RDP4: Detection and analysis of recombination patterns in virus genomes

1In many different groups of viruses, genetic recombination is an important evolutionary process that generates much of the genetic diversity upon which natural selection acts. Recombination patterns that are evident within the genomes of such viruses can reveal a great deal about their biology and evolution. Non-random patterns of sequence exchange between individuals within a species can provide direct evidence of geographical or host-range-imposed population subdivisions that prevent certain individuals from recombining (; Monjane et al. 2014). Similarly, sequence exchange patterns between viruses in different species can reveal otherwise undetectable ecological links between some species and barriers between others. The distributions of recombination breakpoints that are evident within virus genomes can also reveal details of the mechanistic and biochemical processes underlying recombination and the selective forces that constrain the survival and proliferation of recombinants. The epidemiological and/or ecological context of recombinants and the distributions of detected recombination breakpoints can also be crucial in identifying instances where recombinants have been artefactually generated in the laboratory.

2Besides an interest in recombination itself, another important reason for analysing recombination patterns in virus genomes is to minimise the disruptive impact that recombination can have on other phylogeny-based analyses of molecular evolution. Specifically, unaccounted for recombination events within a set of sequences can seriously undermine the accuracy of phylogenetic trees constructed from these sequences. Therefore, it is often desirable to either exclude recombinant sequences or identify recombination breakpoint positions and focus analyses exclusively on those genome regions that are unbroken by these breakpoints prior to carrying out selection, molecular clock, phylogeographic, or any other analyses of virus genome sequences that may be misled by incorrectly inferred phylogenetic trees.

3RDP4 is a computer program that was developed with all of these applications in mind. Given a set of aligned nucleotide sequences, it identifies and characterises individual recombination events, providing detailed information on which sequences in the analysed dataset carry evidence of the same recombination event, the likely positions of recombination breakpoints, and the identities of sequences that are most closely related to the parental sequences. Key elements of the RDP4 program interface are illustrated in Fig. 1.

4Crucially, RDP4 is able to perform recombination analyses without any need for predefined sets of non-recombinant reference sequences: a factor which makes it more generally applicable than many other available recombination analysis tools (see http://www.bioinf.manchester.ac.uk/recombination/programs.shtml; ). RDP4 is able to do this using a range of fast and powerful heuristic recombination detection methods that sequentially test every combination of three sequences in an input alignment for evidence that one of the three sequences is a recombinant and the other two are its parents. Besides the original RDP method, these methods include bootscan, maxchi, chimaera, 3seq, geneconv, lard, and siscan. Following the detection of a ‘recombination signal’ with these methods, RDP4 determines approximate breakpoint positions using a hidden Markov model, burt, and then identifies the recombinant sequence using the phylpro, visrd, and eeep methods (;; see the manual that is distributed with RDP4 for a detailed account of how all of these methods work).

5Having detected all of the recombination signals that are evident within an input alignment, RDP4 will then proceed to infer the minimum number of recombination events needed to account for these signals. It does so by sequentially disassembling identified recombinant sequences into their component parts (i.e., each recombinant sequence is split into two pieces) and iteratively rescanning the resulting expanded dataset until no further recombination signals are evident.

6This fully exploratory approach means that, without any prior information, RDP4 can be used to characterise complex patterns of recombination such as those arising when recombination events occur between parental sequences that are themselves recombinant.

7It is important to note, however, that there are also drawbacks to this approach. Primary among these is that when analysing datasets that contain large numbers of recombinant sequences, it can become very difficult for RDP4 to accurately identify the recombinants. Similarly, when numerous ancient recombination events have occurred such that multiple sequences in a dataset carry evidence of the same ancestral recombination events, RDP4 will often incorrectly attribute recombination signals arising from multiple different recombination events to a single ancestral event (i.e., it will under-count the number of recombination events evident within a dataset).

8To partially rectify such deficits, RDP4 includes an array of tools which can be used to manually check, and correct if necessary, any perceived inference errors that the program has made. These tools are all accessible via a point-and-click graphical user interface and enable a user to directly test alternative hypotheses relating to the misidentification of recombination breakpoints, parental sequences, and groups of sequences sharing evidence of the same ancestral recombination events. Among others, these cross-checking tools include the following: Multiple different phylogenetic tree construction methods that can be used to contrast phylogenetic signals in different parts of an alignment (such as on opposite sides of a recombination breakpoint).Shimodaira–Hasegawa and approximately unbiased phylogenetic tree comparison tests.Matrix-based visualisations of the statistical plausibility of alternative breakpoint locations.Statistical and phylogenetic tests that indicate the degree to which recombination signals that are detectable in two different sequences resemble one another.

