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Members: Eishita Tyagi Sandeep Namburi Aarthi Talla Vinay Vyas Amin Momin Jay Humphrey. COMPUTATIONAL GENOMICS GENOME ASSEMBLY. Contents. Assembly De novo Algorithms Involved Reference Assembly problems Task and Strategy. How do we get Reads?. De novo Assembly. Reads.

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members eishita tyagi sandeep namburi aarthi talla vinay vyas amin momin jay humphrey
Members:

Eishita Tyagi

Sandeep Namburi

Aarthi Talla

Vinay Vyas

Amin Momin

Jay Humphrey

COMPUTATIONAL GENOMICS

GENOME ASSEMBLY

contents
Contents
  • Assembly
    • De novo
      • Algorithms Involved
    • Reference
    • Assembly problems
    • Task and Strategy
de novo assembly
De novo Assembly

Reads

Overlap

Assembly Problems:

-Repeats

-Chimerism

-Gaps

Local Multiple Alignment

Alignment Scoring

Contigs

Scaffolding

Finishing

overlapping reads
Overlapping Reads
  • Greedy Algorithm
  • Overlap-Layout-Consensus Algorithm
  • Eulerian path Algorithm
greedy algorithm
Greedy Algorithm

X = abcbdab

Y = bdcaba,

the lcs is Z= bcba.

LCS = Longest common subsequence

By inserting the non-lcs symbols while preserving the symbol order, we get the scs: = abdcabdab

Shortest common superstring

The union of two strings (X U Y)

overlap layout consensus algorithm
Overlap-Layout-Consensus Algorithm
  • Graph based: G(V,E) How is it executed ??
    • de Bruijn Graph – a directed graph with vertices that represent sequences of symbols from an alphabet, and edges that indicate where the sequence may overlap.
    • Nodes (V) = reads
    • Edges (E) = between overlapping reads
    • Path = Contig (each node occurs at least once)
  • Builds graph – alignments
  • Removing ambiguities
  • Output is a set of nonintersecting simple paths, each path being a contig.
  • Consensus sequence
  • E.g.. Celera Assembler, Arachne
eulerian path algorithm
Eulerian Path Algorithm
  • De-bruijn graph
  • Eulerian path – a path that visits all edges of a graph
  • Breaks reads into overlapping n-mers.
  • Source: n-1 prefix and destination is the n-1 suffix corresponding to an n-mer.
slide9

Build a table of n-mers contained in sequences (single pass through the genome)

  • Generate the pairs from n-mer table

ATG

AT

TGC

TG

GCA

GC

n-mer

CAG

CA

AGG

AG

GGT

HAMILTONIAN (IDURY - WATERMAN

GG

EULER

slide10
MSA

•Correct errors using multiple alignment

•Score alignments

•Accept alignments with good scores

parameters for scoring
Parameters for Scoring
  • length of overlap
  • % identity in overlap region
  • maximum overhang size
contigs
Contigs
  • A continuous sequence of DNA that has been assembled from overlapping cloned DNA fragments.
  • Reads combined into Contigs based on sequence similarity between reads.
scaffolding
Scaffolding

The process through which the read pairing information is used to order and orient the contigs along a chromosome is called Scaffolding.

  • Scaffolding groups contigs -> subsets with known order and orientation.
  • Nodes (V) = contigs.
  • Directed edge (E) – mate pairs between node.
mate pairs or paired end reads

Sameward

Outward

Inward

Mate Pairs or Paired End Reads
  • A library of Paired End reads or Mate pairs are used to determine the orientation and relative positions of contigs.
  • Reads sequenced from the template DNA
  • Known order and orientation (facing in, facing out, or facing the same direction) between reads.
  • Known range of separation between read 5' ends.
  • Approximately 84-nucleotide DNA fragments that have a 44-mer adaptor sequence in the middle flanked by a 20-mer sequence on each side.
  • Mate-pairs allow you to remove gaps & merge islands (contigs) into super-contigs.
slide15

Mate Pairs are Needed to:

  • Order Contigs
  • Orient Contigs
  • Fill Gaps in the assembly

A scaffold of 3 contigs (the thick arrows)

held together by mate pairs

reference assembly
Reference Assembly

Reads

Overlap

Assembly Problems:

-Repeats

-Chimerism

-Gaps

Local Multiple Alignment

Alignment Scoring

Contigs

Map to a reference

Finishing

assembly problems
Assembly Problems
  • Errors from sequencing machines, e.g. missing a base, or misreading a base
  • Even at 8-10 X coverage, there is a probability that some portion of the genome remains unsequenced
  • Repeat problem lead to Misassembly and Gaps
  • Chimeric reads - When two fragments from two different parts of genome are combined together
repeat problems
Repeat Problems
  • Ability of an assembly program to produce 1 contig for a chromosome: limited by regions of the genome that occur in multiple near-identical copies throughout the genome (repeats).
  • Assembler incorrectly collapses the two copies of the repeat leading to the creation of 2 contigs instead of 1.
  • Thus, number of contigs increase with the number of repeats.
  • Repeated sequences within a genome also produce problems with higher level ordering.
slide20

Genome mis-assembled due to a repeat. 

Assembly programs incorrectly may combine the reads from the two copies of a repeat leading to the creation of 2 separate contigs (Contig Level Misassembly)

slide21
Gaps
  • A good Assembler would have to ignore the repeats and generate one contig instead of two.
  • A Gap would be created in the place of the repeat.
  • Higher the number of repeats, the Gaps generated would increase.

Chimeric reads

  • Two fragments from two different parts of genome are combined together.
  • Can give a completely wrong assembly.
finishing
Finishing
  • Process of completing the chromosome sequence.
  • Re-sequence areas with gaps or less than 2x, 3x, 5x coverage
  • Close gaps (usually by PCR or BACs)
  • Expensive and time-consuming.
our task
Our Task
  • To Assemble Neisseria meningitidis strains sequences: M13519 and M16917
    • Strains are Non-groupable
      • M13519 matches Serogroup C (PCR), W135 (SASG)
      • M16917 matches Serogroup Y (PCR), W135 (SASG)
  • No completed genomes available for strains with Serogroup Y and W135.
slide24

Our Strategy

De novo assembly with

Newbler and Mira3

Reference assembly using

AMOScmp and Newbler

Best

Best results from each merged with

Minimus2

Finish by manual alignment

slide25

Important Assembler Metrics

  • Number of large contigs
  • Total size
  • Coverage
  • Average length
  • N50
  • Longest contig
  • % genome assembled
slide26

NEXT PRESENTATION – WEDNESDAY

Initial Results and Lab