1. Genome Sequencing
and Annotation (Part 1)

2. Objective of most genome projects
Sequencing – DNA, mRNA
Identify genes characterize gene features
This chapter
How blocks of DNA seqs. are obtained
How these blocks are assembled into contigs then genomes
Bioinformatics – how to do seq. alignment, such as cDNA/EST, genome seqs.
Annotation of ORF,
Other features of gene – repetition elements, variable distribution of GC content, evolutionary conserved elements
Gene annotation by cross species annotation

3. 2.1 (Part 2) The principle of dideoxy (Sanger) sequencing
Automated DNA sequencing
1974, F. Sanger developed the chain-termination method (Sanger sequencing)
Sanger won his second Noble prize for inventing this process
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4. Automated DNA sequencing
Most current sequencing projects use the chain termination method
Also known as Sanger sequencing, after its inventor
Based on action of DNA polymerase
Adds nucleotides to complementary strand
Requires template DNA and primer
Most large-scale sequencing projects use the chain-termination method, which is also known as Sanger sequencing, after Fred Sanger, who won his second Nobel prize for inventing the process. Chain-termination sequencing is based on the action of DNA polymerase, which adds nucleotides that are complementary to another strand of DNA. For it to work, it needs a template DNA strand that it can copy, as well as a short stretch of DNA, known as a primer, to which it can add nucleotides.

5. Chain-termination sequencing
Dideoxynucleotides (ddA, ddT, ddC or ddG) stop synthesis
Chain terminators (DNA polymerase cannot add another nucleotide)
Included in amounts so as to terminate every time the base appears in the template
Use four reactions
One for each base: A,C,G, and T
3’ ATCGGTGCATAGCTTGT 5’
5’ TAGCCACGTATCGAACA* 3’
5’ TAGCCACGTATCGAA* 3’
5’ TAGCCACGTATCGA* 3’
5’ TAGCCACGTA* 3’
5’ TAGCCA* 3’
5’ TA* 3’
Template
Sequence reaction products
In the Sanger chain-termination method, the nucleotide analog is called a dideoxynucleotide. When the correct amount is added to the solution, the chain will be terminated at each occurrence of the complementary nucleotide in the template. The reason for this reaction is that DNA polymerase cannot add another nucleotide to a dideoxynucleotide. For example, if the right amount of dideoxy A is added, then the chain will be terminated at each occurrence of T in the template. Determining the complete sequence requires a separate reaction for each of the four bases A, T, C, and G. On the top right of this slide is a template strand, and beneath it are the various chains that would be terminated with dideoxy A in the reaction mix.

6. Sequence detection To detect products of sequencing reaction
Include labeled nucleotides
Formerly, radioactive labels (33P or 35S) were used
Now fluorescent labels
Use different fluorescent tag for each nucleotide
Can run all four reactions in a single gel lane or capillary tube
TAGCCACGTATCGAA*
TAGCCACGTATC*
TAGCCACG*
TAGCCACGT*
Once the sequencing reaction has been completed, one has to be able to detect the chains generated by the reaction. This can be done by making the chains radioactive by using radioactively labeled nucleotides in the reaction. As an alternative, today’s automated sequencers all use fluorescent labels. With this approach, each of the four sequencing reactions (for each of the bases A, T, C, and G) uses a different color of fluorescent tag. After the reactions are completed, the four fluorescently labeled reactions can be combined and run in a single gel lane or capillary tube.

7. Sequence separation Terminated chains need to be separated–
Terminated chains need to be separated
Requires one-base-pair resolution
See difference between chains of X and X+1 base pairs
Gel electrophoresis
Very thin gel
High voltage applied
Works with radioactive or fluorescent labels
Negative pole at the top
Determining the placement of each of the bases in the sequence requires separating the terminated chains and resolving them so that one can see differences of one base. This means, for example, that a chain of length 35 bases must be distinguishable from chains of 34 or 36 bases. This step is normally done using electrophoresis through a gel or capillary. Originally, very thin gels were used, with high voltages applied. Either radioactively labeled or fluorescently labeled reaction products can be separated on gels. The image in this slide shows an autoradiogram of a typical sequencing gel with the lanes marked according to the dideoxy terminator used. For gel electrophoresis, the negative pole was at the top and the positive pole at the bottom. +
C A G T C A G T

