Increasing phylogenetic resolution at low taxonomic levels using massively parallel sequencing of chloroplast genomes
© Parks et al; licensee BioMed Central Ltd. 2009
Received: 12 November 2009
Accepted: 2 December 2009
Published: 2 December 2009
Molecular evolutionary studies share the common goal of elucidating historical relationships, and the common challenge of adequately sampling taxa and characters. Particularly at low taxonomic levels, recent divergence, rapid radiations, and conservative genome evolution yield limited sequence variation, and dense taxon sampling is often desirable. Recent advances in massively parallel sequencing make it possible to rapidly obtain large amounts of sequence data, and multiplexing makes extensive sampling of megabase sequences feasible. Is it possible to efficiently apply massively parallel sequencing to increase phylogenetic resolution at low taxonomic levels?
We reconstruct the infrageneric phylogeny of Pinus from 37 nearly-complete chloroplast genomes (average 109 kilobases each of an approximately 120 kilobase genome) generated using multiplexed massively parallel sequencing. 30/33 ingroup nodes resolved with ≥ 95% bootstrap support; this is a substantial improvement relative to prior studies, and shows massively parallel sequencing-based strategies can produce sufficient high quality sequence to reach support levels originally proposed for the phylogenetic bootstrap. Resampling simulations show that at least the entire plastome is necessary to fully resolve Pinus, particularly in rapidly radiating clades. Meta-analysis of 99 published infrageneric phylogenies shows that whole plastome analysis should provide similar gains across a range of plant genera. A disproportionate amount of phylogenetic information resides in two loci (ycf1, ycf2), highlighting their unusual evolutionary properties.
Plastome sequencing is now an efficient option for increasing phylogenetic resolution at lower taxonomic levels in plant phylogenetic and population genetic analyses. With continuing improvements in sequencing capacity, the strategies herein should revolutionize efforts requiring dense taxon and character sampling, such as phylogeographic analyses and species-level DNA barcoding.
Molecular phylogenetic and phylogeographic analyses are typically limited by DNA sequencing costs, and this forces investigators to choose between dense taxon sampling with a small number of maximally informative loci, or genome-scale sampling across a sparse taxon sample [1–4]. Balancing these choices is particularly difficult in studies focused on recently diverged taxa or ancient rapid radiations, as taxon sampling needs to be sufficiently large to define the magnitude of intraspecific variation and the phylogenetic depth of shared alleles [5, 6]. Similarly, broad genome sampling is necessary to offset the low level of genetic divergence among individuals of recent co-ancestry and to overcome low phylogenetic signal to noise ratios characteristic of rapid radiations . Next generation DNA sequencing is poised to bring the benefits of affordable genome-scale data collection to such studies at low taxonomic levels (genera, species, and populations). Massively parallel sequencing (MPS) has increased per instrument sequence output several orders of magnitude relative to Sanger sequencing, with a proportional reduction in per-nucleotide sequencing costs [7, 8]. In principle this could allow the rapid sequencing of large numbers of entire organellar genomes (chloroplast or mitochondria) or nuclear loci, and result in greatly increased phylogenetic resolution . To date, comparatively few plant or animal evolutionary genetic analyses have utilized MPS [10–12], due to associated costs and the technical challenge of assembling large contiguous sequences from micro-reads. These barriers have been largely eliminated through four innovations: development of strategies for targeted isolation of large genomic regions [9, 13–15]; harnessing the capacity of these platforms to sequence targeted regions in multiplex [9, 14, 16]; streamlining sample preparation and improving throughput ; and developing accurate de novo assemblers that reduce reliance upon a predefined reference sequence [18, 19].
In this paper we demonstrate the feasibility and effectiveness of MPS-based chloroplast phylogenomics for one-third of the world's pine species (Pinus), a lineage with numerous unresolved relationships based on previous cpDNA-based studies [20–22]. We also highlight the broad applicability of our approach to other plant taxa, and remark on the potential applications to similar mitochondrial-based studies in animals and plant DNA barcoding. Using multiplex MPS approaches, we sequenced nearly-complete chloroplast genomes (120 kilobases (kb) each total length) from 32 species in Pinus and four relatives in Pinaceae. Our sampling of Pinus includes both subgenera (subg. Pinus, 14 accessions; subg. Strobus, 21 accessions) and species exemplars chosen from all 11 taxonomic subsections  to evenly cover the phylogenetic diversity of the genus. Taxon density is highest for a chosen subsection (subsect. Strobus) as representative of a species-rich clade lacking phylogenetic resolution in previous studies [5, 21–23]. Three species are also represented by two chloroplast genomes each (P. lambertiana, P. thunbergii, P. torreyana).
