fragment assembly
Download
Skip this Video
Download Presentation
Fragment Assembly

Loading in 2 Seconds...

play fullscreen
1 / 48

Fragment Assembly - PowerPoint PPT Presentation


  • 134 Views
  • Uploaded on

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

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Fragment Assembly' - borka


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
fragment assembly
Fragment Assembly

Given N reads…

Where N ~ 30 million…

We need to use a linear-time algorithm

steps to assemble a genome
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
T GA

TACA

| ||

||

TAGA

TAGT

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

|||||||||||||||||

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 reads1
1. Find Overlapping Reads
  • Correcterrors 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
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

repeats errors and contig lengths
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

slide9
3. Link Contigs into Supercontigs

Normal density

Too dense

 Overcollapsed

Inconsistent links

Overcollapsed?

slide10
3. Link Contigs into Supercontigs

Find all links between unique contigs

Connect contigs incrementally, if  2 links

supercontig

(aka scaffold)

slide11
3. Link Contigs into Supercontigs

Fill gaps in supercontigs with paths of repeat contigs

4 derive consensus sequence
4. Derive Consensus Sequence

TAGATTACACAGATTACTGA TTGATGGCGTAA CTA

Derive multiple alignment from pairwise read alignments

TAGATTACACAGATTACTGACTTGATGGCGTAAACTA

TAG TTACACAGATTATTGACTTCATGGCGTAA CTA

TAGATTACACAGATTACTGACTTGATGGCGTAA CTA

TAGATTACACAGATTACTGACTTGATGGGGTAA CTA

TAGATTACACAGATTACTGACTTGATGGCGTAA CTA

Derive each consensus base by weighted voting

(Alternative: take maximum-quality letter)

some assemblers
Some Assemblers
  • PHRAP
      • 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)
      • Overlap  layout  consensus
  • Phusion
      • Overlap  clustering  PHRAP  assemblage  consensus
  • Euler
      • Indexing  Euler graph  layout by picking paths  consensus
quality of assemblies
Quality of assemblies

Celera’s assemblies of human and mouse

quality of assemblies mouse1
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.

history of wga
History of WGA

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
Genomes Sequenced
  • http://www.genome.gov/10002154
evolution at the dna level
Evolution at the DNA level

Deletion

Mutation

…ACGGTGCAGTTACCA…

SEQUENCE EDITS

…AC----CAGTCCACCA…

REARRANGEMENTS

Inversion

Translocation

Duplication

protein phylogenies
Protein Phylogenies
  • Proteins evolve by both duplication and species divergence
orthology and paralogy
Orthology and Paralogy

Yeast

Orthologs:Derived by speciation

Paralogs:

Everything else

HA1 Human

HA2 Human

WA Worm

HB Human

WB Worm

definition
Definition
  • Given N sequences x1, x2,…, xN:
    • Insert gaps (-) in each sequence xi, such that
      • All sequences have the same length L
      • Score of the global map is maximum
  • A faint similarity between two sequences becomes significant if present in many
  • Multiple alignments reveal elements that are conserved among a class of organisms and therefore important in their common biology
  • The patterns of conservation can help us tell function of the element
scoring function sum of pairs
Scoring Function: Sum Of Pairs

Definition:Induced pairwise alignment

A pairwise alignment induced by the multiple alignment

Example:

x: AC-GCGG-C

y: AC-GC-GAG

z: GCCGC-GAG

Induces:

x: ACGCGG-C; x: AC-GCGG-C; y: AC-GCGAG

y: ACGC-GAC; z: GCCGC-GAG; z: GCCGCGAG

sum of pairs cont d
Sum Of Pairs (cont’d)
  • Heuristic way to incorporate evolution tree:

Human

Mouse

Duck

Chicken

  • Weighted SOP:
        • S(m) = k
a profile representation
A Profile Representation
  • Given a multiple alignment M = m1…mn
    • Replace each column mi with profile entry pi
      • Frequency of each letter in 
      • # gaps
      • Optional: # gap openings, extensions, closings
    • Can think of this as a “likelihood” of each letter in each position

