MICROSATELLITE DEVELOPMENT IN
RHODOPHYTA USING HIGH-THROUGHPUT
SEQUENCE DATA

Lucía Couceiro, Isabel Maneiro, Stéphane Mauger, Myriam Valero, José

Miguel Ruiz, Rodolfo Barreiro

► To cite this version:

Lucía Couceiro, Isabel Maneiro, Stéphane Mauger, Myriam Valero, José Miguel Ruiz, et al. MICROSATELLITE DEVELOPMENT IN RHODOPHYTA USING HIGH-THROUGHPUT SEQUENCE DATA. Journal of Phycology, 2011, 47 (6), pp.1258-1265. 10.1111/j.1529-8817.2011.01075.x. hal-04302640

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# MICROSATELLITE DEVELOPMENT IN RHODOPHYTA

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## USING HIGH-THROUGHPUT SEQUENCE DATA

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Lucia Couceiro¹,², Isabel Maneiro¹, Stéphane Mauger², Myriam Valero², Jose Miguel Ruiz¹
and Rodolfo Barreiro¹

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¹ Area de Ecoloxia, Facultade de Ciencias, Universidade da Coruna, Campus de A Zapateira s /n, 1571-A Coruna, Spain

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² CNRS-UPMC, UMR 7144, Equipe BEDIM, Station Biologique de Roscoff, Place Georges Teissier, F-29682 Roscoff Cedex, France

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Shotgun genome sequencing is rapidly emerging as the method of choice for the identification of microsatellite loci in nonmodel organisms. However, to the best of our knowledge, this approach has not been applied to marine algae so far. Herein, we report the results of using the 454 next-generation sequencing (NGS) platform to randomly sample 36.0 and 40.9 Mbp (139,786 and 139,795 reads, respectively) of the genome of two red algae from the northwest Iberian Peninsula [Grateloupia lanceola (J. Agardh) J. Agardh and a still undescribed new member of the family Cruoriaceae]. Using data mining tools, we identified 4,766 and 5,174 perfect microsatellite loci in 4,344 and 4,504 sequences / contigs from G. lanceola and the Cruoriaceae, respectively. After conservative removal of potentially problematic loci (redundant sequences, mobile elements), primer design was possible for 1,371 and 1,366 loci, respectively. A survey of the literature indicates that microsatellite density in our Rhodophyta is at the low end of the values reported for other organisms investigated with the same technology (land plants and animals). A limited number of loci were successfully tested for PCR amplification and polymorphism finding that they may be suitable for population genetic
studies. This study demonstrates that random genome sequencing is a rapid, effective
alternative to develop useful microsatellite loci in previously unstudied red algae.

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29 **Key index words:** 454 sequencing; conservation genetics; expressed sequence tags; microsatellite development; primer design; Rhodophyta; seaweed

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31 **Abbreviations:** bp, base pairs; ESTs, expressed sequence tags; GS-FLX, Genome Sequencer
32 FLX; Mbp, mega base pairs; MIDs, multiplex identifiers; NGS, next-generation sequencing;
33 PAL, potentially amplifiable loci; TE, Tris-EDTA

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35 Population genetics studies are an important tool for developing effective management
36 strategies for endangered species (Frankham et al. 2004). How- ever, obtaining appropriate
37 molecular markers— a prerequisite for such studies—often becomes a limiting step. This is the
38 case for microsatellites. Although they are often considered the marker of choice for population
39 genetics analysis due to their high levels of polymorphism and codominant nature, their de novo
40 development is a costly and time-consuming endeavor (Zane et al. 2002). These difficulties
41 have acted in many cases as a deterrent for the utilization of these markers, particularly in poorly
42 studied taxonomic groups (Squirrell et al. 2003).