Genomic Assemblies and Alignment
Multiplex tags and read count for sampled accession.
Number of Reads
(bp, without tag)
P. lambertiana N
P. lambertiana S
P. torreyana ssp. torreyana
P. torreyana ssp. insularis
Summary of variable and parsimony informative sites in data partitions.
(% of total)
(% of total)
Pines and outgroups
(% of total)
(% of total)
All Nucleotides without ycf1, ycf2
Exon Nucleotides without ycf1, ycf2
Wang et al. 
Gernandt et al. 
Eckert and Hall 
Codon-based Z-test for selection results for exon sequences.
dN > dS
dN < dS
dN > dS
dN < dS
Phylogenetic Resolution in Non-Random and Randomized Data Partitions
Shimodaira-Hasegawa test results.
P. albicaulis, P. lambertianaN, P. parvifloratopologies
Figure 2 vs. 3A
2 vs. 3A
Figure 2 vs. 2
2 vs. 3A
Figure 2 vs. 3A
2 vs. 2
Figure 2 vs. 3B
2 vs. 3B
Figure 2 vs. 3A
2 vs. 3B
Figure 2 vs. 3A
2 vs. 2
Figure 2 vs. 3C
2 vs. 3C
Figure 2 vs. 2
2 vs. 3C
Figure 2 vs. 3C
2 vs. 2
Estimated divergence times of poorly resolved nodes
ML branch length (substitutions/site)
P. krempfii - section Quinquefoliae
P. parviflora -
P. albicaulis -
P. lambertiana N
P. cembra -
Comparisons to Previous PinusPhylogenies
Previous cpDNA based estimates of infrageneric relationships in Pinus [20–22] sampled the same species and/or lineages as our study, and inferred relationships using 2.82 to 3.57 kb of chloroplast DNA. Results of these studies are largely consistent with our results, although highly supported nodes (≥ 95%) accounted for only 13 to 23% of the total ingroup nodes (23% to 42% if [20, 21] adjusted to match our species composition). The empirical results of these studies fell within or close to the 95% prediction intervals established from our jackknife resampling response from our full genome alignment (Figure 7A), indicating that the loci used in prior studies (primarily rbcL and matK) are similarly informative as a comparable sample of random nucleotides from the chloroplast genome.
Meta-Analysis of Published Infrageneric Studies
From our sampling, infrageneric analyses in plants published from 2006 to 2008 were typically based on 2574 aligned bp (95% bootstrap confidence interval: 2,292, 2,864) of sequence data, evaluated 31.7 ingroup species (95% bootstrap confidence interval: 20.2, 43.2), and resolved 22.6% of nodes at ≥ 95% bootstrap support (95% bootstrap confidence interval: 18.6, 26.5). Regression analysis shows that the proportion of highly resolved nodes in these studies is significantly and positively correlated with matrix length (F1,96 = 18.032; r2 = 0.149; P < 0.0001) but not the number of included taxa (F1,97 = 0.546; r 2 = 0.006; P = 0.461), although there was a negative trend in the latter (Figure 7B, C). Our current sample size is typical in the number of taxa sampled, but both matrix length (132.7 kb) and the proportion of highly bootstrap-supported nodes (84.8% parsimony, 90.3% maximum likelihood) were substantially higher.
When considering recent divergence, the disproportionately high mutation rate in ycf1 (and ycf2, to a lesser extent) demonstrated here is of importance, and mirrors findings in other plant taxa [31, 32] and recently in Pinus subsection Ponderosae . These loci should be informative for phylogenetic studies in recently-diverged clades or in population-level studies in a range of plant species. Discretion is advised, however, as ycf1 (and possibly ycf2) appears to be a target of positive selection at least in Pinus and may reflect adaptive episodes rather than neutral genealogies. In likelihood analyses of ycf1 and ycf2, we observed several topological differences from the full alignment at the subsectional level, further demonstrating that caution must be taken in drawing phylogenetic conclusions from these two loci. Although we were able to confidently score small structural changes (indels and stop codon shifts) for all other exons, it was not possible to score indels for ycf1 and ycf2 due to the apparent high rate of indel formation in these loci. In all other loci examined, small structural changes only delineated clades with concurrent high support from nucleotide-based analyses (both in present study and [20–22]), and thus are likely to be of limited use in species or population level discrimination. It is not clear whether this will also be the case in ycf1 and ycf2.