- A G G C T A T C A C C T G

T A G – C T A C C A - - - G

C A G – C T A C C A - - - G

C A G – C T A T C A C – G G

C A G – C T A T C G C – G G

A 1 1 .8

C .6 1 .4 1 .6 .2

G 1 .2 .2 .4 1

T .2 1 .6 .2

- .2 .8 .4 .8 .4

multidimensional dp
Multidimensional DP

Generalization of Needleman-Wunsh:

S(m) = i S(mi)

(sum of column scores)

F(i1,i2,…,iN): Optimal alignment up to (i1, …, iN)

F(i1,i2,…,iN) = max(all neighbors of cube)(F(nbr)+S(nbr))

multidimensional dp1
Multidimensional DP
  • Example: in 3D (three sequences):
  • 7 neighbors/cell

F(i,j,k) = max{ F(i – 1, j – 1, k – 1) + S(xi, xj, xk),

F(i – 1, j – 1, k ) + S(xi, xj, - ),

F(i – 1, j , k – 1) + S(xi, -, xk),

F(i – 1, j , k ) + S(xi, -, - ),

F(i , j – 1, k – 1) + S( -, xj, xk),

F(i , j – 1, k ) + S( -, xj, - ),

F(i , j , k – 1) + S( -, -, xk) }

multidimensional dp2
Multidimensional DP

Running Time:

  • Size of matrix: LN;

Where L = length of each sequence

N = number of sequences

  • Neighbors/cell: 2N – 1

Therefore………………………… O(2N LN)

multidimensional dp3
Multidimensional DP
  • How do gap states generalize?
  • VERY badly!
    • Require 2N – 1 states, one per combination of gapped/ungapped sequences
    • Running time: O(2N 2N  LN) = O(4N LN)

Running Time:

  • Size of matrix: LN;

Where L = length of each sequence

N = number of sequences

  • Neighbors/cell: 2N – 1

Therefore………………………… O(2N LN)

Y

YZ

XY

XYZ

Z

X

XZ

progressive alignment
Progressive Alignment

x

  • When evolutionary tree is known:
    • Align closest first, in the order of the tree
    • In each step, align two sequences x, y, or profiles px, py, to generate a new alignment with associated profile presult

Weighted version:

    • Tree edges have weights, proportional to the divergence in that edge
    • New profile is a weighted average of two old profiles

pxy

y

z

pxyzw

pzw

w

progressive alignment1
Progressive Alignment

x

  • When evolutionary tree is known:
    • Align closest first, in the order of the tree
    • In each step, align two sequences x, y, or profiles px, py, to generate a new alignment with associated profile presult

Weighted version:

    • Tree edges have weights, proportional to the divergence in that edge
    • New profile is a weighted average of two old profiles

y

Example

Profile: (A, C, G, T, -)

px = (0.8, 0.2, 0, 0, 0)

py = (0.6, 0, 0, 0, 0.4)

s(px, py) = 0.8*0.6*s(A, A) + 0.2*0.6*s(C, A)

+ 0.8*0.4*s(A, -) + 0.2*0.4*s(C, -)

Result:pxy= (0.7, 0.1, 0, 0, 0.2)

s(px, -) = 0.8*1.0*s(A, -) + 0.2*1.0*s(C, -)

Result:px-= (0.4, 0.1, 0, 0, 0.5)

z

w

progressive alignment2
Progressive Alignment

x

  • When evolutionary tree is unknown:
    • Perform all pairwise alignments
    • Define distance matrix D, where D(x, y) is a measure of evolutionary distance, based on pairwise alignment
    • Construct a tree (UPGMA / Neighbor Joining / Other methods)
    • Align on the tree

y

?

z

w

heuristics to improve alignments
Heuristics to improve alignments
  • Iterative refinement schemes
  • A*-based search
  • Consistency
  • Simulated Annealing
iterative refinement
Iterative Refinement

One problem of progressive alignment:

  • Initial alignments are “frozen” even when new evidence comes

Example:

x: GAAGTT

y: GAC-TT

z: GAACTG

w: GTACTG

Frozen!