8. Sequence reading of radioactively labeled reactions–
The final step of sequencing is to read the sequence
Radioactive labeled reactions
Gel dried
Placed on X-ray film
Film developed, the position of each band becomes visible
Sequence read from bottom up (the positive pole)
Each of the four lanes giving the position of a different base: A, T, C or G
The final step of sequencing is to read the sequence. After radioactively labeled sequencing reactions are run through a gel, the gel is dried and then a piece of X-ray film is placed on it. After the film is developed, the position of each band becomes visible. The sequence is then read from the bottom of the gel to the top, with each of the four lanes giving the position of a different base: A, T, C, or G. +

9. Sequence reading of fluorescently labeled reactions
Fluorescently labeled reactions scanned by laser as particular point is passed
Color picked up by detector
Output sent directly to computer
The read out is given both in terms of bases and the intensity of each color, so that ambiguous readings are easily identified
For fluorescently labeled reactions that are separated either by gel electrophoresis or through a capillary, the fragments are detected by a laser as they pass a particular point. Each of the four colors is picked up by a detector, and the output is sent directly to a computer. The readout is given both in terms of bases and in terms of the intensity of each color, so that ambiguous readings are easily identified.

10. Summary of chain termination sequencing
A primer is extended by DNA polymerase based on the sequence present in the template strand.
The chain is terminated by different ddNTP that are complementary to the template strand.
Four reactions are separated on a gel that can resolve one-base differences.
The seq. is then read from the bottom
of gel to the top.
This image summarizes the steps in DNA sequencing. A primer is extended by DNA polymerase based on the sequence present in the template strand. The chain is terminated by different dideoxynucleotides that are complementary to the template strand. Four reactions, one for each base, are separated on a gel that can resolve one-base differences. The sequence is then read from the bottom of the gel to the top.

11. High-Throughput Sequencing
The new techniques and equipment include:
(1) Four-color fluorescent dyes have replaced the radioactive label
(2) Rather than stopping the electrophoresis at a particular time, the products are scanned for laser-induced fluorescence just before the run off the end of the electrophoresis medium
(3) Improvements in the chemistry of template purification and the sequencing reaction
(4) Slab gel electrophoresis gave way to capillary electrophoresis with the introduction in 1999 of Applied Biosystem’s ABI Prism 3700 automated sequencers, which in turn were updated with ABI Prism 3730 DNA analyzers in 2003 (deliver extremely high quality, long reads; save time and money)
ABI Prism 3730 DNA analyzers

12. Reading sequence traces
Base-calling – the reading of raw sequence traces
Now routinely performed using automated software that reads bases, aligns similar seqs. and editing
Program – phred
The program assign probability scores to the accuracy of each base call as the trace is read
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13. 2.3 Automated sequence chromatograms
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This seq. shows ‘noiseness’ of the first 30 bp of a run.
The middle two rows show a segment of two seqs. that are polymorphic for both SNPs and an indel.
A decline in seq. quality typically occurs after about 800 bp.

14. Ex. 2.1 Reading a sequence trace
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The base labeled N – due to poor seq. quality
Two peaks of the same height are observed at the same location, the site is heterozygous for a C and T SNP.

15. Figure 2.5 An aligned-reads window in consed
Contig Assembly
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Figure 2.5 An aligned-reads window in consed

16. Assembling DNA seq. fragments
NCBI dbest databases
View the EST statistics
FTP EST files

17. Assembling DNA seq. fragments
IFOM assembler
Multiple EST seqs.  contig
max. number of seqs. you can enter is !!
use gi( , , , , , )
Length (850, 1062, 634, 596, 869, 768) bp
resulting in a single contig consensus seq., can be used for similarity search against db

