Copyright (c) 2008 Daniel Huson. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation
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1 Assembly of the Human Genome Daniel Huson Informatics Research
2 Copyright (c) 2008 Daniel Huson. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license can be found at
3
4 Overview Why Sequence Assembly? A quick look at current sequencing technology BAC-by-BAC Sequencing vs Whole Genome Shotgun brief comparison Compartmentalized Assembly Interim approach to get the best of both worlds Mate-Pair based Contig Assembly Problem and the Greedy Path-Merging Algorithm
5 Goal: Determine DNA Sequence base pairs of DNA (x150000)
6 DNA Sequencing Technology
7 DNA Sequencing Technology 110 capillary array with detection and load bar interface.
8 DNA Sequencing Technology
9
10 Sequencing DNA Current sequencing technology is highly automated and can determine large numbers of base pairs very quickly However, less than 900 base pairs can be read consecutively Hence, large pieces of contiguous DNA ( contigs ) must be assembled from such fragments
11 Three Stages of Genome Sequencing Shotgun Sequence Assembly
12 Human Genome Project 18 countries have human genome research programs. Australia, Brazil, Canada, China, Denmark, European Union, France, Germany, Israel, Italy, Japan, Korea, Mexico, The Netherlands, Russia, Sweden, United Kingdom, and the United States. ~1100 scientists involved The 5 major sequencing centers (USA/UK) are: DOE Joint Genome Institute Baylor College of Medicine Sanger Centre Washington University Genome Sequencing Center Whitehead Institute/MIT Center for Genome Research
13 Clone-by-Clone (Human Genome Project) Genome Physical mapping Minimal tiling set BAC clones ~150kbp For each BAC in tiling: (~ for human) Shotgun sequence Fragment assembly
14 BAC Clone Data from the HGP As of August 2000, GenBank contained ~33500 BAC clones in different phases of assembly: ~3000 phase-0 data sets (~100 pieces of size 1000) ~21000 phase-1/2 data sets (~10-20 pieces of size 5000~25000) ~9500 phase-3 data sets ( finished ~150kbp) Full sequence for chromosomes 21 and 22 published
15 Celera s Sequencing Factory 300 ABI 3700 DNA sequencers 50 production staff 40 support staff 20,000 sq. ft. of wet lab 20,000 sq. ft. of sequencing space Large group of computer scientists 1000 processors Close to 1 Terabyte of main memory 100 Terabytes of disk storage
16 Whole Genome Shotgun (Celera) Genome Shotgun Sequencing Assembly
17 Genome Assembly Two Approaches Clone-by-Clone: Take 7 copies of a page of an encyclopedia and randomly shred them into small pieces. Using the overlaps of pieces, reconstruct the page! Whole Genome Shotgun: Take 7 copies of a whole encyclopedia and randomly shred them. Using the overlap of pieces, reconstruct the whole book!
18 Comparison Clone-by-Clone: + Assembly problem easy and well understood 2 separate processes Clone libraries unstable, maps hard to complete Whole Genome Shotgun: + Single process, few library constructions Computationally harder Assembly of Drosophila proved the feasibility of WGS + Assembly Sequencing libraries must be made for every clone
19 Double-Barreled Shotgun Genome Shotgun Sequence m ~600bp ~600bp Fragments are read in mate pairs of known length m, typically 2k, 10k or 150k m
20 Happy Mates contig AGCTTGCATATAGCCATCNNNNCTACAAAC f g d Fragments that hit (align well to) contig Two mates f,g are happy,, if they are facing each other and d ~ library mean +/- 3*stddev They are unhappy,, if the distance is not correct, or if they are not facing each other
21 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Unitigger Scaffolder RepeatRez Consensus
22 Whole Genome Shotgun Assembly Pipeline Screener Mask contaminants and known repeats Overlapper Unitigger Scaffolder RepeatRez Consensus Repeats in the human genome: Microsatellites of the form x n where x is 3-6bp long and n is very large with 1-2% variation (telomeric and centromeric regions) 1 million Alus of length ~300 with 5-15% variation LINE repeats of length ~ kbp-long RNA pseudo-gene arrays in tandem number of kbp genome duplications many genes are present in multiple copies Repeats make up to 30% of the whole genome
23 Whole Genome Shotgun Assembly Pipeline Screener Find all overlaps 40bp allowing 6% mismatch. (1000X Blast) Overlapper Unitigger True vs repeat-induced overlaps: A B implies true A Scaffolder or B RepeatRez repeat - induced A repeat repeat B Consensus Use breakpoint detection to identify repeat induced overlaps
24 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Assembler Core Compute all consistent sub-assemblies = unitigs Identify those that cover unique DNA = U-unitigs Unitigger Scaffolder Uniquely Assemble-able Contig RepeatRez Consensus Typically we see a 30-fold reduction in pieces and a 100-fold reduction in overlaps.