It is reasonable to ask whether increased resolution is worth the effort of assembling whole plastomes. Considering the conservative nature of bootstrap measures [34–37], systematists often accept bootstrap values of ≥ 70% as reliable indicators of accurate topology . Simulation studies , however, have demonstrated greatly increased accuracy (approximately 42×) with bootstrap values ≥ 95% versus ≥ 70%, and the initial formulation of the phylogenetic bootstrap used ≥ 95% as the threshold for topological significance . Our results similarly support using a 95% bootstrap support cutoff for conclusive evidence as in both areas of topological differences, more than one clade received bootstrap support ≥ 70% by analysis of alternate data partitions. It is probable that conflicting topologies with ≥ 70% but < 95% bootstrap support accurately reflect data partitions yet may not represent the plastome phylogeny, and here the use of entire organelle genomes makes it possible to adopt more conservative criteria of nodal support. There are further biological reasons why an organellar phylogeny (essentially a single-gene estimate) may not accurately represent the organismal phylogeny; these include interspecific hybridization, incomplete lineage sorting, and stochastic properties of the coalescent process. Nonetheless, phylogenetic reconstruction based on complete organellar sequences may facilitate the detection of such phenomena, by reducing errors and uncertainty due to insufficient sampling of DNA sequence.
Plastome sequencing is now a reasonable option for increasing resolution in phylogenetic studies at low taxonomic levels and will continue to become an increasingly simple process. As sequencers evolve to even higher capacity and multiplexing becomes routine in the near future, this will allow more extensive taxon and genomic sampling in phylogenetic studies at all taxonomic levels. It is estimated that sequencing capacity on next generation platforms will approach 100 gigabase pairs per sequencing run by the end of 2009. For perspective, this is sufficient sequence capacity to produce all 100 genus-level data sets used in our meta-analysis (including ours) at greater than 100× coverage depth in a single sequencing run. Based on the estimates of Cronn et al. , this sequencing capacity would also allow the simultaneous sequencing of several thousands of animal mitochondria, which could greatly benefit low-level taxonomic or population-based studies in animals that currently tend to rely on relatively short sequences from many individuals . It is also clear that these improvements could enable other pursuits that are currently hindered by limited sequencing capacity, such as identification of plants by diagnostic DNA sequences (DNA barcoding). The recently agreed upon two locus chloroplast barcode for plants claims only 72% unique identification to species level . Based on results herein, whole plastome sequences have the potential to be more highly discriminating and efficient plant DNA barcodes; in fact, the possibility of plastome- and mitome-scale barcodes has been raised previously . Results in this area (as well as in phylogenetic and phylogeographic analyses) will be impacted particularly if advances in target isolation and enrichment [13–15] and streamlining sample preparation  prove globally effective.
DNA Extraction, Amplification and Sequencing
DNA extraction, amplification and sequencing are described in and followed Cronn et al. , with 4 bp multiplex tags, replacing the original 3 bp tags (Table 1). For one sample, P. ponderosa, additional reads from three non-multiplexed lanes of genomic DNA were also included.
Sequence Assembly and Genome Alignments
Sequence assembly and alignment are described in and followed Whittall et al. . An analysis of interspecific recombination was conducted using RDP(Recombination Detection Program) v. 3.27 . Rather than using the full genomic alignment, which was too memory-intensive, concatenated nucleotide sequences for 71 exons common to all accessions were used (reflective of order on the plastome). Subgenera were investigated separately as members of opposing subgenera appear incapable of hybridization . Each subgenus was checked for recombination events using standard settings for several recombination-detection strategies, including: RDP , GeneConv , Chimaera , MaxChi , BootScan , and SiScan . A total of 24 putative recombination events were identified. On close investigation, all events involved one or more of the following: misalignment, autapomorphic noise coupled with missing data, and amplification of pseudogenes. In cases of misalignment, alignments were corrected prior to subsequent phylogenetic analyses. In cases of amplification of pseudogenes, the entire amplicon for the accession involved was turned to Ns. Inspection of the alignment also revealed that some amplicons in some accessions had failed to amplify, or amplified apparently paralogous loci (evidenced by substantially higher divergence). These regions were masked in affected accessions. The locus matK was determined to be a putative paralog in several accessions, and in four (P. armandii, P. lambertiana S, P. albicaulis, and P. ayacahuite) it was replaced with Sanger sequence . We also replaced 2180 bp of poor quality sequence of the locus ycf1 in P. ponderosa with Sanger sequence. In all accessions amplified by PCR, the regions adjacent to primer sites typically had low coverage, while primers had very high coverage, thus primer-flanking regions (where problematic) and the primers were also excluded. It was also determined through Sanger sequencing that a 600 bp region of the previously published P. koraiensis plastome (positions 48808 to 49634 in GenBank AY228468) is apparently erroneous. This region was removed and reference guided analysis was rerun for this amplicon.
