Downloaded from http://mbe.oxfordjournals.org/ at Duke University on May 27, 2014 and sup- C online), online) pro- ons, please fig. 1 Supplementary the volume of 1 , Â g, empirical data, supplementary table 1 , ´ Ienzyme),2weeksofse- Pst ( ). B ,MLanalysisoftheRAD-seq ble mitochondrial introgression online). n Supplementary Material and more informative sites. Our data set , A Supplementary Material Laure Saune , Â ), we obtained 400 1 site-associated DNA sequencing (RAD- and Jean-Yves Rasplus fig. 1 supplementary materials ciated DNA sequencin ,cstacks) 4 n After 4 days of library preparation following the pecies), fully resolving relationships between Etter et al. (2011) table S2 After a lengthy process of screening loci for vari- online. Supplementary Material Sanger— Details regarding data generation and analysis are de- Whatever the value of RAD-seq— , Julien Foucaud, Material divergences ranged from 1.2S1 to 17 My ( scribed in the ability, primer design andlihood PCR (ML) optimization, analyses maximum of like- sequencesand six from nuclear three markers mitochondrial led totopologies, poorly resolved highlighting and conflicting possi between species ( duced thesupplementary same fig. fully S2 resolved topology ( plementary 10 (parameter protocol by resulted from a stringent lociogy selection and to minimize ensure the forhalf amount of homol- the of individuals missing shouldincluded data. have in sequences More our for than analysis, alowed loci and when to merging the loci be number from all of individuals mismatches varied from al- 4 to data sets (up to 25,425 loci, i.e., one every 12,000 nt, quencing on one lane ofdata a HiSeq 2000 processing flowcell and onCatchen,et a week al. a of 2011 standardSanger data computer and 270 (using Stacks; 1 Thierry Deuve, rRADlibraryusingcurrentbioinformaticandphylogenetic 3 ty for Molecular Biology and Evolution. All rights reserved. For permissi ; ). ra, 34095 Montpellier, France ´ ), whose Maxime Galan, Supplementary Sabine Nidelet, Here, we demonstrate that restriction- ,1,2 Jones et al. 2013 , y 3 ation sequencing, restriction-site asso nomique Fonctionnelle, Montpellier, France ´ ). Here, we empirically ). Sequencing complete 31(5):1272–1274 doi:10.1093/molbev/msu063 Advance Access publication February 3, 2014 McCormack et al. 2012 riate method of library con- um National d’Histoire Naturelle, Paris, France Consequently, Sanger sequenc- ´ ed DNA sequencing (RAD-seq, Emeric Dubois, E-mail: [email protected]. more sites than our Sanger approach (2,262,825 nt/s 1 Mathieu Gautier, Mol. Biol. Evol. Â supplementary fig. S1 ,1 Rubin et al. 2012 ). However, with increasing genetic dis- y , ; Emma Teeling 3My)offeworganisms(e.g., McCormack et al. 2013 phylogenomics, next-gener < online) has been used to infer the recent evolution-

¨lle Genson,

Restriction-site-associat The Author 2014. Published by Oxford University Press on behalf of the Socie These authors contributed equally to this work. MNHN, UMR7205 OSEB, Muse INRA, UMR1062 CBGP, Montferrier-sur-Lez, France Institut de Biologie Computationnelle, 95Montpellier rue GenomiX, de c/o la Institut Gale de Ge (up to 60 My old, number of orthologous loci,to making RAD-seq infer inappropriate Conversely, deeper recent in relationships silico studiesnumber ( suggested of that a markers sufficient could be obtained from distant species ary history ( Nadeau et al.tances, 2013 mutations in the restriction sites may reduce the Baird et al.Material 2008 methods based ongenome reduced appear representations of mostof the adequate data nuclear to across generate many individuals large at amounts reasonable costs. eukaryotic genomes to infer speciespensive, phylogenies time remains consuming, ex- andchondrial unrealistic. genomes Sequencing mito- ismisleading more due affordable, to but introgression trees and can heteroplasmy. be Thus, struction remains challenging. ing of aphylogenies ( few genes is still widely used to infer species phylogenetics, allowing rapid and cost-effective generationmillions of of readshowever, from choosing different an loci approp on nonmodel species; Insecta, Carabidae. Next-generation sequencing opened up new possibilities for seq) generates useful data fortools phylogenomics. produced Analysis 400 of ou ß e-mail: [email protected] 1272 18 nonmodel species of ground ( tested this prediction by comparing theRAD sequencing power approaches of to Sanger resolve and relationships between of eukaryotic species, thoughvantage potential of biases such need data. to beKey evaluated and words: new methodologies developed to take full ad- 18 species of ground beetles (divergences up to 17 My). This suggests that RAD-seq is promising to infer phylogeny Abstract Next-generation sequencing opened up newsample possibilities preparation in remains phylogenetics; challenging. however, choosing an appropriate method of 4 y 1 2 3 Astrid Cruaud,* Gwenae Empirical Assessment of RADPhylogeny Sequencing for Interspecific Associate editor: *Corresponding author:

Brief Communication } RAD Sequencing for Interspecific Phylogeny . doi:10.1093/molbev/msu063 MBE Downloaded from http://mbe.oxfordjournals.org/

