bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P32. ON THE LZ DISTANCE FOR DEREPLICATING
REDUNDANT PROKARYOTIC GENOMES
Raphaël R. Léonard 1,2* , Damien Sirjacobs², Eric Sauvage 1 , Frédéric Kerff 1 & Denis Baurain².
Centre for Protein Engineering, University of Liège 1 ; PhytoSYSTEMS, University of Liège 2 . * rleonard@doct.ulg.ac.be
The fast-growing number of available prokaryotic genomes, along with their uneven taxonomic distribution, is a problem
when trying to assemble broadly sampled genome sets for phylogenomics and comparative genomics. Indeed, most of
the new genomes belong to the same subset of hyper-sampled phyla, such as Proteobacteria and Firmicutes, or even to
single species, such as Escherichia coli (almost 2000 genomes as of Sept 2015), while the continuous flow of newly
discovered phyla prompts for regular updates. This situation makes it difficult to maintain sets of representative genomes
combining lesser known phyla, for which only few species are available, and sound subsets of highly abundant phyla. An
automated straightforward method is required but none are publicly available. The LZ distance, in conjunction with the
quality of the annotations, can be used to create an automated approach for selecting a subset of representative genomes
without redundancy. We are planning to release this tool on a website that will be made publicly available.
INTRODUCTION
The LZ distance (Lempel and Ziv, 1977; Otu and Sayood,
2003) is inspired by compression algorithms, such as gzip
or WinRAR. This distance, amongst others, has already
been used in attempts to produce alignment-free
phylogenetic trees (Bacha and Baurain, 2005; Hohl et al.
2007), though the results were disappointing in such a
context (due to the heterogeneity of the substitution
process at large evolutionary scales). However, the LZ
distance is likely to provide enough resolving power to
identify groups of redundant genomes and to keep only
one representative for each group.
METHODS
For each pair of genomes A and B, the LZ distance is
computed from the gzip-compressed file lengths of the
corresponding nucleotide assemblies s(A) and s(B) and of
their concatenations s(A+B) and s(B+A). These distances,
along with taxonomic information, are stored in a
database.
A clustering method is then applied to regroup the similar
genomes into a user-specified number of groups. For each
of these groups, a representative is chosen based on the
quality of the genomic assemblies (chromosomes rather
than scaffolds) and of the protein annotations (e.g., few
rather than many “unknown proteins”).
RESULTS & DISCUSSION
Our method using the LZ distance is currently under
development using the genomes from the release 28 of
Ensembl Bacteria (ftp://ftp.ensemblgenomes.org/pub/
bacteria/release-28/). It contains 20,950 unique
prokaryotic genomes, composed of 286 Archaea and
20,664 Bacteria. The three most represented phyla are the
Proteobacteria (8642, of which 1980 E. coli), the
Firmicutes (7766) and the Actinobacteria (2673). These
genomes are already the result of a pre-processing step
designed to remove extra assemblies for strains present in
multiple copies (due to parallel sequencing or
resequencing in different labs).
We are working on different approaches for validating our
dereplication method, based on (1) current taxonomy, (2)
16S rRNA phylogeny, and (3) clustering using genomic
signatures (Moreno-Hagelsieb et al. 2013).
First, we compute a central measure of the taxonomic
“purity” of all genome clusters, which reflects the amount
of “mixture” at different taxonomic levels (phylum, class,
order etc). A good clustering should regroup different
genera (or species) without amalgamating distinct classes
(or phyla). Second, we cut the branches of a large 16S
rRNA tree based on the same genome collection to
produce an equal number of groups to compare with our
clustering method. We then compute a statistic of the
overlap between the 16S subtrees and the LZ clusters. A
good clustering should have a reasonable overlap with the
gold standard that is the 16S rRNA tree. Third, using the
same overlap metric, we compare the LZ clusters to
clusters obtained using the genomic signature.
Finally, an interactive tool will be made available through
a website. It will allow the users to download precomputed
sets of representative genomes for either the
complete database or for taxonomic subsets. We are also
planning to allow users to upload their own genomes to
cluster them with the LZ method.
REFERENCES
Ziv, J. and a. Lempel. 1977. ‘A Universal Algorithm for Sequential Data
Compression.’ IEEE Transactions on Information Theory 23.3.
doi:10.1109/TIT.1977.1055714.
Otu, H. H. and K. Sayood. 2003. ‘A New Sequence Distance Measure for
Phylogenetic Tree Construction.’ Bioinformatics 19.16: 2122–2130.
doi:10.1093/bioinformatics/btg295.
Moreno-Hagelsieb, G., Z. Wang, S. Walsh and A. Elsherbiny. 2013.
‘Phylogenomic Clustering for Selecting Non-Redundant Genomes
for Comparative Genomics.’ Bioinformatics 29.1: 947–949.
doi:10.1093/bioinformatics/btt064.
Höhl, M. and M. a Ragan. 2007. ‘Is Multiple-Sequence Alignment
Required for Accurate Inference of Phylogeny?’ Systematic biology
56.2: 206–221. doi:10.1080/10635150701294741.
Bacha, S. and Baurain, D. 2005. ‘Application of Lempel-Ziv complexity
to alignment-free sequence comparison of protein families’.
Benelux Bioinformatics Conference 2005.
http://hdl.handle.net/2268/80179
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