Aligned sequences were annotated using DOGMA (Dual Organellar Genome Annotator)  with manual adjustments to match gene predictions from GenBank and the Chloroplast Genome Database http://chloroplast.cbio.psu.edu/. Exons were evaluated for reading frame and translations, and validity of exon mutations was judged based on presence in de novo sequence, effect on the resulting polypeptide sequence, and sequence coverage depth.
Sequence data was analyzed using all genome positions and concatenated nucleotide sequence from 71 exons common to all pine accessions; both partitions were analyzed with and without the loci ycf1 and ycf2. A relatively short (approximately 630 bp) repetitive stretch of the locus ycf1 of subgenus Strobus accessions was masked in all analyses due to alignment ambiguity. The loci ycf1 and ycf2 (ca. 14 kb combined) were also analyzed individually and together.
Maximum Likelihood (ML) phylogenetic analyses were performed through the Cipres Web Portal http://www.phylo.org/portal/Home.do using RAxML bootstrapping with the general model of nucleotide evolution (GTR+G)  and automatically determined numbers of bootstrap replicates. Bayesian inference analyses (BI) were performed using MrBayes v. 3.1.2  using the GTR+G+I model, which was selected using MrModelTest v. 2.3  under both Aikake Information Criterion and Hierarchical Likelihood Ratio Test frameworks. Each analysis consisted of two runs with four chains each (three hot and one cold chain), run for 1000000 generations with trees sampled every 100 generations. The first 25% percent of trees from all runs were discarded as burn-in. Unweighted maximum parsimony analyses (MP) of data partitions were conducted in PAUP* (Phylogenetic Analysis Using Parsimony (*and other methods)) v. 4.0b10  by heuristic search with 10 replicates of random sequence addition, tree bisection and reconnection branch swapping and a maxtrees limit of 1,000. Non-parametric bootstrap analysis was conducted under the same conditions for 1,000 replicates to determine branch support.
Topological differences between the full alignment topology and each of the three other largest data partitions (full alignment without ycf1 and ycf2, and exon nucleotides both with and without ycf1 and ycf2) were tested for significance using the Shimodaira-Hasegawa test  with resampling estimated log-likelihood (RELL) bootstrapping (1,000 replicates) under the GTR+G model of evolution. To further determine which topological differences were most influential, tests were repeated with the positions of topology-variable accessions alternately modified to match the full alignment topology. In total, the full alignment data set was compared to nine different topologies.
Exon indels and stop codon shifts were mapped onto the topology determined by ML analysis of the full alignment by parsimony mapping using Mesquite v. 2.6 (Maddison and Maddison, http://mesquiteproject.org). Tests of selection for exons were performed in MEGA v. 4.0  using the codon-based Z-test for selection, with pairwise deletion and the Nei-Gojobori (P-distance) model; variance of the differences were computed using the bootstrap method with 500 replicates.
Estimation of Divergence Times for Poorly Resolved Nodes
Divergence times for four nodes with topological uncertainty (P. albicaulis - P. lambertiana N - P. parviflora, P. sibirica - P. cembra - P. koraiensis, P. krempfii-section Quinquefoliae of subgenus Strobus) were estimated according to Pollard et al. . Chloroplast mutation rate was estimated by averaging maximum and minimum mutation rates for Pinaceae chloroplast genomes from two previous studies [59, 60] and assuming a generation time of 50 years . Two estimates were calculated for each node using either low (10,000) or high (100,000) effective population size .
Effect of Character Number on Phylogenetic Resolution
Empirical data from Pinus genomes
Variable-size random subsamples of the full alignment were tested under the parsimony criteria using PAUP* v. 4.0b10 (the faststep option was used for all but the two smallest partitions due to time considerations). Eleven partition sizes were tested (2.5, 5, 10, 20, 30, 40, 50, 60, 80, 100 and 120 kb) in five replicates each, with resolution measured as the percentage of ingroup nodes produced with ≥ 95% jackknife support. Relationships between partition size and ingroup resolution were estimated using least squares regressions, and 95% confidence limits for individual points were estimated based on linear regression using SAS JMP 7.0.1 (S.A.S. Institute, Inc., http://www.jmp.com/). Our full alignment, exon nucleotides and ycf1/ycf2 partitions were analyzed under the same parsimony criteria for comparison, as were the alignments of [20–22]. Accessions from Gernandt et al. and Eckert et al. [20, 21] were pruned to include only taxa common to our sampling; the original analysis of Wang et al.  was used since this data matrix was not available for alternative phylogenetic analyses.