43 The recent accessibility to NGS technologies is revolutionizing life sciences in several research
44 fields (reviewed in Hudson 2008). Even though NGS is typically cited as a fast and cheap route
45 to assemble genomes or transcriptomes, it also provides avenues to many broader purposes,
46 including marker devel- opment (Thomson et al. 2010). In particular, high- throughput
47 sequence data from the Roche 454 platform offer the potential for a rapid identification of
48 microsatellite loci on nonmodel organisms, and several examples of this have been published
49 already (Abdelkrim et al. 2009, Allentoft et al. 2009, Santana et al. 2009, Castoe et al. 2010,
50 Csencsics et al. 2010). Briefly, the Genome Sequencer FLX System (GS-FLX) powered by 454
51 sequencing (454 Life Sciences, Branford, CT, USA) enables a random and extensive survey of
the genome providing thousands of short reads (average length of 400 bp using GS-FLX
Titanium Series Reagents). This huge data set of reads is then screened with simple
bioinformatic tools to detect microsatellite motifs and to design PCR primers. Thus, this
approach eliminates the prolonged, expensive, and laborious steps of constructing genomic
libraries, including cloning, hybridization to detect positive clones, plasmid isolation, and
Sanger sequencing. Herein, we report the results obtained by applying this approach to two
Rhodophyta of conservation concern in Galicia (northwest Iberian Peninsula): G. lanceola and
a still undescribed new member of the Cruoriaceae. Compared to other taxa, seaweeds have not
been traditionally included in the debate about endangered species. However, there is a growing
recognition that many seaweeds are restricted in their
distribution and may thus be vulnerable to stochastic environmental and anthropogenic events
(Brodie et al. 2009). The two species studied herein are candidates for inclusion in the regional
catalog of endangered species (Xunta de Galicia 2007). In Galicia, G. lanceola occurs in only
a few (<30) enclaves mostly concentrated in 30 km of coastline (Peez- Cirera et al. 1989,
Ba'rbara and Cremades 2004). Besides their regional rarity, these populations possess
biogeographic value derived from their marginality and isolation (Eckert et al. 2008) (Galician
populations are located > 1,000 km north of the species' main range in the Atlantic coast of
Africa). The conservation status of the second species also deserves attention. Its Galician
populations were traditionally identified as Furcellaria lumbricalis (Huds.) J. V. Lamour., a
common inhabitant of northern latitudes with a southern limit of distribution in the Iberian
Peninsula (Pe rez-Cirera 1975, Granja et al. 1993). However, some anatomical peculiarities
led us to produce DNA sequence data to determine its taxonomic identity. Surprisingly, both
plastid (rbcL) and nuclear (rDNA LSU) gene sequences have revealed that the plant found in
Galicia is a still undescribed erect member of the Cruoriaceae family (unpublished results). Its
limited presence in Galicia (it is known only from 12 enclaves, Barbara et al. 2006) renders this
new species an interesting case to investigate its population structure and, eventually, to
determine its suitability for inclusion in the regional catalog of seaweeds of conservation concern. Previous experience with conventional protocols for microsatellite isolation suggests that seaweeds may display comparably low genomic frequencies of microsatellites (Wattier and Maggs 2001, Andreakis et al. 2007). Since we were particularly interested in discovering whether or not the use of NGS technology will still support this conventional wisdom, our results with the 454 technology are compared to those for other taxa obtained using comparable high-throughput approaches.

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## MATERIALS AND METHODS

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DNA extraction and pyrosequencing. For each species, a single specimen was used as the sole source of tissue/DNA. Prior to DNA extraction, samples were frozen in liquid nitrogen and homogenized using a mechanical tissue disrupter (Mini-Bead-beater 1; Bio Spec Products Inc., Bartlesville, OK, USA) and steel beads. Total DNA was then extracted with the Wizard Magnetic 96 DNA Plant System (Promega Corp., Madison, WI, USA) following manufacturer's recommendations. To achieve the required amount of DNA, multiple DNA isolations from each specimen were pooled, lyophilized, resuspended in Tris-EDTA (TE) buffer, and purified with QIAquick PCR Purification kit (Qiagen Sciences Inc., Germantown, MD, USA).

A total of 2–5 lg of DNA from each species was used to prepare their respective GS-FLX Titanium general libraries (454 Life Sciences) following manufacturer's protocols and quality control steps. Briefly, genomic DNA was fragmented by nebulization, small DNA fragments (<350 bp) were removed, short adaptors were ligated to the ends of each fragment, and, finally, only those fragments with adaptors in both 5' and 3' ends were retained. Each DNA sample was appropriately tagged using two different multiplex identifiers (MIDs) to enable pooling for simultaneous sequencing. DNA library fragments were then captured onto beads and clonally
ampli- fied within individual emulsion droplets (emPCR). Finally, amplified fragments from both species were evenly mixed and sequenced on ¼ PicoTiterPlate regions using the Titanium GS-FLX chemistry (454 Life Sciences). Libraries preparation, amplification, and sequencing were carried out at the external facilities Centre de Recerca en Agrigenomica (Barcelona, Spain).

