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Evolutionary relationships of species derived by comparing single orthologous genes or groups of genes can be negatively affected by potential horizontal gene transfers, incomplete lineage-sorting, introgression, and the unrecognized comparison of paralogous genes (Delsuc, Brinkmann & Philippe, 2005). However, with the advent of the genomic era, it is now possible for researchers to use the complete genomes of fully sequenced organisms for building trees. Though such trees offer robustness for analysis, it becomes impractical to use traditional methods for constructing large scale alignments and for generating trees from these alignments, mainly because of their large size and their highly heterogeneous nature. As a result, there are now sophisticated methods that don’t rely on alignment and are optimized for large scale data. These methods generally use vector representation of genes (Qi, Luo & Hao, 2004; Stuart, Moffett & Leader, 2002) or features such as gene content (Huson & Steel, 2004; Snel, Bork & Huynen, 1999; Tekaia, Lazcano & Dujon, 1999), gene order (Bourque & Pevzner, 2002; Korbel et al., 2002), intron positions (Roy & Gilbert, 2005), or protein domain structure (Lin & Gerstein, 2000; Yang, Doolittle & Bourne, 2005).

Despite a strong recent interest in the various large-scale non-alignment methods, they are often viewed as somewhat less rigorous and less reliable. In addition, even with the dramatic decrease in the cost of genome sequencing, it is still not attractive to sequence the genomes of those organisms that have little economical value, especially if their genomes are extremely large. On the other hand, the possibility of obtaining a large and representative set of fragments, instead of the whole genome sequence, can be economically feasible even for the lesser known species and can provide a valuable alternative for many types of genomic scale studies, including phylogenomics.

Recently, several approaches have been developed to represent the genome by randomly sampling the entire genome. These approaches give a good reduced representation of the genome and are based on restriction sites on the genome combined with the next generation sequencing methods. Some popular methods include Complexity Reduction of Polymorphic Sequences (CRoPS) (van Orsouw et al., 2007); restriction site-associated DNA sequencing (RAD-seq) (Baird et al., 2008; Etter et al., 2011); Genotyping by Sequencing method (GBS); double-digest RAD-seq (Peterson et al., 2012), and 2bRAD (Wang et al., 2012). All these methods provides good subsamples from homologous locations within genomes and are widely used to study population genetics (Baxter et al., 2011; Hohenlohe et al., 2010). These methods have the potential to uncover detailed information about a wealth of genomic markers. Complex interactions among markers can also be extracted at the population level (Baird et al., 2008; Davey & Blaxter, 2010). Recently, these fragments have also been used for evolutionary studies (Emerson et al., 2010; Rubin, Ree & Moreau, 2012; Yi & Jin, 2013).

A novel class of enzymes, known as Type IIB restriction endonucleases (Roberts et al., 2003b), are site-specific endonucleases that cut both strands of double-stranded DNA upstream and downstream of their recognition sequences. These restriction enzymes have recognition sequences that are generally interrupted and range from 5 to 7 bases long. They produce DNA fragments which are of uniform length, ranging from 21 to 33 base pairs in length (without cohesive ends) (Roberts et al., 2003a). The fragments are generated from throughout the entire length of a genomic DNA providing an excellent fractional representation of the genome. This method of generating fragments using Type IIB enzymes is termed 2bRAD (Wang et al., 2012) and these fragments have been used for various purposes including population studies, digital karyotyping (Stebbins, 1950), for pathogen identification by computational subtraction (Tengs et al., 2004) and genomic profiling to identify and quantitatively analyze genomic DNAs (Dunn et al., 2002). In this study, we show that these fragments can be used for efficient phylogenetic study for determining evolutionary relationships between distinct species. We have tested this method in silico and shown that 13 different types of IIB restriction enzymes can be used to accurately reconstruct the phylogeny of a diverse set of 21 Drosophila species that are currently available.

