Gibson Second Edition

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

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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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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

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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

Template
3 ATCGGTGCATAGCTTGT 5 5 TAGCCACGTATCGAACA
3 5 TAGCCACGTATCGAA 3 5 TAGCCACGTATCGA
3 5 TAGCCACGTA 3 5 TAGCCA 3 5 TA 3
Sequence reaction products
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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 TAG
CCACGT
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Sequence separation
Sequence separation

• Terminated chains need to be separated
• Requires one-base-pair resolution
• See difference between chains of X and X1 base
  pairs
• Gel electrophoresis
• Very thin gel
• High voltage applied
• Works with radioactive or fluorescent labels
• Negative pole at the top

C A G T C A G T
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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

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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

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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.
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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 Biosystems 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
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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 http//www.phrap.org
• The program assign probability scores to the
  accuracy of each base call as the trace is read

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2.3 Automated sequence chromatograms

1. This seq. shows noiseness of the first 30 bp of
   a run.
2. The middle two rows show a segment of two seqs.
   that are polymorphic for both SNPs and an indel.
3. A decline in seq. quality typically occurs after
   about 800 bp.

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• Ex. 2.1 Reading a sequence trace

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.
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Figure 2.5 An aligned-reads window in consed
Contig Assembly
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Assembling DNA seq. fragments

• NCBI dbest databases http//www.ncbi.nlm.nih.gov/D
  atabase/
• View the EST statistics
• FTP EST files

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Assembling DNA seq. fragments

• IFOM assembler
• http//bio.ifom-firc.it/ASSEMBLY/assemble.html
• Multiple EST seqs. ? contig
• max. number of seqs. you can enter is 10000 !!
• use gi(15744427, 19124086, 8147732, 8147734,
  20393914,13728017)
• Length (850, 1062, 634, 596, 869, 768) bp
• resulting in a single contig consensus seq., can
  be used for similarity search against db

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Assembling DNA seq. fragments 6 GI fragments