Data preprocessing and microsatellite discovery. Each read was automatically assigned to its correct library by GS-FLX software (454 Life Sciences) based on sample-specific MID sequence. In an initial attempt to remove redundancy, sequence reads from each species were analyzed using 454 GS De Novo Assembler v2.3 (454 Life Sciences) with default parameters.

The assembled contigs and remaining singletons resulting from this analysis were clustered into a single fasta format file and used as input for the software QDD, a collection of freely available modules (ActivePerl, BLAST, CLUSTALw2 and Primer3) that proceeds in three successive stages (Meglecz et al. 2010). Firstly, it selects sequences that are longer than a user-defined limit and have perfect microsatellites with a minimum number of repeats. Secondly, QDD automatically detects and removes redundancy as well as groups of sequences that potentially are part of a repetitive region of the genome and, consequently, might be problematic for microsatellite amplification (although QDD is not designed to describe mobile elements, this is a conservative method that eliminates many of these sequences). Finally, the software selects sequences that contain a target microsatellite and a flanking region free from nanosatellites and designs PCR primers to amplify these repeats when flanking regions enable it. All QDD parameters, including the parameters of the module Primer3, were set to default values.

Data analysis. Tab-delimited files generated from QDD steps 1 (identified loci) and 3 (potentially amplifiable loci, PAL) were converted to spread-sheet files for use in Microsoft Office Excel 2003 (Microsoft Corporation, Redmond, WA, USA). Microsatellite frequency for each species was expressed as total number of identified microsatellites per mega base pair
(Mbp), and subsequent data analysis included sorting according to microsatellite category (i.e., di-, tri-, tetra-, penta-, and hexanucleotides), specific motifs and number of tandemly repeated units. Reverse-complement repeat motifs and translated or shifted motifs were grouped together so that there were four unique dinucleotides repeats and 10, 33, 102, and 350 unique tri-, tetra-, penta, and hexanucleotides repeats, respectively (Jin et al. 1994). To provide some comparison with other Rhodophyta, microsatellite frequency / attributes were also investigated using exactly the same procedure in seven species with >1,000 expressed sequence tag (EST) sequences available in the public database EST-GenBank: Porphyra yezoen-sis, Furcellaria lumbricalis, Eucheuma denticulatum, Gracilaria changii, Porphyra haitanensis, Chondrus crispus, and Griffithsia okiensis. Portions of this data set were screened for micro-satellite motifs by other authors (e.g., EST data for the two Porphyra; Liu et al. 2005, Sun et al. 2006, Wang et al. 2007, Xie et al. 2009), but they were reanalyzed here under common search criteria to facilitate comparison.

Microsatellite testing. A subset of the PAL for each species was tested for amplification (18 and 23 PAL for the Cruoria-ceae and G. lanceola, respectively). To increase the chance of targeting polymorphic loci, we selected PAL with the highest number of repetitions. Markers producing clear electropherogram patterns were assessed for polymorphism using a number of individuals from various populations in the Iberian Peninsula (32 specimens from four populations in the case of the Cruoriaceae; 26-30 specimens from three populations in G. lanceola). DNA extractions were performed as described above, and PCR reactions (10 µL) contained 10 ng of DNA, 200 nM of each primer (Life Technologies Corp., Carlsbad, CA, USA), 150 µM of dNTPs (Fermentas Inc., Geln Burnie, MA, USA), 1.5 mM of MgCl2 (Promega Corp.), 1-PCR Buffer (Promega Corp.), and 0.35 U Taq DNA polymerase (Promega Corp.). Amplifications were carried out separately for each locus on PTC-200 Thermo Cycler (GMI Inc., Ramsey, MI, USA) with initial denaturing at 94 C for 5 min, followed by 30 cycles of 94 C for 30 s, 57 C-60 C for 30 s, and 72 C for 30 s with a final extension of 72 C for 10 min.
156 DNA fragments were separated on an ABI 3730 Genetic Analyzer (Life Technologies Corp.)
157 with GeneScan 600 LIZ as size standard (Life Technologies Corp.). Finally, electropherograms
158 were analyzed with the software GeneMapper v4.0 (Life Technologies Corp.), and diversity
159 estimates (number of alleles per locus, NA; observed heterozygosity, Ho; expected
160 heterozygosity, He) were calculated with GenALEx 6 software (Peakall and Smouse 2006).