Materials and Methods

Obtaining datasets

Whole genome, nucleotide sequences for the 21 Drosophila species were downloaded from the FlyBase (McQuilton, St Pierre & Thurmond, 2012), NCBI databases and from the Princeton University website (Rebeiz et al., 2009) on July 10, 2010.

Simulated restriction digestion

The PERL program “Phyper” was used to simulate restriction digestion for all 16 Type IIB endonuclease enzymes and for processing the obtained fragments. This program generated a representative list of unique fragments i.e., single-copy fragments (most abundant) and fragments that are present as multiple identical copies (less frequent). The remaining fragments belong to divergent fragment families within a given genome that display one or a few mutations relative to each other and were identified and removed from the analysis. The representative list of fragments were generated for each genome, for each enzyme separately.

Fragment comparisons

The representative lists of fragments were then used with another PERL program “Phyppa” for comparative analyses. This program compares each fragment of a genome with every fragment of another genome in order to find identical fragments and similar fragments (fragments with up to 5 mismatches for ensuring more than 80% similarity among sequences). A total of 210 such comparisons were done in order to generate the full list of shared fragments (identical fragments and similar fragments) for every pair of genomes (both PERL scripts are available upon request). Analyses was performed on a standard laptop with a quad core processor (1.73 GHz Intel Core i7) and with 6 GB RAM. For each enzyme, the scripts required about 6 h to finish for both fragment generation and comparison between all genomes.

Distance calculations

The number of shared fragments between a pair of genomes was then used to calculate the evolutionary distance by calculating the ratio of shared fragment to the total fragments and converting them to negative natural log (Eq. (1)). Conversion to negative natural log was essential to ensure that the distances computed were always positive. Distance=lnIdentical fragments+Similiar fragmentsTotal fragments of both species.

Building trees

Distance measures for all the pairwise comparisons for a particular enzyme were used to build trees using the neighbor program from the Phylip (Felsenstein, 2005) package. A consensus tree was them produced by combining trees for all the enzymes with the consensus program from Phylip. The flowchart for the entire process is given in Fig. 1.

Workflow of the entire process of generating phylogeny from the Type IIB fragments.

Figure 1: Workflow of the entire process of generating phylogeny from the Type IIB fragments.

Results and Discussion


The full nucleotide sequences for 21 Drosophila species downloaded from various sources are listed in Table 1. The genome size ranged from 137.82 mb for D. simulans to 235.52 mb for D. willistoni. D. willistoni had the lowest GC content of all with 37.89% and D. pseudoobscura had the highest GC content (45.43%).

Table 1:
Various Drosophila species and source databases used for the analysis. The GC% for each genome was calculated using infoseq from the EMBOSS package.
Genome GC% Size Source
D. ananassae 42.56 230.99 mb FlyBase
D. biarmipes 41.82 168.58 mb NCBI
D. bipectinata 41.62 166.39 mb NCBI
D. elegans 40.31 170.51 mb NCBI
D. erecta 42.65 152.71 mb FlyBase
D. eugracilis 40.90 156.31 mb NCBI
D. ficusphila 41.93 151.04 mb NCBI
D. grimshawi 38.84 200.46 mb FlyBase
D. kikkawai 41.38 163.57 mb NCBI
D. melanogaster 42.05 168.73 mb FlyBase
D. mojavensis 40.22 193.82 mb FlyBase
D. persimilis 45.29 188.37 mb FlyBase
D. pseudoobscura 45.43 152.73 mb FlyBase
D. rhopaloa 40.07 193.90 mb NCBI
D. santomea 38.52 165.75 mb Princeton University
D. sechellia 42.53 166.57 mb FlyBase
D. simulans 43.06 137.82 mb FlyBase
D. takahashii 40.01 181.00 mb NCBI
D. virrilis 40.80 206.02 mb FlyBase
D. willistoni 37.89 235.51 mb FlyBase
D. yakuba 42.43 165.69 mb FlyBase
DOI: 10.7717/peerj.226/table-1

Type IIB restriction enzymes

The 16 Type IIB restriction endonucleases that could be used for simulating the restriction digestion of Drosophila genomes along with their recognition sites, average distance between the restriction sites assuming random distribution of nucleotides and without any compositional bias, and the size of fragment (blunt) that the enzymes leaves behind are given in Table 2 (Tengs et al., 2004). Unlike traditional Type II enzymes, Type IIB enzymes cleave on both sides of the recognition sequence (about 7–15 bases upstream and downstream, depending on enzyme) generating a fragment of uniform length. Also, the recognition site is usually split into two parts by some fixed number of random bases. They normally leave 2–3 base overhangs on the generated fragment.

