Plastid primers for angiosperm phylogenetics and phylogeography.

PREMISE OF THE STUDY: PCR primers are available for virtually every region of the plastid genome. Selection of which primer pairs to use is second only to selection of the genic region. This is particularly true for research at the species/population interface.
METHODS: Primer pairs for 130 regions of the chloroplast genome were evaluated in 12 species distributed across the angiosperms. Likelihood of amplification success was inferred based upon number and location of mismatches to target sequence. Intraspecific sequence variability was evaluated under three different criteria in four species.
RESULTS: Many published primer pairs should work across all taxa sampled, with the exception of failure due to genomic reorganization events. Universal barcoding primers were the least likely to work (65% success). The list of most variable regions for use within species has little in common with the lists identified in prior studies. DISCUSSION: Published primer sequences should amplify a diversity of flowering plant DNAs, even those designed for specific taxonomic groups. "Universal" primers may have extremely limited utility. There was little consistency in likelihood of amplification success for any given publication across lineages or within lineage across publications.

Whole genome sequencing is more available and less expensive than ever before, yet most scientists continue to rely on targeted, comparative sequencing for phylogenetics and phylogeography. Identifying the most appropriate markers to employ has been challenging. Information for model organisms abounds (e.g., grasses; Saski et al., 2007; Bortiri et al., 2008; Leseberg and Duvall, 2009), and a few studies have specifically screened the same set of markers across a diversity of plant groups, ranking the utility of these markers either explicitly or implicitly (Shaw et al., 2005, 2007, 2014). These studies are exceedingly valuable, demonstrating there is no one-size-fits-all answer to the question “which markers?”. The second critical question to “which markers” is “which primers?”. Hundreds of primer sequences have been published, many designed for specific taxonomic groups. The work presented here was inspired by “The Tortoise and the Hare II” (Shaw et al., 2005), which was the first study to pull together information on a large number of regions commonly in use (at that time) for plant phylogenetics. Our laboratory was also compiling such information, as were many others.

The Tortoise and the Hare II paper was revolutionary in assessing sequence variability for all regions studied across a broad diversity of flowering plants, and providing a ranking of that variability. In the mid-2000s, a small number of complete chloroplast genome sequences were available for land plants and some of those were not annotated (e.g., Medicago truncatula Gaertn. [GenBank NC_003119]; Saski et al., 2005). Grivet et al. (2001) were visionary when they moved beyond analyzing regions commonly being used to design primers for lesser-known and potentially faster-evolving regions of the chloroplast genome. They were the first to take advantage of the new genomic data boom, providing a set of 20 universal chloroplast primers designed around the complete chloroplast data from seven flowering plant species. Around the same time, I developed nondegenerate primers for 36 noncoding regions in the large and small single-copy regions of the chloroplast genome (published here). These near-universal primers were designed based on the complete chloroplast genome sequences of 16 flowering plant species (see Appendix 1).

Grivet et al. (2001) and I designed primers, but Shaw et al. (2007) took an even more applied approach when they examined sequences for three different taxon pairs (Atropa/Nicotiana, Lotus/Medicago, and Saccharum/Oryza), specifically searching for faster-evolving regions. Shaw et al. (2014) go one step further, comparing complete chloroplast genome sequences for 25 (primarily congeneric) sister species pairs. They examined sequence diversity for 107 single-copy noncoding regions, providing the most comprehensive analysis to date.

There are now at least 150 primer pairs available to amplify almost every intergenic, intron, and exon region of the chloroplast genome, including portions of the inverted repeats, thanks to the efforts of Shaw et al. (2005, 2007, 2014) and others (Ebert and Peakall, 2009; Scarcelli et al., 2011; Dong et al., 2012, 2013). Not surprisingly, although all worked independently, many of the same regions were explored (Appendix 2) and, in some cases, identical or nearly identical primers were designed. The push to identify faster-evolving regions was, in part, spurred by groups of organisms with exceptionally slowly evolving chloroplast genomes such as Bromeliaceae (Gaut et al., 1992) and Arecaceae (Asmussen and Chase, 2001). Heinze provided access to a comprehensive database of chloroplast primers in 2007 (Heinze, 2007). The database is periodically updated (last update 18 March 2014) and is available at http://bfw.ac.at/200/2043.html.

In the absence of taxon-specific complete chloroplast genome data, it is possible to mine the wealth of genomic data available in international databases such as GenBank (National Center for Biotechnology Information), EMBL-Bank (European Molecular Biology Laboratory), and DDBJ (DNA Data Bank of Japan). Primer pairs for 130 regions of the chloroplast genome were evaluated relative to representatives of 12 genera, spanning the diversity of flowering plants. Exon regions were avoided because they generally evolve more slowly than intron and intergenic spacer regions. The primers of Shaw et al. (2005, 2007), Scarcelli et al. (2011), and Dong et al. (2012), as well as the primers provided here, were evaluated. Many of the Shaw et al. (2005, 2007) and Scarcelli et al. (2011) primers are degenerate, improving the breadth of taxa they can be used on, but reducing their efficiency during the amplification process. The Dong et al. (2012) primers are primarily used for barcoding, thus amplify a diversity of taxa, but may not target the most quickly evolving regions of the genome. The likelihood of amplification success was estimated based upon the number and position of mismatches between the primer and the target sequence. These data were then evaluated in the context of Shaw et al. (2014) to provide generalizations, by taxonomic group, for primer utility in conjunction with sequence variability.

Finally, a small number of plant species have sequences available for multiple accessions or different subspecific taxa including Fragaria vesca L. (Rosaceae, N = 2), Gossypium herbaceum L. (Malvaceae, N = 2), Olea europaea L. (Oleaceae, N = 4), and Oryza sativa L. (Poaceae, N = 3). Shaw et al. (2014) specifically excluded species pairs with very low and very high levels of sequence divergence. Very high levels of divergence made alignment difficult, and very low levels provide too few characters for reasonable comparison across all flowering plants. Here I compare the variation at the subspecific level to that of higher-level relationships to determine if the same regions are useful at multiple taxonomic levels.

METHODS

Primers designed here

Sixteen chloroplast genomes, representing a diversity of flowering plants, were downloaded from GenBank (see Appendix 1). Homologous gene sequences were aligned in Se-Al version 2.0a11 (Rambaut, 1996). Primers were designed based on simultaneous viewing of the Se-Al file and an Oligo 4.02 (Rychlik, 2002) file, using a single sequence from the pool. Primers were anchored in coding regions and were designed to have a minimum number of hair-pins and primer-primer interactions, annealing temperatures between 50°C and 64°C, and a 3′ GC clamp if possible, targeting regions 400–1800 bp in length. Primer details are provided in Table 1, and are provided in the order of appearance in the tobacco genome (Nicotiana tabacum L. [GenBank Z00044.1]). The tobacco genome was the genome of choice for describing the location of primers prior to the recent accumulation of genomic data. A total of three different trnS primers were designed, corresponding to the three trnS genes encoded by the chloroplast genome (trnS-GCU, trnS-UGA, and trnS-GGA). Gene order is highly conserved on the chloroplast genome of flowering plants, but does vary and can be highly informative, for example, as in the 22-kb inversion in almost all Asteraceae (Jansen and Palmer, 1987a, 1987b) and the 78-kb inversion in Fabaceae subtribe Phaseolinae (Bruneau et al., 1990). Some primer combinations are not useful in particular groups of plants due to structural rearrangements. In some cases, the downloaded genomes differ in the identification of specific genes.

