1. DNA Assembly Sanger Reads
Arthur Gruber
Instituto de Ciências Biomédicas Universidade de São Paulo
AG-ICB-USP

2. Why assemble a genome?
Current DNA sequencing methods generate reads of bp – resolution limit of electrophoresis
Whole genomes or large clones need to be fragmented - clone library
Short fragments are randomly sequenced (shotgun approach) – reads are assembled to form final consensus sequence
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3. Shotgun Sequencing I – random phase
Sheared DNA: kb
BAC clone: kb
Random
Reads
Sequencing
Templates
Modified from BCM-HGSC
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4. Shotgun Sequencing II - assembly
Low Base
Quality
Single
Stranded
Region
Mis-Assembly
(Inverted)
Sequence
Gap
Consensus
Modified from BCM-HGSC
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5. Shotgun Sequencing III - finishing
Low Base
Quality
Single
Stranded
Region
Mis-Assembly
(Inverted)
Sequence
Gap
Consensus
Modified from BCM-HGSC
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6. Shotgun Sequencing III - finishing
Single
Stranded
Region
Mis-Assembly
(Inverted)
Sequence
Gap
Consensus
Modified from BCM-HGSC
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7. Shotgun Sequencing III - finishing
Mis-Assembly
(Inverted)
Sequence
Gap
Consensus
Modified from BCM-HGSC
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8. Shotgun Sequencing III - finishing
Sequence
Gap
Consensus
Modified from BCM-HGSC
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9. Shotgun Sequencing III - finishing
Consensus
High Accuracy Sequence:
< 1 error/ 10,000 bases
Modified from BCM-HGSC
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10. How to deal with the enormous amount of reads generated by the high throughput DNA sequencers?
Sanger Institute
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11. Sanger Institute - Hinxton - UK
ABI3700 DNA sequencers
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12. Sanger Institute - Hinxton - UK
ABI3730 DNA sequencers
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13. Sanger Institute - Hinxton - UK
ABI3700 DNA sequencers
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14. Sanger Institute - Hinxton - UK
Colony-picking robots
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15. Sanger Institute - Hinxton - UK
Colony-picking robot
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16. Sanger Institute - Hinxton - UK
Plasmid miniprep robots
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17. Sanger Institute - Hinxton - UK
Plasmid miniprep rooom
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18. Sanger Institute - Hinxton - UK
Thermocycler room
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19. Exponential growth of sequence generation
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20. Exponential growth of sequence generation
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21. Exponential growth of sequence generation
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22. Exponential growth of sequence generation
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23. Exponential growth of sequence generation
Genetic Sequence Data Bank - October
NCBI-GenBank Flat File Release 192.0
Distribution Release Notes:
loci,
bases
…from reported sequences
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24. Phred/Phrap/Consed Package
Phred/Phrap/Consed is a worldwide distributed package for:
a. Trace file (chromatograms) reading;
b. Quality (confidence) assignment to each individual base;
c. Vector and repeat sequences identification and masking;
d. Sequence assembly and error probability assignment to the consensus sequence;
e. Assembly viewing and editing;
f. Automatic finishing.
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25. Phred/Phrap/Consed Pipeline
Directories:
chromat_dir
phd_dir
edit_dir
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26. Phred Genome Research 8: 175-185, 1998
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27. Phred Genome Research 8: 186-194, 1998
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28. Phred Phred is a program that performs several tasks:
a. Reads trace files – compatible with most file formats: SCF (standard chromatogram format), ABI (373/377/3700), ESD (MegaBACE) and LI- COR.
b. Calls bases – attributes a base for each identified peak with a lower error rate than the standard base calling programs.
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29. Phred
c. Assigns quality values to the bases – a “Phred value” based on an error rate estimation calculated for each individual base.
d. Creates output files – base calls and quality values are written to output files.