18. Assembling DNA seq. fragments – 6 GI fragments
>gi| |gb|BI |BI F1 NIH_MGC_114 Homo sapiens cDNA clone IMAGE: ', mRNA sequenceCGGGGTGCTGCGAGCGCGGGGCCAGACCAAGGCGGGCCCGGAGCGGAACTTCGGTCCCAGCTCGGTCCCCGGCTCAGTCCCGACGTGGAACTCAGCAGCGGAGGCTGGACGCTTGCATGGCGCTTGAGAGATTCCATCGTGCCTGGCTCACATAAGCGCTTCCTGGAAGTGAAGTCGTGCTGTCCTGAACGCGGGCCAGGCAGCTGCGGCCTGGGGGTTTTGGAGTGATCACGAATGAGCAAGGCGTTTGGGCTCCTGAGGCAAATCTGTCAGTCCATCCTGGCTGAGTCCTCGCAGTCCCCGGCAGATCTTGAAGAAAAGAAGGAAGAAGACAGCAACATGAAGAGAGAGCAGCCCAGAGAGCGTCCCAGGGCCTGGGACTACCCTCATGGCCTGGTTGGTTTACACAACATTGGACAGACCTGCTGCCTTAACTCCTTGATTCAGGTGTTCGTAATGAATGTGGACTTCACCAGGATATTGAAGAGGATCACGGTGCCCAGGGGAGCTGACGAGCAGAGGAGAAGCGTCCCTTTCCAGATGCTTCTGCTGCTGGAGAAGATGCAGGACAGCCGGCAGAAAGCAGTGCGGCCCCTGGAGCTGGCTACTGCCTGCAGAAGTGCAACGTGCCCTTGTTTGTCCAACATGATGCTGCCAACTGTACCTCAAACTCTGGAACCTGATTAAGGACCAGATCACTGATGTGCACTTGGTGGAGAGACTGCAGGCCCTGTATATGATCCGGGTGAAGGACTCCTTGATATGCGTTGACTGTGCCATGGGAGAGTAGCAGAAAACAGCAGCATGCTCAACCTCCCACTTTCTCTATTGGATGTGGACTCAAAGCCCT
>gi| |gb|BM |BM AGENCOURT_ NIH_MGC_124 Homo sapiens cDNA clone IMAGE: ', mRNA sequenceGTCCGGAATTCCCGGGATCTCAGCAGCGGAGGCTGGACGCTTGCATGGCGCTTGAGAGATTCCATCGTGCCTGGCTCACATAAGCGCTTCCTGGAAGTGAAGTCGTGCTGTCCTGAACGCGGGCCAGGCAGCTGCGGCCTGGGGGTTTTGGAGTGATCACGAATGAGCAAGGCGTTTGGGCTCCTGAGGCAAATCTGTCAGTCCATCCTGGCTGAGTCCTCGCAGTCCCCGGCAGATCTTGAAGAAAAGAAGGAAGAAGACAGCAACATGAAGAGAGAGCAGCCCAGAGAGCGTCCCAGGGCCTGGGACTACCCTCATGGCCTGGTTGGTTTACACAACATTGGACAGACCTGCTGCCTTAACTCCTTGATTCAGGTGTTCGTAATGAATGTGGACTTCACCAGGATATTGAAGAGGATCACGGTGCCCAGGGGAGCTGACGAGCAGAGGAGAAGCGTCCCTTTCCAGATGCTTCTGCTGCTGGAGAAGATGCAGGACAGCCGGCAGAAAGCAGTGCGGCCCCTGGAGCTGGCCTACTGCCTGCAGAAGTGCAACGTGCCCTTGTTTGTCCAACATGATGCTGCCCAACTGTACCTCAAACTCTGGAACCTGATTAAGGACCAGATCACTGATGTGCACTTGGTGGAGAGACTGCAGGCCCTGTATACGATCCGGGTGAAGGACTCCTTGATTTGCGTTGACTGTGCCATGGAGAGTAGCAGAAACAGCAGCATGCTCACCCTCCCACTTTCTCTTTTTGATGTGGACTCAAAGCCCCTGGAAGACACTGGAGGACGCCCTGCACTGCTTCTTCCAGCCCAGGAGTTATCAAGCAAAAGCAAGTGCTTCTGTGAGAACTGTGGGAAGAAGACCCGCGGGGAACAGGGTCCTGAAACCTGACCATTTTGCCCCAGACCTTGACCAATCCACCTCATGGCGATTCTCCCTCCAGGAATTCCCCGACCGAGAAAAAATTGGCCACTTCCCCGGAATTTCCCCCCAAAAACTTGGAATTTCACCCAAAACCTTTCCCATGTAAACCCGGAAACCCTGGGGAAGGCT
>gi| |gb|AW |AW EST MAGE resequences, MAGE Homo sapiens cDNA, mRNA sequenceGAACTAGTGGATCCCCCGGGCTGCAGGAATTCGGCACGAGTGGAGCTGGCCTACTGCCTGCAGAAGTGCAACGTGCCCTTGTTTGTCCAACATGATGCTGCCCAACTGTACCTCAAACTCTGGAACCTGATTAAGGACCAGATCACTGATGTGCACTTGGTGGAGAGACTGCAGGCCCTGTATATGATCCGGGTGAAGGACTCCTTGATTTGCGTTGACTGTGCCATGGAGAGTAGCAGAAACAGCAGCATGCTCACCCTCCCACTTTCTCTTTTTGATGTGGACTCAAAGCCCCTGAAGACACTGGAGGACGCCCTGCACTGCTTCTTCCAGCCCAGGGAGTTATCAAGCAAAAGCAAGTGCTTCTGTGAGAACTGTGGGAAGAAGACCCGTGGGAAACAGGTCTTGAAGCTGACCCATTTGCCCCAGACCCTGACAATCCACCTCATGCGATTCTTCATCAGGAATTCACAGACGAGAAAGATCTGCCACTCCCTGTACTTCCCCCAGAGCTTGGATTTCAGCCAGAACCTTCCAATGAAGCGAGAATCTTGTGAAGCTGAAGAACAGTCTGGAAGGCAAGATGAGCTTTTTGCTGGGAATGCGCACGTGGAAAGGCAGAATTCGGTCATAA
>gi| |gb|AW |AW EST MAGE resequences, MAGE Homo sapiens cDNA, mRNA sequenceGGAGCTGGCCTACTGCCTGCAGAAGTGCAACGTGCCCTTGTTTGTCCAACATGATGCTGCCCAACTGTACCTCAAACTCTGGAACCTGATTAAGGACCAGATCACTGATGTGCACTTGGTGGAGAGACTGCAGGCCCTGTATATGATCCGGGTGAAGGACTCCTTGATTTGCGTTGACTGTGCCATGGAGAGTAGCAGAAACAGCAGCATGCTCACCCTCCCACTTTCTCTTTTTGATGTGGACTCAAAGCCCCTGAAGACACTGGAGGACGCCCTGCACTGCTTCTTCCAGCCCAGGGAGTTATCAAGCAAAAGCAAGTGCTTCTGTGAGAACTGTGGGAAGAAGACCCGTGGGAAACAGGTCTTGAAGCTGACCCATTTGCCCCAGACCCTGACAATCCACCTTATGCGATTCTCCATCAGGAATTCACAGACGAGAAAGATCTGCCACTCCCTGTACTTCCCCCAGAGCTTGGATTTCAGCCAGATCCTTCCAATGAAGCGAGAGTCTTGTGATGCTTGAGGAGCAATCTGGAGGGCATATGAGCTTTTTGCTGTGATTGCGCACCTGGGAATGCAAAACTCCGTCATTACTG