• gtgi15744427gbBI752849.1BI752849 603022060F1
  NIH_MGC_114 Homo sapiens cDNA clone IMAGE5192510
  5', mRNA sequenceCGGGGTGCTGCGAGCGCGGGGCCAGACCAAGGC
  GGGCCCGGAGCGGAACTTCGGTCCCAGCTCGGTCCCCGGCTCAGTCCCGA
  CGTGGAACTCAGCAGCGGAGGCTGGACGCTTGCATGGCGCTTGAGAGATT
  CCATCGTGCCTGGCTCACATAAGCGCTTCCTGGAAGTGAAGTCGTGCTGT
  CCTGAACGCGGGCCAGGCAGCTGCGGCCTGGGGGTTTTGGAGTGATCACG
  AATGAGCAAGGCGTTTGGGCTCCTGAGGCAAATCTGTCAGTCCATCCTGG
  CTGAGTCCTCGCAGTCCCCGGCAGATCTTGAAGAAAAGAAGGAAGAAGAC
  AGCAACATGAAGAGAGAGCAGCCCAGAGAGCGTCCCAGGGCCTGGGACTA
  CCCTCATGGCCTGGTTGGTTTACACAACATTGGACAGACCTGCTGCCTTA
  ACTCCTTGATTCAGGTGTTCGTAATGAATGTGGACTTCACCAGGATATTG
  AAGAGGATCACGGTGCCCAGGGGAGCTGACGAGCAGAGGAGAAGCGTCCC
  TTTCCAGATGCTTCTGCTGCTGGAGAAGATGCAGGACAGCCGGCAGAAAG
  CAGTGCGGCCCCTGGAGCTGGCTACTGCCTGCAGAAGTGCAACGTGCCCT
  TGTTTGTCCAACATGATGCTGCCAACTGTACCTCAAACTCTGGAACCTGA
  TTAAGGACCAGATCACTGATGTGCACTTGGTGGAGAGACTGCAGGCCCTG
  TATATGATCCGGGTGAAGGACTCCTTGATATGCGTTGACTGTGCCATGGG
  AGAGTAGCAGAAAACAGCAGCATGCTCAACCTCCCACTTTCTCTATTGGA
  TGTGGACTCAAAGCCCT
• gtgi19124086gbBM807263.1BM807263
  AGENCOURT_6574903 NIH_MGC_124 Homo sapiens cDNA
  clone IMAGE5732238 5', mRNA sequenceGTCCGGAATTCCC
  GGGATCTCAGCAGCGGAGGCTGGACGCTTGCATGGCGCTTGAGAGATTCC
  ATCGTGCCTGGCTCACATAAGCGCTTCCTGGAAGTGAAGTCGTGCTGTCC
  TGAACGCGGGCCAGGCAGCTGCGGCCTGGGGGTTTTGGAGTGATCACGAA
  TGAGCAAGGCGTTTGGGCTCCTGAGGCAAATCTGTCAGTCCATCCTGGCT
  GAGTCCTCGCAGTCCCCGGCAGATCTTGAAGAAAAGAAGGAAGAAGACAG
  CAACATGAAGAGAGAGCAGCCCAGAGAGCGTCCCAGGGCCTGGGACTACC
  CTCATGGCCTGGTTGGTTTACACAACATTGGACAGACCTGCTGCCTTAAC
  TCCTTGATTCAGGTGTTCGTAATGAATGTGGACTTCACCAGGATATTGAA
  GAGGATCACGGTGCCCAGGGGAGCTGACGAGCAGAGGAGAAGCGTCCCTT
  TCCAGATGCTTCTGCTGCTGGAGAAGATGCAGGACAGCCGGCAGAAAGCA
  GTGCGGCCCCTGGAGCTGGCCTACTGCCTGCAGAAGTGCAACGTGCCCTT
  GTTTGTCCAACATGATGCTGCCCAACTGTACCTCAAACTCTGGAACCTGA
  TTAAGGACCAGATCACTGATGTGCACTTGGTGGAGAGACTGCAGGCCCTG
  TATACGATCCGGGTGAAGGACTCCTTGATTTGCGTTGACTGTGCCATGGA
  GAGTAGCAGAAACAGCAGCATGCTCACCCTCCCACTTTCTCTTTTTGATG
  TGGACTCAAAGCCCCTGGAAGACACTGGAGGACGCCCTGCACTGCTTCTT
  CCAGCCCAGGAGTTATCAAGCAAAAGCAAGTGCTTCTGTGAGAACTGTGG
  GAAGAAGACCCGCGGGGAACAGGGTCCTGAAACCTGACCATTTTGCCCCA
  GACCTTGACCAATCCACCTCATGGCGATTCTCCCTCCAGGAATTCCCCGA
  CCGAGAAAAAATTGGCCACTTCCCCGGAATTTCCCCCCAAAAACTTGGAA
  TTTCACCCAAAACCTTTCCCATGTAAACCCGGAAACCCTGGGGAAGGCT
• gtgi8147732gbAW958049.1AW958049 EST370119 MAGE
  resequences, MAGE Homo sapiens cDNA, mRNA
  sequenceGAACTAGTGGATCCCCCGGGCTGCAGGAATTCGGCACGAGTG
  GAGCTGGCCTACTGCCTGCAGAAGTGCAACGTGCCCTTGTTTGTCCAACA
  TGATGCTGCCCAACTGTACCTCAAACTCTGGAACCTGATTAAGGACCAGA
  TCACTGATGTGCACTTGGTGGAGAGACTGCAGGCCCTGTATATGATCCGG
  GTGAAGGACTCCTTGATTTGCGTTGACTGTGCCATGGAGAGTAGCAGAAA
  CAGCAGCATGCTCACCCTCCCACTTTCTCTTTTTGATGTGGACTCAAAGC
  CCCTGAAGACACTGGAGGACGCCCTGCACTGCTTCTTCCAGCCCAGGGAG
  TTATCAAGCAAAAGCAAGTGCTTCTGTGAGAACTGTGGGAAGAAGACCCG
  TGGGAAACAGGTCTTGAAGCTGACCCATTTGCCCCAGACCCTGACAATCC
  ACCTCATGCGATTCTTCATCAGGAATTCACAGACGAGAAAGATCTGCCAC