Table 1.

Region, primer name, primer sequence, amplicon position, and amplicon length for plastid noncoding regions relative to the Nicotiana tabacum L. (GenBank Z00044.1) genome.

Region	Primer name	Tm (°C)a	Primer sequence	Amplicon position	Amplicon length (bp)
trnQ(UUG)–psbK IGS	trnQ-IGSR	62.7	ACCCGTTGCCTTACCGCTTGG	7457–8018	562
	psbK-IGSR	50.9	ATCGAAAACTTGCAGCAGCTTG
psbK–trnS (GCU) IGS	psbK-IGSF	47.9	CCAATCGTAGATGTTATGCC	7937–8719	783
	trnS_GCU-IGSF	56.1	GGAGAGATGGCTGAGTGGA
trnG(UCC)–atpA IGS	trnG_UCC-IGSF	56.3	CCTTCCAAGCTAACGATGCG	10,219–10,796	577
	atpA-IGSF	50.3	TGGACAGGTGAAGAAATTTC
atpF intron	atpF-E2R	47.3	CTCTGTTTTCGATTATCTAATAAAT	12,582–13,372	791
	atpF-E1F	48.1	AGCAACAAATCCAATAAATCT
atpF–atpH IGS	atpF-E1R	46.5	TAGATTTATTGGATTTGTTGC	13,352–13,927	575
	atpH-IGSF	48.5	CTTTTATGGAAGCTTTAACAATTTA
atpH–atpI IGS	atpH-IGSR	56.9	CCAGCAGCAATAACGGAAGC	14,059–15,400	1341
	atpI-IGSF	48.2	GTTGTTGTTCTTGTTTCTTTAG
rpoC1 intron	rpoC1-intR	49.9	AAGTGGGATGCTGTATTTC	23,004–23,976	973
	rpoC1-intF	49.2	ACGAAGGTATCAAATGGG
trnS (UGA)–psbZ IGS	trnS_UGA-IGSR	55.0	ATCAACCACTCGGCCATC	37,209–37,620	412
	psbZ-IGS	45.6	AATAGCCAATTGAAAAGC
psaA–ycf3 IGS	psaA-IGSR	50.2	CGGCGAACGAATAATCAT	43,469–44,295	827
	ycf3-E3F	48.4	CCCGGTAATTATATTGAAGC
ycf3 intron 2	ycf3-E3R	54.5	ATCTCCCTGTCGAATGGC	44,362–45,193	832
	ycf3-E2F	53.2	GGCCGTGATCTGTCATTAC
ycf3 intron 1	ycf3-E2R	50.0	TTCCGCGTAATTTCCTTC	45,370–46,163	794
	ycf3-E1F	48.1	CATTTACCTATTACAGAGATGG
ycf3–trnS (GGA) IGS	ycf3-E1R	45.5	ACAATTGAAAAGGTCTTATC	46,214–47,174	961
	trnS_GGA-IGSR	47.9	CAAAAGCCTACATAGCAG
rpS4-trnT (UGU)	rpS4-IGSR1	56.2	TCCTCGGTAACGCGACAT	48,065–48,570	506 max.
	rpS4-IGSR2	45.9	GGCTTTTTATTAGTTAGTCC
	trnT_UGU-IGSF1	53.0	AGGTTAGAGCATCGCATTTG
	trnT_UGU-IGSF2	47.9	GAGCATCGCATTTGTAAT
trnF (GAA)–ndhJ IGS	trnF-IGSF	56.4	ATCCTCGTGTCACCAGTTCAAA	50,277–51,024	747
	ndhJ-IGSF	49.3	RCCCCTAATTTYTATGAAATACA
ndhC–trnV (UAC) IGS	ndhC-IGSR	52.9	ATCATATTCGTGAAGCAGAAACAT	52,644–53,776	1132
	trnV_UAC-E2F	58.3	GGTTCGAGTCCGTATAGCCCT
trnV (UAC) intron	trnV_UAC-E2R	57.1	GGGCTATACGGACTCGAACC	53,757–54,380	624
	trnV_UAC-E1F	52.8	GTAGAGCACCTCGTTTACAC
trnV (UAC)–atpE IGS	trnV_UAC-E1R	52.8	GTGTAAACGAGGTGCTCTAC	54,361–55,032	672
	atpE-IGSF	56.6	AGTGACATTGATCCRCAAGAAGC
atpB–rbcL IGS	atpB-IGSR	48.4	AAGTAGTAGGATTGATTCTCAT	56,756–57,615	859
	rbcL-IGSR	53.9	AGTCTCTGTTTGTGGTGACAT
rbcL–accD IGS	rbcL-IGSF	58.5	GCTGCTGCTTGTGAGGTATGG	58,960–59,865	905
	accD-IGSR	51.1	AATTGAACCCACATTTTTCCATA
accD–psaI IGS	accD-IGSF	48.2	GGTAAAAGAGTAATTGAACAAAC	61,143–62,161	1018
	psaI-IGSR	49.7	ATAAAGAAGCCATTGCAATTG
psaI–ycf4 IGS	psaI-IGSF	51.8	CCTAGTCTTTCCGGCAAT	62,127–62,682	556
	ycf4-IGSR	49.5	CCCCGTTATAAGTTCTATCC
ycf4–ycf10 IGS	ycf4-IGSF	47.0	ATTAGCCTATTTCTTGCG	63,153–63,541	389
	ycf10-IGSR	51.9	GCCCAGTATTCCACCAA
petA–psbJ IGS	petA-IGSF	50.8	GAAACAGTTTGAGAAGGTTCA	65,255–66,388	1133
	psbJ-IGSF	55.8	ATTCCGCATTGGGCTCATC
petL–psaJ IGS	petL-IGSF	48.4	TCTATTAGCGGCTTTAACTATA	68,322–69,671	1350
	psaJ-IGSR	52.4	GCATCCGGGAATAAACGA
psaJ–rpL20 IGS	psaJ-IGSF	46.5	ATGCGAGATCTAAAAACATA	69,565–71,404	1840
	rpL20-IGSF	46.6	CAGAATTAAACGGGGATATA
rpL20–rpS12 IGS	rpL20-IGSR	51.3	CGTCTCCGAGCTATATATCC	71,372–72,319	947
	rpS12-IGSF	47.3	CAACTTATTAGAAACACAAGAC
clpP intron 2	clpP-E3R	51.6	TTGCCTGTTCTTTGTACATAAAC	72,573–73,466	893
	clpP-E2F	50.9	GCTATTTATGACGCTATGCAA
clpP intron 1	clpP-E2R	50.9	TTGCATAGCGTCATAAATAGC	73,446–74,451	1005
	clpP-E1F	54.9	TTGGGTTGACATATAGTGCGAC
clpP–psbB IGS	clpPE1-IGSR	52.2	AGGGACTTTTGGAACACC	74,481–74,970	490
	psbB-IGSR	51.5	ATACCAAGGCAAACCCAT
psbH–petB IGS	psbH-IGSF	48.5	AACTACTCCTTTGATGGG	77,214–78,377	1163
	petB-E2R	44.1	TAGTAAAAAGTCATAGCAAA
petB–petD IGS	petBE2-IGSF	50.8	ATGCACTTTCCAATGATACG	78,805–79,760	956
	petD-E2R	59.8	CCCGAGGGAACCGGACAT
rpS3–rpS19 IGS	rpS3-IGSR	50.5	CAGTCTGAAACCAAGTGG	85,863–86,504	642
	rpS19-IGSF	45.9	TTTATATAACGGATAGTATGGT
ccsA-ndhD IGS	ccsA-IGSF	45.5	ATGATATTTTCAACCTTAGA	116,344–117,614	1271
	ndhD-IGSF	43.6	CCGTAATAGGTATTGGTAT
psaC–ndhE IGS	psaC-IGSR	44.9	TCCTATACACGTATCATAAA	119,351–119,713	363
	ndhE-IGSF	42.4	TTCATCAATTTATCGTAAC
ndhE–ndhI IGS	ndhE-IGSR	45.6	GAAAATAAATAGGCACTCAA	119,912–121,251	1340
	ndhI-IGSF	46.9	CAATGACCGAAGAATATGA
rpS15–ycf1 IGS	rpS15-IGSR	47.7	GCAATTCTAAATGTGAAGTAAG	125,374–126,001
	ycf1-IGSR	45.6	ATTATCGATTAGAAGATTTAGC