Table 2:
List of enzymes used for the fragment generation from the 21 Drosophila species.
Frequency indicates estimated distance between cut sites given a random sequence with all the 4 bases in equal probability and length refers to blunt tag length.
Enzyme Recognition sequence Frequency Length
BslFI GGGAC 512 21
DOI: 10.7717/peerj.226/table-2

Fragment analyses

The numbers of representative fragments obtained from each genome for each enzyme are listed in Table 3. The most frequent cutting enzymes such as BslFI had generally higher numbers of fragments within all genomes compared to other enzymes. Also, D. pseudoobscura and D. persimilis had relatively higher numbers of fragments compared to other genomes with most of the enzymes. Following fragment extraction, the original genomic sequences downloaded from various source databases were represented as a collection of fragments of uniform length. For each genome a total of 16 fragment sets were generated by using 16 different type IIB enzymes. The number of fragments generated by each genome was not closely related to the size of their genomes but they were related to the GC content. Most of the enzymes used in the analysis recognized a GC rich recognition site which is reflected in the number of fragments generated with GC rich genomes. The genomes that were GC rich such as D. pseudoobscura and D. persimilis had higher numbers of fragments compared to other genomes. Similarly the genomes that had lower GC content such as D. willistoni and D. grimshawi generated fewer fragments. Overall, the number of fragments obtained for each species were within the range of expected fragments based on their genome size and estimated distance between restriction cut sites (assuming random sequence without GC content bias). Most enzymes predicted to be frequent cutters generated large number of fragments like BslFI. Predicted rare cutters like PsrI, PpiI, AloI and CspCI generated fewer fragments than other enzymes.

Table 3:
Total number of fragments generated using 13 different Type IIB restriction enzymes for each of the 21 Drosophila genomes.
Genomes AlfI AloI BaeI BcgI BplI BsaXI BslFI Bsp24I CspCI FalI HaeIV PpiI PsrI
D. ananassae 34804 11421 6151 51646 21457 52433 101183 46042 16405 38109 74174 11193 8344
D. biarmipes 41242 12667 6875 63518 22752 51248 109404 44554 18178 41284 75291 12177 10210
D. bipectinata 35642 10893 6616 51208 20363 50001 98937 45563 17131 39286 73197 10545 8622
D. elegans 43207 11314 6068 59905 18764 45496 93763 43259 18466 41866 75238 11027 9753
D. erecta 42781 10517 5914 60434 18119 43684 85735 40020 17793 31931 66412 9979 8677
D. eugracilis 36455 10170 5699 51988 18236 43177 86365 42020 17568 40795 72398 9682 8335
D. ficusphila 38374 11698 5338 60448 20161 47056 89928 39223 17489 37380 69222 11070 8868
D. grimshawi 49667 5891 5212 61420 17341 30379 58175 35658 16642 34409 64560 8062 6977
D. kikkawai 39192 10361 5516 54698 21908 50258 99784 44066 16846 40965 68593 10765 8126
D. melanogaster 39711 9908 6037 59203 16840 41168 81877 39221 17651 31350 68204 9243 8303
D. mojavensis 54782 6294 5234 64186 21048 33289 60708 36674 14774 33071 65210 9090 8012
D. persimilis 43327 10706 7567 59923 25287 53206 113002 48862 16329 31779 76473 12267 8940
D. pseudoobscura 43650 10461 7466 60237 25174 53269 111423 48990 16358 31417 74808 12175 8774
D. rhopaloa 36920 10920 6177 56203 18139 44894 93524 41357 17133 40153 76711 10442 9247
D. santomea 40344 9877 5957 56771 17044 41850 80010 38107 17037 32142 67070 9414 8378
D. sechellia 39876 10371 5808 59204 17430 42659 83936 39380 17276 31541 68359 9792 8289
D. simulans 38549 9815 5547 56820 16777 40735 79826 37436 16666 30304 64321 9148 7773
D. takahashii 37489 11463 5431 58887 19189 45240 91825 39992 26269 37277 74002 10801 8987
D. virrilis 58785 6943 5774 64912 18097 31951 66710 38679 15733 37692 65275 9290 8551
D. willistoni 34033 7083 6177 43299 15103 35578 70085 39996 17240 42202 77102 7941 9626
D. yakuba 42202 10300 6165 59442 17885 43748 83095 39920 18007 33024 69632 9887 8765
DOI: 10.7717/peerj.226/table-3

