1. COMPUTATIONAL GENOMICS GENOME ASSEMBLY
Members:
Eishita Tyagi
Sandeep Namburi
Aarthi Talla
Vinay Vyas
Amin Momin
Jay Humphrey

2. Contents Assembly De novo Reference Assembly problems
Algorithms Involved
Reference
Assembly problems
Task and Strategy

3. How do we get Reads?

4. De novo Assembly Reads Overlap Local Multiple Alignment
Assembly Problems:
-Repeats
-Chimerism
-Gaps
Local Multiple Alignment
Alignment Scoring
Contigs
Scaffolding
Finishing

5. Overlapping Reads Greedy Algorithm Overlap-Layout-Consensus Algorithm
Eulerian path Algorithm

6. 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)

7. 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

8. 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.

9. Generate the pairs from n-mer table
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

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

11. Parameters for Scoring
length of overlap
% identity in overlap region
maximum overhang size

12. 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.

13. 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.

14. 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

15. 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

16. Reference Assembly Reads Overlap Local Multiple Alignment
Assembly Problems:
-Repeats
-Chimerism
-Gaps
Local Multiple Alignment
Alignment Scoring
Contigs
Map to a reference
Finishing

17. Mapping contigs to a reference

18. 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

19. 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.

20. 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)

21. 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.

22. 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.

23. Our Task
To Assemble Neisseria meningitidis strains sequences: M13519 and M16917
Strains are Non-groupable
M matches Serogroup C (PCR), W135 (SASG)
M matches Serogroup Y (PCR), W135 (SASG)
No completed genomes available for strains with Serogroup Y and W135.

24. Best results from each merged with
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

25. Important Assembler Metrics
Number of large contigs
Total size
Coverage
Average length
N50
Longest contig
% genome assembled

26. NEXT PRESENTATION – WEDNESDAY
Initial Results and Lab