This document discusses the MASURCA genome assembler, which uses a hybrid assembly approach combining advantages of de Bruijn graphs and overlap layout consensus. MASURCA first extends reads into "super reads" by merging overlapping k-mers, then constructs a de Bruijn graph from the super reads for efficiency. It then uses a modified CABOG assembler on only maximal super reads with additional data to produce a draft assembly. Tests on bacterial and mouse genomes showed MASURCA able to produce high-quality assemblies comparable or better than other assemblers as measured by metrics like N50 and NGA50.

1. Sequence Assembly
&
MASURCA as a hybrid
approach
ZEHADY ABDULLAH KHAN
PhD 1st Year Student,
Department Of Computer Science,
Purdue University

2. DNA Sequencing
The process of determining the precise order of
neucleotides in a DNA molecule.

10. Overlap Layout Consensus
● Compute all pairwise overlaps between reads.
● Creates a layout
o An alignment of all overlapping reads.
● Extracts a consensus sequence
o by scanning the multiread alignment, column by column.
● Celera Assembler (Miller et al., 2008; Myers et al., 2000), PCAP (Huang, 2003), Arachne
(Batzoglou et al., 2002) and Phusion (Mullikin and Ning, 2003).
● Benefits:
o Flexibility with respect to read lengths
o Robustness to sequencing errors.
● Problem:
o Exponential computation.

12. The De Bruijin Graph

13. The De Bruijin Graph
● Allpaths-LG (Gnerre et al., 2010), SOAPdenovo (Li et al., 2008), Velvet (Zerbino and Birney,
2008), EULER-SR (Chaisson and Pevzner, 2008) and ABySS (Simpson et al., 2009)
● Any path through the graph that visits every edge exactly once, formally known as an Eulerian
path, forms a draft assembly of the read.
o Given all reads are perfect, which will match the de Bruijn graph of the genome
● Practically reads are not perfect.
● These graphs are complex with many intersecting cycles, and many alternative Eulerian paths

14. The De Bruijin Graph

15. What is Masurca?
● A new hybrid method
o Computational efficiency of the De Bruijn graph
method.
o Flexibility of the OLC assembly

16. Super Read
● Extend each original read forwards and backwards, base by base, as long as the extension is
unique.
● k-mer count look-up table
o An efficient hash table
o Determine quickly how many times each k-mer occurs in our reads
● Given a k-mer found at the end of a read, there are four possible k-mers for the next k-mer.
o The strings formed by appending A, C, G or T to the last k-1 bases in the read
● If only one of the four possible k-mers occurs, we say the read has a unique following k-mer
and we append that base to the read.

17. k-Unitig
● A k-mer is called a k-mer simple if it has a unique preceding k-mer and a unique following k-
mer.
● A k-unitig is a string of maximal length such that every k-mer in it is simple except for the first
and the last.
● By the construction, no k-mer can belong to more than one k-unitig.
● If a read has a k-mer that occurs in a k-unitig, the read and the k-unitig can be aligned to one
another.
● Use individual reads to merge the k-unitigs that overlap them into a single longer super-read.

18. Super Reads from paired-reads
● If the reads are paired,
o We examine each pair of reads
o Map each read to the k-unitigs,
o Look for a unique path of k-unitigs connected by k-unitig overlaps that connects the two
reads.
o If we find such a path, then we extend both paired-end reads to a new super-read.
 Merge the k-unitigs on this unique path

19. Assembler in Masurca
● Modified version of the CABOG assembler
● Only super-reads used are maximal super-reads
o Those that are not exact substrings of another super-reads.
● Use of other data in assembly
o Jumping libraries
o 454 read data
o Sanger read data
o Mate pairs
● Coverage of the genome by maximal super-reads typically varies from 2–3x
o Independent of the raw read coverage
● MaSuRCA automatically chooses the k-mer size for creating super-reads.

20. Results: Choice of Genome
● Reference Paper:
The MaSuRCA genome assembler
Aleksey V. Zimin, Guillaume Marc ̧ ais, Daniela Puiu, Michael Roberts, Steven L. Salzbergand
James A. Yorke
● bacterium R.sphaeroides str. 2.4.1 (Rhodobacter)
● chromosome 16 of M.musculus lineage B6 (mouse)

21. Metrics of Evaluation
● N50 :
o The length for which the collection of all contigs of that length or longer contains at least
half of the sum of the lengths of all contigs.
● NGA50:
o The value N such that 50% of the finished sequence is contained in contigs whose
alignments to the finished sequence are of size N or larger .

22. Results

23. Results

24. The End