Meta-Analysis of Published Studies
We evaluated 99 phylogenetic analyses from 86 studies published between 2006 and 2008 in Systematic Botany, Systematic Biology, American Journal of Botany, Taxon, Molecular Phylogenetics and Evolution, and Annals of the Missouri Botanical Garden [see additional file 2]. Analyses were selected based on: 1) the presented phylogeny was based solely on chloroplast DNA sequence; 2) the analysis included ≥ 10 species from a monophyletic genus; 3) there were more inter- than intra-specific taxa analyzed within the genus; 4) parsimony-based bootstrap or jackknife values were presented. Ingroup branches with bootstrap support ≥ 95%, the number of ingroup taxa and the aligned base pairs used in the analysis were recorded for each case. The authors' taxonomic interpretations were accepted in instances of taxonomic uncertainty. Conspecific clades were treated as one taxon unless clearly differentiated from one another, and internal bootstrap values were disregarded. The number of branches with bootstrap support ≥ 95% was regressed both on the number of aligned base pairs and the number of taxa (both log-transformed to meet assumptions of normality and equal variances).
Illumina sequencing reads and quality scores have been deposited in the NCBI SRA database as accession SRA009802. New sequences have been deposited in GenBank as accessions FJ899555-FJ899583.
Accession numbers cited in manuscript
[GenBank FJ899555-FJ899583, EU998739-EU998746, SRA009802]
massively parallel sequencing.
We thank Mariah Parker-deFeniks and Sarah Sundholm for lab assistance, Uranbileg Daalkhaijav, Zachary Foster and Brian Knaus for computing assistance, Linda Raubeson for providing a chloroplast isolation of Larix occidentalis, Christopher Campbell and Justen Whittall for DNA samples, David Gernandt, Chris Pires, Jonathan Wendel and Mark Fishbein for editorial comments, and Steffi Ickert-Bond for timely questions. We also thank Mark Dasenko, Scott Givan, Chris Sullivan and Steve Drake of the OSU Center for Genome Research and Biocomputing. This work was supported by National Science Foundation grants (ATOL-0629508 and DEB-0317103 to A.L. and R.C.), the Oregon State University College of Science Venture Fund and the US Forest Service Pacific Northwest Research Station.
- Moore MJ, Bell CD, Soltis PS, Soltis DE: Using plastid genome-scale data to resolve enigmatic relationships among basal angiosperms. Proc Natl Acad Sci USA. 2007, 104: 19363-10.1073/pnas.0708072104.PubMed CentralView ArticlePubMedGoogle Scholar
- Delsuc F, Brinkmann H, Philippe H: Phylogenomics and the reconstruction of the tree of life. Nature Rev Genet. 2005, 6: 361-375. 10.1038/nrg1603.View ArticlePubMedGoogle Scholar
- Philippe H, Frederic D, Henner B, Lartillot N: Phylogenomics. Annu Rev Ecol Evol Syst. 2005, 36: 541-542. 10.1146/annurev.ecolsys.35.112202.130205.View ArticleGoogle Scholar
- Jansen RK, Cai Z, Raubeson LA, Daniell H, Depamphilis CW, Leebens-Mack J, Muller KF, Guisinger-Bellian M, Haberle RC, Hansen AK: Analysis of 81 genes from 64 plastid genomes resolves relationships in angiosperms and identifies genome-scale evolutionary patterns. Proc Natl Acad Sci USA. 2007, 104: 19369-10.1073/pnas.0709121104.PubMed CentralView ArticlePubMedGoogle Scholar
- Liston A, Parker-Defeniks M, Syring JV, Willyard A, Cronn R: Interspecific phylogenetic analysis enhances intraspecific phylogeographical inference: a case study in Pinus lambertiana. Mol Ecol. 2007, 16: 3926-3937. 10.1111/j.1365-294X.2007.03461.x.View ArticlePubMedGoogle Scholar
- Whitfield JB, Lockhart PJ: Deciphering ancient rapid radiations. Trends Ecol Evol. 2007, 22: 258-265. 10.1016/j.tree.2007.01.012.View ArticlePubMedGoogle Scholar
- Hudson ME: Sequencing breakthroughs for genomic ecology and evolutionary biology. Molecular Ecology Resources. 2008, 8: 3-17. 10.1111/j.1471-8286.2007.02019.x.View ArticlePubMedGoogle Scholar