FIG.1. ML phylogenies (RAxML 7.2.8-ALPHA, Stamatakis 2006;10,000bootstrapreplicates)(A)fromthesixnuclear(3,659bp)and(B)threemito- at Duke University on May 27, 2014 chondrial (1,925 bp) markers sequenced using Sangertechnology.Nodeswithbootstrapsupports(BP)< 50% are collapsed (see also supplementary fig. S4, Supplementary Material online). Dashed arrows show possible cases of mitochondrial introgression. (C)MLphylogeny(RAxML,10,000bootstrap replicates, ncstacks =10)inferredfromtheRADdatasets(thesametopologywasrecoveredwhateverthevalueof n,seesupplementary fig. S2, Supplementary Material online). BP = 100 for all nodes but three (stars) for which BP = 99. Synapomorphies supporting nodes as calculated with MacClade 4.08 (Maddison DR and Maddison WP 2005)arebetweenbrackets.Nodemeanages,takenfromDeuve et al. (2012),andmissingdataare given as follows: ages/percentage of missing data. (D)ComparisonbetweenSangerandRAD-seqdatasets.Percentagesofmissingdatawereobtained using Geneious 6.1.6. (Drummond et al. 2010). Percentages of variable and informativesiteswerecalculatedusingMEGA5.2.2(Tamura et al. 2011). See details for all data sets in supplementary table S2, Supplementary Material online. different from the nuclear and mitochondrial Sanger trees. online). Furthermore, missing data did not bias reconstruc- These two last topologies were rejected by statistical tests tion, as topologies obtained from the complete matrices (P < 0.001). Although significant loss of RAD markers oc- (loci shared by all 18 species) were similar to those inferred curred for the oldest DNA sample (78.3%–75.4%, Carabus from the incomplete matrices (supplementary fig. S3, olympiae,extractionperformedin1998),enoughsignalre- Supplementary Material online). mained for its placement. Loss of RAD markers also oc- Overall, this study illustrates the power of RAD-seq to infer curred with increasing genetic distances, though enough shallow relationships, and we believe that this approach may information was retained to accurately resolve the relation- be generalized to many groups (~90% of the genomes ships within Carabus (Deuve et al. 2012). When n was set range between 100 and 800 Mb, supplementary fig. S5, to 10, more polymorphic loci were included and missing Supplementary Material online). Here, we used only one par- data reached 68.6% for a divergence of 17 My (fig. 1C and tition and relied on current evolutionary models to analyze supplementary fig. S2, Supplementary Material online). RAD-seq data. Testing alternative partitioning schemes or Preliminary tests are thus recommended to optimize the developing more appropriate models to deal with the speci- amount of allowed mismatches within a RAD locus to ficities of such data (e.g., modeling gain/loss of restriction avoid loci overkill while ensuring loci homology across in- sites) might be promising to improve inferences. dividuals. Nevertheless, percentages of missing data were Finally, RAD-seq definitely opens new avenues for phylo- comparable between Sanger and RAD-seq data sets geneticists but possibly also a Pandora’s box of analytical (fig. 1D, supplementary table S2, Supplementary Material issues. These issues will need to be explored to avoid being

1273 Cruaud et al. . doi:10.1093/molbev/msu063 MBE misled by systematic (Lemmon EM and Lemmon AR 2013)or Catchen JM, Amores A, Hohenlohe P,CreskoW,PostlethwaitJH.2011. other biases (Arnold et al. 2013), which may differ depending Stacks: Building and genotyping loci de novo from short-read sequences. G3 1:171–182. on the level of genetic divergence among samples. Deuve T, Cruaud A, Genson G, Rasplus J-Y. 2012. Molecular systematics and evolutionary history of the genus Carabus (Col. Carabidae). Mol Supplementary Material Phylogenet Evol. 65:259–275. Supplementary figures S1–S5 and tables S1 and S2 are avail- Drummond AJ, Ashton B, Buxton S, Cheung M, Cooper A, Heled J, able at Molecular Biology and Evolution online (http://www. Kearse M, Moir R, Stones-Havas S, Sturrock S, et al. 2010. mbe.oxfordjournals.org.) Geneious v6.1.6. San Francisco (CA): Biomatters Inc. [cited 2014 Feb 17]. Available from: http://www.geneious.com. Acknowledgments Etter PD, Bassham S, Hohenlohe PA, Johnson EA, Cresko WA. 2011. SNP discovery and genotyping for evolutionary genetics using RAD The authors thank the SeqGen plateform (CeMEB Labex, sequencing. In: Orgogozo V, Rockman MV, editors. Molecular France) for facilitating access to the Covaris S220 instrument methods for evolutionary genetics. New York: Humana Press. p. 157–178. and the CBGP HPC computational platform on which anal- Jones JC, Fan SH, Franchini P, Schartl M, Meyer A. 2013. The evolutionary yses were performed. The authors also thank E. Artige (CBGP) history of Xiphophorus fish and their sexually selected sword: a for her help on the field, I. Meusnier (CBGP) for her assistance genome-wide approach using restriction site-associated DNA se- for lab work, P.A. Gagnaire for P1 adapters, K. Gharbi and C. quencing. Mol Ecol. 22:2986–3001. Eland (GenePool, UK) for their assistance with transferring Lemmon EM, Lemmon AR. 2013. High-throughput genomic data Downloaded from RAD-seq protocol to CBGP and two anonymous reviewers in systematics and phylogenetics. Annu Rev Ecol. Evol Syst. 44: 99–121. for their valuable comments on a previous version of this Maddison DR, Maddison WP. 2005. MacClade 4: Analysis of phylog- manuscript. This work was based upon financial support eny and character evolution. Version 4.08a. Sunderland (MA): received from the division “Plant Health and the Sinauer Associates [cited 2014 Feb 17]. Available from: http://mac Environment” of the French National Institute for clade.org. http://mbe.oxfordjournals.org/ Agricultural Research (INRA) and from the network “Library McCormack JE, Hird SM, Zellmer AJ, Carstens BC, Brumfield RT. 2013. Applications of next-generation sequencing to phylogeography and of Life” funded by the National Center for Scientific Research phylogenetics. Mol Phylogenet Evol. 66:526–538. (CNRS), the National Museum of Natural History (MNHN), McCormack JE, Maley JM, Hird SM, Derryberry EP, Graves GR, Brumfield the French National Agronomic Institute (INRA), and the RT. 2012. Next-generation sequencing reveals population genetic French Alternative Energies and Atomic Energy Commission structure and a species tree for recent bird divergences. Mol (CEA) (Genoscope). Sanger sequences were deposited in Phylogenet Evol. 62:397–406. GenBank under accession numbers KJ158754-KJ158836. Nadeau NJ, Martin SH, Kozak KM, Salazar C, Dasmahapatra KK, Davey JW, Baxter SW, Blaxter ML, Mallet J, Jiggins CD. 2013. Genome-wide RAD-seq data sets can be downloaded from http://www1.