9Multiple different phylogenetic tree construction methods that can be used to contrast phylogenetic signals in different parts of an alignment (such as on opposite sides of a recombination breakpoint).

10Shimodaira–Hasegawa and approximately unbiased phylogenetic tree comparison tests.

11Matrix-based visualisations of the statistical plausibility of alternative breakpoint locations.

12Statistical and phylogenetic tests that indicate the degree to which recombination signals that are detectable in two different sequences resemble one another.

13In cases where recombination is only being analysed with the intention of minimising its impact on other molecular evolution analyses, RDP4 can export sequence alignments in a multitude of formats either with recombinant sequences/fragments of sequences removed or with recombinant sequences split into their constituent parts. Such alignments will be stripped of all readily detectable evidence of individual recombination events and can then be used with other computer programs such as BEAST or HYPHY (Kosakovsky-Pond et al. 2005) to make more accurate estimates of evolutionary rates or less error-prone inferences of positive selection.

14RDP4 can also be used to directly construct minimum evolution (with FastTree2; ) and maximum-likelihood (with RAxML8; ) phylogenetic trees that account for the recombination events that it has detected. Specifically, it will construct trees using edited versions of the input alignment where fragments of sequence derived through recombination have either been removed altogether or have been re-added to the alignment as new sequences. Further, the program can carry out ‘recombination aware’ inferences of ancestral sequences using parsimony (with PHYLIP; ), maximum likelihood (with RAxML8; ), or Bayesian (with MrBayes3.2; ) approaches.

15In cases where the underlying mechanistic or selective causes of detectable recombination patterns are of interest, RDP4 provides a range of useful tools including: Tests for the presence of recombination hot- and cold spots (,; Fig. 2a–c).Tests of purifying selection acting against recombination induced misfolding of either proteins or nucleic acid secondary structures.Tests of association between recombination breakpoint locations and user-specified genome features (such as gene boundaries, the junctions between protein domains or nucleotides that are base-paired within secondary structures).Tests for, and matrix-based visualisations of, the types of imbalanced coinheritance of nucleotide pairs that are expected to occur within recombinant genomes evolving under selection acting against the disruption of favourable epistatic interactions (Fig. 2d; )Phylogenetic incompatibility visualisations of the overall phylogenetic impacts of recombination within datasets (Fig. 2e;;;;; ).

16Tests for the presence of recombination hot- and cold spots (,; Fig. 2a–c).

17Tests of purifying selection acting against recombination induced misfolding of either proteins or nucleic acid secondary structures.

18Tests of association between recombination breakpoint locations and user-specified genome features (such as gene boundaries, the junctions between protein domains or nucleotides that are base-paired within secondary structures).

19Tests for, and matrix-based visualisations of, the types of imbalanced coinheritance of nucleotide pairs that are expected to occur within recombinant genomes evolving under selection acting against the disruption of favourable epistatic interactions (Fig. 2d; )

20Phylogenetic incompatibility visualisations of the overall phylogenetic impacts of recombination within datasets (Fig. 2e;;;;; ).

21RDP4 can be used to productively analyse datasets containing up to 200 million nucleotides within 72 hours on a standard 2 GHz processor with 2 GB of RAM. Such datasets might, e.g., consist of sixty 3-Mb-long bacterial genome sequences, or 1,500 10-kb-long viral genome sequences. With default program settings, RDP4 can analyse 100 10-kb-long sequences in 10 minutes on a standard desktop computer.

22RDP4 is available for free download from http://web.cbio.uct.ac.za/∼darren/rdp.html. It is distributed along with programs for generating (SDT; ) and aligning (IMPALE) datasets and an extensive manual that contains detailed descriptions of the various methods implemented in RDP4 and a step-by-step guide describing how best to use these. The manual and RDP4 site also contain information on how RDP4 can be run on Mac and Linux computers.