Melting temperature (Tm) based on 50 mM NaCl solution.

Primer utility

The chloroplast genomes for species of eight genera (Acorus L., Amborella Baill., Canna L., Ceratophyllum L., Cymbidium Sw., Helianthus L., Magnolia L., and Nelumbo Adans.) and for subspecies of F. vesca, G. herbaceum, O. europaea, and O. sativa were compared to 130 primer pairs published by Shaw et al. (2005, 2007), Scarcelli et al. (2011), Dong et al. (2012), and those designed here. Complete chloroplast genome sequences were downloaded from GenBank (accession numbers, taxonomic identity, and original publication information provided in Appendix 3) and aligned manually in Sequencher (Gene Codes Corporation, Ann Arbor, Michigan, USA). A separate file containing the primer sequences was imported and automatically assembled using the settings “dirty data” and 100% sequence similarity with a minimum overlap of 16 bp. Additional rounds of alignment were conducted with successively lower levels of sequence similarity. Primers that failed to align automatically, or that aligned incorrectly, were realigned manually whenever possible (guided by the GenBank annotations). Alignment of the two Gossypium sequences required inversion of a large region of one taxon (arbitrarily selected as G. herbaceum subsp. africanum (G. Watt) Vollesen) approximately corresponding to bases 115,132–135,355 in the final alignment. The Oryza alignment includes O. nivara Sharma & Shastry because it is a potential progenitor of O. sativa (Li et al., 2006; but see Huang et al., 2012 for an alternative view point).

As mentioned above, degenerate primers provide broader utility, but reduced amplification efficiency. If a mismatch was detected in the last five bases at the 3′ end of the primer, the mismatch was inferred to be fatal (IDT, 2009). If more than three mismatches were detected within any given primer, amplification was inferred to be unsuccessful. These criteria are arbitrary but have worked for me personally and are probably more strict than necessary.

Sequence variability within species

The sequences of F. vesca, G. herbaceum, O. europaea, and O. sativa were examined manually to assess the variation of the 130 regions. Length of the inferred amplicon was noted along with the number of mismatched bases (aka inferred substitutions; excluding primer regions), the number of insertion/deletion (indel) events, and the number of inversions. These data provided an estimate of the utility of the regions for inferring phylogeny among closely related subspecies, and potential for application to phylogeographic studies. Shaw et al. (2014) specifically avoided these types of comparisons due to the very small number of parsimony informative characters. Sequence diversity was estimated using three criteria calculated as: (1) [(number of substitutions*2)+(number of indels)+(number of inversions)]/amplicon length, (2) number of substitutions+indels+inversions, and (3) sequence diversity (number of substitutions/sequence length). The first criterion (criterion 1) is a weighted rank, and includes information on the number of inferred substitutions (weighted twice as heavily as the other two components), indels, and inversions. Substitutions were weighted more heavily because chloroplast indels may be more homoplasious (Kelchner and Clark, 1997), especially among closely related taxa. Inversions are often low in homoplasy (Graham et al., 2000) and thus could be weighted more heavily, but are relatively rare so weighting was not employed. The 10 most variable regions for each species were identified, as measured under each criterion. Frequency of any specific “top 10” primer pair was summed across the four species.

RESULTS

The 72 primers targeted noncoding regions of the chloroplast genome with amplicon sizes of 500–1800 bp. Degenerate primers were avoided because they were assumed to decrease priming efficiency, as were mismatches within the last five bases at the 3′ end of the primer. Only two primers required degenerate bases: one primer with two degenerate bases and another primer with one degenerate base. None of these degeneracies were located within the last five bases. In contrast, 17 of the Scarcelli et al. (2011) primers have at least one degenerate base in the last five bases at the 3′ end of the primer, and so are assumed to fail for at least some taxa.

Primer evaluation

Three of the four sets of primers examined here were equally likely to amplify target chloroplast regions (81–85% should work; see Table 2). The Dong et al. (2012) primers were least likely to work based on the 12 species examined here (65% on average) and were particularly poorly matched to the Oryza genome (29% amplification success predicted), and only moderately suited for Amborella (52%), Cymbidium (52%), and Helianthus (57%). However, the Dong et al. (2012) primer pair trnH-psbA was not expected to work on any of the target species, possibly due, in part, to an extra “A” near the 3′ end of the published sequence for the trnH primer. The primers designed here were poorly matched to three of the four monocots (Cymbidium, Oryza, and Canna; 61%, 64%, and 67%, respectively), despite being a good match for Acorus (81%). Scarcelli et al. (2011) primers were designed with monocots in mind and did an exceptional job matching the monocot genomes examined here, with amplification success ranging from 82–97%. They were almost equally good for the dicots examined here, with amplification success of 72–93%. The Shaw et al. (2005, 2007) primers were useful across the angiosperm phylogeny, with all anticipated amplification success percentages above 78%.

Table 2.

Summary of amplification success probability for 130 pairs of chloroplast primers.

