Bioinformatic detection of horizontally transferred DNA
in bacterial genomes
Morgan GI Langille and Fiona SL Brinkman*
Address: Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC, Canada V5A 1S6
*Corresponding author: Fiona SL Brinkman (brinkman@sfu.ca)
F1000 Biology Reports 2009,1:25 (doi: 10.3410/B1-25)
The electronic version of this article is the complete one and can be found at: http://F1000.com/Reports/Biology/content/1/25
Abstract
We highlight a selection of recent research on computational methods and associated challenges
surrounding the prediction of bacterial horizontal gene transfer. This research area continues to face
controversy, but is becoming more critical as the importance of horizontal gene transfer in medically
and ecologically important prokaryotic evolution is further appreciated.
Introduction and context
Horizontal gene transfer (HGT) is an important driving
force in prokaryotic evolution that allows bacteria to
quickly share genes not only from similar strains, but
also from distantly related species, including phages [1].
This enables bacteria to adapt to changing environmen-
tal pressures, but can also lead to problems with treating
bacterial illnesses, due to the exchange of antibiotic
resistance genes or virulence factors [2].
Although HGT has been shown to be widespread across
bacterial strains, the rate of HGT is still debated. Some
argue that HGT is so prevalent among bacteria that the
ability to reconstruct a tree of life should be seriously
reconsidered [3], while other recent research indicates
that HGT may not be very prevalent [4]. Overall,
methods for the prediction of HGT in bacteria continue
to improve, and many would agree that construction of
species trees, despite the prevalence of HGT, is worth-
while if appropriate methodologies are applied [5].
Most HGT computational prediction methods can be
roughly grouped into two main categories: composi-
tional methods, which identify anomalous sequence
signatures within a prokaryotic genome suggestive of a
region of HGT, and phylogenetic methods, which
analyse the incongruence of a gene tree versus its
associated species tree. We briefly highlight here some
of the recent improvements in such methodologies and
the challenges still faced.
Major recent advances
Sequence composition methods
Sequence composition methods depend on different
species having differences in their genome signatures.
These methods identify HGT by searching for genomic
regions that have an abnormal sequence composition
(G+C, dinucleotide bias, and so on) compared to the rest
of the genome.
An in-depth study of HGT in the Salmonella lineage
indicated that ancient horizontally transferred gene
sequences tended to share a greater similarity in
sequence composition with their host compared to
more recently acquired genes [6], clearly supporting the
idea that transferred genes ameliorate to their host
genome over time [7]. Notably, however, very recently
acquired prophage elements tended to have sequence
compositions that were more similar to the host
genome, not representing amelioration but rather
specialization and adaptation to their hosts [6].
Although this study may suggest that more sensitive
measures of sequence composition are needed to better
predict HGT events, these methods must be carefully
designed so that they do not result in an increase in false
positives. For example, a recent study of large viruses
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further supported previous work indicating that many
genes with atypical sequence composition were not
horizontally acquired and, instead, the anomalous
sequence composition was likely related to certain
functions and gene features such as expression level
[8]. A recent comparison of several HGT prediction
programs showed that these sequence-composition-
based methods can predict very different classes of
genes, warning that the use of a single method could give
biased results [9]. Even with these disadvantages,
detecting HGT by sequence composition is still an
attractive method, since it usually does not require
more than the query genome for analysis.
New research is also producing more intelligent meth-
ods; one such method takes into account that single
transfer events often include multiple genes and uses the
genome location of putative HGTs to further refine
predictions [10]. Similarly, there have been many
methods focused on the prediction of genomic islands
(large regions of HGT) and the accuracy of such genomic
island predictors has been increased through the
coupling of sequence composition analysis with the
identification of additional gene features such as the
presence of mobility genes (e.g. integrases and transpo-
sases) or tRNAs and direct repeats (known integration
sites) [11–15]. This research direction is likely to
continue as more sophisticated composition-based
methods are developed that also examine other sequence
features.
Phylogenetic methods
Many HGT prediction methods look for incongruence
between gene trees and an associated species tree. Such
methods could increasingly benefit from having a more
universal ‘tree of life’ to use as a species tree reference.