patterns of divergence and gene flowacrossabutterflyradiation. at Duke University on May 27, 2014 montpellier.inra.fr/CBGP/NGS/Files/Datasets_Cruaud_et_al_ Mol Ecol. 22:814–826. 2014_RAD_MBE.zip. Rubin BER, Ree RH, Moreau CS. 2012. Inferring phylogenies from RAD sequence data. PLoS One 7:e33394. References Stamatakis A. 2006. RAxML-VI-HPC: maximum likelihood-based phylo- Arnold B, Corbett-Detig RB, HartlD,BombliesK.2013.RADsequnder- genetic analyses with thousands of taxa and mixed models. estimates diversity and introduces genealogical biases due to non- Bioinformatics 22:2688–2690. random haplotype sampling. Mol Ecol. 22:3179–3190. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S. 2011. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, Selker MEGA5: molecular evolutionary genetics analysis using maximum EU, Cresko WA, Johnson EA. 2008. Rapid SNP discovery and genetic likelihood, evolutionary distance, and maximum parsimony meth- mapping using sequenced RAD markers. PLoS One 3:e3376. ods. Mol Biol Evol. 28:2731–2739.

1274 Supplementary data

Materials and Methods

Taxonomic sampling

Thirteen species of the genus Carabus (Coleoptera, Carabidae) were included in our analyses

(seven from the subgenus Chrysocarabus, considered as ingroup species, and six from other subgenera, considered as outgroup species, Table S1). As inferred by a previous study based on 99 Carabus species (127 specimens) and three outgroups (Deuve, et al. 2012), divergence times between those species ranged from 1.2 to 17 Myr. To the exception of one specimen of

Chrysocarabus (C. olympiae, JRAS03456_0101) sampled in 1998, all specimens were collected between June and December 2012. Specimens were collected alive and fixed in 96% ethanol.

DNA extraction

Genomic DNA was extracted from the thoracic muscles of each individual, using standard phenol / chloroform extraction and isopropanol precipitation procedure. Samples were treated with RNase A (Qiagen) during extraction, to remove residual RNA. Genomic DNA of C. olympiae was extracted from four legs in 1998 using the Qiagen DNA Blood and Tissue kit according to manufacturer's protocol. Integrity of DNA was verified on a 1.5% agarose gel and concentration determined using a Qubit® 2.0 Fluorometer (Invitrogen). A smear of DNA from high to low molecular weights was observed for C. olympiae, indicating a degradation of the genomic DNA. Nevertheless, we decided to keep that sample to test whether RAD-seq could be performed on heavily degraded DNA (i.e. whether a sufficient number of RAD markers could end up in the size range of 300 – 600 bp excised from gel during library preparation, Fig. S1)

Sanger Data set preparation and sequencing

Three mitochondrial (Cytochrome oxydase I, COI ; Cytochrome b, Cytb ; 16S rRNA) and six nuclear markers (28s rRNA, Internal transcribed spacer 2, ITS2; Phosphoenolpyruvate carboxykinase, PepCK ; an ‘‘Anonymous’’ DNA region (Sota and Vogler 2001) ; long wavelength rhodopsin, LWRh ; the Ms6103 loci (Rasplus, et al. 2001)) were selected.

Primer sequences and amplification protocols followed Deuve et al. (2012) for Cytb, COI, 16s rRNA, 28S rRNA, PepCK and ‘‘Anonymous’’, and Rasplus (2001) for Ms6103. ITS2 was amplified using CASp8sFcM 5’-CGAACATCGACAAGTCGAACGCACA-3’ and

CAS28sB1D 5’-TTCTTTTCCTCCSCTTAYTRATATGCTTAA-3’ (Ji, et al. 2003). The amplification protocol involved 4 min denaturation at 94°C, then 35 cycles of 1 min denaturation at 94°C, 1 min annealing at 52°C, 1 min extension at 72°C and a final extension of 5 min at 72°C. LWRh was amplified using LWRhFor 5’-

AATTGCTATTAYGARACNTGGGT-3’ and LWRhRev 5’-

ATATGGAGTCCANGCCATRAACC-3’ (Danforth, et al. 2004). The amplification protocol involved 3 min denaturation at 94°C, then 35 cycles of 30 s denaturation at 94°C, 1 min annealing at 55°C, 1 min extension at 72°C and a final extension of 10 min at 72°C. PCR products were purified using Exonuclease I and Phosphatase, and sequenced directly using the BigDyeTerminator V3.1 kit (Applied Biosystems) and an ABI3730XL sequencer at

Genoscope, Evry, France. Both strands for each overlapping fragment were assembled using the sequence editing software Geneious 6.1.6. (Drummond, et al. 2010). All sequences were deposited in GenBank (sequences generated for the purpose of this study: KJ158754-

KJ158836; sequences from Deuve et al. (2012): AY183518, AY183520, AY183574,

JQ646552, JQ646575, JQ646591, JQ646599, JQ646611, JQ646620, JQ646648, JQ646673,

JQ646682, JQ646703, JQ646716, JQ646839, JQ646861, JQ646866, JQ646886, JQ646904,

JQ646920, JQ646926, JQ646936, JQ646943, JQ646953, JQ647108, JQ647135, JQ647144, JQ647160, JQ647168, JQ647182, JQ647332, JQ647357, JQ647366, JQ647381, JQ647389,

JQ647403)

RAD-seq library preparation and sequencing (see Fig S1 for a summary of the protocol)

Library preparation was performed at CBGP, following Baird et al. (2008) and Etter et al.

(2011) with some modifications as detailed below. As no reference genome was available to design our experiment, we decided to duplicate seven samples (shaded in grey in Table S1) to test different sequencing depths. Thus, 31 samples were included in our RAD-seq library.