  TCCCTGTACTTCCCCCAGAGCTTGGATTTCAGCCAGAACCTTCCAATGAA
  GCGAGAATCTTGTGAAGCTGAAGAACAGTCTGGAAGGCAAGATGAGCTTT
  TTGCTGGGAATGCGCACGTGGAAAGGCAGAATTCGGTCATAA
• gtgi8147734gbAW958051.1AW958051 EST370121 MAGE
  resequences, MAGE Homo sapiens cDNA, mRNA
  sequenceGGAGCTGGCCTACTGCCTGCAGAAGTGCAACGTGCCCTTGTT
  TGTCCAACATGATGCTGCCCAACTGTACCTCAAACTCTGGAACCTGATTA
  AGGACCAGATCACTGATGTGCACTTGGTGGAGAGACTGCAGGCCCTGTAT
  ATGATCCGGGTGAAGGACTCCTTGATTTGCGTTGACTGTGCCATGGAGAG
  TAGCAGAAACAGCAGCATGCTCACCCTCCCACTTTCTCTTTTTGATGTGG
  ACTCAAAGCCCCTGAAGACACTGGAGGACGCCCTGCACTGCTTCTTCCAG
  CCCAGGGAGTTATCAAGCAAAAGCAAGTGCTTCTGTGAGAACTGTGGGAA
  GAAGACCCGTGGGAAACAGGTCTTGAAGCTGACCCATTTGCCCCAGACCC
  TGACAATCCACCTTATGCGATTCTCCATCAGGAATTCACAGACGAGAAAG
  ATCTGCCACTCCCTGTACTTCCCCCAGAGCTTGGATTTCAGCCAGATCCT
  TCCAATGAAGCGAGAGTCTTGTGATGCTTGAGGAGCAATCTGGAGGGCAT
  ATGAGCTTTTTGCTGTGATTGCGCACCTGGGAATGCAAAACTCCGTCATT
  ACTG
• gtgi20393914gbBQ213074.1BQ213074
  AGENCOURT_7559959 NIH_MGC_72 Homo sapiens cDNA
  clone IMAGE6055692 5', mRNA sequenceAGATCTGCCACTC
  CCTGTACTTCCCCCAGAGCTTGGATTTCAGCCAGATCCTTCCAATGAAGC
  GAGAGTCTTGTGATGCTGAGGAGCAGTCTGGAGGGCAGTATGAGCTTTTT
  GCTGTGATTGCGCACGTGGGAATGGCAGACTCCGGTCATTACTGTGTCTA
  CATCCGGAATGCTGTGGATGGAAAATGGTTCTGCTTCAATGACTCCAATA
  TTTGCTTGGTGTCCTGGGAAGACATCCAGTGTACCTACGGAAATCCTAAC
  TACCACTGGCAGGAAACTGCATATCTTCTGGTTTACATGAAGATGGAGTG
  CTAATGGAAATGCCCAAAACCTTCAGAGATTGACACGCTGTCATTTTCCA
  TTTCCGTTCCTGGATCTACGGAGTCTTCTAAGAGATTTTGCAATGAGGAG
  AAGCATTGTTTTCAAACTATATAACTGAGCCTTATTTATAATTAGGGATA
  TTATCAAAATATGTAACCATGAGGCCCCTCAGGTCCTGATCAGTCAGAAT
  GGATGCTTTCACCAGCAGACCCGGCCATGTGGCTGCTCGGTCCTGGGTGC
  TCGCTGCTGTGCAAGACATTAGCCCTTTAGTTATGAGCCTGTGGGAACTT
  CAGGGGTTCCCAGTGGGGAGAGCAGTGGCAGTGGGAGGCATCTGGGGGGC
  CAAGGGCAGTGGCAGGGGGTATTTCAGTATTATACCACTGCTGTGACCAG
  ACTTGTATACTGGCTGAATATCAGGGCTGGTTGTAATTTTTTCCCTTTGA
  AGAAACACCATTAATTTCCTAATGAATCCAAGTGGTTTGTAACTTGCCTA
  TTCCTTTTATTCCAGCAAAAAATTAATTGATCATCCCCTCCCCCAAAAAA
  TAGGGG
• gtgi13728017gbBG206330.1BG206330 RST25778
  Athersys RAGE Library Homo sapiens cDNA, mRNA
  sequenceTCCTGGGAAGACATCCAGTGTACCTACGGAAATCCTAACTAC
  CACTGGCAGGAAACTGCATATCTTCTGGTTTACATGAAGATGGAGTGCTA
  ATGGAAATGCCCAAAACCTTCAGAGATTGACACGCTGTCATTTTCCATTT
  CCGTTCCTGGATCTACGGAGTCTTCTAAGAGATTTTGCAATGAGGAGAAG
  CATTGTTTTCAAACTATATAACTGAGCCTTATTTATAATTAGGGATATTA
  TCAAAATATGTAACCATGAGGCCCCTCAGGTCCTGATCAGTCAGAATGGA
  TGCTTTCACCAGCAGACCCGGCCATGTGGCTGCTCGGTCCTGGGTGCTCG
  CTGCTGTGCAAGACATTAGCCCTTTAGTTATGAGCCTGTGGGAACTTCAG
  GGGTTCCCAGTGGGGAGAGCAGTGGCAGTGGGAGGCATCTGGGGGCCAAA
  GGTCAGTGGCAGGGGGTATTTCAGTATTATACAACTGCTGTGACCAGACT
  TGTATACTGGCTGAATATCAGTGCTGTTTGTAATTTTTCACTTTGAGAAC
  CAACATTAATTCCATATGAATCAAGTGTTTTGGAACTGCTATTCATTTAT
  TCAGCAAATATTTATTGGTCATCTTTTCTCCATAAGATAGTGTGATAAAC
  ACAGCATGAATAAAGGTATTTTCCACACAGACAAGTGTTTTTTCACAAAA
  TTATTNATTTTGNTGGGGCTGTGGCGGCCGCTTCCTTTATGGGGGGGAAT
  TTAGAACCCGTTCCTGACGCGGGGGN

