DNA Sequencing

CS262 Lecture 9, Win07, Batzoglou DNA sequencing How we obtain the sequence of nucleotides of a species …ACGTGACTGAGGACCGTG CGACTGAGACTGACTGGGT CTAGCTAGACTACGTTTTA TATATATATACGTCGTCGT ACTGATGACTAGATTACAG ACTGATTTAGATACCTGAC TGATTTTAAAAAAATATT…

CS262 Lecture 9, Win07, Batzoglou Which representative of the species? Which human? Answer one: Answer two: it doesn’t matter Polymorphism rate: number of letter changes between two different members of a species Humans: ~1/1,000 Other organisms have much higher polymorphism rates  Population size!

CS262 Lecture 9, Win07, Batzoglou

Human population migrations Out of Africa, Replacement  Single mother of all humans (Eve) ~150,000yr  Single father of all humans (Adam) ~70,000yr  Humans out of Africa ~40000 years ago replaced others (e.g., Neandertals)  Evidence: mtDNA Multiregional Evolution  Fossil records show a continuous change of morphological features  Proponents of the theory doubt mtDNA and other genetic evidence

CS262 Lecture 9, Win07, Batzoglou Why humans are so similar A small population that interbred reduced the genetic variation Out of Africa ~ 40,000 years ago Out of Africa H = 4Nu/(1 + 4Nu)

CS262 Lecture 9, Win07, Batzoglou Migration of human variation

CS262 Lecture 9, Win07, Batzoglou Migration of human variation

CS262 Lecture 9, Win07, Batzoglou Migration of human variation

CS262 Lecture 9, Win07, Batzoglou Human variation in Y chromosome

CS262 Lecture 9, Win07, Batzoglou

DNA Sequencing – Overview Gel electrophoresis  Predominant, old technology by F. Sanger Whole genome strategies  Physical mapping  Walking  Shotgun sequencing Computational fragment assembly The future—new sequencing technologies  Pyrosequencing, single molecule methods, …  Assembly techniques Future variants of sequencing  Resequencing of humans Resequencing of humans  Microbial and environmental sequencing Microbial and environmental sequencing  Cancer genome sequencing Cancer genome sequencing

CS262 Lecture 9, Win07, Batzoglou DNA Sequencing Goal: Find the complete sequence of A, C, G, T’s in DNA Challenge: There is no machine that takes long DNA as an input, and gives the complete sequence as output Can only sequence ~500 letters at a time

CS262 Lecture 9, Win07, Batzoglou DNA Sequencing – vectors + = DNA Shake DNA fragments Vector Circular genome (bacterium, plasmid) Known location (restriction site)

CS262 Lecture 9, Win07, Batzoglou Different types of vectors VECTORSize of insert Plasmid 2,000-10,000 Can control the size Cosmid40,000 BAC (Bacterial Artificial Chromosome) 70, ,000 YAC (Yeast Artificial Chromosome) > 300,000 Not used much recently

CS262 Lecture 9, Win07, Batzoglou DNA Sequencing – gel electrophoresis 1.Start at primer(restriction site) 2.Grow DNA chain 3.Include dideoxynucleoside (modified a, c, g, t) 4.Stops reaction at all possible points 5.Separate products with length, using gel electrophoresis

CS262 Lecture 9, Win07, Batzoglou Pyrosequencing / Image credits: 454 Life Sciences

CS262 Lecture 9, Win07, Batzoglou Solexa / ABI SOLiD 19 Image credits: Illumina, Applied Biosystems

CS262 Lecture 9, Win07, Batzoglou Illumina / Affymetrix genotyping 20 Image credits: Illumina, Affymetrix

CS262 Lecture 9, Win07, Batzoglou $10.00 $1.00 $0.10 $0.01 Cost per finished base: DNA sequencing’s Moore’s law

CS262 Lecture 9, Win07, Batzoglou Human Genome Project (1990 – 2003) $3 billion3 billion bases ,000 billion bases ,000 billion bases ,700,000 billion bases Growth of DNA Sequencing Capacity

CS262 Lecture 9, Win07, Batzoglou 100 million species Each individual has different DNA Within individual, some cells have different DNA (i.e. cancer) Sequencing Applications

CS262 Lecture 9, Win07, Batzoglou What genes are on/off, when, and in which cells? Where do molecules bind to DNA? Sequencing Applications

