Members: Eishita Tyagi Sandeep Namburi Aarthi Talla Vinay Vyas Amin Momin Jay Humphrey

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

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

3. How do we get Reads?

4. Assembly • De novo assembly • Newbler • MIRA3 • CABOG • EULER • VELVET • Reference assembly • Newbler • AMOScmp • CELERA • Phred • Phrap

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

6. Overlapping Reads • Greedy Algorithm • Overlap-Layout-Consensus Algorithm • Eulerian path Algorithm

7. Greedy Algorithm • Build a rough map of fragment overlaps • Pick the largest scoring overlap • Merge the two fragments • Repeat until no more merges can be done • Easy to implement - Dynamic Programming • Ignores long-range relationships between reads. • E.g. PHRAP, TIGR Assembler, CAP3

8. Set of strings (reads) – {s1,s2,s3….sN} • T=lowest string such that every si с T • If 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 • In a gist, it’s the union of two strings (X U Y)

9. 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

10. 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. • Try all pairs – must consider ~ n2 pairs • Smarter solution: only n x coverage pairs are possible

11. Build a table of n-mers contained in sequences (single pass through the genome) • Generate the pairs from n-mer table (single pass through k-mer table) n-mer

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

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

14. 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.

15. 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 , if in different contigs, have a 1% chance of being neighbors.

16. 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.

17. 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

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

19. Mapping contigs to a reference

20. 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

21. 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.

22. 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)

23. 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.

24. Finishing • Process of completing the chromosome sequence. • Close all gaps (usually by PCR, but large gaps in big genomes can be sent back to make BACs for resequencing) • Re-sequence areas with less than 2x, 3x, 5x coverage (depending on quality standard) –same procedure as gaps • Check and manually assemble unresolved repeat regions • Check for mis-assembly by analyzing the overlap graph • Expensive and time-consuming.

25. Our Task • To Assemble Neisseria meningitidis strains sequences: M13159 and M16159 • The Data Provided: • 2 SFF (Standard Flowgram Format) files • sequence information • quality scores of basecalls • clipping positions • flowgram values • No Pair End Data Provided • Strains are Non-groupable • M13159 matches Serogroup C (PCR), W135 (SASG) • M16159 matches Serogroup Y (PCR), W135 (SASG) • No completed genomes available for strains with Serogroup Y and W135.

26. Our Strategy De novo assembly with Newbler and Mira3 Reference assembly using AMOScmp and Newbler Best Best results from each merged with Minimus2 Finish using MAUVE

27. Important Assembler Metrics • Number of large contigs • Total size • Coverage • Average length • N50 • Longest contig • # of Large Contigs • % genome assembled • quality • % Gap fill

28. NEXT PRESENTATION – WEDNESDAY Initial Results and Lab