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Assembling DNA seq. fragments
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Assembling DNA seq. fragments
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Assembling DNA seq. fragments
Assembled mRNA sequence
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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

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Box 2.1 Pairwise Sequence Alignment
Same true alignment arise through different
evolutionary events Scoring scheme substitution
? -1, indel ? -5, match ? 3
indel
Score 9 5
4
4
Figure A Common evolutionary events and their
effects on alignment
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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 http//www.usc.edu/dept/LAS/biosci/
  faculty/waterman.html

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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
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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) 0 3 3 c(A,A)
  c(1,1)
• S(0,1) c(A, -) -5 -5 -10 c(A, -)
  c(1, -)
• S(1,0) c(-, A) -5 -5 -10 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).

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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,
-10-5 -2
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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 ACGCTGA 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
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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 ABL
• 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 gt 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

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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 53 2(-1) 13
• Second/Third alignment score 63 2(-5)
  8
• (2) First alignment score 53 2(-1) 13
• Second/Third alignment score 63 2(-1)
  16
• A more serious problem identify the wrong
  alignment

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Exercise 2.2 Computing an optimal sequence
alignment
Gap penalty -5
Gap penalty -1
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Emerging Sequencing Methods

• Costs of genome sequencing
• Mid-2000 - 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

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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
(???) 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 20-25 nucleotide seq. as
shown. These can then be assembled
computationally into a contiguous seq.
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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
• http//www.sciencemag.org/feature/plus/sfg/human/t
  imeline1.shtml
• 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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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

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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 2.8 Cloning vectors used in genome
sequencing
Cloning vectors used in genome sequencing
38
Genome Sequencing hierarchical sequencing

• DNA libraries
• By restriction enzyme (RE) or sonication (??????)
• 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 (???) 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

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Figure 2.9 Hierarchical assembly of a
sequence-contig scaffold (supercontig)
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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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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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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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 lt 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

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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 ? gt90 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 lt
  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

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.

Figure 2.11 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 (????) seqs. generated from BAC clones
by public genomes sequencing efforts.
Figure 2.12 Proportion of fly and human genomes
in large scaffolds
46

• NCTS http//math.cts.nthu.edu.tw/Mathematics/confe
  rence-PT2005.html
• UCSD
• http//research.calit2.net/recomb-workshop05/