JDF509 Bioinformatics

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Contents 1 2011 Spring 1.1 Software 1.2 Chapter 1 1.2.1 Exercise#1 1.2.2 Exercise#2 1.2.3 Exercise#3 1.3 Chapter 2 1.3.1 Exercise 1.3.1.1 Exercise#1 Finding ORFs 1.3.1.2 Exercise#2 Using randomized sequences 1.3.1.3 Exercise#3 1.4 Chapter 3 1.4.1 Exersize#1 1.4.2 Excercise #2 Use Blast 1.4.3 Excercise #3 Alignment comparison 1.4.4 Excercise #4 Multiple sequence alignment 1.5 Chapter 3 1.5.1 Exersize#1 1.5.2 Excercise #2 Use Blast 1.5.3 Excercise #3 Find homologs 1.5.4 Excercise #4 Multiple sequence alignment 1.6 Chapter 4 1.6.1 Exercise#1 1.6.2 Exercise#2 1.6.3 Excercise#3 1.7 Chapter 5 1.7.1 Exercise#1 1.8 Chapter 6 2 2010 Spring 3 2009 Spring 3.1 Human Genome 3.2 Diagnosis test

2011 Spring

Textbook

• Introduction to Computational Genomics
• Textbook website - Software & Data

숙제

• 단원이 끝나면 단원 맨뒤에 있는 연습문제(Exercise)풀어서 제출

Schedule

3/15/11 Introduction to Python programming by 노한성

노한성 발표자료 3-15-2011

Midterm rest (4/19/11) - no exam

No class - 5/10/11 (Budda's birthday)

Chapter	Name	Pages	Presentation	숙제 업로드
1		21	03/22/11	03/18/11
2		16	03/29/11	03/25/11
3		23	04/05/11	03/28/11
4		17	04/12/11	04/08/11
5		18	04/26/11	04/22/11			
6		14	05/03/11	04/29/11
7		18	05/10/11	05/06/11
8		12	05/24/11	05/20/11
9               18      05/31/11	05/27/11
10		21	06/07/11	06/03/11
Final			Project

Software

Python

about Python

Introduction to Programming using Python

파이썬 설치하기 (for windows system)

1. 파이썬엑스와이 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...)
2. 바이오파이썬 Biopython 1.56 (Mar 2011) download & install

Biopython Tutorial & Cookbook(참고문서)

Chapter 1

Chapter 1 The first look at a genome

Chaos Game Representation

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:
    

• 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

1. GeneSweep result
2. Type I & Type II errors
3. Statitical power

Exercise

1. Find all ORFs in Human, Chimp and Mouse mtDNA
2. Repeat the ORF search on randomized mtDNA. The longest ORF in the randomized sequence?
3. 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

• producing randomized genome

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

Local & Global alignment of X79493 and AY707088

>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 result File:Pax drome.needle.txt

Smith-Waterman (Local alignment) - download result File:Pax drome.water.txt

Excercise #2 Use Blast

Run Blast with X79493

Protein Blast (Blastp program)

Tips

The Statistics of Sequence Similarity Scores

P-values

Excercise #3 Alignment comparison

use DNA or protein sequence of Exercise #1

Excercise #4 Multiple sequence alignment

use following sequence set (File:SampleMSA.txt) to make a multiple sequence alignment

ClustalW links

Clustalw@genome.jp

Clustalw@EBI

Chapter 3

Scoring matrix: PAM matrices

What is dynamic programming? by Sean Eddy

Dynamic Programming Exercises from UCSC BME 205 class homework

Exersize#1

Local & Global alignment of X79493 and AY707088

>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

The Statistics of Sequence Similarity Scores

P-values

Excercise #3 Find homologs

Excercise #4 Multiple sequence alignment

Clustalw@genome.jp

Clustalw@EBI

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

• 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

Exercise 1 Measure Ka/Ks ratio for various mtDNA genes

target gene - human mitochondiral cytochrome C protein (protein sequence)

>gi|49457409|emb|CR542208.1| Homo sapiens full open reading frame cDNA clone RZPDo834D0125D for gene COX7A2L, cytochrome c oxidase subunit VIIa polypeptide 2 like; complete cds, incl. stopcodon
ATGTACTACAAGTTTAGTGGCTTCACGCAGAAGTTGGCAGGAGCATGGGCTTCGGAGGCCTATAGCCCGC
AGGGATTAAAGCCTGTGGTTTCCACAGAAGCACCACCTATCATATTTGCCACACCAACTAAACTGACCTC
CGATTCCACAGTGTATGATTATGCTGGGAAAAACAAAGTTCCAGAGCTACAAAAGTTTTTCCAGAAAGCT
GATGGTGTGCCCGTCTACCTGAAACGAGGCCTGCCTGACCAAATGCTTTACCGGACCACCATGGCGCTGA
CTGTGGGAGGGACCATCTACTGCCTGATCGCCCTCTACATGGCTTCGCAGCCCAAAAACAAATGA

find homologs of chimp & mouse (or other animals) using BLAST (protein sequence)