Now clear correct y = GA-CTT

iterative refinement1
allow y to vary

x,z fixed projection

Iterative Refinement

Algorithm (Barton-Stenberg):

  • For j = 1 to N,

Remove xj, and realign to x1…xj-1xj+1…xN

  • Repeat 4 until convergence

z

x

y

iterative refinement2
Iterative Refinement

Example: align (x,y), (z,w), (xy, zw):

x: GAAGTTA

y: GAC-TTA

z: GAACTGA

w: GTACTGA

After realigning y:

x: GAAGTTA

y: G-ACTTA + 3 matches

z: GAACTGA

w: GTACTGA

iterative refinement3
Iterative Refinement

Example not handled well:

x: GAAGTTA

y1: GAC-TTA

y2: GAC-TTA

y3: GAC-TTA

z: GAACTGA

w: GTACTGA

  • Realigning any single yi changes nothing
consistency
Consistency

zk

z

xi

x

y

yj

yj’

consistency1
Consistency

zk

z

Basic method for applying consistency

  • Compute all pairs of alignments xy, xz, yz, …
  • When aligning x, y during progressive alignment,
    • For each (xi, yj), let s(xi, yj) = function_of(xi, yj, axz, ayz)
    • Align x and y with DP using the modified s(.,.) function

xi

x

y

yj

yj’

real world protein aligners
Real-world protein aligners
  • MUSCLE
    • High throughput
    • One of the best in accuracy
  • ProbCons
    • High accuracy
    • Reasonable speed
muscle at a glance
MUSCLE at a glance
  • Fast measurement of all pairwise distances between sequences
    • DDRAFT(x, y) defined in terms of # common k-mers (k~3) – O(N2 L logL) time
  • Build tree TDRAFT based on those distances, with UPGMA
  • Progressive alignment over TDRAFT, resulting in multiple alignment MDRAFT
    • Only perform alignment steps for the parts of the tree that have changed
  • Measure new Kimura-based distances D(x, y) based on MDRAFT
  • Build tree T based on D
  • Progressive alignment over T, to build M
  • Iterative refinement; for many rounds, do:
    • Tree Partitioning: Split M on one branch and realign the two resulting profiles
    • If new alignment M’ has better sum-of-pairs score than previous one, accept
probcons at a glance
PROBCONS at a glance
  • Computation of all posterior matrices Mxy : Mxy(i, j) = Prob(xi ~ yj), using a HMM
  • Re-estimation of posterior matrices M’xy with probabilistic consistency
    • M’xy(i, j) = 1/N sequence zk Mxz(i, k)  Myz (j, k); M’xy = Avgz(MxzMzy)
  • Compute for every pair x, y, the maximum expected accuracy alignment
    • Axy: alignment that maximizes aligned (i, j) in AM’xy(i, j)
    • Define E(x, y) = aligned (i, j) in AxyM’xy(i, j)
  • Build tree T with hierarchical clustering using similarity measure E(x, y)
  • Progressive alignment on T to maximize E(.,.)
  • Iterative refinement; for many rounds, do:
    • Randomized Partitioning: Split sequences in M in two subsets by flipping a coin for each sequence and realign the two resulting profiles
some resources
Some Resources

Genome Resources

Annotation and alignment genome browser at UCSC

http://genome.ucsc.edu/cgi-bin/hgGateway

Specialized VISTA alignment browser at LBNL

http://pipeline.lbl.gov/cgi-bin/gateway2

ABC—Nice Stanford tool for browsing alignments

http://encode.stanford.edu/~asimenos/ABC/

Protein Multiple Aligners

http://www.ebi.ac.uk/clustalw/

CLUSTALW – most widely used

http://phylogenomics.berkeley.edu/cgi-bin/muscle/input_muscle.py

MUSCLE – most scalable

http://probcons.stanford.edu/

PROBCONS – most accurate

ad