CS262 Lecture 9, Win07, Batzoglou Comparison TechnologyRead length (bp)Pairingbp / $de novo Sanger1,000long-range1,000yes short-range10,000yes Solexa/ABI30-120short-range200,000maybe SNP chips1no5,000no 25 ApplicationSanger454Solexa/ABI SNP chips Bacterial sequencing  probably Mammalian sequencing ?probably not Mammalian resequencing expensive Genotypingexpensive

CS262 Lecture 9, Win07, Batzoglou Method to sequence longer regions cut many times at random (Shotgun) genomic segment Get one or two reads from each segment ~500 bp

CS262 Lecture 9, Win07, Batzoglou Reconstructing the Sequence (Fragment Assembly) Cover region with ~7-fold redundancy (7X) Overlap reads and extend to reconstruct the original genomic region reads

CS262 Lecture 9, Win07, Batzoglou Definition of Coverage Length of genomic segment:L Number of reads: n Length of each read:l Definition: Coverage C = n l / L How much coverage is enough? Lander-Waterman model: Assuming uniform distribution of reads, C=10 results in 1 gapped region /1,000,000 nucleotides C

CS262 Lecture 9, Win07, Batzoglou Repeats Bacterial genomes:5% Mammals:50% Repeat types: Low-Complexity DNA (e.g. ATATATATACATA…) Microsatellite repeats (a 1 …a k ) N where k ~ 3-6 (e.g. CAGCAGTAGCAGCACCAG) Transposons  SINE (Short Interspersed Nuclear Elements) e.g., ALU: ~300-long, 10 6 copies  LINE (Long Interspersed Nuclear Elements) ~4000-long, 200,000 copies  LTR retroposons (Long Terminal Repeats (~700 bp) at each end) cousins of HIV Gene Families genes duplicate & then diverge (paralogs) Recent duplications ~100,000-long, very similar copies

CS262 Lecture 9, Win07, Batzoglou Sequencing and Fragment Assembly AGTAGCACAGA CTACGACGAGA CGATCGTGCGA GCGACGGCGTA GTGTGCTGTAC TGTCGTGTGTG TGTACTCTCCT 3x10 9 nucleotides 50% of human DNA is composed of repeats Error! Glued together two distant regions

CS262 Lecture 9, Win07, Batzoglou What can we do about repeats? Two main approaches: Cluster the reads Link the reads

CS262 Lecture 9, Win07, Batzoglou What can we do about repeats? Two main approaches: Cluster the reads Link the reads

CS262 Lecture 9, Win07, Batzoglou What can we do about repeats? Two main approaches: Cluster the reads Link the reads

CS262 Lecture 9, Win07, Batzoglou Sequencing and Fragment Assembly AGTAGCACAGA CTACGACGAGA CGATCGTGCGA GCGACGGCGTA GTGTGCTGTAC TGTCGTGTGTG TGTACTCTCCT 3x10 9 nucleotides C R D ARB, CRD or ARD, CRB ? ARB

CS262 Lecture 9, Win07, Batzoglou Sequencing and Fragment Assembly AGTAGCACAGA CTACGACGAGA CGATCGTGCGA GCGACGGCGTA GTGTGCTGTAC TGTCGTGTGTG TGTACTCTCCT 3x10 9 nucleotides

CS262 Lecture 9, Win07, Batzoglou Strategies for whole-genome sequencing 1.Hierarchical – Clone-by-clone i.Break genome into many long pieces ii.Map each long piece onto the genome iii.Sequence each piece with shotgun Example: Yeast, Worm, Human, Rat 2.Online version of (1) – Walking i.Break genome into many long pieces ii.Start sequencing each piece with shotgun iii.Construct map as you go Example: Rice genome 3.Whole genome shotgun One large shotgun pass on the whole genome Example: Drosophila, Human (Celera), Neurospora, Mouse, Rat, Dog

CS262 Lecture 9, Win07, Batzoglou Hierarchical Sequencing

CS262 Lecture 9, Win07, Batzoglou Hierarchical Sequencing Strategy 1.Obtain a large collection of BAC clones 2.Map them onto the genome (Physical Mapping) 3.Select a minimum tiling path 4.Sequence each clone in the path with shotgun 5.Assemble 6.Put everything together a BAC clone map genome

CS262 Lecture 9, Win07, Batzoglou Methods of physical mapping Goal: Make a map of the locations of each clone relative to one another Use the map to select a minimal set of clones to sequence Methods: Hybridization Digestion

CS262 Lecture 9, Win07, Batzoglou 1. Hybridization Short words, the probes, attach to complementary words 1.Construct many probes 2.Treat each BAC with all probes 3.Record which ones attach to it 4.Same words attaching to BACS X, Y  overlap p1p1 pnpn