find each nucleotide sequence

• Bos taurus - cow

>gb|BC102552.1|:48-392 Bos taurus cytochrome c oxidase subunit VIIa polypeptide 2 like, mRNA (cDNA clone MGC:127892 IMAGE:30957826), complete cds
ATGTATTATAAGTTCAGTGGCTTCACGCAGAAGTTGGCCGGAGCATGGGCTTCCGACGCCTATAGCCCTC
AGGGATTAAGACCTGTTGTTTCCACAGAAGCACCGCCTATCATATTTGCCACACCAACTAAACTGAGCTC
CGGTCCCACTGCATATGACTATGCTGGGAAAAACACAGTTCCAGAGCTGCAGAAGTTTTTCCAGAAATCT
GACGGTGTGCCCATCCACCTGAAACGAGGCCTGCCTGACCAAATGCTTTACCGGACCACCATGGCGCTAA
CAGTGGGAGGGACCATCTACTGCCTGATCGCCCTCTACATGGCATCACAGCCCAGAAACAAATGA

• Mus musculus - mouse

>gi|226958510:59-394 Mus musculus cytochrome c oxidase subunit VIIa polypeptide 2-like (Cox7a2l), nuclear gene encoding mitochondrial protein, transcript variant 2, mRNA
ATGTACTACAAGTTTAGCAGTTTCACGCAGAAGTTGGCTGGAGCTTGGGCTTCGGAAGCCTACACCCCGC
AGGGGTTAAAGCCAGTTTCCACAGAAGCACCACCTATCATATTTGCCACACCAACCAAACTGACCTCCAG
TGTGACAGCATATGATTATTCTGGGAAGAACAAAGTTCCAGAGCTGCAGAAGTTTTTCCAGAAGGCTGAT
GGTTTCCACCTGAAACGAGGCCTTCCAGACCAAATGCTTTACCGGACCACCATGGCTCTGACACTGGGAG
GGACCATCTACTGCCTGATCGCCCTCTACATGGCCTCGCAGCCCAGAAACAAATGA

Do alignment - ClustalW Online by EBI

>human-gi|49457409|emb|CR542208/1-345
ATGTACTACAAGTTTAGTGGCTTCACGCAGAAGTTGGCAGGAGCATGGGCTTCGGAGGCCTATAGCCCGCAG
GGATTAAAGCCTGTGGTTTCCACAGAAGCACCACCTATCATATTTGCCACACCAACTAAACTGACCTCCGAT
TCCACAGTGTATGATTATGCTGGGAAAAACAAAGTTCCAGAGCTACAAAAGTTTTTCCAGAAAGCTGATGGT
GTGCCCGTCTACCTGAAACGAGGCCTGCCTGACCAAATGCTTTACCGGACCACCATGGCGCTGACTGTGGGA
GGGACCATCTACTGCCTGATCGCCCTCTACATGGCTTCGCAGCCCAAAAACAAATGA
>cow-gb|BC102552.1|_48-392/1-345
ATGTATTATAAGTTCAGTGGCTTCACGCAGAAGTTGGCCGGAGCATGGGCTTCCGACGCCTATAGCCCTCAG
GGATTAAGACCTGTTGTTTCCACAGAAGCACCGCCTATCATATTTGCCACACCAACTAAACTGAGCTCCGGT
CCCACTGCATATGACTATGCTGGGAAAAACACAGTTCCAGAGCTGCAGAAGTTTTTCCAGAAATCTGACGGT
GTGCCCATCCACCTGAAACGAGGCCTGCCTGACCAAATGCTTTACCGGACCACCATGGCGCTAACAGTGGGA
GGGACCATCTACTGCCTGATCGCCCTCTACATGGCATCACAGCCCAGAAACAAATGA
>mouse-gi|226958510_59-394/1-336
ATGTACTACAAGTTTAGCAGTTTCACGCAGAAGTTGGCTGGAGCTTGGGCTTCGGAAGCCTACACCCCGCAG
GGGTTAAAGCC---AGTTTCCACAGAAGCACCACCTATCATATTTGCCACACCAACCAAACTGACCTCCAGT
GTGACAGCATATGATTATTCTGGGAAGAACAAAGTTCCAGAGCTGCAGAAGTTTTTCCAGAAGGCTGATGGT
TT------CCACCTGAAACGAGGCCTTCCAGACCAAATGCTTTACCGGACCACCATGGCTCTGACACTGGGA
GGGACCATCTACTGCCTGATCGCCCTCTACATGGCCTCGCAGCCCAGAAACAAATGA

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!

2010 Spring

• SNP Fact Sheet - FAQs such as What are SNPs? etc.
• Nature Milestones (2007) - DNA technologies
• Suggested Reading for a term paper (due date: 6/17(Thu))

1. 게놈의 기적 (과학도서관 링크)
2. 신의 언어 : 유전자 지도에서 발견한 신의 존재 (과학도서관 링크)
3. 지루한 사람과 어울리지 마라 : 과학에서 배우는 삶의 교훈 (과학도서관 링크)

2009 Spring

• 질병유전체학

Human Genome

• dbSNP@NCBI(Summary)
  • rs#: Reference, ss#: Submitted
• HapMap project

Diagnosis test

• Diagnostic performance test - Basic statistics