			Basal dicot grade/Magnoliids		Monocots				Basal eudicot grade		Eurosids I	Eurosids II	Euasterids I	Euasterids II
Publicationa	No. of regions	Average % ampl.	Amborella	Magnolia	Acorus	Cymbidium	Oryza	Canna	Ceratophyllum	Nelumbo	Fragaria	Gossypium	Olea	Helianthus
Dong	21	65	11 (52%)	16 (76%)	14 (67%)	11 (52%)	6 (29%)	15 (71%)	15 (71%)	17 (81%)	16 (76%)	14 (67%)	17 (81%)	12 (57%)
Current study	36	81	31 (86%)	32 (89%)	29 (81%)	22 (61%)	23 (64%)	24 (67%)	32 (89%)	32 (89%)	28 (78%)	33 (92%)	31 (86%)	32 (89%)
Scarcelli	99	83	71 (72%)	92 (93%)	96 (97%)	92 (93%)	81 (82%)	87 (88%)	71 (72%)	88 (89%)	73 (74%)	80 (81%)	79 (80%)	75 (76%)
Shaw	33	85	27 (82%)	31 (94%)	29 (88%)	26 (79%)	26 (79%)	29 (88%)	28 (85%)	28 (85%)	27 (82%)	27 (82%)	29 (88%)	28 (85%)

Dong et al., 2011; Scarcelli et al., 2011; Shaw et al., 2005, 2007.

On average, the Shaw et al. (2005, 2007) and Scarcelli et al. (2011) primers are more degenerate, yet they were only slightly more likely to amplify the target sequences than the nondegenerate primers designed here, at least for nonmonocot taxa. With so many different primers available, most regions could be amplified in almost all target taxa provided an appropriate primer pair was selected. Indeed, many primer pairs should work in all 12 species examined here. Details of the inferred priming success are provided in Appendix S1, and species-specific notes on primer/sequence mismatches are provided in Appendix S2.

Primer utility × sequence variability

Shaw et al. (2014) conveniently summarized sequence variability across the chloroplast genome including the identification of the 13 fastest-evolving regions for six taxonomic groups (magnoliids, monocots, eurosids I, eurosids II, euasterids I, and euasterids II). Summing across these major groups, 28 different regions were identified as the most variable. Primers to amplify those 28 regions are detailed in Table 3, along with the Shaw et al. (2014) rank for each region (in bold typeface above each primer region), for each taxon examined here. Multiple primer pairs are available for each of the 28 regions except the trnT-trnL (Shaw et al., 2005 only), ycf4-ycf10 (or cemA; current study only), and ndhD-psaC (none of the publications examined). The ndhD-psaC region was ranked 10th fastest for eurosids I, but as there are no primers to be evaluated this region will not be discussed further. Primers are available for each of the remaining 27 regions.

Table 3.

Amplification success prediction for the 28 fastest Shaw et al. (2014) regions.

