Members:Eishita TyagiSandeep NamburiAarthi TallaVinay VyasAmin MominJay Humphrey

COMPUTATIONAL GENOMICS

GENOME ASSEMBLY

Contents

• Assembly– De novo

• Algorithms Involved

– Reference– Assembly problems– Task and Strategy

How do we get Reads?

De novo AssemblyReads

Overlap

Local Multiple Alignment

Contigs

Scaffolding

Alignment Scoring

Finishing

Assembly Problems:

-Repeats

-Chimerism

-Gaps

• Greedy Algorithm• Overlap-Layout-Consensus Algorithm• Eulerian path Algorithm

Overlapping Reads

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

• 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

• 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.

n-mer

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

– Generate the pairs from n-mer table

ATGTGC

GCA

CAG

AGG

GGTHAMILTONIAN (IDURY - WATERMAN

AT

TG

GC

CA

AG

GGEULER

MSA

•Correct errors using multiple alignment•Score alignments•Accept alignments with good scores

Parameters for Scoring

• length of overlap• % identity in overlap region• maximum overhang size

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.

ScaffoldingThe 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

• 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.

Sameward

Outward

Inward

A scaffold of 3 contigs (the thick arrows) held together by mate pairs

Mate Pairs are Needed to:

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

Reference Assembly

Reads

Overlap

Local Multiple Alignment

Contigs

Map to a reference

Alignment Scoring

Finishing

Assembly Problems:

-Repeats

-Chimerism

-Gaps

Mapping contigs to a reference

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

• 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.

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)

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.

•Two fragments from two different parts of genome are combined together.•Can give a completely wrong assembly.

Chimeric reads

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• 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.

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

Our Strategy

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

Important Assembler Metrics

NEXT PRESENTATION – WEDNESDAY

Initial Results and Lab