DNA Sequencing

DNA sequencing How we obtain the sequence of nucleotides of a species …ACGTGACTGAGGACCGTG CGACTGAGACTGACTGGGT CTAGCTAGACTACGTTTTA TATATATATACGTCGTCGT ACTGATGACTAGATTACAG ACTGATTTAGATACCTGAC TGATTTTAAAAAAATATT…

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! Just beneath the surface of the ocean floats a tiny egg. Within it's hull of follicle cells the embryo of Ciona, a sea squirt or Ascidian, is beginning to develop. The shape of the egg does not give any clue of the creature that it is going to be. Now about a third of a millimeter it is waiting to become a larva, and this larva is a rather surprising creature. The larva is developing within a few weeks. A head and a tail are evolving. When you look carefully you can see a rod that stiffens the tail. We know such a device as a notochord. A trained zoologist would identify the animal as belonging to the Phylum Chordata, and that is the same group we humans belong to. (See footnote). Also the internal organs are beginning to show. But the image is a bit deceiving. What seems to be an eye is in fact a device for equilibrium called the 'Otolith'. Above it we find the light sense organ, the 'Ocellus' So there is something suspicious about these so-called sea squirts. Freed from it's shell a familiar form has appeared. Clearly the resemblance with a tadpole larva is seen. The little creature swims for a few hours to find a good spot somewhere on a solid surface. Then a surprising thing happens. This larva is not going to evolve into a fish, amphibian or anything like that. With the front of it's head it attaches itself to a surface. Within minutes resorption of the larva tail commences and the sea squirt will stay on that same spot for all it's life

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

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)

Migration of human variation http://info.med.yale.edu/genetics/kkidd/point.html

Migration of human variation http://info.med.yale.edu/genetics/kkidd/point.html

Migration of human variation http://info.med.yale.edu/genetics/kkidd/point.html

Human variation in Y chromosome

DNA Sequencing – Overview 1975 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 Microbial and environmental sequencing Cancer genome sequencing 2015

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

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

Different types of vectors Size of insert Plasmid 2,000-10,000 Can control the size Cosmid 40,000 BAC (Bacterial Artificial Chromosome) 70,000-300,000 YAC (Yeast Artificial Chromosome) > 300,000 Not used much recently

DNA Sequencing – gel electrophoresis Start at primer (restriction site) Grow DNA chain Include dideoxynucleoside (modified a, c, g, t) Stops reaction at all possible points Separate products with length, using gel electrophoresis

Electrophoresis diagrams

Challenging to read answer

Challenging to read answer

Challenging to read answer

Reading an electropherogram Filtering Smoothening Correction for length compressions A method for calling the letters – PHRED PHRED – PHil’s Read EDitor (by Phil Green) Several better methods exist, but labs are reluctant to change

Output of PHRED: a read A read: 500-1000 nucleotides A C G A A T C A G …A 16 18 21 23 25 15 28 30 32 …21 Quality scores: -10log10Prob(Error) Reads can be obtained from leftmost, rightmost ends of the insert Double-barreled sequencing: (1990) Both leftmost & rightmost ends are sequenced, reads are paired

Pyrosequencing / 454 Image credits: 454 Life Sciences

Solexa / ABI SOLiD Image credits: Illumina, Applied Biosystems

Illumina / Affymetrix genotyping Image credits: Illumina, Affymetrix

Other technologies in development

Mammalian resequencing Comparison Technology Read length (bp) Pairing bp / $ de novo Sanger 1,000 long-range yes 454 250 short-range 10,000 Solexa/ABI 30 100,000 maybe SNP chips 1 no 5,000 Application Sanger 454 Solexa/ABI SNP chips Bacterial sequencing   probably Mammalian sequencing ? probably not Mammalian resequencing expensive Genotyping