>gi| |gb|BQ |BQ AGENCOURT_ NIH_MGC_72 Homo sapiens cDNA clone IMAGE: ', mRNA sequenceAGATCTGCCACTCCCTGTACTTCCCCCAGAGCTTGGATTTCAGCCAGATCCTTCCAATGAAGCGAGAGTCTTGTGATGCTGAGGAGCAGTCTGGAGGGCAGTATGAGCTTTTTGCTGTGATTGCGCACGTGGGAATGGCAGACTCCGGTCATTACTGTGTCTACATCCGGAATGCTGTGGATGGAAAATGGTTCTGCTTCAATGACTCCAATATTTGCTTGGTGTCCTGGGAAGACATCCAGTGTACCTACGGAAATCCTAACTACCACTGGCAGGAAACTGCATATCTTCTGGTTTACATGAAGATGGAGTGCTAATGGAAATGCCCAAAACCTTCAGAGATTGACACGCTGTCATTTTCCATTTCCGTTCCTGGATCTACGGAGTCTTCTAAGAGATTTTGCAATGAGGAGAAGCATTGTTTTCAAACTATATAACTGAGCCTTATTTATAATTAGGGATATTATCAAAATATGTAACCATGAGGCCCCTCAGGTCCTGATCAGTCAGAATGGATGCTTTCACCAGCAGACCCGGCCATGTGGCTGCTCGGTCCTGGGTGCTCGCTGCTGTGCAAGACATTAGCCCTTTAGTTATGAGCCTGTGGGAACTTCAGGGGTTCCCAGTGGGGAGAGCAGTGGCAGTGGGAGGCATCTGGGGGGCCAAGGGCAGTGGCAGGGGGTATTTCAGTATTATACCACTGCTGTGACCAGACTTGTATACTGGCTGAATATCAGGGCTGGTTGTAATTTTTTCCCTTTGAAGAAACACCATTAATTTCCTAATGAATCCAAGTGGTTTGTAACTTGCCTATTCCTTTTATTCCAGCAAAAAATTAATTGATCATCCCCTCCCCCAAAAAATAGGGG
>gi| |gb|BG |BG RST25778 Athersys RAGE Library Homo sapiens cDNA, mRNA sequenceTCCTGGGAAGACATCCAGTGTACCTACGGAAATCCTAACTACCACTGGCAGGAAACTGCATATCTTCTGGTTTACATGAAGATGGAGTGCTAATGGAAATGCCCAAAACCTTCAGAGATTGACACGCTGTCATTTTCCATTTCCGTTCCTGGATCTACGGAGTCTTCTAAGAGATTTTGCAATGAGGAGAAGCATTGTTTTCAAACTATATAACTGAGCCTTATTTATAATTAGGGATATTATCAAAATATGTAACCATGAGGCCCCTCAGGTCCTGATCAGTCAGAATGGATGCTTTCACCAGCAGACCCGGCCATGTGGCTGCTCGGTCCTGGGTGCTCGCTGCTGTGCAAGACATTAGCCCTTTAGTTATGAGCCTGTGGGAACTTCAGGGGTTCCCAGTGGGGAGAGCAGTGGCAGTGGGAGGCATCTGGGGGCCAAAGGTCAGTGGCAGGGGGTATTTCAGTATTATACAACTGCTGTGACCAGACTTGTATACTGGCTGAATATCAGTGCTGTTTGTAATTTTTCACTTTGAGAACCAACATTAATTCCATATGAATCAAGTGTTTTGGAACTGCTATTCATTTATTCAGCAAATATTTATTGGTCATCTTTTCTCCATAAGATAGTGTGATAAACACAGCATGAATAAAGGTATTTTCCACACAGACAAGTGTTTTTTCACAAAATTATTNATTTTGNTGGGGCTGTGGCGGCCGCTTCCTTTATGGGGGGGAATTTAGAACCCGTTCCTGACGCGGGGGN