Approx. Nicotiana order			Basal dicot grade/Magnoliids		Monocots				Basal eudicot grade		Eurosids I	Eurosids II	Euasterids I	Euasterids II
	Genomic region	Publicationb	Amborella	Magnolia	Acorus	Cymbidium	Oryza	Canna	Ceratophyllum	Nelumbo	Fragaria	Gossypium	Olea	Helianthus	Average
1	trnH-psbA IGS												8c
	trnH-psbA IGS	Dong et al.	NO**	NO**	NO	NO**	NO	NO	NO	NO**	NO	NO	NO	NO**	0%
	trnH-psbA IGS	Scarcelli et al.	YES	YES	YES	YES	YES	NO	YES	NO	YES	YES	YES	YES	83%
	trnH-psbA IGS	Shaw et al.	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	YES	YES	92%
5	matK exon		12c								6c	12c
	trnK (including matK)	Dong et al.	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	YES	YES	92%
	matK exon	Scarcelli et al.	YES	YES	YES	YES*	YES	YES	YES	YES	YES	YES	YES	YES	100%
7	trnK-rps16 IGS		13c		5c						13c		7c	12c
	trnK-rps16	Scarcelli et al.	YES	YES	YES	YES	YES	YES	YES	YES	NO	YES	YES	YES*	92%
	trnK-3′rpS16	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
8	rps16 intron				4c						3c			5c
	rps16 intron	Scarcelli et al.	YES	YES	YES	YES	YES	YES	NO	YES	NO	YES	YES	YES	83%
	rpS16 intron	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
9	rps16-trnQ IGS		2c								11c		1c	13c
	rps16-trnQ	Dong et al.	YES	YES	YES	YES	NO	NO	YES	YES	NO	NO	NO	YES	58%
	rps16-trnQ	Scarcelli et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
	5′rpS16-trnQ	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
12	trnS-trnG IGS				11c						2c		12c
	trnS-trnG (and intron)	Dong et al.	NO	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	NO	75%
	trnS-trnG	Scarcelli et al.	NO	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	NO	75%
	trnS-trnG	Shaw et al.	YES	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	NO	83%
16	atpF intron										5c
	atpF intron	Prince (here)	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
	atpF intron/exon	Scarcelli et al.	NO	YES	YES	NO	YES	YES	NO	YES	NO	YES	YES	YES	67%
18	atpH-atp IGS		9c								12c		4c
	atpH-atpI	Dong et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	NO	YES	YES	92%
	atpH-atpI	Prince (here)	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
	atpH-atpI	Scarcelli et al.	YES	YES	YES	YES	YES	YES	YES	YES	NO	YES	YES	YES	92%
	atpH-atpI	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
26	rpoB-trnC IGS				8c							10c	11c	7c
	rpoB-trnC	Dong et al.	YES	YES	YES	NO	NO	NO	YES	YES	YES	YES	YES	NO	67%
	rpoB-trnC	Scarcelli et al.	NO	YES	YES	YES	YES	YES	NO	YES	YES	YES	YES	NO	75%
	rpoB-trnC	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	NO	YES	NO	83%
29–31	petN-psbM IGS												6c	10c
	petN-trnD	Scarcelli et al.	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	YES	NO	83%
	petN-psbM	Dong et al.	NO	NO	NO	NO	NO	NO	NO	NO	NO	NO	YES	YES	17%
	ycf6-psbM	Shaw et al.	YES	YES	YES	NO	YES	NO	YES	YES	YES	YES	NO	YES	75%
32	psbM-trnD IGS		8c		3c							9c
	psbM-trnD	Dong et al.	YES	YES	YES	NO	NO	YES	YES	YES	YES	YES	YES	YES	83%
	psbM-trnD	Shaw et al.	NO	NO	YES	NO	YES	YES	YES	YES	YES	YES	YES	NO	67%
33	trnE-trnT IGS											8c		6c
	trnD-trnT	Scarcelli et al.	YES	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	NO	83%
	trnD-trnT	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	NO	92%
34	trnT-psbD IGS		4c								8c	4c		8c
	trnT-psbD	Dong et al.	NO	YES	YES	YES	NO	YES	YES	YES	NO	YES	YES	NO	67%
	trnT-psbD	Scarcelli et al.	NO	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	YES	83%
	trnT-psbD	Shaw et al.	YES	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	YES	92%
38–41	psbZ-trnG IGS										7c	2c
	trnS-trnG	Dong et al.	YES	YES	YES	YES	NO	YES	YES	YES	YES	YES	NO	YES	83%
	trnS-trnfM	Shaw et al.	YES	YES	NO	YES	YES	YES	YES	NO	YES	NO	NO	YES	67%
	psbZ-trnfM	Scarcelli et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
50	trnT-trnL IGS		11c		9c							3c
	trnT-trnL	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
55	ndhC-trnV IGS		5c		2c								3c	3c
	ndhC-trnV	Dong et al.	YES	YES	YES	YES*	YES	YES	YES	YES	YES	YES	YES	YES	100%
	ndhC-trnV	Prince (here)	YES	YES	YES	YES	YES	YES	YES	YES	NO	YES	YES	YES	92%
	ndhC-trnV	Scarcelli et al.	YES	YES	YES	YES*	YES	YES	YES	YES	YES	YES	YES	YES	100%
	ndhC-trnV	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	NO	YES	YES	YES	92%
60	atpB-rbcL IGS										9c
	atpB-rbcL	Prince (here)	YES	YES	YES	NO	YES	YES	YES	YES	NO	NO	YES	YES	75%
	atpB-rbcL	Scarcelli et al.	NO	YES	YES	NO	YES	YES	YES	YES*	NO	YES	YES	NO	67%
62	rbcL-accD IGS				12c								13c
	rbcL-accD	Dong et al.	NO	YES	YES	YES	NO	YES	YES	YES	YES	YES	YES	NO	75%
	rbcL-accD	Prince (here)	YES	YES	NO	YES	NO	NO	YES	NO	NO	YES	NO	NO	42%
	rbcL-accD	Scarcelli et al.	NO	NO	NO	NO	NO	YES	NO	NO	NO	NO	NO	NO	8%
64	accD-psaI IGS		10c		10c
	accD-psaI	Dong et al.	NO	YES	NO	NO	NO	YES	YES	YES	YES	YES	YES	YES	67%
	accD-psaI	Prince (here)	NO	YES	NO	YES	NO	YES	YES	YES	YES	YES	YES	NO	67%
	accD-psaI	Scarcelli et al.	NO	YES	NO	YES	NO	YES	NO	YES	NO	YES	YES	YES	58%
	accD-psaI	Shaw et al.	YES	YES	NO	YES	NO	NO	YES	YES	YES	YES	YES	YES	75%
67	ycf4-cemA (ycf10) IGS											11c
	ycf4-ycf10	Prince (here)	YES	YES	YES	YES	YES	NO	NO	YES	NO	YES	YES	YES	75%
70	petA-psbJ IGS		6c		6c							5c	5c
	petA-psbJ	Dong et al.	YES	YES	YES	NO	NO	YES	YES	YES	YES	YES	YES	NO	75%
	petA-psbJ	Prince (here)	YES	YES	YES	NO	YES	NO	YES	YES	YES	NO	NO	YES	67%
	petA-psbJ	Shaw et al.	YES	YES	YES	NO	YES	YES	YES	YES	NO	NO	YES	YES	75%
72	psbE-petL IGS		7c		7c						4c	13c		9c
	psbE-petL	Dong et al.	NO	NO	NO	YES*	NO	YES	YES	NO	YES	NO	YES	YES	50%
	psbE-petL	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	100%
76, 77	psaJ-rpl33 IGS				13c
	trnP-rps18	Scarcelli et al.	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	YES	NO	92%
	psaJ-rpL20	Prince (here)	NO	YES	NO	NO	NO	YES	YES	YES	NO	YES	YES	YES	58%
116	ndhF-rpl32 IGS		3c		1c							1c	9c	2c
	ndhF-rpl32	Scarcelli et al.	YES	YES	YES	YES	YES	NO	YES	YES	YES	NO	YES	YES	83%
	ndhF-rpl32	Shaw et al.	NO	YES	YES	YES	NO	NO	NO	YES	YES	YES	YES	YES	67%
118	rpl32-trnL IGS		1c									6c	2c	1c
	rpL32-trnL	Dong et al.	NO	YES	YES	NO	YES	YES	YES	YES	YES	YES	YES	YES	83%
	rpL32-trnL	Shaw et al.	YES	YES	YES	YES	YES	YES	YES	YES	NO	YES	YES	YES	92%
121.5	ndhD-psaC IGS										10c
127	ndhA intron										1c		10c	11c
	ndhA intron	Dong et al.	NO	NO	NO	YES*	YES	NO	NO	NO	NO	YES	NO	NO	25%
	ndhA intron	Scarcelli et al.	YES	YES	YES	YES*	YES	YES	YES	YES	YES	YES	YES	YES	100%
	ndhA intron	Shaw et al.	YES	YES	YES	YES*	NO	YES	YES	YES	YES	YES	YES	YES	92%
129	rps15-ycf1 IGS											7c		4c
	rpS15-ycf1	Prince (here)	YES	YES	YES	NO	NO	YES	YES	YES	YES	YES	YES	YES	83%
	rps15-ycf1	Scarcelli et al.	YES	NO	YES	YES	NO	YES	YES	NO	YES	YES	NO	YES	67%

YES* = will not work for at least one species in the genus; NO** = will work if psbA primer is synthesized with one fewer A at the 3′ end.

Shaw et al., 2005, 2007; Scarcelli et al., 2011; Dong et al., 2012.

Shaw et al. (2014) rank for the region within the specified taxonomic group.

Among the basal dicot grade (Amborella and Magnolia), successful primers are available for all 27 regions. Primer selection is more challenging for Amborella than for Magnolia. The top ranked region was the rpl32-trnL intergenic spacer (IGS). Shaw et al. (2007) primers will work for both taxa; Dong et al. (2012) primers will not. In contrast, rps16-trnQ, the second highest ranked region, has three sets of primers available (Shaw et al., 2007; Scarcelli et al., 2011; and Dong et al., 2012), all of which should work.

Among the monocots sampled (Acorus, Cymbidium, Oryza, and Canna), Acorus was the least difficult sequence to match and Oryza the most difficult. Structural rearrangements are the primary reason for failure to amplify across all available primers (e.g., rbcL-accD in Oryza and petA-psbJ in Cymbidium). One region cannot be amplified in Acorus—the accD-psaI IGS, despite the availability of four different primer pairs. In all, four regions cannot be amplified in Cymbidium with the primers studied here: petN-psbM, psbM-trnD, atpB-rbcL, and petA-psbJ. The ndhA region can be amplified in only some species of Cymbidium due to fatal substitutions in some species for all three primer pairs evaluated here. In Oryza, the trnS[GCU]-trnG[GCC], trnT-psbD, rbcL-accD, accD-psaI, and rps15-ycf1 cannot be amplified using any primer pair. In Canna, ndhF-rpl32 will not amplify with either of the available primer pairs. Unfortunately, according to Shaw et al. (2014), ndhF-rpl32 is the most variable and psbM-trnD is the third most variable region for monocots.