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

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

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

Repeats Bacterial genomes: 5% Mammals: 50% Repeat types: Low-Complexity DNA (e.g. ATATATATACATA…) Microsatellite repeats (a1…ak)N where k ~ 3-6 (e.g. CAGCAGTAGCAGCACCAG) Transposons SINE (Short Interspersed Nuclear Elements) e.g., ALU: ~300-long, 106 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

Sequencing and Fragment Assembly AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT 3x109 nucleotides 50% of human DNA is composed of repeats Error! Glued together two distant regions

What can we do about repeats? Two main approaches: Cluster the reads Link the reads

What can we do about repeats? Two main approaches: Cluster the reads Link the reads

What can we do about repeats? Two main approaches: Cluster the reads Link the reads

Sequencing and Fragment Assembly AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT 3x109 nucleotides A R B ARB, CRD or ARD, CRB ? C R D

Sequencing and Fragment Assembly AGTAGCACAGACTACGACGAGACGATCGTGCGAGCGACGGCGTAGTGTGCTGTACTGTCGTGTGTGTGTACTCTCCT 3x109 nucleotides

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

Hierarchical Sequencing

Hierarchical Sequencing Strategy a BAC clone map genome Obtain a large collection of BAC clones Map them onto the genome (Physical Mapping) Select a minimum tiling path Sequence each clone in the path with shotgun Assemble Put everything together

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

1. Hybridization p1 pn Short words, the probes, attach to complementary words Construct many probes Treat each BAC with all probes Record which ones attach to it Same words attaching to BACS X, Y  overlap

Hybridization – Computational Challenge p1 p2 …………………….pm Matrix: m probes  n clones (i, j): 1, if pi hybridizes to Cj 0, otherwise Definition: Consecutive ones matrix 1s are consecutive in each row & col Computational problem: Reorder the probes so that matrix is in consecutive-ones form Can be solved in O(m3) time (m > n) 0 0 1 …………………..1 C1 C2 ……………….Cn 1 1 0 …………………..0 1 0 1…………………...0 pi1pi2…………………….pim 1 1 1 0 0 0……………..0 0 1 1 1 1 1……………..0 0 0 1 1 1 0……………..0 Cj1Cj2 ……………….Cjn 0 0 0 0 0 0………1 1 1 0 0 0 0 0 0 0………0 1 1 1

Hybridization – Computational Challenge pi1pi2………………………………….pim pi1pi2…………………….pim 1 1 1 0 0 0……………..0 0 1 1 1 1 1……………..0 0 0 1 1 1 0……………..0 Cj1Cj2 ……………….Cjn Cj1Cj2 ……………….Cjn 0 0 0 0 0 0………1 1 1 0 0 0 0 0 0 0………0 1 1 1 If we put the matrix in consecutive-ones form, then we can deduce the order of the clones & which pairs of clones overlap

Hybridization – Computational Challenge p1 p2 …………………….pm Additional challenge: A probe (short word) can hybridize in many places in the genome Computational Problem: Find the order of probes that implies the minimal probe repetition Equivalent: find the shortest string of probes such that each clone appears as a substring APX-hard Solutions: Greedy, probabilistic, lots of manual curation 0 0 1 …………………..1 C1 C2 ……………….Cn 1 1 0 …………………..0 1 0 1…………………...0

2. Digestion Restriction enzymes cut DNA where specific words appear Cut each clone separately with an enzyme Run fragments on a gel and measure length Clones Ca, Cb have fragments of length { li, lj, lk }  overlap Double digestion: Cut with enzyme A, enzyme B, then enzymes A + B

Online Clone-by-clone The Walking Method

The Walking Method Build a very redundant library of BACs with sequenced clone-ends (cheap to build) Sequence some “seed” clones “Walk” from seeds using clone-ends to pick library clones that extend left & right

Walking: An Example

Advantages & Disadvantages of Hierarchical Sequencing ADV. Easy assembly DIS. Build library & physical map; redundant sequencing Whole Genome Shotgun (WGS) ADV. No mapping, no redundant sequencing DIS. Difficult to assemble and resolve repeats The Walking method – motivation Sequence the genome clone-by-clone without a physical map The only costs involved are: Library of end-sequenced clones (cheap) Sequencing

Walking off a Single Seed Low redundant sequencing Many sequential steps

Walking off a single clone is impractical Cycle time to process one clone: 1-2 months Grow clone Prepare & Shear DNA Prepare shotgun library & perform shotgun Assemble in a computer Close remaining gaps A mammalian genome would need 15,000 walking steps !

