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

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

How do we get Reads?

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

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

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

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

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)

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

Generate the pairs from n-mer table (single pass through k-mer table) 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

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.

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.

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

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

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

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.

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.

Best results from each merged with 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

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

NEXT PRESENTATION – WEDNESDAY Initial Results and Lab