Basal eudicots were not evaluated by Shaw et al. (2014) in detail, so direct comparisons cannot be made here. Fortunately, at least one primer pair was successful for each of the 27 fastest-evolving regions, with the exception of the ycf4-ycf10 region. The only available primers for this region were designed here, and they will not work for Ceratophyllum. In general, Ceratophyllum was more difficult to match than was Nelumbo.

Shaw et al. (2014) detailed variability of higher eudicots for four major groups: eurosids I, eurosids II, euasterids I, and euasterids II. Only a single species representing each group was included here. Fragaria (eurosids I) could not be amplified for a single region, the ycf4-ycf10 IGS. According to Shaw et al. (2014), the fastest region for this clade was the ndhA intron. Both the Shaw et al. (2007) and Scarcelli et al. (2011) primers should work, but the Dong et al. (2012) primers will not. The second fastest region was the trnS[GCU]-trnG[GCC], which should amplify with any of the primer pairs (Shaw et al., 2005; Scarcelli et al., 2011; or Dong et al., 2012).

The sole representative of eurosids II and euasterids I (Gossypium and Olea, respectively) could successfully be amplified by at least one pair of primers studied here. The fastest region for eurosids II was the ndhF-rpl32 IGS. The Shaw et al. (2007) primer pair should work, but the Scarcelli et al. (2011) primer pair likely will not. The second most variable region was the psbZ-trnG IGS. For this region, both the Scarcelli et al. (2011) and Dong et al. (2012) primers should work, but the Shaw et al. (2005; as trnfM-trnS) primers will not. In euasterids I, the fastest region was the rps16-trnQ IGS. For Olea, the Shaw et al. (2007) and Scarcelli et al. (2011) primers should work, but not so the Dong et al. (2012) primers. The next-fastest region was the rpl32-trnL IGS. Both the Shaw et al. (2007) and Dong et al. (2012) primers should work.

Primer failure in Helianthus (euasterids II) was primarily due to structural rearrangements (e.g., trnS[GCU]-trnG[GCC], rpoB-trnC, trnE-trnT, rbcL-accD). rpl32-trnL IGS was the fastest region according to Shaw et al. (2014), and either the Shaw et al. (2007) or Dong et al. (2012) primers should successfully amplify this region. The adjacent ndhF-rpl32 IGS was the second most variable region. Both the Shaw et al. (2007) or the Scarcelli et al. (2011) primers should work.

Subspecific sequence variability

Intraspecific sequence variation was evaluated in four species: F. vesca, G. herbaceum, O. europaea, and O. sativa. This represents a tiny fraction of angiosperm diversity, but is the first analysis of subspecific diversity across the entire chloroplast genome for multiple species, in the context of available primer resources. Appendix S3 identifies the fastest-evolving regions among the four species, under three different criteria. On average, only five inversions per chloroplast genome were detected here and the distribution across species was very different. Gossypium and Oryza each had 10 inversions, Fragaria none, and Olea only one. Details of subspecific comparisons for all regions are provided in Appendix S2.

No single genic region was identified as the top 10 fastest for all four species. Pooling data across all three criteria, the most frequently identified genic region was the psbZ-trnfM IGS with eight occurrences out of a maximum of 12 possible, followed by the trnS (GCU)-trnG (GCC) IGS, with six occurrences, rps16-trnQ IGS and trnT (GGU)-psbD IGS each with five, and rps12-psbB IGS and rps4-trnT (UGU) IGS each with four occurrences. Data for individual species have limited general application, but are provided below.

Oryza sativa, the only monocot in this comparison, showed highest variation, based on rank, for clpP-psbB (0.0195, 924 bp), atpB-rbcL (0.0168, 1070 bp), and psbM-trnD (GUC) (0.0150, 523 bp). Two of the same regions were identified as fastest under criterion 2, atpB-rbcL (12 characters, 1070 bp) and clpP-psbB (11 characters, 924 bp), plus rbcL-accD (13 characters, 1824 bp). Sequence divergence was highest in and around the clpP region including what would be the clpP intron 2 (1.9455%, 257 bp), clpP intron 1 (1.0050%, 199 bp), and clpP-psbB (0.7576%, 924 bp). In contrast, the three fastest regions per Shaw et al. (2014) for monocots were ndhF-rpl32 (rank 1), ndhC-trnV (rank 2), and psbM-trnD (rank 3).

The highest variation for Fragaria under criterion 1 was for trnW (CCA)-psaJ (0.0101, 789 bp), trnT (GGU)-psbD (0.0098, 1527 bp), and trnP (UGG)-rps18 (0.0090, 1563 bp). Under criterion 2: trnT (GGU)-psbD (eight characters; 1527 bp), trnP (UGG)-rps18 (eight characters, 1563 bp), and petN-trnD (seven characters, 2504 bp). Under criterion 3, the top three regions were trnT (GGU)-psbD (0.4584%, 1527 bp), psbB-psbH (0.4451%, 674 bp), and rps4-trnT (UGU) (0.4435%, 451 bp). Shaw et al. (2014) eurosids I top three regions were ndhA intron (rank 1), trnS (GCU)-trnG (GCC) (rank 2), and rps16 intron (rank 3).

In Gossypium, the most informative regions under criterion 1 were psbZ-trnfM (CAU) (0.0534, 1179 bp), trnH (GUG)-psbA (0.0444, 496 bp), and rps4-trnT (UGU) (0.0425, 635 bp). Criterion 2 fastest regions were trnS (UGA)-trnG (GCC) with 39 variable characters over 1673 bp, followed by psbZ-trnfM (CAU) with 37 characters for 1179 bp, and trnT (UGU)-trnL (UAA) with 33 characters over 1470 bp. Sequence divergence (criterion 3) was highest for psbZ-trnfM (CAU) (2.2053%, 1179 bp), then trnS (UGA)-trnG (GCC) (1.6736%, 1673 bp), and finally the rps16 intron (1.6181%, 927 bp). Eurosids II top three regions for Shaw et al. (2014) were ndhF-rpl32 (rank 1), psbZ-trnG (rank 2), and trnT-trnL (rank 3).

For Olea, the most informative regions under criterion 1 were psbC-psbZ (0.0411, 1045 bp), trnS (UGA)-trnfM (0.0333, 1203 bp), and clpP intron 2 (0.0313, 702 bp). The highest number of variable characters (criterion 2) were found in rps16-trnQ (29 characters, 2739 bp), psbC-psbZ (22 characters, 1045 bp), and trnS (UGA)-trnfM (21 characters, 1203 bp). Criterion 3 (percent sequence divergence) was highest in the same three regions as under criterion 1: psbC-psbZ (2.0096%, 1045 bp), trnS (UGA)-trnfM (1.5794%, 1203 bp), and clpP intron 2 (1.4245%, 702 bp). Shaw et al. (2014) euasterids I top three included rps16-trnQ (rank 1), rpl32-trnL (rank 2), and ndhC-trnV (rank 3).