Walking off several seeds in parallel Efficient Inefficient Few sequential steps Additional redundant sequencing In general, can sequence a genome in ~5 walking steps, with <20% redundant sequencing

Using Two Libraries Most inefficiency comes from closing a small gap with a much larger clone Solution: Use a second library of small clones

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 100-200 kb read a 500-900 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

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

Fragment Assembly (in whole-genome shotgun sequencing)

We need to use a linear-time algorithm Fragment Assembly Given N reads… Where N ~ 30 million… We need to use a linear-time algorithm

Steps to Assemble a Genome Some Terminology read a 500-900 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 1. Find overlapping reads 2. Merge some “good” pairs of reads into longer contigs 3. Link contigs to form supercontigs 4. Derive consensus sequence ..ACGATTACAATAGGTT..

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

1. Find Overlapping Reads Find pairs of reads sharing a k-mer, k ~ 24 Extend to full alignment – throw away if not >98% similar TACA TAGT || TAGATTACACAGATTAC T GA TAGA | || ||||||||||||||||| TAGATTACACAGATTAC Caveat: repeats A k-mer that occurs N times, causes O(N2) read/read comparisons ALU k-mers could cause up to 1,000,0002 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

1. Find Overlapping Reads Create local multiple alignments from the overlapping reads TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAGATTACACAGATTACTGA TAG TTACACAGATTATTGA TAGATTACACAGATTACTGA

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

2. Merge Reads into Contigs Overlap graph: Nodes: reads r1…..rn Edges: overlaps (ri, rj, shift, orientation, score) Reads that come from two regions of the genome (blue and red) that contain the same repeat Note: of course, we don’t know the “color” of these nodes

2. Merge Reads into Contigs repeat region Unique Contig Overcollapsed Contig We want to merge reads up to potential repeat boundaries

2. Merge Reads into Contigs repeat region Ignore non-maximal reads Merge only maximal reads into contigs

2. Merge Reads into Contigs Remove transitively inferable overlaps If read r overlaps to the right reads r1, r2, and r1 overlaps r2, then (r, r2) can be inferred by (r, r1) and (r1, r2)

2. Merge Reads into Contigs

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

Overlap graph after forming contigs Unitigs: Gene Myers, 95

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

2. Merge Reads into Contigs Insert non-maximal reads whenever unambiguous

3. Link Contigs into Supercontigs Normal density Too dense  Overcollapsed Inconsistent links  Overcollapsed?

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

3. Link Contigs into Supercontigs Fill gaps in supercontigs with paths of repeat contigs

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

Some Assemblers PHRAP Celera Arachne Phusion Euler Early assembler, widely used, good model of read errors Overlap O(n2)  layout (no mate pairs)  consensus Celera First assembler to handle large genomes (fly, human, mouse) Overlap  layout  consensus Arachne Public assembler (mouse, several fungi) Phusion Overlap  clustering  PHRAP  assemblage  consensus Euler Indexing  Euler graph  layout by picking paths  consensus

Quality of assemblies Celera’s assemblies of human and mouse

Quality of assemblies—mouse

Quality of assemblies—mouse 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 50th percentile.

Quality of assemblies—rat

History of WGA 1982: -virus, 48,502 bp 1995: h-influenzae, 1 Mbp 1997 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 Let’s sequence the human genome with the shotgun strategy That is impossible, and a bad idea anyway Phil Green Gene Myers

Genomes Sequenced http://www.genome.gov/10002154