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163 RESULTS

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165 Undescribed Cruoriaceae species. A total of 139,795 reads and 40,896,217 bp were obtained
166 from the 454 shotgun library for the Cruoriaceae (mean read length 293 bp). When sequence
167 reads were assembled using 454 GS De Novo Assembler v 2.3 with default parameters, the new
168 data set consisted in 4,329 contigs plus 51,622 singletons. A total of 5,174 microsatellite loci
169 (di-, tri-, tetra-, penta-, and hexanucleotides) with ‡4 repeats were identified in 4,504 of these
170 sequences amounting to a yield of 0.037 microsatellites per initial read and 126.5 microsatellites
171 per mega base pairs (Mbp). Most loci contained di- and trinucleotide repeats (4,309 and 699,
172 respectively), followed far behind by hexa- (65), tetra- (64), and pentanucleotides (37) (Table
173 1). After a conservative removal of redun- dancy and potentially problematic sequences (e.g.,
174 those located in repetitive regions of the genome), PCR primers design was possible in 1,366
175 loci (26% of the loci identified). Again, these potentially PAL were dominated by di- and
176 trinucleotide repeats (1,178 and 168 loci, respectively), while larger motifs lagged well behind
177 (£8 loci in each repeat category). A detailed description of these results including a unique
178 accession for each PAL, Gen- Bank accession number, repeat monomer sequence, number of
179 perfect tandemly repeated units, and forward / reverse primer sequences is provided in
180 Appendix S1 (see the supplementary material).
The relative numbers of perfect tandemly repeated units were fairly similar across the various microsatellite categories (Table 1). In all five categories, there were many short repeats and fewer long repeats although the relative abundance of loci with the lowest number of repeated units (four) decreased with microsatellite complexity (from >70% for di- and tri-nucleotides to <55% for larger motifs). Differences were even more pronounced in PAL where most loci (>95%) only contained four to five repeat units. Since di- and trinucleotides were 97% of all identified microsatellite loci and 99% of all PAL, differences in the relative abundance of specific motifs were only investigated in these two categories (Fig. 1). For the dinucleotide repeats, three of the four possible motifs were evenly represented and accounted 88% of the identified loci, while AT was less frequent (12%). Among the trinucleotides, one of the 10 possible motifs (CCG) was clearly overrepresented and nearly doubled the frequency expected simply by chance; the other nine motifs showed comparable frequencies except for two motifs (ACT and AGT) with frequencies <5%. In general, the same pattern was applicable to PAL data. Twelve of the 18 PAL tested for amplification (Table 1) yielded a product of the expected size. Reliable scoring of allele sizes (one to two clear peaks, no multiband pattern) was possible for seven of them, and they were used for subsequent polymorphism assessment. Among these seven loci, five were polymorphic and heterozygous in at least one individual (Table S1 in the supplementary material) with three to four (mean 3.4) alleles per locus. The observed and expected heterozygosities ranged from 0.031 to 0.969 (mean 0.569) and from 0.254 to 0.656 (mean 0.515), respectively. The five polymorphic loci produced 28 distinct multilocus genotypes in 32 individuals (88%). Grateloupia lanceola. The 454 shotgun library for G. lanceola provided a number of reads very close to those obtained for the Cruoriaceae (139,786 reads) but a slightly lower number of base pairs (36,013,235 bp, mean length read 258 bp). The pre-liminary assembling with the 454 GS De Novo Assembler v 2.3 resulted in 2,526 contigs plus 67,005 singletons. A total of 4,766 microsatellite loci (di-, tri-, tetra-, penta-, and hexanucleotides) with 4 repeats were identified in 4,344 of these sequences amounting to a
yield of 0.034 microsatellites per initial read or 132.3 microsatellites per Mbp (Table 1). As in the other species, most loci contained di- and trinucleotide repeats (3,959 and 686, respectively) followed far behind by tetra- (59), hexa- (33), and pentanucleotides (29). After removing redundancy in its widest sense, the pro- portion of loci for which PCR primers design was possible was again very similar to that obtained for the other red alga (1,371 loci, 29% of the identified loci). Likewise, PAL were dominated by di- and tri- nucleotide repeats (1,176 and 165, respectively), while more complex motifs lagged well behind (±14 loci). A detailed description of these results includ- ing a unique accession of each PAL, GenBank acces- sion number, repeat monomer sequence, number of perfect tandemly repeated units, and for- ward/reverse primers sequences is provided in Appendix S2 (see the supplementary material).