Distance matrices and phylogenetic trees

A comparison of fragments between genomes provided a list of fragments that were shared by those genomes. Closely related organisms are expected to share higher numbers of similar fragments (including identical fragments) compared to other distantly related genomes. Similar fragments are defined as those with 6 or fewer mismatches. Since the average length of fragments generated from various enzymes was around 27 bases, allowing 5 bases mismatch ensured at least 80% similarity among the sequences. The fragments being compared between 2 genomes ranged from 21 bp to 33 bp long (average size of 27 bp). The identical fragments between the 2 genomes are most likely to represent homologous or even orthologous sections of the genomes. Even for a fragment length of 21 bp (smallest fragment size produced by these enzymes), the probability that a particular 21 bp sequence exists one or more times in a genome of 150 Mb is 0.00341%. The pair-wise distance matrices constructed using the similar fragments detected by each enzyme were used to estimate phylogenetic trees (Fig. 2). The individual NJ trees obtained for each enzyme were largely consistent with the currently accepted relationships among the various Drosophila groups and subgroups, as was the single consensus tree obtained (Fig. 3). Per cent support values were calculated based on number of enzymes supporting the particular branch.

The consensus phylogenetic tree obtained by combining the trees obtained for each of the 13 enzymes.

Figure 2: The consensus phylogenetic tree obtained by combining the trees obtained for each of the 13 enzymes.

The phylogenetic tree for each enzyme was calculated by extracting the corresponding fragments and then counting the number of shared fragment between every pair of species. The upper branch support values represent the percentage agreement over 13 enzymes and the bottom values indicate number of enzymes out of total 13 enzymes supporting the branch.
Single enzyme tree (AloI enzyme) showing the branch length.

Figure 3: Single enzyme tree (AloI enzyme) showing the branch length.


The 21 species of Drosophila used here included the subgenus Sophophora and the subgenus Drosophila. The Sophophora group was represented by melanogaster, obscura and willistoni and the Drosophila group was represented by virilis, repleta and mojavensis. Out of the 12 subgroups within the melanogaster group, 9 subgroups viz., ananassae, montium, melanogaster, suzukii, takahashii, ficusphila, elegans, rhopaloa and eugracilis were represented by 15 species. Of these, only 2 subgroups had multiple members within our data set, but both displayed a monophyletic arrangement within the final tree shown in Fig. 2. The placement of the 12 well-studied Drosophila species viz., D. simulans, D. sechellia, D. melanaogaster, D. erecta, D. ananassae, D. yakuba, D. pseudoobscura, D. persimilis, D. willistoni, D. mojavensis, D. virilis and D. grimshawi within our tree corresponds exactly to the currently accepted phylogeny (Clark et al., 2007; Hahn, Han & Han, 2007; Haubold & Pfaffelhuber, 2012; Stark et al., 2007).