For each sample, 400 ng of genomic DNA was digested with the PstI restriction enzyme to obtain a high density of RAD markers. P1 adaptors (synthesized by IDT, USA) containing nucleotide barcodes (MIDs, for Multiplex IDentifiers) were then ligated to allow multiplexing of all samples on one sequencing lane. Low sample diversity is an intrinsic problem of libraries generated by restriction enzyme digestion (Krueger, et al. 2011). To increase the diversity of our library and reduce phasing errors, which can result in loss of sequence quality

(Elshire, et al. 2011), we used a set of 5 and 6 bp MIDs. The set was designed to maximize the balance of the bases at each position and MIDs differed by at least three nucleotides to reduce erroneous sample assignment due to sequencing error. Ligated DNA from all samples were pooled and sheared using a S220 ultra-sonicator (Covaris, Inc.) (duty cycle 10%, intensity 5, cycles/burst 200, duration 70s) and a size range of 300 – 600 bp was isolated using gel excision and purification with NucleoSpin Gel and PCR Clean-up columns

(Macherey-Nagel). Subsequent reactions were purified using AMPure XP beads (Agencourt).

After end-repair and 3’ A-tailing, DNA was ligated to P2 adapters (including Illumina primers for paired-end sequencing). Final enrichment PCR was carried out in 6 independent 25µL reactions. We increased the starting amount of DNA to 20 ng and reduced the number of PCR cycles to 12 to limit PCR duplicates. The 6 reactions were pooled into one library for sequencing. The library was quantified by qPCR using the « Library Quantification Kit -

Illumina/Universal » from KAPA (KK4824). It was then denatured using NaOH and diluted to 7 pM prior to clustering. Clustering and 100nt paired-end read sequencing were performed on a single lane of a HiSeq 2000 flowcell at Montpellier GenomiX (MGX), Montpellier,

France, following the manufacturer's instructions. A low-concentration spike-in (1%) of PhiX

Control v3 was used as an in-lane positive control for alignment calculations and quantification efficiency. Image analyses and base calling were performed using the HiSeq

Control Software (HCS) and Real-Time Analysis (RTA) software (Illumina). The quality of the data was assessed using FastQC (Babraham Institute, Bioinformatics Group http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc) and the Sequence Analysis Viewer

(SAV) software (Illumina).

Estimates of the number of PstI cut sites were performed using the radcounter_v4.xls spreadsheet available from the UK RAD Sequencing Wiki

(www.wiki.ed.ac.uk/display/RADSequencing/Home). No reference genome was available for

Carabus but we assumed a 300 Mb approximate genome size (Hanrahan and Johnston 2011;

Sota, et al. 2013) and a 42% GC content (estimated from the EST data on Carabus granulatus available on NCBI (Theodorides, et al. 2002)). Based on those estimates, 49,068 PstI cut sites were expected with a sequencing depth of 48x per RAD marker. Given the relatively low genome size and the pioneering nature of the experiment in our lab, we deliberately chose a frequent-cutter restriction enzyme and a low level of multiplexing to obtain a high number of markers and a high sequencing depth. Nevertheless, with the same restriction enzyme, a higher level of multiplexing could be used (up to 64 individual/lane), further reducing the costs. This may also allow increasing the representativeness of the sampling, therefore contributing to reduce long branch attraction artefacts that could be frequent in phylogenomics studies with many genes but few samples (Philippe, et al. 2011). As emphasized by Etter et al. (2011), experimental design is crucial. Level of multiplexing and restriction enzyme must be carefully chosen given estimated genome size and budgetary constraints. It appears that the approximate genome size and EST data were good proxies to design our experiment. Indeed a sequencing depth of 48x per sample was largely enough and sample duplicates allowed us to determine that less than 1‰ of the nucleotide calls were different. Library preparation at our lab costed about 400 € (500 USD) for 31 samples, while sequencing service costed about 2300 € (3100 USD).

RAD-seq data processing

The 2*151,303,473 reads that passed the filtering step performed by the Illumina Real Time

Analysis (RTA) software were considered as raw data. To the exception of custom scripts all programs hereafter mentioned belong to the suite Stacks version 0.99994 (Catchen, et al.

2011). Raw data were cleaned and demultiplexed using the program process_radtags. Default parameters were used and we did not attempt to rescue reads by automatically correcting errors in barcode or restriction site sequences. Using an awk custom script (written by M.

Gautier), reads 1 were then trimmed to remove restriction sites and reads 1 and 2 were end- trimmed to a length of 89 bp. We then used the program clone_filter (default parameters) to remove PCR clones. After this step, we focused on the analysis of reads 1 only, to estimate their phylogenetic informativeness. Reads from each barcode were assembled into loci using the program ustacks. The minimum depth of coverage required to create a stack (m parameter) and the maximum distance (in nucleotides) allowed between stacks (M parameter) were set to 2 (default values), and the maximum distance allowed to align secondary reads to primary stacks (N parameter) was set to M+2 to discard highly divergent reads. Loci from each barcode were then merged into a catalog of loci using the program cstacks. To capture loci that were fixed within individuals / species but variable between individuals / species, we tested different values (n = 4, 6, 8, 10) for the number of mismatches allowed between loci, when generating the catalog. Number of loci and loci including sequences with higher levels of divergence should increase with increasing values of n, decreasing the relative amount of missing data for distant species. It appeared from our study that preliminary tests are required on every dataset and further studies are needed to determine the optimal amount of mismatches for distant species to be represented (i.e. avoid loci over-kill because n is set too low), while ensuring analysed loci remain homologous. The use of reads 2 could help detecting the presence of paralogs within certain loci.

Using custom R and awk scripts (written by M. Gautier), we performed a stringent selection of individuals and loci included in our phylogenetic analyses. Thus, we only retained i) individuals with more than 10,000 loci (which resulted in the loss of 8 individuals) and loci for which at least 12 individuals were represented. We obtained four RAD-seq data sets (n=4,

6, 8, 10) that can be downloaded from http://www1.montpellier.inra.fr/CBGP/NGS/Files/Datasets_Cruaud_et_al_2014_RAD_MBE. zip.

