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Low Coverage Genome Assembly Using Fuzzy Hash Maps
John Healy
Department of Computing & Mathematics
Galway-Mayo Institute of Technology
Ireland
john.healy@gmit.ie
Abstract
Despite the high-throughput of sequence reads that
characterise second generation sequencing
technologies, genome assemblers require a large
degree of over-sampling to produce a complete
genomic sequence. Using a novel approach to
comparative genome assembly, based on the
application of fuzzy hash maps, low coverage sequence
reads can be rapidly ordered and orientated in an
assembly scaffold with a low error rate and a vastly
increased N50 length.
1. Introduction
The advent of second generation sequencing (SGS)
technology, capable of rapidly sequencing a massive
number of short-length reads, has resulted in a reappraisal
of existing approaches to sequence alignment
and genome assembly. The twin characteristics of large
read number and short read length has resulted in a
move away from assembly strategies based on the
traditional overlap graph to more k-mer centric
approaches such as de Bruijn graphs and sequence
graphs. While the newer k-mer centric genome
assemblers are ideal for use with short-length reads, the
requirement for a large degree of oversampling, or
coverage, renders these approaches unsuitable for
assembling genomes of draft coverage or lower.
Although comparative assemblers have been developed
for assembling draft genomes, the underlying assembly
model is invariably based on the traditional overlap
graph. We describe how a fuzzy hash-map can be
applied to rapidly and accurately assemble a
prokaryotic genome, sampled at varying levels of low
coverage, against the reference genome of a closely
related species.
2. Assembly with Fuzzy Hash Maps
Hash-tables or maps are dictionary data structures
that use a key and a hashing function to provide
constant time, O(1), insertion, deletion and retrieval
operations. By generating a unique hash code from a
given key, hash tables provide a rapid mapping from a
domain of unique keys to a range of possible values.
Fuzzy Hash Maps (FHM) leverage the power of objectoriented
languages to allow a degree of variability in
the composition of a hash key. In the Java programming
language, a degree of fuzziness can be applied to a hash
key by manipulating the contract between the
hashCode() and equals() methods in the object used as
159
Desmond Chambers
Department of Information Technology
National University of Ireland Galway
Ireland
des.chambers@nuigalway.ie
the hash key. In contrast to traditional hash maps, which
seek to avoid collisions, FHMs encourage initial
collisions in the map by reducing the size of the key
used to compute a hash code. Dynamic programming
algorithms can then be implemented in the equals()
method to establish whether a full collision is permitted.
Using a FHM as the underlying data structure, a de
Bruijn graph approach can be used to anchor a set of
draft sequence against a reference genome and
assemble the draft reads into contiguous sequences [1-
2].
3. Results
The results of assembling the 0.58Mb genome of
M.genitalium at varying levels of coverage are shown in
Table 1. The 0.81Mb genome of M.pneumoniae was
used as a reference sequence and anchored 65.56% of
the M.genitalium reads.
Table 1. Summary of Assembly Results at
Varying Levels of Coverage
Coverage N50
Contig
N50
Scaffold
%
Ordering
Errors
%
Orientation
Errors
Time
(s)
2.0 2141 51215 1.21 0.12 19.2
1.8 1918 14787 4.60 1.00 17.3
1.6 1798 14853 1.12 0.17 16.2
1.4 1739 9734 0.39 0.59 14.6
1.2 1456 10322 2.18 0.46 12.8
1.0 1269 6001 1.24 0.97 11.1
0.8 1228 8240 1.55 0.69 7.5
0.6 992 4743 0.92 0.46 7.8
0.4 -­‐ 2450 2.07 0.00 6.0
0.2 -­‐ 2539 1.38 2.07 4.1
4. Conclusions
The FHM approach is capable of rapidly and
accurately ordering and orientating low coverage
sequence reads without sacrificing the execution speed
inherent in hash maps.
5. References
[1] J. Healy and D. Chambers, "Fast and Accurate Genome
Anchoring Using Fuzzy Hash Maps", Proceedings 5th
International Conference on Practical Applications of
Computational Biology & Bioinformatics, 2011, pp. 149-
156.
[2] J. Healy and D. Chambers, "De Novo Draft Genome
Assembly Using Fuzzy K-mers", Proceedings 3rd
International Conference on Bioinformatics,
Biocomputational Systems and Biotechnologies, 2011.