CS262 Lecture 9, Win07, Batzoglou 2.Digestion Restriction enzymes cut DNA where specific words appear 1.Cut each clone separately with an enzyme 2.Run fragments on a gel and measure length 3.Clones C a, C b have fragments of length { l i, l j, l k }  overlap Double digestion: Cut with enzyme A, enzyme B, then enzymes A + B

CS262 Lecture 9, Win07, Batzoglou Some Terminology insert a fragment that was incorporated in a circular genome, and can be copied (cloned) vector the circular genome (host) that incorporated the fragment BAC Bacterial Artificial Chromosome, a type of insert–vector combination, typically of length kb read a long word that comes out of a sequencing machine coverage the average number of reads (or inserts) that cover a position in the target DNA piece shotgun the process of obtaining many reads sequencing from random locations in DNA, to detect overlaps and assemble

CS262 Lecture 9, Win07, Batzoglou Whole Genome Shotgun Sequencing cut many times at random genome forward-reverse paired reads plasmids (2 – 10 Kbp) cosmids (40 Kbp) known dist ~500 bp

CS262 Lecture 9, Win07, Batzoglou Fragment Assembly (in whole-genome shotgun sequencing)

CS262 Lecture 9, Win07, Batzoglou Fragment Assembly Given N reads… Where N ~ 30 million… We need to use a linear-time algorithm

CS262 Lecture 9, Win07, Batzoglou Steps to Assemble a Genome 1. Find overlapping reads 4. Derive consensus sequence..ACGATTACAATAGGTT.. 2. Merge some “good” pairs of reads into longer contigs 3. Link contigs to form supercontigs Some Terminology read a long word that comes out of sequencer mate pair a pair of reads from two ends of the same insert fragment contig a contiguous sequence formed by several overlapping reads with no gaps supercontig an ordered and oriented set (scaffold) of contigs, usually by mate pairs consensus sequence derived from the sequene multiple alignment of reads in a contig

CS262 Lecture 9, Win07, Batzoglou 1. Find Overlapping Reads aaactgcagtacggatct aaactgcag aactgcagt … gtacggatct tacggatct gggcccaaactgcagtac gggcccaaa ggcccaaac … actgcagta ctgcagtac gtacggatctactacaca gtacggatc tacggatct … ctactacac tactacaca (read, pos., word, orient.) aaactgcag aactgcagt actgcagta … gtacggatc tacggatct gggcccaaa ggcccaaac gcccaaact … actgcagta ctgcagtac gtacggatc tacggatct acggatcta … ctactacac tactacaca (word, read, orient., pos.) aaactgcag aactgcagt acggatcta actgcagta cccaaactg cggatctac ctactacac ctgcagtac gcccaaact ggcccaaac gggcccaaa gtacggatc tacggatct tactacaca

CS262 Lecture 9, Win07, Batzoglou 1. Find Overlapping Reads Find pairs of reads sharing a k-mer, k ~ 24 Extend to full alignment – throw away if not >98% similar TAGATTACACAGATTAC ||||||||||||||||| T GA TAGA | || TACA TAGT || Caveat: repeats  A k-mer that occurs N times, causes O(N 2 ) read/read comparisons  ALU k-mers could cause up to 1,000,000 2 comparisons Solution:  Discard all k-mers that occur “ too often ” Set cutoff to balance sensitivity/speed tradeoff, according to genome at hand and computing resources available

CS262 Lecture 9, Win07, Batzoglou 1. Find Overlapping Reads Create local multiple alignments from the overlapping reads TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA

CS262 Lecture 9, Win07, Batzoglou 1. Find Overlapping Reads Correct errors using multiple alignment TAGATTACACAGATTACTGA TAGATTACACAGATTATTGA TAGATTACACAGATTACTGA TAG-TTACACAGATTACTGA TAGATTACACAGATTACTGA TAG-TTACACAGATTATTGA TAGATTACACAGATTACTGA TAG-TTACACAGATTATTGA insert A replace T with C correlated errors— probably caused by repeats  disentangle overlaps TAGATTACACAGATTACTGA TAG-TTACACAGATTATTGA TAGATTACACAGATTACTGA TAG-TTACACAGATTATTGA In practice, error correction removes up to 98% of the errors