Phylogenetic analyses

Alignments of Sanger markers were reconstructed with MAFFT 6.864 (L-INS-i option)

(Katoh, et al. 2005). Alignments of the protein coding genes were translated to amino acids using MEGA 5.2.2 (Tamura, et al. 2011) to detect frameshift mutations and premature stop codons, which may indicate the presence of pseudogenes. Alignments of individual gene regions obtained with each strategy were first analysed separately to produce single-gene trees and check for potential paralogs. Alignments were then concatenated, resulting in two combined matrices (1 mtDNA, 1 nuDNA). No alignment was required for the RAD-seq data set. Percentages of missing data for the two data sets (Sanger, RAD-seq) were obtained using Geneious 6.1.6. (Drummond, et al. 2010). Percentages of variable and informative sites were calculated using MEGA 5.2.2 (Tamura, et al. 2011).

We performed maximum likelihood analyses (ML) and associated bootstrapping on every data sets using the MPI-parallelized RAxML 7.2.8-ALPHA (Stamatakis 2006b). GTRCAT approximation of models was used for ML bootstrapping (10,000 replicates) (Stamatakis

2006a). The Sanger nuDNA data set was partitioned as follows: one partition per gene fragment. The Sanger mtDNA was partitioned as follows: one partition for protein coding genes, one partition for 16S rRNA. The four RAD-seq data sets (n=4, 6, 8, 10) were not partitioned by locus, to avoid over-parameterization. Given that α and the proportion of invariable sites cannot be optimized independently from each other (Gu 1995) and following

Stamatakis’ personal recommendations (RAxML manual, 2006a), a GTR + Γ model was applied to each partition of the Sanger data sets and to the whole RAD-seq data sets. We used a discrete gamma approximation (Yang 1994) with four categories. Apomorphy lists for given nodes were generated using MacClade 4.08 (Maddison and Maddison 2005).

Test of alternative topologies

To compare RAD-seq and Sanger topologies, we performed AU (Shimodaira 2002) and SH

(Shimodaira and Hasegawa 1999) tests implemented in the program package CONSEL

(Shimodaira and Hasegawa 2001). The program makermt was used to generates K=10 sets of bootstrap replicates. Each set consisted of 100,000 replicates of the row sums (10 times the default number of replicates). Default scales parameters were used (r1=0.5, r2=0.6, r3=0.7, r4=0.8, r5=0.9, r6=1, r7=1.1, r8=1.2 r9=1.3, r10=1.4), meaning that in the k-th set of replicates,

Nk = rkN sites were randomly chosen with replacement to calculate the row sums (with

N=total number of sites). RAxML was used to compute the per-site log likelihoods for all trees.

References

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Authors’ contribution

JYR designed the study. AC, JYR, TD contributed samples. MGal, JF adapted RAD-seq library preparation protocol to CBGP context. GG, LS performed DNA extraction, PCR amplification, Sanger and RAD-seq library preparation. ED, HP performed Illumina sequencing of the RAD-seq library at Montpellier GenomiX. AC, MGau and JYR analysed the data. AC and JYR wrote the manuscript. All authors commented on the manuscript.

Supplementary Table and Figure legends

Table S1. Sampling used in the study. As no reference genome was available to design our experiment, we decided to duplicate seven samples (shaded in grey) to test different sequencing depths.

Table S2. Comparison between Sanger (a) and RAD-seq (b) data sets.

Figure S1: RAD-seq library preparation and sequencing: summary of the workflow.

Figure S2: Maximum likelihood trees (RAxML 7.2.8-ALPHA, 10 000 bootstrap replicates) obtained from the analyses of the RAD-seq data sets, with a) ncstacks = 4, b) ncstacks = 6, c) ncstacks=8, d) ncstacks = 10. Mean ages, taken from Deuve et al. (2012) and missing data are given as follows: Mean ages / percentage of missing data. Bootstrap supports are reported at nodes.

Figure S3: Maximum likelihood trees (RAxML 7.2.8-ALPHA) obtained from the analyses of the complete matrices (no missing data, 18 species, GTR + Γ4, 10 000 bootstrap replicates) of the a) RAD ncstacks=4 data set (233 loci of 89 bp each), b) RAD ncstacks=6 data set (355 loci of

89 bp each), c) RAD ncstacks=8 data set (428 loci of 89 bp each), d) RAD ncstacks=10 data set (478 loci of 89 bp each). Bootstrap supports are reported at nodes. Although sometimes less resolved because of a smaller number of loci (n=4, 6), the topologies were similar to those obtained with larger matrices (ie that included missing data).

Figure S4: Maximum likelihood phylogenies (RAxML 7.2.8-ALPHA, 10 000 bootstrap replicates) a) from the six nuclear (3659 bp) and b) three mitochondrial (1925 bp) markers sequenced using the Sanger technology. Bootstrap supports are reported at nodes.

Figure S5: Insect genome sizes (863 species) extracted from the Animal Genome Size Database (Gregory 2014) and updated with recent literature and published genomic resources. When several estimates were available for one species, we used the largest genome size. Median = 606 Mb.

Table S1.