Overall, the topology of our 21 species tree agrees precisely with those presented by van der Linde et al. (2010), Haubold & Pfaffelhuber (2012) and Yang et al. (2012) and all the branches were completely resolved. The subgenus Sophophora was clearly distinguished into old world clades melanogaster/obscura and neo world clade willistoni in our tree (van der Linde & Houle, 2008). The largest group melanogaster, had multiple subgroups viz., melanogaster, montium, ananassae and oriental subgroup cluster (eugaracilis, suzukii, takahashii, elegans, rhopaloa, ficusphila). Many previous studies have failed to completely resolve the nodes within the oriental subgroup cluster (Da Lage et al., 2007; Toda, 1991). In our tree, ananassae group formed the earliest branch in the melanogaster group followed by montium subgroup with strong branch support values. Most of the earlier studies confirmed this topology (Da Lage et al., 2007; Kopp, 2006; Prud’homme et al., 2006) except for two studies that placed them together as a sister clade from the rest of the subgroups (Schawaroch, 2002) or reversed the order of branching (Yang et al., 2004). Both these studies had poor branch support. The oriental subgroups cluster formed three sub-clades. The first sub-clade included elegans and rhopaloa with ficusphila as the sister sub-group, the second sub-clade included suzukii and takahashii and the third sub-clade included the eugracilis sub-group. The placements of these sub-clades were controversial among the literature surveyed and was attributed to the explosive radiation of these oriental groups (van der Linde & Houle, 2008). The eugracilis clade consisting of D. eugracilis is most inconsistently placed clade and it is either placed as sister species of melanogaster sub group, as in our tree (Haubold & Pfaffelhuber, 2012; Pelandakis & Solignac, 1993; van der Linde et al., 2010) or as sister species of the sub clade formed by suzukii and takahashii (Yang et al., 2004) or as sister species of elegans and rhopaloa within the elegansrhopaloaficusphila clade (Yang et al., 2012). The placements of the other two clades, suzukiitakahashii and elegansrhopaloaficusphila within the melanogaster group in our tree is in agreement with other published studies (Kopp, 2006; Kopp & True, 2002). The sub-clade formed by suzukii and takahashii is well supported by most studies including ours with the strong branch support (Da Lage et al., 2007; Kopp & True, 2002; Schawaroch, 2002; Yang et al., 2004). Most studies have confirmed that the rhopaloa subgroup is the sister group of the elegans subgroup but the ficusphila sub group is considered to be polytomic branching clade in the melanogaster group (van der Linde & Houle, 2008). However, in our tree ficusphila sub group is presented as the sister species of rhopaloaelegans subgroups, albeit with low branch support. Within the Drosophila subgenus, all three groups (virilis, repleta and grimshawi) exhibited a topology frequently observed in other studies (van der Linde & Houle, 2008).

A variety of sub-genomic sampling methods have been used previously for population studies and are especially effective on non-model organisms, but are rarely used for generating phylogenies for a diverse set of distinct species. We show here that multi-locus data obtained from short sub-genomic fragment sets, essentially 2b-RAD, provides good phylogenetic signal and produces a well resolved and well-supported species phylogeny. The wide adoption of various RAD-like methods is due to the fact that deep sequencing of the fragments produced can be easily accomplished following two simple steps: adapter ligation, and then PCR. These methods are applicable to any organism irrespective of its genome size. The 2b-RAD approach to fragment generation and characterization in particular is simple, quick and cost effective (Wang et al., 2012). This method also shares some similarity with the recently described, alignment free multi-locus “co-phylog” method (Yi & Jin, 2013). Both use a large number of short homologous fragments and, consequently, both can be profitably applied to short sequence reads derived via next generation sequencing, even prior to assembly. However, the co-phylog method is distinct in that it makes use of standard alignment algorithms applied to each locus to generate estimates of relatedness for building phylogenies. Effective application of the co-phylog method generally requires that the genomes being compared be closely related, and this would be expected to be true for our method as well, since effective matching of homologous short fragments in either case requires a significant degree of local sequence similarity. Despite this expected limitation, we note that the Drosophila species compared herein are relatively diverse, spanning approximately 40–50 million years of evolution.