CS262 Lecture 9, Win07, Batzoglou 2. Merge Reads into Contigs Overlap graph:  Nodes: reads r 1 …..r n  Edges: overlaps (r i, r j, shift, orientation, score) Note: of course, we don’t know the “color” of these nodes Reads that come from two regions of the genome (blue and red) that contain the same repeat

CS262 Lecture 9, Win07, Batzoglou 2. Merge Reads into Contigs We want to merge reads up to potential repeat boundaries repeat region Unique Contig Overcollapsed Contig

CS262 Lecture 9, Win07, Batzoglou 2. Merge Reads into Contigs Ignore non-maximal reads Merge only maximal reads into contigs repeat region

CS262 Lecture 9, Win07, Batzoglou 2. Merge Reads into Contigs Remove transitively inferable overlaps  If read r overlaps to the right reads r 1, r 2, and r 1 overlaps r 2, then (r, r 2 ) can be inferred by (r, r 1 ) and (r 1, r 2 ) rr1r1 r2r2 r3r3

CS262 Lecture 9, Win07, Batzoglou 2. Merge Reads into Contigs

CS262 Lecture 9, Win07, Batzoglou 2. Merge Reads into Contigs Ignore “hanging” reads, when detecting repeat boundaries sequencing error repeat boundary??? b a a b …

CS262 Lecture 9, Win07, Batzoglou Overlap graph after forming contigs Unitigs: Gene Myers, 95

CS262 Lecture 9, Win07, Batzoglou Repeats, errors, and contig lengths Repeats shorter than read length are easily resolved  Read that spans across a repeat disambiguates order of flanking regions Repeats with more base pair diffs than sequencing error rate are OK  We throw overlaps between two reads in different copies of the repeat To make the genome appear less repetitive, try to:  Increase read length  Decrease sequencing error rate Role of error correction: Discards up to 98% of single-letter sequencing errors decreases error rate  decreases effective repeat content  increases contig length

CS262 Lecture 9, Win07, Batzoglou Insert non-maximal reads whenever unambiguous 2. Merge Reads into Contigs

CS262 Lecture 9, Win07, Batzoglou 3. Link Contigs into Supercontigs Too dense  Overcollapsed Inconsistent links  Overcollapsed? Normal density

CS262 Lecture 9, Win07, Batzoglou Find all links between unique contigs 3. Link Contigs into Supercontigs Connect contigs incrementally, if  2 links supercontig (aka scaffold)

CS262 Lecture 9, Win07, Batzoglou Fill gaps in supercontigs with paths of repeat contigs 3. Link Contigs into Supercontigs

CS262 Lecture 9, Win07, Batzoglou 4. Derive Consensus Sequence Derive multiple alignment from pairwise read alignments TAGATTACACAGATTACTGA TTGATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAAACTA TAG TTACACAGATTATTGACTTCATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAA CTA TAGATTACACAGATTACTGACTTGATGGGGTAA CTA TAGATTACACAGATTACTGACTTGATGGCGTAA CTA Derive each consensus base by weighted voting (Alternative: take maximum-quality letter)

CS262 Lecture 9, Win07, Batzoglou Some Assemblers PHRAP Early assembler, widely used, good model of read errors Overlap O(n 2 )  layout (no mate pairs)  consensus Celera First assembler to handle large genomes (fly, human, mouse) Overlap  layout  consensus Arachne Public assembler (mouse, several fungi) Overlap  layout  consensus Phusion Overlap  clustering  PHRAP  assemblage  consensus Euler Indexing  Euler graph  layout by picking paths  consensus

CS262 Lecture 9, Win07, Batzoglou Quality of assemblies Celera’s assemblies of human and mouse

CS262 Lecture 9, Win07, Batzoglou Quality of assemblies—mouse

CS262 Lecture 9, Win07, Batzoglou Quality of assemblies—mouse N50 contig length Terminology: N50 contig length If we sort contigs from largest to smallest, and start Covering the genome in that order, N50 is the length Of the contig that just covers the 50 th percentile.

CS262 Lecture 9, Win07, Batzoglou Quality of assemblies—rat

CS262 Lecture 9, Win07, Batzoglou History of WGA 1982: -virus, 48,502 bp 1995: h-influenzae, 1 Mbp 2000: fly, 100 Mbp 2001 – present  human (3Gbp), mouse (2.5Gbp), rat *, chicken, dog, chimpanzee, several fungal genomes Gene Myers Let’s sequence the human genome with the shotgun strategy That is impossible, and a bad idea anyway Phil Green 1997

CS262 Lecture 9, Win07, Batzoglou Genomes Sequenced