Number of Number of reads reads after after cleaning and PCR clones demultiplexing removal MIDs Specimen_code Species Locality (process_radtags) (clone_filter) AAACAA JRAS03779_0101 C. (Chrysocarabus) auronitens auronitens France, Forêt de Soignes 3156421 2145146 AGCTA JRAS03779_0101 C. (Chrysocarabus) auronitens auronitens France, Forêt de Soignes 4882363 3195412 ACGTCA JRAS03942_0101 C. (Chrysocarabus) auronitens festivus France, Forêt de Somail 1555799 1244143 AAGGG JRAS03911_0101 C. (Chrysocarabus) auronitens punctatoauratus France, Hautacam 1949228 1541416 ACAAT JRAS03934_0101 C. (Chrysocarabus) auronitens punctatoauratus France, Bélesta 690 604 GAAGTC JRAS03806_0101 C. (Chrysocarabus) hispanus France, Col du Minier 1039 949 CGTCAG JRAS03952_0101 C. (Chrysocarabus) hispanus France, Auriac 2891652 2034211 ATTCC JRAS03952_0101 C. (Chrysocarabus) hispanus France, Auriac 4758848 3174064 GATAA JRAS03886_0101 C. (Chrysocarabus) lineatus lateralis Spain, nr Aldeia de S. Joao 3551564 2769028 GCCGC JRAS03895_0101 C. (Chrysocarabus) lineatus lateralis Spain, O Cadavo 2136676 1778930 CTAGA JRAS03903_0101 C. (Chrysocarabus) lineatus lineatus Spain, nr Calabrez 2626889 2157873 CGGAC JRAS03877_0101 C. (Chrysocarabus) lineatus troberti France, nr Macaye 889 804 CACCT JRAS03877_0101 C. (Chrysocarabus) lineatus troberti France, nr Macaye 46501 38802 TCCTTC JRAS03456_0101 C. (Chrysocarabus) olympiae Italy, Piemont (Rearing from Antibes) 270100 228235 TTCAG JRAS03957_0101 C. (Chrysocarabus) rutilans jeannei Spain, Prades 6213012 4012742 TGTGT JRAS03954_0101 C. (Chrysocarabus) rutilans rutilans France, Fontfrède 2473040 1927472 CCTTG JRAS03954_0101 C. (Chrysocarabus) rutilans rutilans France, Fontfrède 5428999 3718109 GCGAGC JRAS03788_0101 C. (Chrysocarabus) solieri France, Barles, Le Curneyer 2334986 1835320 TGCCCA JRAS03940_0101 C. (Chrysocarabus) solieri France, Saint-Cassien-des-bois 1718 1465 CTTATC JRAS03872_0101 C. (Chrysocarabus) splendens France, nr Bélus 3544444 2596744 TTGGCC JRAS03950_0101 C. (Chrysocarabus) splendens France, Sougraigne 84738 72935 ATGAAG JRAS03941_0101 Calosoma sycophanta France, Saint-Cassien-des-bois 688 618 GGACG JRAS03941_0101 Calosoma sycophanta France, Saint-Cassien-des-bois 1683 1488 GTTGGG JRAS03793_0101 C. (Carabus) granulatus France, Forêt d'Orient 2324187 1872575 CAAAGG JRAS03787_0101 C. (Chaetocarabus) intricatus France, Vescou, Rau. de Ronson 4659399 3121728 TACGAG JRAS03939_0101 C. (Macrothorax) morbillosus France, Puget-sur-Argens 1956653 1616003 TAATC JRAS03939_0101 C. (Macrothorax) morbillosus France, Puget-sur-Argens 4355861 3242045 AGTAGA JRAS03944_0101 C. (Megodontus) violaceus France, Forêt de Somail 4936094 3635616 GGTTCC JRAS03815_0101 C. (Mesocarabus) problematicus Belgique, Forêt de Soignes 2412018 1842828 GTGTT JRAS03815_0101 C. (Mesocarabus) problematicus Belgique, Forêt de Soignes 2669143 2079619 CCCAAA JRAS03874_0101 C. (Tachypus) cancellatus France, nr Bélus 4387288 3199968

Table S2 Comparison between Sanger (a) and RAD-seq (b) data sets. a)

Method Sanger Genome nuclear mitochondrial Number of loci 6 3 Matrix Carabus Chrysocarabus Carabus Chrysocarabus (18 ind./13 spp.) (12 ind./7 spp.) (18 ind./13 spp.) (12 ind./7 spp.) Alignment length 3 659 bp 1 925 bp Data set size 65 862 nt 43 908 nt 34 650 nt 23 100 nt Missing data 26.8% 20.8% 12.4% 12.3% without counting the degraded DNA sample (C.olympiae) Missing data for the degraded 65.9% 64.8% DNA sample (C. olympiae) Variable sites 583 (15.9%) 281 (7.7%) 447 (23.2%) 254 (13.2%) Informative sites 213 (5.8%) 119 (3.3%) 282 (14.6%) 145 (7.5%) Sample preparation and 1 week / ca. 1200 USD sequencing (about 1 month of marker screening, Primer design and PCR condition optimization) Raw Data Analysis 1 day (sequence correction) / Geneious 6.1.6 / computer = quad-core, 2.5GHz Processor, 8GB RAM) Phylogenetic analyses 1 day (single and combined gene tree) / 8-cores on a 16-cores Linux, 2.9GHz, 64GB RAM computer RAxML (10,000 BP replicates)

b) Method RAD-seq n parameter n = 4 n = 6 n = 8 n = 10 (cstacks)

Number of loci 18 916 22 366 24 293 25 425 (89 bp each) Alignment 1 683 524 bp 1 990 574 bp 2 162 077 bp 2 262 825 bp length matrix Carabus Chrysocarabus Carabus Chrysocarabus Carabus Chrysocarabus Carabus Chrysocarabus (18 ind./13 spp.) (12 ind./7 spp.) (18 ind./13 spp.) (12 ind./7 spp.) (18 ind./13 spp.) (12 ind./7 spp.) (18 ind./13 spp.) (12 ind./7 spp.) Data set size 30 303 432 nt 20 202 288 nt 35 830 332 nt 23 886 888 nt 38 917 386 nt 25 944 924 nt 40 730 850 nt 27 153 900 nt Missing data 29.6% 12.5% 28.2% 12.8% 27.4% 13.1% 26.9% 13.3% without counting the degraded DNA sample (C. olympiae) Missing data 78.3% 76.6% 75.9% 75.4% for the degraded DNA sample (C. olympiae) Variable sites 169 146 (10.0%) 112 023 (6.7%) 254 410 (12.8%) 159 399 (8.0%) 319 203 (14.8%) 192 227 (8.9%) 367 813 (16.3%) 216 300 (9.6%) Informative 57 985 (3.4%) 45 870 (2.7%) 88 914 (4.5%) 66 150 (3.3%) 113 088 (5.2%) 80 690 (3.7%) 132 186 (5.8%) 91 420 (4.0%) sites Sample 3 weeks / ca. 3600 USD preparation (1 week for library preparation, ca 500 USD / 2 weeks of sequencing ca 3100 USD) and sequencing Raw data 3 days to 1 week (depending on the value of the n parameter of cstacks) analysis One quad-core, 2.5GHz Processor, 8GB RAM