25 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Assembler Core Compute all consistent sub-assemblies = unitigs Identify those that cover unique DNA = U-unitigs Unitigger Scaffolder RepeatRez Consensus
26 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Unitigger Scaffolder Build Scaffolds Mated reads unitigs Two unitigs are scaffolded if they are joined by at least 2 mates and if one is a U-unitig (error probability is 1 in ) scaffold RepeatRez Consensus Sequence gaps or repeat gaps (with estimated distances)
27 Whole Genome Shotgun Assembly Pipeline Screener Build Scaffolds Overlapper Unitigger Scaffolder RepeatRez Consensus
28 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Fill Gaps using Rocks,, Stones and Pebbles Unitigger Scaffolder U-unitig Unitig>0 U-unitig RepeatRez 2 confirming mates or 3 confirming, 1 conflicting Consensus
29 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Fill Gaps using Rocks,, Stones and Pebbles Unitigger Scaffolder RepeatRez Consensus
30 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Fill Gaps using Rocks, Stones and Pebbles Unitigger Scaffolder RepeatRez Stones Confirming o-path Anchored mate and confirming overlap path Consensus
31 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Fill Gaps using Rocks, Stones and Pebbles Unitigger Scaffolder RepeatRez Consensus
32 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Fill Gaps using Rocks, Stones and Pebbles Unitigger Scaffolder RepeatRez Greedy overlap path between placed pieces, using quality values for scoring. Consensus
33 Whole Genome Shotgun Assembly Pipeline Screener Overlapper Fill Gaps using Rocks, Stones and Pebbles Unitigger Scaffolder RepeatRez Consensus
34 Whole Genome Shotgun Assembly Pipeline Screener Repeat Rez: 50kbp gap illustration Overlapper Unitigger Scaffolder RepeatRez Consensus
35 Whole Genome Shotgun Assembly Pipeline Screener Compute consensus sequence from fragments (using Baysian SNP consensus) Overlapper Unitigger Scaffolder RepeatRez Consensus
36 Whole Genome Shotgun of Drosophila Screener 8:37 Input: Overlapper Unitigger Scaffolder RepeatRez Consensus 86:25 38:29 4:12 30:53 25:00 Total: ~180 CPU hours millions Celera reads in 2kbp and 10kbp pairs 12,152 BAC-end pairs from the BDGP & EDGP Output: 114mbp of sequence Published in Science (March 2000)
37 Scaffold Lengths for Drosophila 25 scaffolds >100kbp (total = 116mbp) 813 mini-scafs: hetero-chromatic, breakup, gap-filling 97.7% contiguous
38 Contig Lengths for Drosophila There are 312 contigs >100kbp totalling 81mbp Mean contig length is 50kbp
39 Gap Lengths for Drosophila 3.04 Mbp in 1645 intra-scaffold gaps Only 13 larger than 10kbp Only 455 larger than 1kbp 75% are less than 1kbp long ~ 75% are sequencing gaps
40 Validation Against STS-map for Drosophila 50 scaffolds (114.8mbp) were aligned against the BDGP STS- content map All scaffolds spanning 2 or more STSs were checked for order discrepancies. 17 STS sites out of 2167 (.78%) were out of order, well within the estimated error rate of the STS map. 9 have been determined to be incorrect. BDGP STS Order 4 3L 3R 2L 2R X Celera Scaffold and STS Order
41 Whole Genome Shotgun of Human Incremental design Distributed design End-to-end run takes > CPU hours High water-mark of 32 GB of main memory
42 Compartmentalized Assembler ( Best of Both Worlds ) Interim approach using both the public clone-by-clone and Celera s WGS data Produces good results earlier than either individual strategy Started in January (2-5 people) Reuse of WGS code base (in C) Use of LEDA (C++)