19. List of assembled fragments
Assembling DNA seq. fragments
List of assembled fragments

20. Assembling DNA seq. fragments
Overlap details

21. Assembling DNA seq. fragments
End of overlap details
Assembled mRNA sequence

22. Box 2.1 Pairwise Sequence Alignment
The most important class of bioinformatics tools – pairwise alignment of DNA and protein seqs.
alignment 1 alignment 2
Seq. 1 ACGCTGA ACGCTGA
Seq. 2 A - - CTGT ACTGT - -
Seeks alignments  high seq. identity, few mismatchs and gaps
Assumption – the observed identity in seqs. to be aligned is the result of either random or of a shared evolutionary origin
Identity ≠ similarity
Sequence identity = Homology (a risky assumption)
Sequence identity ≠ Homology

23. Box 2.1 Pairwise Sequence Alignment
Same true alignment arise through different evolutionary events
Scoring scheme: substitution  -1, indel  -5, match  3
indel
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Score
Figure A Common evolutionary events and their effects on alignment

24. Box 2.1 Pairwise Sequence Alignment
Find the optimal score  the best guess for the true alignment
Find the optimal pairwise alignment of two seqs.  inserted gaps into one or both of them  maximize the total alignment score
Dynamic programming (DP) – Needleman and Wunsch (1970), Smith and Waterman (1980), this algorithm guarantees that we find all optimal alignments of two seqs. of lengths m and n
BLAST is based on DP with improvement on speed
Prof. Waterman

25. Box 2.1 Pairwise Sequence Alignment
The score for alignment of i residues of sequence 1 against
j residues of sequence 2 is given by
where
c(i,j) = the score for alignment of residues i and j and takes the value 3 for a match or -1 for a mismatch,
c(-,j) = the penalty for aligning a residue with a gap, which takes the value of -5

26. Box 2.1 Pairwise Sequence Alignment
The entry for S(1,1) is the maximum of the following three events:
S(0,0) + c(A,A) = = [c(A,A) = c(1,1)]
S(0,1) + c(A, -) = = [c(A, -) = c(1, -)]
S(1,0) + c(-, A) = = [c(- ,A) = c(-, 1)]
Similarly, one finds S(2,1) as the maximum of three values: (-5)-1=-6; 3-5=-2; and (-10)-5=-15  the best is entry is the addition of the C indel to the A-A match, for a score of -2 (see next page).

27. Box 2.1 Pairwise Sequence Alignment
The alignment matrix of sequences 1 and 2
S(2,1)
= max {S(1,0) + c(2,1),
S(1,1) + c(2,-), S(2,0) + c(-,1)}
= max { S(1,0) + c(C,A),
S(1,1) + c(C,-), S(2,0) + c(-,A) }
= max { -5-1, 3-5, }
= -2

28. Box 2.1 Pairwise Sequence Alignment
Traceback  determine the actual alignment
From the top right hand corner  the (7,5) cell
For example the 1 in the (7,5) cell could only be reached by the addition of the mismatch A-T
ACGCTGA
A - - CTGT or
AC - - TGT
4 matches
1 mismatch
2 indels
Ambiguity – has to do with which C in seq. 1 aligns with the C in seq. 2

29. Box 2.1 Pairwise Sequence Alignment
Parameters settings - Gap penalties
Default settings are the easiest to use but they are not necessarily yield the correct alignment
constant penalty  independent of the length of gap, A
proportional penalty  penalty is proportional to the length L of the gap, BL (that is what we used in the this lecture)
affine gap penalty  gap-opening penalty + gap-extension penalty = A+BL
There is no rule for predicting the penalty that best suits the alignment
Optimal penalties vary from seq. to seq.  it is a matter of trial and error
Usually A > B, because of opening a gap (usually A/B ~ 10)
Hint: (1) compare distantly related seqs. high A and very low B often give the best results  penalized more on their existence than on their length, (2) compare closely related seqs., penalize both of extension and extension

30. Exercise 2.2 Computing an optimal sequence alignment
Two score schemes
Gap penalty = -5, mismatch = -1, match =3
Gap penalty = -1, mismatch = -1, match =3
First alignment score = 5*3 + 2*(-1) =13
Second/Third alignment score = 6*3 + 2*(-5) = 8
(2) First alignment score = 5*3 + 2*(-1) =13
Second/Third alignment score = 6*3 + 2*(-1) = 16
A more serious problem – identify the wrong alignment
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31. Exercise 2.2 Computing an optimal sequence alignment
Gap penalty = -5
Gap penalty = -1

32. Emerging Sequencing Methods
Costs of genome sequencing
Mid $30-50 Million dollars to sequencing a mammalian genome
Target $1000 per human genome by the year 2010
J. Craig Benter Foundation - $500,000 award for the first person to achieve this goal
New technologies
Sequencing by hybridization (SBH) – detect whether an exact match is present in a sample of DNA or not
Mass spectrophotometric technique – ionized fragment, time of flight
Nanopore sequencing strategies - Ultrafast and relative inexpensive sequencing of long DNA fragments
Single-molecule approach – Solexa, Visigen and Genovoxx
Single-molecule polony sequencing