DISCUSSION

A large number of “universal” primers have been published for amplification of various chloroplast regions. Some are more degenerate than others, presumably to be more widely applicable. Degeneracy is not required, however, and may not lead to greater success in the laboratory. On the other hand, nondegenerate primers with poor fit are likely to fail, and some primers published as “universal” are not necessarily so. The universal barcoding primers of Dong et al. (2012) were the least likely to be useful across the 12 taxa examined here, with an average success rate of 65%, and a very poor 29% success rate in Oryza. In contrast, the primers designed by Scarcelli et al. (2011) specifically for monocots were exceedingly well-matched to the monocots sampled (97% in Acorus, 93% in Cymbidium, 92% in Oryza, and 88% in Canna), and a good match across all angiosperms.

Unlike previous analyses, this study used published genomes and primer sequences to infer the likelihood of amplification success. Only a small number of published primers were evaluated, and additional primers will be added to future analyses. Indeed, as mentioned in the introduction, Ebert and Peakall (2009) and Dong et al. (2013) have primers that could be evaluated as well as those of Doorduin et al. (2011) designed for species of Asteraceae. The evaluation conducted here shows parallels to prior studies in that general conclusions or recommendations are difficult to distill. For each region, there may be a number of primer pair options. Which primer pair is best is highly variable and depends upon the taxon being investigated. Scarcelli et al. (2011) primers are the best option for monocots in general, but will fail in specific combinations (e.g., trnH-psbA for Canna, atpF intron/exon for Cymbidium, and trnD-trnT for Oryza). Dong et al. (2012) primers are generally less successful, but they are the only primers that will work for psbM-trnD in Amborella and Magnolia. In several instances, a primer will work for some, but not all species in a genus, like the Scarcelli et al. (2011) matK primers in Cymbidium or the trnK-rps16 primers in Helianthus. Table 3 provides a quick summary of primer match for the top regions according to Shaw et al. (2014).

Prior studies have done an excellent job assessing variability of various noncoding regions across a diversity of angiosperms, particularly the recent work of Shaw et al. (2014). Those studies focused on infrageneric or even intergeneric comparisons. Here I compare sequence variability within species to see if the same markers are identified as the most variable, under slightly different criteria. This comparison was specifically avoided by Shaw et al. (2014) due to the small number of variable characters. The fastest regions identified here for Oryza were (depending upon criterion) clpP-psbB, atpB-rbcL, psbM-trnD, and rbcL-accD. In contrast, Shaw identified ndhF-rpl32, ndhC-trnV, and psbM-trnD as the fastest regions for monocots, with only one region of overlap between the two. For Fragaria (eurosids I), the list has no overlap at all. Olea (eurosids II) and Gossypium (euasterids I) each only overlap for a single region between the two studies. The lack of consensus over which region is the most variable at lower taxonomic levels has been pointed out by a number of papers including Särkinen and George (2013) for Solanum, and for 19 species pairs as demonstrated by Shaw et al. (2014). The comparison made here only adds to the argument that there is an acute need for additional comparative information.

Shaw et al. (2014) provided a solid foundation for which markers evolve the most quickly in major angiosperm clades, yet the fastest regions identified here for subspecies comparisons share little overlap with Shaw’s regions. This finding suggests the need for a thorough exploration of markers prior to undertaking a large comparative sequencing project. The methods employed here to examine expected primer utility can easily be applied to any taxon, provided complete chloroplast genomic data are available. When complete genome data are lacking, the results presented here can provide a rough estimate of the “best primers,” but this remains a work in progress.

Click here for additional data file.

Click here for additional data file.

Click here for additional data file.

Appendix 2.

Comparison of chloroplast regions with published primer pairs.