43 Compartmentalized Assembler HGP BAC data 1 million contigs, average 4kbp clone-by-clone contigs fragment recruiter WGS mate pairs Celera data 28 million fragments, 70% in pairs Run overlapper on contigs and fragments, looking for good overlap alignments ( hits ) Assemble unfinished BAC clones using fragment matepairs Curate tiling path and assemble components of tiling recruited frags frag-contig hits combine assembler BAC clone assemblies Tiling and WGA on components Unrecruited fragments WGS assembler additional assemblies 5 million fragments
44 Combining Assembly Given a collection of contigs and a collection of recruited fragments Use mate-pair information to order and orient contigs into scaffolds Use mates to extend and merge contigs Main application: Phase-1/2 BAC data
45 Path-Merging Algorithm Mate links Contigs of BAC clone a:5000 c:5000 e:5000 d:1000 b:1000 Fragments that hit contigs
46 Path-Merging Algorithm Mate links m:500,w:2 Contigs of BAC clone a:5000 c:5000 e:5000 d:1000 b:1000 Bundle mate links Fragments that hit contigs
47 Path-Merging Algorithm Mate links a:5000 c:5000 e:5000 d:1000 b:1000 m:2000,w:4 m:500,w:2 Contigs of BAC clone Bundle mate links Fragments that hit contigs
48 Path-Merging Algorithm m:500,w:2 Mate links a:5000 c:5000 m:2000,w:4 m:2000,w:4 Contigs of BAC clone c:5000 e:5000 d:1000 b:1000 Bundle mate links Fragments that hit contigs
49 Path-Merging Algorithm m:500,w:2 Mate links a:5000 c:5000 m:2000,w:4 m:2000,w:4 Contigs of BAC clone c:5000 e:5000 d:1000 b:1000 m:500,w:2 Fragments that hit contigs Bundle mate links
50 Path-Merging Algorithm m:500,w:2 Mate links a:5000 c:5000 m:2000,w:4 m:2000,w:4 Contigs of BAC clone c:5000 e:5000 d:1000 b:1000 m:500,w:2 Fragments that hit contigs Bundle mate links m:500,w:2
51 Path-Merging Algorithm m:500,w:2 a:5000 c:5000 m:2000,w:4 m:2000,w:4 c:5000 e:5000 d:1000 b:1000 m:500,w:2 Bundle mate links m:500,w:2 Merge fragments into contigs
52 Path-Merging Algorithm m:500,w:2 a:5000 c:5000 m:2000,w:4 m:2000,w:4 c:5000 e:5000 d:1000 b:1000 m:500,w:2 Bundle mate links m:500,w:2 Merge fragments into contigs Greedily consider mate edges
53 Path-Merging Algorithm m:500,w:2 a:5000 c:5000 m:2000,w:4 b:1000 e:5000 d:1000 m:2000,w:4 m:500,w:2 Bundle mate links Merge fragments into contigs Greedily consider mate edges m:500,w:2
54 Path-Merging Algorithm m:500,w:2 a:5000 c:5000 m:2000,w:4 b:1000 e:5000 d:1000 m:2000,w:4 Bundle mate links m:500,w:2 Merge fragments into contigs Greedily consider mate edges m:500,w:2 m:500,w:2
55 Path-Merging Algorithm m:500,w:2 b:1000 a:5000 c:5000 m:2000,w:4 e:5000 d:1000 m:500,w:2 m:2000,w:4 m:500,w:2 Bundle mate links Merge fragments into contigs Greedily consider mate edges m:500,w:2
56 Path-Merging Algorithm m:500,w:2 a:5000 b:1000 c:5000 m:2000,w:4 e:5000 d:1000 m:500,w:2 m:2000,w:4 m:500,w:2 Bundle mate links Merge fragments into contigs Greedily consider mate edges
57 Path-Merging Algorithm a:5000 m:500,w:2 b:1000 m:500,w:2 c:5000 d:1000 e:5000 m:500,w:2 m:2000,w:4 m:2000,w:4 Bundle mate links Merge fragments into contigs Greedily consider mate edges Scaffolds are represented by yellow-blue paths in contig graph
58 Path-Merging Algorithm Bundle mate links Merge fragments into contigs Greedily consider mate edges Final result is scaffolding of contigs
59 Path-Merging Algorithm: Interleaving
60 Path-Merging Algorithm: Interleaving u w 0 e 0 v 0
61 Path-Merging Algorithm: Interleaving u e 0 w 0 v 0
62 Path-Merging Algorithm: Interleaving u w 1 v 0 e 0 w 0 v 1 e 1
63 Path-Merging Algorithm: Interleaving u w 1 v 1 e 1
64 Path-Merging Algorithm: Interleaving u e w 1 1 v 1
65 Path-Merging Algorithm: Interleaving u w 2 e 1 w 1 e 2 v 1 v 2
