ComGen Course

From CSBLwiki

Contents 1 2011 Spring 2 Chapters 2.1 Chapter 1 2.1.1 Exercise#1 2.1.2 Exercise#2 2.1.3 Exercise#3 Most frequent word 2.2 Chapter 2 2.2.1 Exercise#1 Finding ORFs 2.3 Chapter 3 2.3.1 Exersize#1 2.4 Chapter 4 2.4.1 Exercise#1 2.5 Chapter 5 2.5.1 Exercise#1 2.6 Chapter 6 2.7 Chapter 7 2.8 Chapter 8 2.9 Chapter 9 3 A Thinking Chair 4 Links 5 Programming 5.1 Languages 5.2 Packages 6 Previous years 6.1 2009 Schedule

2011 Spring

Textbook

Introduction to Computational Genomics

Textbook website - Software & Data

Presentation by students

One chapter per each student

Schedule

Chapter	Name	Pages	Presentation	Due date
1		21	03/16/11	03/14/11
2		16	03/23/11	03/21/11
3		23	03/30/11	03/28/11
4		17	04/06/11	04/04/11
5		18	04/13/11	04/11/11
Midterm			Project
6		14	04/27/11	04/25/11
7		18	05/04/11	05/02/11
8		12	05/11/11	05/09/11
9		18	05/18/11	05/16/11
10		21	05/25/11	05/23/11
Final			Project

Chapters

Chapter 1

• Reading (download a PDF)
• 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

Chapter 3

• What is dynamic programming? by Sean Eddy

Exersize#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

• Where did the BLOSUM62 alignment score matrix come from? by Sean Eddy

Exercise#1

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
2. 12S rRNA sequence of Saber-Tooth Tiger can tell which of modern felines is most closest one.
3. 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)
• Ans: a pairwise distance matrix

     Whale  Hippo Cow 
Whale 0     
Hippo 0.222 
Cow   0.226 0.226

• Download all files

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

Packages

• R (R packages)
  • Manuals
  • Contributed Documents

Previous years

2009 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 Review(검토)메뉴의 "Trace Changes" 기능을 이용하여 수정하여 제출