- Mardis ER: The impact of next-generation sequencing technology on genetics. Trends Genet. 2008, 24: 133-141.View ArticlePubMedGoogle Scholar
- Cronn R, Liston A, Parks M, Gernandt DS, Shen R, Mockler T: Multiplex sequencing of plant chloroplast genomes using Solexa sequencing-by-synthesis technology. Nucleic Acids Res. 2008, 36: e122-10.1093/nar/gkn502.PubMed CentralView ArticlePubMedGoogle Scholar
- Gilbert MTP, Drautz DI, Lesk AM, Ho SYW, Qi J, Ratan A, Hsu CH, Sher A, Dalen L, Gotherstrom A: Intraspecific phylogenetic analysis of Siberian woolly mammoths using complete mitochondrial genomes. Proc Natl Acad Sci USA. 2008, 105: 8327-10.1073/pnas.0802315105.PubMed CentralView ArticlePubMedGoogle Scholar
- Moore MJ, Dhingra A, Soltis PS, Shaw R, Farmerie WG, Folta KM, Soltis DE: Rapid and accurate pyrosequencing of angiosperm plastid genomes. BMC Plant Biol. 2006, 6: 17-10.1186/1471-2229-6-17.PubMed CentralView ArticlePubMedGoogle Scholar
- Ossowski S, Schneeberger K, Clark RM, Lanz C, Warthmann N, Weigel D: Sequencing of natural strains of Arabidopsis thaliana with short reads. Genome Res. 2008, 18: 2024-10.1101/gr.080200.108.PubMed CentralView ArticlePubMedGoogle Scholar
- Gnirke A, Melnikov A, Maguire J, Rogov P, LeProust EM, Brockman W, Fennell T, Giannoukos G, Fisher S, Russ C: Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nature Biotech. 2009, 27: 182-189. 10.1038/nbt.1523.View ArticleGoogle Scholar
- Porreca GJ, Zhang K, Li JB, Xie B, Austin D, Vassallo SL, LeProust EM, Peck BJ, Emig CJ, Dahl F, Gao Y, Church GM, Shendure J: Multiplex amplification of large sets of human exons. Nature Meth. 2007, 4: 931-936. 10.1038/nmeth1110.View ArticleGoogle Scholar
- Herman DS, Hovingh GK, Iartchouk O, Rehm HL, Kucherlapati R, Seidman JG, Seidman CE: Filter-based hybridization capture of subgenomes enables resequencing and copy-number detection. Nature Meth. 2009, 6: 507-510. 10.1038/nmeth.1343.View ArticleGoogle Scholar
- Craig DW, Pearson JV, Szelinger S, Sekar A, Redman M, Corneveaux JJ, Pawlowski TL, Laub T, Nunn G, Stephan DA: Identification of genetic variants using bar-coded multiplexed sequencing. Nature Meth. 2008, 5: 887-893. 10.1038/nmeth.1251.View ArticleGoogle Scholar
- Quail MA, Kozarewa I, Smith F, Scally A, Stephens PJ, Durbin R, Swerdlow H, Turner DJ: A large genome center's improvements to the Illumina sequencing system. Nature Meth. 2008, 5: 1005-1010. 10.1038/nmeth.1270.View ArticleGoogle Scholar
- Hernandez D, Francois P, Farinelli L, Osteras M, Schrenzel J: De novo bacterial genome sequencing: millions of very short reads assembled on a desktop computer. Genome Res. 2008, 18: 802-809. 10.1101/gr.072033.107.PubMed CentralView ArticlePubMedGoogle Scholar
- Zerbino DR, Birney E: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res. 2008, 18: 821-829. 10.1101/gr.074492.107.PubMed CentralView ArticlePubMedGoogle Scholar
- Eckert AJ, Hall BD: Phylogeny, historical biogeography, and patterns of diversification for Pinus (Pinaceae): Phylogenetic tests of fossil-based hypotheses. Mol Phylogenet Evol. 2006, 40: 166-182. 10.1016/j.ympev.2006.03.009.View ArticlePubMedGoogle Scholar
- Gernandt DS, Lopez G, Garcia SO, Liston A: Phylogeny and classification of Pinus. Taxon. 2005, 54: 29-42. 10.2307/25065300.View ArticleGoogle Scholar
- Wang XR, Tsumura Y, Yoshimaru H, Nagasaka K, Szmidt AE: Phylogenetic relationships of Eurasian pines (Pinus, Pinaceae) based on chloroplast rbcL, matK, rpl20-rps18 spacer, and trnV intron sequences. Am J Bot. 1999, 86: 1742-1753. 10.2307/2656672.View ArticlePubMedGoogle Scholar
- Syring J, Farrell K, Businsky R, Cronn R, Liston A: Widespread genealogical nonmonophyly in species of Pinus subgenus Strobus. Syst Biol. 2007, 56: 163-181. 10.1080/10635150701258787.View ArticlePubMedGoogle Scholar
- Lidholm J, Gustafsson P: The chloroplast genome of the gymnosperm Pinus contorta : a physical map and a complete collection of overlapping clones. Curr Genet. 1991, 20: 161-166. 10.1007/BF00312780.View ArticlePubMedGoogle Scholar