66 Path-Merging Algorithm: Interleaving u w 2 e 2 v 2
67 Path-Merging Algorithm: Interleaving u e 2 w 2 v 2
68 Path-Merging Algorithm: Interleaving u e 2 v 2 w 2
69 Path-Merging Algorithm: Interleaving u
70 Path-Merging Algorithm: Interleaving w 0 e 0 v 0
71 Path-Merging Algorithm: Interleaving w 0 e 0 v 0
72 Path-Merging Algorithm: Interleaving w 1 e 1 v 1 w 0 e 0 v 0
73 Path-Merging Algorithm: Interleaving w 1 e 1 v 1
74 Path-Merging Algorithm: Interleaving w 1 e 1 v 1
75 Path-Merging Algorithm: Interleaving e 1 w 1 v 1
76 Path-Merging Algorithm: Interleaving
77 When Do We Accept a Merge? Increase happy mates > increase unhappy mates f g Fragments that hit (align well to) contig d Two mates f,g are happy,, if they are facing each other and d ~ library mean +/- 3*stddev
78 When Do We Accept a Merger? All induced must -overlaps are found Increase in happiness must be larger than increase in unhappiness,, I.e.: H(P)-H(P 1 )+H(P 2 ) > U(P)-U(P 1 )-U(P 2 ) This can be based on: bundled mate edges, or individual mates.
79 Mate-Pair Based Contig Ordering Problem The problem Does scaffolding exist that has at least t happy mates? is NP-complete Reduction of BANDWIDTH (D.H.H., Knut Reinert and Gene Myers, RECOMB 01)
80 Tiling Graph Definition: Nodes are all public BACs and all unrecruited scaffolds Two nodes are connected by an edge if they represent overlapping or adjacent sequence Manually curate the graph to distinguish between true edges and repeat induced ones or ones caused by chimeric BACs
81 Tiling Graph
82 Compartmentalized Assembly 8.5mbp scaffold on chromosome 20
83 Reassembly of Chromosomes Input: Sequence of whole chromosome e.g. as assembled by Celera e.g. as assembled by Haussler ( golden path ) Recruit fragments (whole genome takes weeks on compute farm) Reassemble chromosome using path merging algorithm (whole genome: overnight on 16 processor machine)
84 Results The Sequence of the Human Genome is in press. J.Craig Venter et al. (approx. 280 authors)
85 Assembly Comparison: Chromosome 15 Publicly funded Project Fragment-based dot plot Celera Assembly
86 Assembly Comparison: Chromosome 15 Publicly funded Project 0 Celera Assembly 80 Mbp Fragment-based rearrangement plot
87 Assembly Comparison: Chromosome Mbp Mate pair coverage
88 Assembly Comparison: Chromosome Mbp Unhappy coverage
89 Assembly Comparison: Chromosome Mbp Unhappy coverage
90 Assembly Comparison: Chromosome Mbp Breakpoint detection
91 Assembly Comparison: Chromosome Mbp Close up
92 Chromosome Mbp
93 Chromosome Mbp
94 Assembly Comparison: Chromosome Mbp
95 Assembly Comparison: Chromosome Mbp Finished sequence
96 The Mouse as a Comparative Model Celera is currently sequencing mouse (4.5x coverage) Syntenic humanized-mousemouse WGS mouse Will reveal many genes not detectable by standard methods
97 PAX 6 affects eye development The fly is blind... The mouse is blind... The child is blind.
98 Sequencing the genome is just the beginning... DNA RNA Proteins Modified Proteins Biological Function Transcription Translation Post-Translation Modification 20-80,000 Genes > 1,000,000 Proteins
99 Credits WGS Team Regional Team Map Team Granger Sutton, Randy Bolanos, Ian Dew, Art Delcher, Michael Flanigan, Dan Fasulo, Saul Kravitz, Clark Mobarry, Knut Reinert, Karin Remington, Gene Myers Daniel Huson, Knut Reinert, Aaron Halpern, Saul Kravitz, Karin Remington, Art Delcher, Gene Myers Qing Zhang, Ellen Beasley, Rhonda Brandon, Lin Chen, Pat Dunn, Aaron Halpern, Zhongwu Lai, Yong Liang, Deborah Nusskern, Ming Zhan, Holly Zheng and many others...
100
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