As already observed in the Cruoriaceae, the rela- tive abundance of perfect tandemly repeated units was fairly similar across microsatellite categories (Table 1) with abundant short repeats and fewer long repeats. However, the relative abundance of loci with the lowest number of repeated units (four) decreased with microsatellite complexity (86, 75, 69, 62, and 60% for di-, tri-, tetra-, penta-, and hexanu- cleotides, respectively). The higher relative abundance of loci with a small number of repeated units was even more marked in PAL as >95% loci only contained four to five repeats.

Both di- and trinucleotide loci revealed notable changes in the relative abundance of specific motifs and, more interestingly, substantial differences with the other species (Fig. 1). For the dinucleotide repeats, one of the four possible motifs (AG) accounted for 50% of the identified loci, while AC showed a frequency similar to that expected by chance (31%) and the other two motifs (AT and CG) were <15%. Among the trinucleotides, one of the 10 possible motifs (AAG) doubled the frequency expected by chance; the other nine motifs exhibited comparable frequencies (10%) except for AGC and CCG with frequencies <7%. PAL showed the same general pattern of motif frequencies.
A large proportion of the PAL tested for this species showed good, size-expected amplification of 232 products (20 of 23 tested PAL, 87%) and easily scorable profiles (19 of 23 tested PAL, 83%) of 233 (Table 1). Twelve of these loci were monomorphic, and the seven remaining ones were 234 polymorphic; however, six were heterozygous in at least one individual, while the last one was 235 homozygous across all samples. The mean number of different alleles per locus was lower 236 than in the Cruoriaceae (2.4 vs. 3.4, two to four alleles per locus), and the observed and expected 237 heterozygosities ranged from 0 to 0.933 (mean 0.196) and from 0.034 to 0.531 (mean 0.204), 238 respectively (Table S1). Likewise, the seven loci produced 13 distinct multilocus genotypes in 239 30 individuals (43%). The low levels of polymorphism detected in G. lanceola are consistent 240 with the low genetic diversity found in this species using AFLP markers (unpublished results). 241 Microsatellite frequency / attributes in other Rhodophyta. A total of 60,444 public EST 242 sequences (27 Mbp) from the other seven red algae were screened for perfect tandemly repeats 243 units with QDD using the same criteria applied to our shotgun sequencing data (Table S2 in the 244 supplementary material). Micro-satellite frequency oscillated from 261.8 counts Æ Mbp)1 in 245 E. denticulatum to 860.89 counts Æ Mbp)1 in G. changii. As in our Cruoriaceae and G. 246 lanceola, most of the microsatellite loci (>95%) contained di- and trinucleotide repeats. Relative 247 abundances of perfect tandemly repeated units were also similar to our results with genome 248 sequencing data. More than 65% of the detected loci contained just four repeats, while the 249 percentage of microsatellites with >10 repeats was <1% in three species (E. denticulatum, F. 250 lumbricalis, and G. changii), ranged 1%-5% in C. crispus and G. okiensis, and was >5% in the 251 two Porphyra. The relative abundance of specific motifs also showed considerable variation. 252 AG was the most abundant dinucleotide motif in three species (E. denticulatum, P. haitanensis, 253 and G. changii; 34.4%-44.9%), AC dominated in F. lumbricalis (49.2%) as well as in G. 254 okiensis (39.4%), and CG was the most common motif in C. crispus (44.4%) and P. yezoensis 255 (48.2%). Among the trinucleotides, CCG prevailed in all species except F. lumbricalis, but its 256 relative abundance was variable. 257
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Population genetics studies are still comparatively scarce in seaweeds (Valero et al. 2001) and have mostly focused on a limited number of genera / species (Andreakis et al. 2007). This shortage of intraspecific studies has been attributed, at least in part, to a lack of appropriate molecular markers. In particular, the detection of useful microsatellite loci has been challenging, and it has been suggested that their genomic frequency in this group might be low (Olsen et al. 2002, Andreakis et al. 2007). Our relatively extensive and random survey of the genome of two Rhodophyta should allow some testing of this conventional wisdom.