Approx. Nicotiana ordera	Primary type	Locationb	Genomic region	Shaw et al., 2005, 2007	Ebert and Peakall, 2009	Scarcelli et al., 2011	Dong et al., 2012	Dong et al., 2013	Current study
1	IGS	LSC	trnH (GUG)-psbA	✓		✓	✓	✓
2	Exon	LSC	psbA exon			✓		✓
3	IGS	LSC	psbA-trnK (UUU)	✓		✓		✓
4	IGS	LSC	3′trnK (UUU)-matK	✓	✓
5	Exon	LSC	matK exon			✓	*	✓
6	IGS	LSC	matK-trnK5′	✓	✓			✓
7	IGS	LSC	trnK (UUU)-rps16	✓	✓	✓		✓
8	Intron	LSC	rps16 intron	✓	✓	✓		✓
9	IGS	LSC	rps16-trnQ (UUG)	✓	✓	✓	✓	✓
10	IGS	LSC	trnQ (UUG)-psbK		✓	✓		*	✓
11	IGS	LSC	psbK-trnS (GCU)		✓	✓		*	✓
12	IGS	LSC	trnS (GCU)-trnG (UCC) and intron	✓	✓	✓	✓	*
13	Intron	LSC	trnG (UCC) intron	✓	✓	✓
14	IGS	LSC	trnG (UCC)-atpA		*	✓		✓	✓
15	Exon	LSC	atpA exon			✓		✓
16	IGS	LSC	atpA-atpF		✓			✓
17	Intron	LSC	atpF intron		✓	✓		✓	✓
18	IGS	LSC	atpF-atpH		✓	✓		✓	✓
19	IGS	LSC	atpH-atpI	✓	✓	✓	✓	✓
20	Exon	LSC	atpI exon			✓		✓
21	IGS	LSC	atpI-rps2		✓	✓		✓
22	Exon	LSC	rps2 exon			✓		*
23	IGS	LSC	rps2-rpoC2		✓	✓
24	IGS	LSC	rpoC2-rpoC1			✓		*
25	Intron	LSC	rpoC1 intron/exon 1		✓	✓		✓	✓
26	Exon	LSC	rpoC1 exon 2			✓		✓
27	Exon	LSC	rpoB2 exon					✓
28	IGS	LSC	rpoB-trnC (GCU)	✓	✓	✓	✓	✓
29	IGS	LSC	trnC (GCU)-ycf6	✓
30	IGS	LSC	trnC (GCU)-petN		✓	✓		✓
31	IGS	LSC	petN-trnD			✓
32	IGS	LSC	petN-psbM		✓		✓	✓
33	IGS	LSC	ycf6-psbM	✓
34	IGS	LSC	psbM-trnD (GUC)	✓	✓		✓	✓
35	IGS	LSC	trnD (GUC)-trnT (GGU)		✓	✓		✓
36	IGS	LSC	trnT (GGU)-psbD	✓	✓	✓	✓	✓
37	Exon	LSC	psbD exon			✓		✓
38	Exon	LSC	psbC exon			✓		✓
39	IGS	LSC	psbC-psbZ		✓	✓		*
40	IGS	LSC	trnS (UGA)-trnG (GCC)				✓
41	IGS	LSC	trnG (GCC)-rpS14		✓
42	IGS	LSC	trnS (UGA)-trnfM	✓
43	IGS	LSC	trnS (UGA)-psbZ						✓
44	IGS	LSC	psbZ-trnfM (CAU)			✓
45	IGS	LSC	trnfM (CAU)-psaB			✓
46	Exon	LSC	psaB exon					✓
47	Exon	LSC	psaA exon					✓
48	IGS	LSC	psaA-ycf3		✓	✓		✓	✓
49	Intron	LSC	ycf3 intron 2		✓	✓		✓	✓
50	Intron	LSC	ycf3 intron 1		✓	✓		✓	✓
51	IGS	LSC	ycf3-trnS (GGA)		✓				✓
52	IGS	LSC	ycf3-rps4			✓		✓
53	IGS	LSC	trnS (GGA)-rpS4-trnT (UGU)	✓
54	IGS	LSC	rpS4-trnT (UGU)					*	✓
55	IGS	LSC	trnT (UGU)-trnL (UAA)	✓	✓			*
56	Intron	LSC	trnL (UAA) intron	✓		✓		*
57	IGS	LSC	trnL (UAA)-trnF (GAA)	✓				*
58	IGS	LSC	trnL (UAA)-ndhJ	✓		✓		✓
59	IGS	LSC	trnF (GAA)-ndhJ		✓				✓
60	IGS	LSC	ndhJ-ndhC					✓
61	IGS	LSC	ndhC-trnV (UAC)	✓	✓	✓	✓	✓	✓
62	Intron	LSC	trnV (UAC) intron		✓	✓		✓	✓
63	IGS	LSC	trnV (UAC)-atpE					✓	✓
64	IGS	LSC	trnV (UAC)-atpB			✓		✓
65	Exon	LSC	atpB exon			✓		✓
66	IGS	LSC	atpB-rbcL		✓	✓		✓	✓
67	Exon	LSC	rbcL exon			✓		✓
68	IGS	LSC	rbcL-accD		✓	✓		✓	✓
69	Exon	LSC	accD exon			✓		✓
70	IGS	LSC	accD-psaI	✓	✓	✓	✓	*	✓
71	IGS	LSC	psaI-ycf4		✓	✓		*	✓
72	Exon	LSC	ycf4 exon			✓		✓
73	IGS	LSC	ycf4-ycf10 (cemA)		*			✓	✓
74	Exon	LSC	cemA					✓
75	IGS	LSC	ycf4-petA			✓		*
76	Exon	LSC	petA exon			✓		✓
77	IGS	LSC	petA-psbJ	✓	✓		✓	✓	✓
78	IGS	LSC	psbJ-psbE					✓
79	IGS	LSC	petA-psbL			✓
80	IGS	LSC	psbE-petL	✓	✓		✓	✓
81	IGS	LSC	petL-psaJ						✓
82	IGS	LSC	petL-trnP (UGG)			✓		✓
83	IGS	LSC	trnW (CCA)-psaJ				✓	✓
84	IGS	LSC	trnP (UGG)-rps18		*	✓
85	IGS	LSC	psaJ-rpl20		*			*	✓
86	IGS	LSC	rps18-rps12			✓		*
87	IGS	LSC	rpl20-rps12	✓				*	✓
88	IGS	LSC	rps12-psbB			✓
89	IGS	LSC	rps12-clpP		✓	✓		*
90	Intron	LSC	clpP intron 2		✓	✓	✓	✓	✓
91	Intron	LSC	clpP intron 1		✓	✓	✓	✓	✓
92	IGS	LSC	clpP-psbB		✓	✓		✓	✓
93	Exon	LSC	psbB exon			✓		✓
94	IGS	LSC	psbB-psbH	✓				✓
95	IGS	LSC	psbH-petBE2		✓			✓	✓
96	Intron	LSC	petB intron/exon 2			✓		✓
97	IGS	LSC	petBE2-petDE2		✓	✓	✓	✓	✓
98	Intron	LSC	petD intron/exon 2			✓		✓
99	IGS	LSC	petD-rpoA			✓		✓
100	Exon	LSC	rpoA exon					✓
101	IGS	LSC	rpoA-rps11					✓
102	IGS	LSC	rps11-rps8		✓	✓		✓
103	Exon	LSC	rps8 exon					✓
104	IGS	LSC	rpl36-rpl14	✓
105	IGS	LSC	rps8-rpl16		✓	✓		✓
106	Intron	LSC	rpl16 intron	✓		✓
107	IGS	LSC	rpl16-rps3		✓	✓		✓
108	Exon	LSC	rps3 exon			✓		✓
109	IGS	LSC	rps3-rps19		✓			*	✓
110	IGS	LSC	rpl22-rpl2			✓		*
111	Intron	IRb	rpl2 intron/exon 1-2			✓		✓
112	IGS	IRb	rpl23-ycf2			✓		*
113	Exon	IRb	ycf2 exon					✓
114	IGS	IRb	ycf2-ndhB			✓		✓
115	Exon	IRb	ndhB exon 2			✓		✓
116	Intron	IRb	ndhB intron/exon 1			✓		✓
117	IGS	IRb	ndhB-rps7			✓		✓
118	IGS	IRb	rps7-rps12					✓
119	Intron	IRb	rps12 intron/exon			✓
120	IGS	IRb	rps12-trnV (GAC)			✓		✓
121	IGS	IRb	trnV (GAC)-rrn16			✓		✓
122	Exon	IRb	rrn16 exon			✓		✓
123	IGS	IRb	rrn16-trnl (GAU)			✓		✓
124	Intron	IRb	trnI (GAU) intron			✓		*
125	Intron	IRb	trnA (UGC) intron			✓		*
126	IGS	IRb	trnA (UGC)-rrn23			✓		*
127	Exon	IRb	rrn23 exon					✓
128	IGS	IRb	rrn4,5-trnN (GUU)			✓		✓
129	IGS	IRb	trnN (GUU)-ycf1					✓
130	IGS	IRb/SSC	ycf1-ndhF					✓
131	Exon	SSC	ndhF exon				✓	✓
132	IGS	SSC	ndhF-rpl32	✓		✓		✓
133	IGS	SSC	rpl32-ccsA			✓		✓
134	IGS	SSC	rpl32-trnL (UAG)	✓			✓
135	Exon	SSC	ccsA exon			✓		✓
136	IGS	SSC	ccsA-ndhD			✓		✓	✓
137	Exon	SSC	ndhD exon			✓		✓
138	IGS	SSC	ndhD-ndhE					✓
139	IGS	SSC	psaC-ndhE						✓
140	IGS	SSC	psaC-ndhG			✓
141	IGS	SSC	ndhE-ndhI					✓	✓
142	Exon	SSC	ndhG exon			✓		*
143	IGS	SSC	ndhG-ndhI			✓		*
144	Intron	SSC	ndhA intron	✓		✓	✓	✓
145	IGS	SSC	ndhA-ndhH					✓
146	Exon	SSC	ndhH exon			✓		✓
147	IGS	SSC	ndhH-rps15					✓
148	IGS	SSC/IRa	rps15-ycf1			✓			✓
149	IGS	IRa	ycf1-rrn5			✓
Bonus	IGS	LSC	rbcL-psaI						✓
Bonus	IGS	LSC	trnS-psbD				✓

Several regions overlap.

IR = inverted repeat; LSC = large single-copy region; SSC = small single-copy region.

Slightly different region from that listed.