- Palmer JD: Comparative organization of chloroplast genomes. Annu Rev Genet. 1985, 19: 325-354. 10.1146/annurev.ge.19.120185.001545.View ArticlePubMedGoogle Scholar
- Drescher A, Ruf S, Calsa T, Carrer H, Bock R: The two largest chloroplast genome-encoded open reading frames of higher plants are essential genes. Plant J. 2000, 22: 97-104. 10.1046/j.1365-313x.2000.00722.x.View ArticlePubMedGoogle Scholar
- Fishbein M, Hibsch-Jetter C, Oltis DES, Hufford L: Phylogeny of Saxifragales (angiosperms, eudicots): analysis of a rapid, ancient radiation. Syst Biol. 2001, 50: 817-847. 10.1080/106351501753462821.View ArticlePubMedGoogle Scholar
- Wortley AH, Rudall PJ, Harris DJ, Scotland RW: How much data are needed to resolve a difficult phylogeny? Case study in Lamiales. Syst Biol. 2005, 54: 697-709. 10.1080/10635150500221028.View ArticlePubMedGoogle Scholar
- Wang XR, Szmidt AE, Nguyên HN: The phylogenetic position of the endemic flat-needle pine Pinus krempfii (Pinaceae) from Vietnam, based on PCR-RFLP analysis of chloroplast DNA. Plant Syst Evol. 2000, 220: 21-36. 10.1007/BF00985368.View ArticleGoogle Scholar
- Syring J, Willyard A, Cronn R, Liston A: Evolutionary relationships among Pinus (Pinaceae) subsections inferred from multiple low-copy nuclear loci. Am J Bot. 2005, 92: 2086-2100. 10.3732/ajb.92.12.2086.View ArticlePubMedGoogle Scholar
- Neubig KM, Whitten WM, Carlsward BS, Blanco MA, Endara L, Williams NH, Moore M: Phylogenetic utility of ycf 1 in orchids: a plastid gene more variable than matK. Plant Syst Evol. 2009, 277: 75-84. 10.1007/s00606-008-0105-0.View ArticleGoogle Scholar
- Chung SM, Gordon VS, Staub JE: Sequencing cucumber (Cucumis sativus L.) chloroplast genomes identifies differences between chilling-tolerant and-susceptible cucumber lines. Genome. 2007, 50: 215-225. 10.1139/G07-003.View ArticlePubMedGoogle Scholar
- Gernandt DS, Hernández-León S, Salgado-Hernández E, Pérez de la Rosa JA: Phylogenetic Relationships of Pinus Subsection Ponderosae Inferred from Rapidly Evolving cpDNA Regions. Syst Bot. 2009, 34: 481-491. 10.1600/036364409789271290.View ArticleGoogle Scholar
- Alfaro ME, Zoller S, Lutzoni F: Bayes or bootstrap? A simulation study comparing the performance of Bayesian Markov chain Monte Carlo sampling and bootstrapping in assessing phylogenetic confidence. Mol Biol Evol. 2003, 20: 255-266. 10.1093/molbev/msg028.View ArticlePubMedGoogle Scholar
- Douady CJ, Delsuc F, Boucher Y, Doolittle WF, Douzery EJ: Comparison of Bayesian and maximum likelihood bootstrap measures of phylogenetic reliability. Mol Biol Evol. 2003, 20: 248-254. 10.1093/molbev/msg042.View ArticlePubMedGoogle Scholar
- Hillis DM, Bull JJ: An empirical test of bootstrapping as a method for assessing confidence in phylogenetic analysis. Syst Biol. 1993, 42: 182-192.View ArticleGoogle Scholar
- Suzuki Y, Glazko GV, Nei M: Overcredibility of molecular phylogenies obtained by Bayesian phylogenetics. Proc Natl Acad Sci USA. 2002, 99: 16138-16143. 10.1073/pnas.212646199.PubMed CentralView ArticlePubMedGoogle Scholar
- Felsenstein J: Confidence limits on phylogenies: an approach using the bootstrap. Evolution. 1985, 39: 783-791. 10.2307/2408678.View ArticleGoogle Scholar
- Patenaude NJ, Portway VA, Schaeff CM, Bannister JL, Best PB, Payne RS, Rowntree VJ, Rivarola M, Baker CS: Mitochondrial DNA diversity and population structure among southern right whales (Eubalaena australis). J Hered. 2007, 98: 147-157. 10.1093/jhered/esm005.View ArticlePubMedGoogle Scholar
- Hollingsworth PM, Forrest LL, Spouge JL, Hajibabaei M, Ratnasingham S, Bank van der M, Chase MW, Cowan RS, Erickson DL, Fazekas AJ, et al: A DNA barcode for land plants. Proc Natl Acad Sci USA. 2009, 106: 12794-12797. 10.1073/pnas.0905845106.PubMed CentralView ArticleGoogle Scholar