33. Figure 2.6 Single-molecule polony sequencing
Emerging Sequencing Methods
Dilute solution of DNA are plated
onto a glass microscope slide.
In situ PCR produces thousands of
tiny colonies of DNA, which incorporated of single dye-labeled dNTPs.
Polony – PCR colonies (聚集區)
The slide is read after each cycle of
Incorporation of a new base, allowing
short seqs. to be determined.
Each numbered polony produces a short nucleotide seq. as shown.
These can then be assembled computationally into a contiguous seq.
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Figure 2.6 Single-molecule polony sequencing

34. Figure 2.7 (Part 1) Hierarchical versus shotgun sequencing
Genome Sequencing
Whole genome seqs. are assembled from
~105 of fragments, each typically between
500 and 1000 bp in length.
Two general approaches for fragmentation
and assembly: (1) hierarchical seq. (2) shotgun
seq.
For historical overview, see
Hierarchical seq.
* First develop a low resolution physical alignment to measure the seq. is obtained in large order pieces.
* Break the genome into small fragments and use computer algorithms to assemble them, see Figure 2.7
Most new genome projects adopt the shotgun approach.
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Figure 2.7 (Part 1) Hierarchical versus shotgun sequencing

35. Genome Sequencing – hierarchical sequencing
Top down, map-based or clone-by-clone strategy
~ late 1980
Genome  break into small fragments
The relative locations of the fragments are known BEFORE sequencing
Advantages
It fostered (help develop) assembly of high-resolution physical and genetic maps
Allow groups working around the global
Technology for cloning large fragments of genomes are progressed rapidly throughout the1990s, such as E. coli, S. cerevisiae, C. elegans. A. thaliana.
Top-down seq.  clone seqs. as managable units of framgments (50 – 200 kb in length)
Clone vectors – BAC (~300 kb), PAC (~100 kb), phage-derived cosmids

36. Figure 2.7 (Part 2) Shotgun sequencing
Genome Sequencing – Shotgun sequencing
In the shotgun approach, no attempt is made to order the clones in advance, Instead, the whole genome is
assembled using computer algorithms that order contigs based on their overlapping sequences.
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Figure 2.7 (Part 2) Shotgun sequencing

37. Figure 2.8 Cloning vectors used in genome sequencing
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Figure 2.8 Cloning vectors used in genome sequencing

38. Genome Sequencing – hierarchical sequencing
DNA libraries
By restriction enzyme (RE) or sonication (以超音波處理)
Fragments are ligated into a multiple cloning site (mcs) in the vector
Aim for 5- to 10-fold redundancy  larger than 5 to 10 times in the genome library
Each clone will have different ends  possible to select a scaffold of clones that forms a contiguous seq. coverage – a tiling (貼瓷磚) path
By aligning the regions of overlap (Fig. 2.9)
The tiling path can be assembled using a combination of 3 methods: (1) hybridization, (2) fingerprinting, and (3) end-sequencing

39. Genome Sequencing – hierarchical sequencing
A minimal tiling path through a library of aligned BAC clones that ensures complete coverage of the chromosome is chosen.
After sequencing independent shotgun libraries for each BAC.
Small gaps in the sequenced clone contigs remain.
These are closed as far as possible by merging the two BAC sequences, as well as by the addition of mate-pair information (yellow) and cDNA structural information (red), which establishes the orientation and distance between cloned segments.
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Figure 2.9 Hierarchical assembly of a sequence-contig scaffold (supercontig)

40. Genome Sequencing – hierarchical sequencing
Hybridization
All of the clones in a library that carry a particular seq. can be identified rapidly by hybridizing a small radioactively or chemically labeled probe containing the seq. to a filter on which is printed an array of ~10000 of clones (Fig. 2.10A)
Fingerprinting
Study the Restriction Enzyme (RE) patterns
Assemble contigs of large insert clones is to compare and
align them according to RE
RE ~ 6 bp  46 = 212 ~ 4000 bp
For BAC, 100 kb  100 kb/4 kbp ~ 20 – 30 fragments
these fragments can be separated by electrophesis  Fingerprint profile  BAC alignment by gel  software alignment  overlapping  Contigs  assemble of ~Mb length contigs
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41. Figure 2.10 Aligning BAC clones by hybridization and fingerprinting
Genome Sequencing – hierarchical sequencing
(A) A macroarray of BAC clones is probed
with a short, radioactive fragment to
identify all BACs that carry a specific
fragment.
These clones are digested with a RE, end-
labeled, and separated by gel electrophoresis,
Software converts the bands to a virtual
profile, shown hypothetically for a small
portion of four bands (high-ligated box in
part B). Shared bands (red or blue) imply
that the two clones share the same seq.
Green indicates the vector band common to
all clones.
The fingerprint profile is then converted into
a BAC alignment, In this example, clone 2
does not share any bands with the others and
so is placed into a seq. BAC contig, while the
other three clones form a tiling path.
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Figure Aligning BAC clones by hybridization and fingerprinting