One notable study attempted to build such a tree by
identifying genes that were present in all species that did
not show signs of HGT [16]. Identifying genes that have
never been horizontally transferred is a difficult problem
that remains controversial, however, some studies have
suggested that particular genes could be more resistant to
HGT and could therefore be better candidates for
construction of a reference tree [17]. However, genes
subject to HGT can still provide valuable phylogenetic
information, and one study actually embraced using
HGTs for tree construction, demonstrating that ancient
gene transfers to the ancestor of red algae and green
plants can act as informative events that support a
common origin of these two groups [18]. Another
method that tries to construct a large genome tree,
using a selected list of genes that are shared across most
genomes, is AMPHORA (a pipeline for AutoMated
PHylogenOmic inference) [19]. An automated pipeline
was developed that uses 31 ‘marker’ genes, a hidden
Markov model (HMM)-based multiple alignment pro-
gram, and maximum likelihood to construct an organ-
ism tree for 578 species. The construction of these large
trees is likely to lead to new insights and aid other
analyses, but it is appreciated that they do not fully
reflect bacterial evolution due to their lack of representa-
tion of HGTs. Therefore, complementing these
approaches are phylogenetic methods that incorporate
or predict HGT events [20–23]. These tools allow for
reticulate evolutionary events, such as HGT, and result in
a network-like phylogenetic tree that is often represented
as a rooted, directed, acyclic graph; this is the same
structure that is used by the Gene Ontology project [24].
A software package called PhyloNet [23] was recently
published and includes many tools to carry out predic-
tion of HGT and tree construction that should be useful
for many researchers. It makes use of a recently created
eNewick ('extended Newick') format for containing
network-like trees [25], which is based on the well
established, classic Newick format.
Despite these promising advances, limitations of phylo-
genetic based HGT prediction methods still exist that
must be considered; transfers between sister branches in
a tree (often very closely related species) can’t usually be
detected, and sparsely distributed genes may not be
detected if the gene tree is consistent (or inconclusive)
with the species tree. Future research is likely to try to
overcome or at least minimize these limitations, either
through increased species sampling or by combining the
power of phylogenetic and sequence composition based
approaches.
Metagenomics
Prediction of HGTs in metagenomic datasets is some-
what limited due to the novelty of this type of genomic
data, the fact that the organism sources of the sequences
are unknown, and use of short sequence read lengths
that can lower the statistical power of HGT prediction
methods. However, one recent study has designed novel
composition and phylogenetic methods for the detection
of HGT in several environmental samples [26]. They
show that their composition method and phylogenetic
method detect different levels of HGT at 0.8–1.5% and
2–8%, respectively. The authors note that these differ-
ences are likely to be due to the types of HGT detected by
each method, illustrating just how far we still have to go
in HGT prediction and the significant potential there is
to improve HGT prediction by integrating approaches.
Future directions
Regions of HGT are being repeatedly found to contain
virulence genes or other genes of medical and/or
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ecological importance, so improved prediction of such
regions from primary sequence data will continue to be
of significant interest. Considering that science is still
working out the details of how genes are transferred by
conjugation [27], and we are unsure of the origins of
most regions of predicted HGT, we should not be
surprised that prediction of HGT still has a long way to
go. New computational methods are likely to be
developed that improve on algorithm design by inclu-
sion of new biological insights gained from increased
sampling of our genetic world, or by better statistical
modelling. The role of phages and other vehicles of HGT,
in particular, may help shape some predictive methods
[28]. Prediction of the more specific boundaries of
regions of HGT is one research area that needs more
focus. More accurate bioinformatic methods are becom-
ing even more important now, and should be a major
goal, as the number of completed microbial genomes
increases dramatically and the number of sequences
from metagenomic studies and next-generation sequen-
cing eclipses all other sequence data combined. Research
that provides unbiased analysis and reviews of the
accuracy of HGT methods should be encouraged so
that researchers can utilize those methods that work best
for their data (akin to what has been done for
phylogenomics methods [29] and genomic island
prediction methods [12]). As sequence coverage of our
genetic world continues to grow and HGT prediction
methods continue to improve, hopefully the origins of
many HGT events will become clearer, and we will better
understand these events that have played such a pivotal
role in bacterial adaptation.
Abbreviations
HGT, horizontal gene transfer.
Competing interests
The authors declare that they have no competing
interests.
Acknowledgements
We would like to acknowledge the reviewers of this
article, including Dr Robert Beiko, who waived his
anonymity and contributed useful suggestions.
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