- Erickson DL, Spouge J, Resch A, Weigt LA, Kress JW: DNA barcoding in land plants: developing standards to quantify and maximize success. Taxon. 2008, 57: 1304-1316.PubMed CentralPubMedGoogle Scholar
- Whittall JB, Syring J, Parks M, Buenrostro J, Dick C, Liston A, Cronn R: Finding a (pine) needle in a haystack: chloroplast genome sequence divergence in rare and widespread pines. Mol Ecol. 2009.Google Scholar
- Martin DP, Williamson C, Posada D: RDP2: recombination detection and analysis from sequence alignments. 2005, 21: 260-262.Google Scholar
- Price RA, Liston A, Strauss SH: Phylogeny and Systematics of Pinus. Ecology and Biogeography of Pinus. Edited by: Richardson DM. 1998, Cambridge: Cambridge University Press, 49-68.Google Scholar
- Martin D, Rybicki E: RDP: detection of recombination amongst aligned sequences. 2000, 16: 562-563.Google Scholar
- Padidam M, Sawyer S, Fauquet CM: Possible emergence of new geminiviruses by frequent recombination. Virology. 1999, 265: 218-225. 10.1006/viro.1999.0056.View ArticlePubMedGoogle Scholar
- Posada D, Crandall KA: Modeltest: testing the model of DNA substitution. Bioinformatics. 1998, 14: 817-818. 10.1093/bioinformatics/14.9.817.View ArticlePubMedGoogle Scholar
- Smith JM: Analyzing the mosaic structure of genes. J Mol Evol. 1992, 34: 126-129.PubMedGoogle Scholar
- Martin DP, Posada D, Crandall KA, Williamson C: A modified bootscan algorithm for automated identification of recombinant sequences and recombination breakpoints. AIDS Research & Human Retroviruses. 2005, 21: 98-102.View ArticleGoogle Scholar
- Gibbs MJ, Armstrong JS, Gibbs AJ: Sister-scanning: a Monte Carlo procedure for assessing signals in recombinant sequences. 2000, 16: 573-582.Google Scholar
- Wyman SK, Jansen RK, Boore JL: Automatic annotation of organellar genomes with DOGMA. Bioinformatics. 2004, 20: 3252-3255. 10.1093/bioinformatics/bth352.View ArticlePubMedGoogle Scholar
- Stamatakis A: A rapid bootstrap algorithm for the RAxML web servers. Syst Biol. 2008, 57: 758-771. 10.1080/10635150802429642.View ArticlePubMedGoogle Scholar
- Ronquist F, Huelsenbeck JP: MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003, 19: 1572-1574. 10.1093/bioinformatics/btg180.View ArticlePubMedGoogle Scholar
- Nylander JAA: MrModeltest v2. Program distributed by the author Evolutionary Biology Centre, Uppsala University. 2004Google Scholar
- Swofford DL: PAUP*. Phylogenetic Analysis Using Parsimony (*and Other Methods) . Version 4. 2000, Sunderland, Massachusetts: Sinauer Associates,Google Scholar
- Shimodaira H, Hasegawa M: Multiple comparisons of log-likelihoods with applications to phylogenetic inference. Mol Biol Evol. 1999, 16: 1114-1116.View ArticleGoogle Scholar
- Tamura K, Dudley J, Nei M, Kumar S: MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol. 2007, 24: 1596-1599. 10.1093/molbev/msm092.View ArticlePubMedGoogle Scholar
- Pollard DA, Iyer VN, Moses AM, Eisen MB, McAllister BF: Widespread discordance of gene trees with species tree in Drosophila : evidence for incomplete lineage sorting. PLoS Genet. 2006, 2: e173-10.1371/journal.pgen.0020173.PubMed CentralView ArticlePubMedGoogle Scholar
- Gernandt DS, Magallon S, Geada Lopez G, Zeron Flores O, Willyard A, Liston A: Use of simultaneous analyses to guide fossil-based calibrations of Pinaceae phylogeny. Int J Plant Sci. 2008, 169: 1086-1099. 10.1086/590472.View ArticleGoogle Scholar
- Willyard A, Syring J, Gernandt DS, Liston A, Cronn R: Fossil calibration of molecular divergence infers a moderate mutation rate and recent radiations for Pinus. Mol Biol Evol. 2007, 24: 90-101. 10.1093/molbev/msl131.View ArticlePubMedGoogle Scholar
- Bouille M, Bousquet J: Trans-species shared polymorphisms at orthologous nuclear gene loci among distant species in the conifer Picea (Pinaceae): implications for the long-term maintenance of genetic diversity in trees. Am J Bot. 2005, 92: 63-73. 10.3732/ajb.92.1.63.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.