ComGen Course
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===Chapter 9=== | ===Chapter 9=== |
Revision as of 04:38, 7 June 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 04/19/11 04/08/11 Midterm - no exam (04/26/11) 5 HJKim 18 05/03/11 04/29/11 5/10/11 (Budda's birthday) 6 JWLee 14 05/17/11 05/13/11 7 HSRoh 18 05/24/11 05/20/11 9 BHKim 05/31/11 05/27/11 8 JISong 12 06/07/11 06/03/11 10 Final 21 06/14/11 (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
- Tutorials
- A Primer on Python for Life Science Researchers - PLoS Compbio
- Eric Talevich - Check his presentation files
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
- related topics
Exercise
- Find all ORFs in Human, Chimp and Mouse mtDNA
- Repeat the ORF search on randomized mtDNA. The longest ORF in the randomized sequence?
- Find ORFs in H. influenzae
Exercise#1 Finding ORFs
>>> 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
- From Eric Talevich's presentation
# define function 1 - translate a given sequences in all 6 frames def translate_six_frames(seq, table=2): rev = seq.reverse_complement() for i in range(3): yield seq[i:].translate(table) yield rev[i:].translate(table) # define function 2 - translate given sequences in 6 reading frames # & return ORFs, min_prot_len = 'k' def translate_orfs(sequences, min_prot_len=60): for frame in translate_six_frames(seq): for prot in frame.split('*'): if len(prot) >= min_prot_len: yield prot # actual procedure from Bio import SeqIO from Bio.SeqRecord import SeqRecord from Bio.Seq import Seq seq = hum.seq proteins = translate_orfs(seq) seqrecords = (SeqRecord(seq,id='orf'+str(i)) for i, seq in enumerate(proteins)) for rec in seqrecords: print ">%s lenght=%s\n%s"%(rec.id,len(rec.seq),rec.seq)
Exercise#2 Using randomized sequences
from Bio import Entrez, SeqIO Entrez.email = '' han1 = Entrez.efetch(db="nucleotide",id="NC_001807",rettype="fasta") hum = SeqIO.read(han1,"fasta") import random from Bio.SeqRecord import SeqRecord from Bio.Seq import Seq nuc_list = list(hum.seq) #shuffle sequence random.shuffle(nuc_list) seq = Seq(''.join(nuc_list)) def translate_six_frames(seq, table=2): rev = seq.reverse_complement() for i in range(3): yield seq[i:].translate(table) yield rev[i:].translate(table) def translate_orfs(sequences, min_prot_len=60): for frame in translate_six_frames(sequences): for prot in frame.split('*'): if len(prot) >= min_prot_len: yield prot from Bio.SeqRecord import SeqRecord proteins = translate_orfs(seq) seqrecords = (SeqRecord(seq,id='orf'+str(i+1)) for i, seq in enumerate(proteins)) for rec in seqrecords: print ">%s lenght=%s\n%s"%(rec.id,len(rec.seq),rec.seq)
Exercise#3
- see, Excercis #1
import sys #min = int(sys.argv[1]) #threshold min = 60 from Bio.SeqRecord import SeqRecord proteins = translate_orfs(hinf.seq,min) seqrecords = (SeqRecord(seq,id='orf'+str(i+1)) for i, seq in enumerate(proteins))
Chapter 3
- Scoring matrix: PAM matrices
- What is dynamic programming? by Sean Eddy
- Dynamic Programming Exercises from UCSC BME 205 class homework
Exersize#1
>sp|O18381|PAX6_DROME Paired box protein Pax-6 OS=Drosophila melanogaster GN=ey PE=2 SV=3 MRNLPCLGTAGGSGLGGIAGKPSPTMEAVEASTASHRHSTSSYFATTYYHLTDDECHSGV NQLGGVFVGGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRP RAIGGSKPRVATAEVVSKISQYKRECPSIFAWEIRDRLLQENVCTNDNIPSVSSINRVLR NLAAQKEQQSTGSGSSSTSAGNSISAKVSVSIGGNVSNVASGSRGTLSSSTDLMQTATPL NSSESGGASNSGEGSEQEAIYEKLRLLNTQHAAGPGPLEPARAAPLVGQSPNHLGTRSSH PQLVHGNHQALQQHQQQSWPPRHYSGSWYPTSLSEIPISSAPNIASVTAYASGPSLAHSL SPPNDIESLASIGHQRNCPVATEDIHLKKELDGHQSDETGSGEGENSNGGASNIGNTEDD QARLILKRKLQRNRTSFTNDQIDSLEKEFERTHYPDVFARERLAGKIGLPEARIQVWFSN RRAKWRREEKLRNQRRTPNSTGASATSSSTSATASLTDSPNSLSACSSLLSGSAGGPSVS TINGLSSPSTLSTNVNAPTLGAGIDSSESPTPIPHIRPSCTSDNDNGRQSEDCRRVCSPC PLGVGGHQNTHHIQSNGHAQGHALVPAISPRLNFNSGSFGAMYSNMHHTALSMSDSYGAV TPIPSFNHSAVGPLAPPSPIPQQGDLTPSSLYPCHMTLRPPPMAPAHHHIVPGDGGRPAG VGLGSGQSANLGASCSGSGYEVLSAYALPPPPMASSSAADSSFSAASSASANVTPHHTIA QESCPSPCSSASHFGVAHSSGFSSDPISPAVSSYAHMSYNYASSANTMTPSSASGTSAHV APGKQQFFASCFYSPWV
>gi|51872083|gb|AAU12168.1| paired box gene 6 isoform a [Homo sapiens] MQNSHSGVNQLGGVFVNGRPLPDSTRQKIVELAHSGARPCDISRILQVSNGCVSKILGRYYETGSIRPRA IGGSKPRVATPEVVSKIAQYKRECPSIFAWEIRDRLLSEGVCTNDNIPSVSSINRVLRNLASEKQQMGAD GMYDKLRMLNGQTGSWGTRPGWYPGTSVPGQPTQDGCQQQEGGGENTNSISSNGEDSDEAQMRLQLKRKL QRNRTSFTQEQIEALEKEFERTHYPDVFARERLAAKIDLPEARIQVWFSNRRAKWRREEKLRNQRRQASN TPSHIPISSSFSTSVYQPIPQPTTPVSSFTSGSMLGRTDTALTNTYSALPPMPSFTMANNLPMQPPVPSQ TSSYSCMLPTSPSVNGRSYDTYTPPHMQTHMNSQPMGTSGTTSTGLISPGVSVPVQVPGSEPDMSQYWPR LQ
- Tools - EMBOSS package - packing various sequence analysis programs
- Emboss windows version - download site
- Needlman-Wunsch (Global alignment) - download alignment File:Pax drome.needle.txt
- Smith-Waterman (Local alignment) - download alignment File:Pax drome.water.txt
Excercise #2 Use Blast
Excercise #3 Find homologs
Excercise #4 Multiple sequence alignment
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
Exercise#2
- Draw the topology of a 2-state HMM emitting symbols (1~10) with even & odd states
Excercise#3
- Sketch the general architecture of an ORF finding HMM
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
- Exercise 1 Measure Ka/Ks ratio for various mtDNA genes
- target gene - human mitochondiral cytochrome C protein (protein sequence)
- find homologs of chimp & mouse (or other animals) using BLAST (protein sequence)
- find each nucleotide sequence
- use [1] Online KaKs Calculator!
- Exercise 2 Viral genomes
- Exercise 3 Practice with free online software tools for measuring Ka/Ks
- [2] Online KaKs Calculator!
Chapter 7
- neighbor joining alogrithm
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