Microsatellite frequency is known to vary to a considerable extent among eukaryotes. Even when complete genomes are screened for microsatellite occurrence with consistent procedures, among-taxa differences can be as high as three orders of magnitude (Sharma et al. 2007). A review of the literature reveals that microsatellite frequency is likewise variable in taxa investigated with a genome sequencing approach comparable to ours (Table S3 in the supplementary material). Estimates for a number of plants and animals range from 11.7 microsatellite counts per Mbp to 544.0 counts Æ Mbp)1 (mean 148.4 counts Æ Mbp)1). In this context, our results may seem comparable to those obtained for various animals and higher than the estimates reported for some plants. However, our review also shows that search parameters vary between studies, and, consequently, caution must be exercised in doing this comparison.

In particular, the minimum number of repeats for microsatellite detection can be especially influential. Most studies used cutoff values slightly larger than ours, especially for the smaller motifs that often constitute a major fraction of the detected loci. When our estimates were recalculated for a higher cutoff (five for every category), the new microsatellite frequencies (21.9 and 28.8 counts Æ Mbp)1) placed our two red algae in the lower end of the values reported in the literature (Table S3).
As our study is the first case where 454 shotgun genome sequencing was used to detect microsatellite motifs in algae, it is difficult to assess to what extent our estimates may be relevant for other taxa. However, it is worth mentioning that our two taxa yielded very similar microsatellite frequencies despite the fact that they belong to different orders and show obvious differences in other microsatellite attributes. Thus, the genome of the Cruoriaceae was particularly enriched in CG motifs, while G. lanceola was dominated by AG motifs. This variation is consistent with the great differences in di- and trinucleotide motifs reported in other photosynthetic organisms (Von Stackelberg et al. 2006). It also suggests that microsatellite attributes are species specific rather than group specific, an observation with obvious implications for the enrichment protocols often used for isolating microsatellite loci (Zane et al. 2002) as inappropriate a priori choices about repeat size and motif sequence may lower the efficiency of obtaining useful microsatellite loci.

In contrast with the absence of random genomic sequencing data for Rhodophyta, the growing body of publicly available DNA sequences—including vast collections of ESTs—can be used to explore the density/attributes of repeat motifs in the genome of other red algae. Our analyses of EST data for seven species of Rhodophyta (covering four orders and six families) reveal that microsatellite density is 2- to 7-fold higher in EST sequences than in our 454 sequencing data, suggesting that EST collections may be a more effective source for microsatellite mining. As in our random sequencing, EST-derived loci were dominated (>95%) by di- and trinucleotide repeats (Table S2); also, dinucleotides were three to five times more abundant than trinucleotides in all taxa except the two Porphyra. This prevalence of dimer repeats is in agreement with the higher density of dinucleotides found in nine of the 25 photosynthetic organisms investigated by Von Stackelberg et al. (2006), and contrasts with the notion that EST-derived microsatellites may be prone to trinucleotides (Varshney et al. 2002, Rota et al. 2005, Wang et al. 2006). However, the particulars of our search criteria may have had some influence on our
estimates, and alternative estimates for the two Porphyra can be found in the literature (Liu et al. 2005, Sun et al. 2006, Wang et al. 2007, Xie et al. 2009).