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30. Trace File High quality read: - no ambiguities (Ns) - no noise
- peaks very well spaced
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31. Trace File Good quality read: - no ambiguities (Ns)
- some noise (notice baseline)
- peaks very well spaced
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32. Trace File Poor quality read: - some ambiguities (Ns)
- bad noise (notice baseline)
- overlapping peaks
- can be caused by bad quality template, bad matrix, low signal to noise rate
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33. Trace File Poor quality read: - many ambiguities (Ns) - noise
- caused by homopolymeric region/polymerase slippage
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34. Trace File
Sudden drop artifact: - good quality region is followed by a sudden drop of signal
- caused by secondary structure
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35. Trace File High quality region: - no ambiguities (Ns) - no noise
- peaks very well spaced
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36. Trace File Medium quality region: - some ambiguities (Ns) - no noise
- peaks very well spaced
- some homopolymeric strectches are not well resolved
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37. Trace File
Poor quality region - diffusion effects and decrease in the relative mass difference between the sequence products: - overlapping peaks, peaks not evenly spaced
- low resolution
- low confidence to base assignment
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38. Phred Analysis steps
a) Predicts idealized (expected) peaks (amplitudes) based effectively on the best region of the trace
b) Identifies observed peaks
c) Compares observed and expected peaks (divides the peaks into matched and unmatched)
d) Unmatched peaks are analyzed for any peak that could be called, but was not called in step c
Modified from Evan Eichler, Ph.D
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39. Phred value formula q = - 10 x log10 (p) where q - quality value
p - estimated probability error for a base call
Examples:
q = 20 means p = 10-2 (1 error in 100 bases)
q = 40 means p = 10-4 (1 error in 10,000 bases)
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40. The structure of a phd file
BEGIN_SEQUENCE 01EBV10201A02.g
BEGIN_COMMENT
CHROMAT_FILE: EBV10201A02.g
ABI_THUMBPRINT:
PHRED_VERSION: g
CALL_METHOD: phred
QUALITY_LEVELS:99
TIME: Thu May 24 00:18:
TRACE_ARRAY_MIN_INDEX: 0
TRACE_ARRAY_MAX_INDEX: 12153
TRIM:
CHEM: term
DYE: big
END_COMMENT
BEGIN_DNA
t 8 5
c 13 17
a 19 26
c 19 32 t a a a g c c t g g g g t g c c t a a t g t g t c g n c t t c t c c c t c g g a g g t a g t c a g c n c n n n n n n n c n
END_DNA
END_SEQUENCE
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41. AG-ICB-USP

42. c t g a t g t g a c a g c t c t c a c t c t a g a g g c t g t t g c a g a g g t c c g c g a t t c c t t g c a g c t g c a t a c t a c a
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43. AG-ICB-USP

44. t c g t a t g c c c c a c c a g g g a g a t t c g g a c c g g a c a g t a a t c g a a t t c c c g c g g c c g c c a t g g c g g c c g g g a g c a t
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45. AG-ICB-USP

46. g c g a c g t c g g g c c c a a t t c g c c c t a t a g t g a g t c g t a t t a c a t t c a c t g g c c g c g t t t t t t a c a t a a a g g
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47. Phred/Phrap/Consed Pipeline
Directories:
chromat_dir
phd_dir
edit_dir
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48. Conversion of phd files into FASTA files phd2fasta script
Features:
- Phred creates single-sequences files containing the sequence itself plus the quality assignments (phd files)
- The input file for cross_match and phrap programs is a multiple sequence file in FASTA format
- A Perl script named phd2fasta converts the phd files into two multiple sequence FASTA format files, containing the sequence information and the basecall quality information respectively
- phredPhrap script automatically executes phd2fasta before running cross_match and phrap!
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49. Phred/Phrap/Consed Pipeline
Directories:
chromat_dir
phd_dir
edit_dir
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50. Vector screening Features:
This step removes or screen out vector sequence before running phrap
Program:
Cross_match – a program for rapid sequence comparison and database search based on na efficient implementation of the Smith-Waterman-Gotoh algorithm.
Command:
cross_match seq_file1 [seq_file2...] [-optionvalue] – [optionvalue]
- seq_file is a file containing sequences in a FASTA format
- all sequences in seq_file1 (query) are compared to sequences in seq_file2 (subject)
- matches meeting relevant criteria are written to the standard output
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51. Vector screening Example:
cross_match seqfile.fasta vector.seq –minmatch 10 –minscore 20 –screen >screen.out
where:
- ‘seqfile.fasta’ is a file containing multiple reads in FASTA format
- ‘vector.seq’ is a file containing the vector sequences
- ‘-minmatch’ and ‘-minscore’ are parameters for pairwise alignment
- ‘-screen’ creates a file named seqfile.fasta.screen containing vector-masked versions of the original sequences. Any region matching any part of a vector sequence is replaced by Xs.
- ‘screen.out’ contains a list of the matches found
- the .‘screen’ file is the input for phrap
- if a ‘.qual’ file was created (i.e. seqfile.fasta.qual) , it has to be renamed to (seqfile.fasta.screen.qual) – phredPhrap script automatically performs this step!
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52. Phred/Phrap/Consed Pipeline
Directories:
chromat_dir
phd_dir
edit_dir
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53. Phrap - Phragment Assembly Program or… Phil’s Revised Assembly Program
Phrap is a program for assembling shotgun DNA sequence data
Command:
phrap –seq_file1 [seq_file2...] [-optionvalue] – [optionvalue]
- seq_file is a file containing multiple sequences in a FASTA format
- the current version only handles a single sequence file
- all the sequences in the seq_file are compared to each other
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54. Phrap a. Uses the entire read content – no need for trimming.
Key Features:
a. Uses the entire read content – no need for trimming.
b. User supplied (i.e. Repbase) + internally computed data – better accuracy of assembly in the presence of repeats.
c. Contig sequence is constituted by a mosaic of the highest quality parts of the reads – it’s not a consensus!