42. Genome Sequencing – hierarchical sequencing
End-sequencing
Fill in the gaps after fingerprinting. How ?
sequencing both ends of the collection of BAC clones
Once a critical threshold of seqs. have been achieved  overlap
For example, along a 10 Mbp genome, end seqs. of 10,000 BAC clones,  provide a seq. tag every 5kb (for a 5-fold coverage)
Along a 10 Mbp genome 10 Mbp/10000 BAC  1 kbp/BAC
Five fold  10 Mb/2000 BAC ~ 5 kb (a seq. tag distance)
Given this tag density, it is possible to close gap < 50 kb
Once the Tiling path is chosen  shotgun the BAC clones into small fragments
Subcloning, use M13 phagemid (~1 kb, exist as dsDNA and ssDNA
or clone 2 ~ 3 kb fragments into a plasmid vector

43. Genome Sequencing – Shotgun sequencing
Use computer algorithm to assemble the seqs. (~100,000)
About 5 ~ 10 folds redundancy for each fragment
Library - From a single whole genome
After MSA  screen out repetitive seqs., overlap reads of the same seq.  generate
unitigs and scaffolds  >90% of the seqs. are assembled
Finishing phase – closing gaps, cleaning up ambiguities  take as much time as
the shotgun phase
Users are asked to trust the assemblies
Celera Genomics used the following software to assemble the seqs.
Screener – to mask (not removed) seqs. that contain repetitive DNA
(such as microsatellites, LINE, Alu repeats, retrotransposons and ribosomal DNA)
Overlapper – compares every unscreened read against every other unscreened read,
searching for overlaps of a predetermined length and identity.
Parallel processing on 40 supercomputers, each with 4GB RAM, allowed the 27 M
screened human seqs. reads to be overlapped in < 5 days !
Repeat-induced overlaps of a seq. are resolved using the Unitigger (see Figure 2.11).
Scaffolder – uses mate-pair information to link U-unitigs into scaffold contigs
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44. Genome Sequencing – Shotgun sequencing
Figure 2.11
Seq. alignment between two or more shotgun clones can arise between unique seqs. (left) or repetitive seqs. (right).
(B) The Overlapper aligns unitigs, which are identified as unique seq. alignments (U-untigs) or overcollapsed repeats (blue).
Two contigs can be aligned and
oriented by using mate-pair seq.
information from the ends of longer (10- or 50-kb) clones, as shown at the bottom, while mate-pairs from 2-kb fragments allow assembly of scaffolds despite the presence of simple repeats such as microsatellites (blue) that are masked before performing alignments.
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Figure U-unitigs and repeat resolution

45. Genome Sequencing – Shotgun sequencing
Figure 2.12 shows the estimated coverage of the fly and human whole genomes after initial assembly: in both cases, 84% or more of the genomes was covered by scaffolds at least 100 kb in length, while most scaffolds were in the Mb range.
 seq. coverage from 5x to 10x  a 10%
 in the proportion of scaffolds of lengths up to 1 Mb.
The plot shows the percentage of
Scaffolds that have a length greater than that indicated for the fly 10x, human 8x (CSA) and human 5x (whole genome assembly WGA) seqs. generated by Celera. The fly and CSA assemblies include shredded (撕成碎片) seqs. generated from BAC clones by public genomes sequencing efforts.
pgs2e-fig jpg
Figure Proportion of fly and human genomes in large scaffolds

46. NCTS http://math.cts.nthu.edu.tw/Mathematics/conference-PT2005.html
UCSD