A survey of service providers indicates that many companies guarantee the development of 10 poly-morphic microsatellite loci in 1-3 months with a cost of $5,000-10,000 (Abdelkrim et al. 2009). Using shotgun sequencing, we were able to develop 12 polymorphic microsatellite loci in a similar period of time with a final cost of €7,000. Polymorphism (as number of alleles) in our loci was comparable to values reported for other red algae (Guillemin et al. 2005, Andreakis et al. 2007). In this regard, previous analysis with dominant AFLPs markers has revealed very low levels of genetic diversity in one of the species included in this study (G. lanceola; I. Maneiro, L. Couceiro, I. Ba'rbara, J. Cremades, J. M. Ruiz, and R. Barreiro, unpublished), and the slightly lower number of alleles found for this species, as well as the high proportion of monomorphic loci (12/19), seems consistent with these previous results. Therefore, our approach shows a time- and cost-effectiveness comparable to those of traditional methods of microsatellite development. However, the genome sequencing approach still offers a number of alternatives that may enhance its effectiveness. Using microsatellite-enriched DNA before pyrosequencing, Santana et al. (2009) reported that 25%-97% of their initial reads contained microsatellite motifs (estimates vary depending on the species and enrichment protocol used), values that clearly surpass the <10% found in our two red algae. Likewise, our microsatellite search was restricted to perfect motifs, but there is some evidence that complex repeated core motifs (i.e., multiple or interrupted repeats) dominate in other red algae (Wattier et al. 1997, Luo et al. 1999, Guillemin et al. 2005, Andreakis et al. 2007). The possibility that complex patterns may be more abundant in red algae surely warrants further investigation and suggests that the development of polymorphic loci from NGS data could benefit from less stringent screening criteria. Although interrupted repeats are expected to have lower mutation rates compared to pure ones (Buschiazzo and Gemmell 2006), empirical data from red algae reveal
334 that many useful microsatellite loci have complex patterns (Wattier et al. 1997, Luo et al. 1999,
335 Guillemin et al. 2005, Andreakis et al. 2007).

336 Microsatellite detection with genome sequencing data has other additional benefits. The access
337 to a large volume of sequences allows the detection of putative mobile elements by spotting
338 sequence similarity groups (Meglecz et al. 2010). Using the package QDD (Meglecz et al.
339 2010), we removed 25.8%–26.2% of the sequences with potential loci, possibly saving time
340 and money in primer design and laboratory testing for useless loci since PCR amplification can
341 be seriously affected by microsatellite and mobile element associations. On the other hand,
342 genome sequencing produces a large number of novel genomic resources that may be useful
343 for both related and unrelated research such as gene content, gene functions, or even the
344 association of PAL with coding regions (Sharma et al. 2007, Tang-phatsornruang et al. 2009).

345 In summary, we have reported the cost- and labor-effective development of microsatellite
346 markers in two red algae using shotgun sequencing data (454 technology). To our knowledge,
347 this is the first time that random genome sequencing is used for microsatellite identification in
348 seaweeds. Our results suggest that microsatellite density in our two red algae is below the levels
349 reported for other taxa studied with shotgun technology. However, and despite the use of a
350 stringent filtering procedure to avoid redundancy, we still obtained a substantial number of
351 PAL. A limited number of these PAL were successfully tested for PCR amplification and
352 polymorphism finding that they may be suitable for population genetic studies. Shotgun
353 sequencing appears as an efficient alternative for the fast development of microsatellite loci
354 in difficult and/or poorly studied red algae species.

