ComGen Course
From CSBLwiki
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;Download a genome sequence & do basic statistical analysis | ;Download a genome sequence & do basic statistical analysis | ||
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| - | + | ;GC-content? | |
| - | + | :Solution = GC content of NC_01415 is '??? %' | |
| - | + | :Code | |
| + | |||
<pre> | <pre> | ||
>>> from Bio import Entrez, SeqIO | >>> from Bio import Entrez, SeqIO | ||
| + | >>> Entrez.mail = 'your@email.address' | ||
>>> handle = Entrez.efetch(db="nucleotide",id="NC_001416",rettype="fasta") | >>> handle = Entrez.efetch(db="nucleotide",id="NC_001416",rettype="fasta") | ||
>>> record = SeqIO.read(handle,"fasta") | >>> record = SeqIO.read(handle,"fasta") | ||
| Line 74: | Line 76: | ||
49.857737825244321 | 49.857737825244321 | ||
</pre> | </pre> | ||
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*<b>GC-content scanning with window size 500 bps?</b> | *<b>GC-content scanning with window size 500 bps?</b> | ||
**<b>ANS:</b><br> | **<b>ANS:</b><br> | ||
Revision as of 05:00, 22 March 2011
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2011 Spring
- Textbook
- Introduction to Computational Genomics
- Textbook website - Software & Data
- Presentation by students
- One chapter per each student
- No exam (Project submission)
- (Temporary) Schedule
Chapter Name Pages Presentation Due date python HSRoh Introduction 3/15/11 1 SJKim 21 03/22/11 03/18/11 2 JHLee 16 03/29/11 03/25/11 3 BHKim 23 04/05/11 03/28/11 4 JISong 17 04/12/11 04/08/11 Midterm (4/19/11) - no exam 5 HJKim 18 04/26/11 04/22/11 6 JWLee 14 05/03/11 04/29/11 7 TBA 18 05/10/11 05/06/11 5/10/11 (Budda's birthday) 8 TBA 12 05/24/11 05/20/11 9 BHKim 05/31/11 05/27/11 10 TBA 21 06/07/11 06/03/11 Final Project
Software
- Installing Python & related Modules (Windows & Linux only)
- Python(x,y)-2.6.5.6 (Mar 2011) - Free scientific and engineering development software download & install
- including almost every very useful scientific modules (Numpy, Scipy...)
- Biopython 1.56 (Mar 2011) download & install
Chapters
- Introduction to Python Programming
- 노한성 발표자료 3-15-2011
Chapter 1
Exercise#1
- Download a genome sequence & do basic statistical analysis
- GC-content?
- Solution = GC content of NC_01415 is '??? %'
- Code
>>> from Bio import Entrez, SeqIO
>>> Entrez.mail = 'your@email.address'
>>> 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
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
Exercise#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)
- Ans: a pairwise distance matrix
Whale Hippo Cow
Whale 0
Hippo 0.222
Cow 0.226 0.226
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
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
