fasta") 13: hipp = SeqIO.read(h2,"fasta") 14: cow = SeqIO.read(h3,"fasta") 19: seqs = [blue, hipp, cow] 20: h4 = open("seq.fasta","w") 21: SeqIO.write(seqs,h4,"fasta") 22: h4.close()
- Do alignment with multiple sequence alignment program (e.g. Clustalw)
- Calculate genetic distance based on the model (e.g. Juke-Cantor model) by Phylip Package (or any GUI program for phylogenetic analysis - MEGA)
Chapter 6
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