355

356 The authors are deeply indebted to Ignacio Barbara, Javier Cremades, Viviana Pena, and Pilar
357 Diaz for their assistance with sampling and species identification. This research was supported
358 by Spain's Secretaria de Estado de Investigacion, Ministerio de Ciencia e Innovacion (project
359 CTM2007-61011).
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<table><caption>TABLE 1. Counts of loci for each combination of microsatellite category (di-, tri-, tetra-, penta-, and hexanucleotides) and number of perfect tandemly repeat units in each species. Within each cell, values are given for identified/potentially amplifiable microsatellite loci.</caption><thead><tr><th rowspan="2"></th><th colspan="5">Cruoriaceae species</th><th colspan="5">Grateloupia lanceola</th></tr><tr><th>di-</th><th>tri-</th><th>tetra-</th><th>penta-</th><th>hexa-</th><th>di-</th><th>tri-</th><th>tetra-</th><th>penta-</th><th>hexa-</th></tr></thead><tbody><tr><td>4</td><td>3,409/1,019</td><td>515/131</td><td>27/5</td><td>20/4*</td><td>25/4</td><td>3,385/1,052</td><td>513/124</td><td>41/8</td><td>18/3</td><td>20/7</td></tr><tr><td>5</td><td>536/118</td><td>85/18</td><td>11/1*</td><td>5/0</td><td>9/3*</td><td>385/92</td><td>107/31</td><td>11/5*</td><td>8/1</td><td>6/3*</td></tr><tr><td>6</td><td>134/21</td><td>38/8</td><td>3/1*</td><td>4/0</td><td>4/0</td><td>77/16</td><td>35/5</td><td>3/0</td><td>0/1</td><td>4/0</td></tr><tr><td>7</td><td>69/5</td><td>12/6*</td><td>7/0</td><td>0/0</td><td>0/0</td><td>30/7</td><td>4/0</td><td>2/1*</td><td>3/0</td><td>0/0</td></tr><tr><td>8</td><td>37/7</td><td>14/2*</td><td>4/1*</td><td>1/0</td><td>2/0</td><td>11/4*</td><td>6/2*</td><td>1/0</td><td>0/0</td><td>2/1*</td></tr><tr><td>9</td><td>23/5*</td><td>9/0</td><td>2/0</td><td>2/0</td><td>4/0</td><td>15/2*</td><td>5/2*</td><td>0/0</td><td>0/0</td><td>0/0</td></tr><tr><td>≥10</td><td>73/3*</td><td>21/3*</td><td>8/0</td><td>3/0</td><td>17/0</td><td>54/3*</td><td>16/1*</td><td>1/0</td><td>0/0</td><td>1/0</td></tr><tr><td>≥25</td><td>17/0</td><td>3/0</td><td>1/0</td><td>2/0</td><td>4/1*</td><td>2/0</td><td>0/0</td><td>0/0</td><td>0/0</td><td>0/0</td></tr><tr><td>≥50</td><td>11/0</td><td>2/0</td><td>1/0</td><td>0/0</td><td>0/0</td><td>0/0</td><td>0/0</td><td>0/0</td><td>0/0</td><td>0/0</td></tr><tr><td>Total</td><td>4,309/1,178</td><td>699/168</td><td>64/8</td><td>37/4</td><td>65/8</td><td>3,959/1,176</td><td>686/165</td><td>59/14</td><td>29/5</td><td>33/11</td></tr></tbody></table>

468

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<footnote>*, Denotes categories of potentially amplifiable loci that were tested for amplification and polymorphism.</footnote>
<figure><img src="image_2.png" /><figcaption>FIG. 1. Motif frequency in di- and trinucleotide repeats. Number of identified microsatellite loci (black) and subset for which PCR primers could be designed (potentially amplifiable loci, PAL; white) for different repeat sequence motifs in (a) undescribed Cruoriaceae and (b) *Grateloupia lanceola*.</figcaption></figure>

Supplementary Material

The following supplementary material is available for this article:

Appendix S1. Details of Cruoriaceae PAL (GenBank accession, number of perfect tandemly repeated units, repeat monomer sequence) and designed primers (forward and reverse sequences, melting temperatures).

Appendix S2. Details of *Grateloupia lanceola* PAL. (GenBank accession, number of perfect tandemly repeated units, repeat monomer sequence) and designed primers (forward and reverse sequences, melting temperatures).

Table S1. Characteristics of five and seven polymorphic microsatellite loci in the undescribed Cruoriaceae and *Grateloupia lanceola*, respectively (attributes/primer sequences for monomorphic loci are available from authors upon request). *N*: sample size; *N*<sub>A</sub>: number of alleles; *H*<sub>e</sub>: observed heterozygosity; *H*<sub>c</sub>: expected heterozygosity.

Table S2. Frequency/attributes of EST-derived microsatellites for seven Rhodophyta species.

Table S3. Microsatellite frequencies (microsatellite counts per Mbp) reported for genome sequencing approaches in this (as shaded data) and other studies.

This material is available as part of the online article.

Please note: Wiley-Blackwell are not responsible for the content or functionality of any supplementary materials supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.