Phylogenetic A couple of hours to 3 days (depending on the value of the n parameter of cstacks) analyses 8-cores on a 16-cores Linux, 2.9GHz, 64GB RAM computer RAxML (10,000 BP replicates)

C. (Chrysocarabus) rutilans rutilans JRAS03954_0101 100 C. (Chrysocarabus) rutilans rutilans JRAS03954_0101 100

C. (Chrysocarabus) rutilans jeannei JRAS03957_0101 100

C. (Chrysocarabus) hispanus JRAS03952_0101 100 C. (Chrysocarabus) hispanus JRAS03952_0101 100

C. (Chrysocarabus) lineatus lateralis JRAS03895_0101 100 C. (Chrysocarabus) lineatus lateralis JRAS03886_0101 100

C. (Chrysocarabus) lineatus lineatus JRAS03903_0101

100 C. (Chrysocarabus) auronitens auronitens JRAS03779_0101 100 C. (Chrysocarabus) auronitens auronitens JRAS03779_0101 100

100 C. (Chrysocarabus) auronitens festivus JRAS03942_0101 98 100 C. (Chrysocarabus) auronitens punctatoauratus JRAS03911_0101

C. (Chrysocarabus) splendens JRAS03872_0101

100 C. (Chrysocarabus) olympiae JRAS03456_0101 100 C. (Chrysocarabus) solieri JRAS03788_0101

100 C. (Macrothorax) morbillosus JRAS03939_0101 100 C. (Macrothorax) morbillosus JRAS03939_0101 100 C. (Megodontus) violaceus JRAS03944_0101