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55. Phrap
Key Features:
e. Handles very large datasets – hundreds of thousands of reads are easily manipulated.
f. Generate output files – contain some important data and enable visualization by other programs
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56. Phrap output files *.contigs – fasta file containing the contigs
Contigs with more than one read
Singletons (single reads with a match to some other contig but that couldn’t be merged consistently to it)
*.singlets – fasta file of the singlet reads
Reads with no match to other read
*.ace – allows for viewing the assembly using Consed
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57. Phred/Phrap/Consed Pipeline
Directories:
chromat_dir
phd_dir
edit_dir
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58. Consed Genome Research 8: 195-202, 1998
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59. Consed
Consed is a program for viewing and editing assemblies produced by Phrap
Key Features:
a. Assembly viewer - allows for visualization of contigs, assembly (aligned reads), quality values of reads and final sequence.
b. Trace file viewer – single and multiple trace files can be visualized allowing for comparison of a given sequence in several reads.
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60. Consed
Consed is a program for viewing and editing assemblies produced by Phrap
Key Features:
c. Navigation – identify and list regions which are below a given quality threshold, contain high quality discrepancies, single- strand coverage, etc.
d. Autofinish – automatic set of functions for: gap closure, improvement of sequence quality, determination of relative orientation of contigs, identification of regions covered by a single read or by reads of a single strand. The program automatically performs primer picking and chooses the templates.
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70. Phred/Phrap/Consed Pipeline
Directories:
chromat_dir
phd_dir
edit_dir
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71. Autofinish Genome Research 11: 614-625, 2001
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72. Autofinish Features: - Autofinish is part of the Consed package.
- It automatically chooses finishing reads in order to finish a project.
- The “finished” status is defined by the user according to pre-defined parameters
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73. Autofinish Autofinish allows the user to:
- Figure out how contigs are ordered and oriented
- Close gaps
- Improve the error rate
- Cover every base by reads from at least 2 different subclones
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74. Autofinish
Autofinish will suggest any of the following types of reads:
Forward universal primer terminator reads
Reverse universal primer terminator reads
Custom primer reads with subclone template
Custom primer reads with whole clone template
Minilibraries
PCR
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75. Autofinish Finishing procedure: Assemble new reads with existing reads
Autofinish suggests reads
Assemble new reads with existing reads
Shotgun reads
Make reads in lab
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76. How to get the programs - Solaris Supported platforms: Internet site:
- Linux computers (i686, i386, EM64T, AMD64 )
- Mac (OS X)
Note: there are commercial versions of Phred/Phrap for DOS/Windows platform (no Consed version so far)
Internet site:
- academic version
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77. Contacts - Phrap/Cross_match/Swat – Phil Green – phg@u.washington.edu
To obtain the programs, questions, bug reports, suggestions:
- Phrap/Cross_match/Swat – Phil Green –
- Phred – Brent Ewing –
- Consed – David Gordon –
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78. The Staden Package Medical Research Council – Laboratory of Molecular Biology (MRC-LMB) – UK
(no more supported by the original team) – now open source
Preparing sequence trace data for analysis for assembly
pregap4
Graphical user interface
Prepare trace data
Automation
Trace format conversion
Quality analysis
Vector clipping
Contaminant screening
Repeat searching.
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79. The Staden Package Assembly program Gap4 and Gap5 Assembly
Contig joining
Assembly checking
Repeat searching
Experiment suggestion
Read pair analysis
Contig editing
Graphical views of contigs
Database
Note: ace files produced by a special version of Phrap can be viewed by Gap4
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85. The Staden Package - Sun Solaris - http://staden.sourceforge.net/
Supported platforms:
- Sun Solaris
- Compaq Tru64 UNIX (Alpha)
- SGI Irix
- Linux
- MS Windows (Win9x, NT, 2000)
Availability: -
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86. CAP3 - Sequence Assembly Program Genome Research 9: 868-877, 1999
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87. CAP3 - Sequence Assembly Program
Characteristics:
- Makes use of quality values – qual files produced by Phred can be used by CAP3
- Produces an ace file compatible with Consed
- Can also be used in Gap4 (Staden Package)
- Program available at
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88. Finishing Problems Finishing can be a boring and difficult task due:
DNA sequencing problems
a. High GC content – genomes presenting a high GC content are more prone to generate artifacts as compressions, sudden drops, bad quality regions. Try to use Dye Primer instead of Dye Terminator, change chemistry, add DMSO, increase annealing temperature, use deaza-dGTP instead of dGTP, etc.
b. Palindromic regions – lead to strong secondary structures causing sudden drops. Try to use deaza-dGTP instead of dGTP, amplify the problematic region by PCR and sequence the product.
c. Homopolymeric regions – can reduce DNA synthesis efficiency for some chemistries. Try to use Dye Primer instead of Dye Terminator, change chemistry (dRhodamine instead of BigDye).
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89. Finishing Problems Finishing can be a boring and difficult task due:
DNA assembly problems
a. High repeat content – highly repeated elements reduce accuracy of DNA assembly. Identify the repeat unit, screen it with Cross_Match or Repeat_Masker and mask it. Try to assemble again and add the repetitive region only at the end. Map the repetitive region using restriction enzymes to estimate its size and number of repeat units.
b. High AT content – some highly biased genomes (i.e. Plasmodium falciparum; plastid genomes) can pose a problem for assembly programs. Very difficult to solve. Try to determine a restriction map and associate mapping with DNA sequencing data.
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