99 C. (Carabus) granulatus JRAS03793_0101

C. (Mesocarabus) problematicus JRAS03815_0101 100 C. (Mesocarabus) problematicus JRAS03815_0101

C. (Tachypus) cancellatus JRAS03874_0101

C. (Chaetocarabus) intricatus Toudon, Vescou, Rau. de Ronson JRAS03787_0101

0.0040 C. (Chrysocarabus) rutilans rutilans JRAS03954_0101

100

C. (Chrysocarabus) rutilans jeannei JRAS03957_0101 100

C. (Chrysocarabus) hispanus JRAS03952_0101

100 C. (Chrysocarabus) lineatus lateralis JRAS03895_0101

100

C. (Chrysocarabus) lineatus lateralis JRAS03886_0101 100

C. (Chrysocarabus) lineatus lineatus JRAS03903_0101 100

C. (Chrysocarabus) auronitens festivus JRAS03942_0101

100

C. (Chrysocarabus) auronitens auronitens JRAS03779_0101 100

100 C. (Chrysocarabus) auronitens punctatoauratus JRAS03911_0101 100

C. (Chrysocarabus) splendens JRAS03872_0101

100 C. (Chrysocarabus) solieri JRAS03788_0101

100

C. (Chrysocarabus) olympiae JRAS03456_0101

100

C. (Macrothorax) morbillosus JRAS03939_0101

100 C. (Megodontus) violaceus JRAS03944_0101

100 C. (Carabus) granulatus JRAS03793_0101

C. (Mesocarabus) problematicus JRAS03815_0101

C. (Tachypus) cancellatus JRAS03874_0101

C. (Chaetocarabus) intricatus Toudon, Vescou, Rau. de Ronson JRAS03787_0101

0.0060 C. (Chrysocarabus) lineatus lateralis JRAS03895_0101

100

C. (Chrysocarabus) lineatus lateralis JRAS03886_0101 100

C. (Chrysocarabus) lineatus lineatus JRAS03903_0101

100 C. (Chrysocarabus) rutilans rutilans JRAS03954_0101

100

C. (Chrysocarabus) rutilans jeannei JRAS03957_0101 100

C. (Chrysocarabus) hispanus JRAS03952_0101 100

C. (Chrysocarabus) auronitens auronitens JRAS03779_0101

100

C. (Chrysocarabus) auronitens festivus JRAS03942_0101 100

100 C. (Chrysocarabus) auronitens punctatoauratus JRAS03911_0101 100

C. (Chrysocarabus) splendens JRAS03872_0101

100 C. (Chrysocarabus) olympiae JRAS03456_0101

100

C. (Chrysocarabus) solieri JRAS03788_0101

100

C. (Macrothorax) morbillosus JRAS03939_0101

100 C. (Megodontus) violaceus JRAS03944_0101

100 C. (Carabus) granulatus JRAS03793_0101

C. (Mesocarabus) problematicus JRAS03815_0101

C. (Tachypus) cancellatus JRAS03874_0101

C. (Chaetocarabus) intricatus Toudon, Vescou, Rau. de Ronson JRAS03787_0101

0.0070 C. (Chrysocarabus) lineatus lateralis JRAS03886_0101

100

C. (Chrysocarabus) lineatus lateralis JRAS03895_0101 100

C. (Chrysocarabus) lineatus lineatus JRAS03903_0101

99 C. (Chrysocarabus) rutilans rutilans JRAS03954_0101

100

C. (Chrysocarabus) rutilans jeannei JRAS03957_0101 100

C. (Chrysocarabus) hispanus JRAS03952_0101 100

C. (Chrysocarabus) auronitens auronitens JRAS03779_0101

100

C. (Chrysocarabus) auronitens festivus JRAS03942_0101 100

99 C. (Chrysocarabus) auronitens punctatoauratus JRAS03911_0101 99

C. (Chrysocarabus) splendens JRAS03872_0101

100 C. (Chrysocarabus) olympiae JRAS03456_0101

100

C. (Chrysocarabus) solieri JRAS03788_0101

100

C. (Macrothorax) morbillosus JRAS03939_0101

100 C. (Megodontus) violaceus JRAS03944_0101

100 C. (Carabus) granulatus JRAS03793_0101

C. (Mesocarabus) problematicus JRAS03815_0101

C. (Tachypus) cancellatus JRAS03874_0101

C. (Chaetocarabus) intricatus Toudon, Vescou, Rau. de Ronson JRAS03787_0101

0.0080 C. (Chrysocarabus) rutilans rutilans JRAS03954_0101 100

100 C. (Chrysocarabus) rutilans rutilans JRAS03954_0101

100 C. (Chrysocarabus) rutilans jeannei JRAS03957_0101

C. (Chrysocarabus) hispanus JRAS03952_0101 100

82 C. (Chrysocarabus) hispanus JRAS03952_0101

C. (Chrysocarabus) lineatus lateralis JRAS03886_0101 55

51 100 C. (Chrysocarabus) lineatus lateralis JRAS03895_0101

C. (Chrysocarabus) lineatus lineatus JRAS03903_0101

C. (Chrysocarabus) splendens JRAS03872_0101 100

C. (Chrysocarabus) auronitens auronitens JRAS03779_0101 100

95 C. (Chrysocarabus) auronitens auronitens JRAS03779_0101

100 C. (Chrysocarabus) auronitens festivus JRAS03942_0101 100

C. (Chrysocarabus) auronitens punctatoauratus JRAS03911_0101

C. (Macrothorax) morbillosus JRAS03939_0101 100

100 C. (Macrothorax) morbillosus JRAS03939_0101 82

C. (Chrysocarabus) olympiae JRAS03456_0101 100

100 C. (Chrysocarabus) solieri JRAS03788_0101

C. (Megodontus) violaceus JRAS03944_0101 44

C. (Carabus) granulatus JRAS03793_0101

C. (Tachypus) cancellatus JRAS03874_0101

C. (Mesocarabus) problematicus JRAS03815_0101 100

C. (Mesocarabus) problematicus JRAS03815_0101

C. (Chaetocarabus) intricatus Toudon, Vescou, Rau. de Ronson JRAS03787_0101

0.0030 C. (Chrysocarabus) rutilans rutilans JRAS03954_0101 100

100 C. (Chrysocarabus) rutilans jeannei JRAS03957_0101

C. (Chrysocarabus) hispanus JRAS03952_0101

68

C. (Chrysocarabus) lineatus lateralis JRAS03886_0101 75

100 C. (Chrysocarabus) lineatus lateralis JRAS03895_0101

C. (Chrysocarabus) lineatus lineatus JRAS03903_0101 100

C. (Chrysocarabus) auronitens auronitens JRAS03779_0101 100

100 C. (Chrysocarabus) auronitens festivus JRAS03942_0101

73 96 C. (Chrysocarabus) auronitens punctatoauratus JRAS03911_0101

C. (Chrysocarabus) splendens JRAS03872_0101

100 C. (Chrysocarabus) olympiae JRAS03456_0101 100

C. (Chrysocarabus) solieri JRAS03788_0101 100

C. (Macrothorax) morbillosus JRAS03939_0101

100

C. (Megodontus) violaceus JRAS03944_0101

50 C. (Carabus) granulatus JRAS03793_0101

C. (Mesocarabus) problematicus JRAS03815_0101

C. (Tachypus) cancellatus JRAS03874_0101

C. (Chaetocarabus) intricatus Toudon, Vescou, Rau. de Ronson JRAS03787_0101

0.0040 C. (Chrysocarabus) lineatus lateralis JRAS03895_0101 92

100 C. (Chrysocarabus) lineatus lateralis JRAS03886_0101

C. (Chrysocarabus) lineatus lineatus JRAS03903_0101

69

C. (Chrysocarabus) rutilans rutilans JRAS03954_0101 100

100 C. (Chrysocarabus) rutilans jeannei JRAS03957_0101

C. (Chrysocarabus) hispanus JRAS03952_0101 100

C. (Chrysocarabus) auronitens auronitens JRAS03779_0101 100

100 C. (Chrysocarabus) auronitens festivus JRAS03942_0101

95 91 C. (Chrysocarabus) auronitens punctatoauratus JRAS03911_0101

C. (Chrysocarabus) splendens JRAS03872_0101

100 C. (Chrysocarabus) solieri JRAS03788_0101 100

C. (Chrysocarabus) olympiae JRAS03456_0101 100

C. (Macrothorax) morbillosus JRAS03939_0101

100

C. (Megodontus) violaceus JRAS03944_0101

78 C. (Carabus) granulatus JRAS03793_0101

C. (Mesocarabus) problematicus JRAS03815_0101

C. (Tachypus) cancellatus JRAS03874_0101

C. (Chaetocarabus) intricatus Toudon, Vescou, Rau. de Ronson JRAS03787_0101

0.0050 C. (Chrysocarabus) lineatus lateralis JRAS03886_0101 100

100 C. (Chrysocarabus) lineatus lateralis JRAS03895_0101

C. (Chrysocarabus) lineatus lineatus JRAS03903_0101

95

C. (Chrysocarabus) rutilans rutilans JRAS03954_0101 100

100 C. (Chrysocarabus) rutilans jeannei JRAS03957_0101

C. (Chrysocarabus) hispanus JRAS03952_0101 100

C. (Chrysocarabus) auronitens festivus JRAS03942_0101 100

100 C. (Chrysocarabus) auronitens auronitens JRAS03779_0101

95 96 C. (Chrysocarabus) auronitens punctatoauratus JRAS03911_0101

C. (Chrysocarabus) splendens JRAS03872_0101

100 C. (Chrysocarabus) solieri JRAS03788_0101 100

C. (Chrysocarabus) olympiae JRAS03456_0101 100

C. (Macrothorax) morbillosus JRAS03939_0101

100

C. (Megodontus) violaceus JRAS03944_0101

80 C. (Carabus) granulatus JRAS03793_0101

C. (Mesocarabus) problematicus JRAS03815_0101

C. (Tachypus) cancellatus JRAS03874_0101

C. (Chaetocarabus) intricatus Toudon, Vescou, Rau. de Ronson JRAS03787_0101

0.0060