Study species and study design

For the first step of the method development, we used samples of the butterfly Lycaena helle (Lepidoptera, Lycaenidae) (Table 1). Three recently collected, ethanol-preserved samples from Romania, France and Kazakhstan were used for generating the RAD probes, in order to cover variation within the full species range. Genomic libraries to be enriched by sequence capture were built using eight samples which included seven museum dry-pinned specimens from Finland (4 collected in 1985 and 3 collected in 1957) and one recently collected and ethanol-preserved specimen from Romania. Using these eight samples we compared the outputs between fresh and historical DNA of different age, and tested the importance of DNA sonication in each case. The museum samples were loaned from the Finnish Museum of Natural History in Helsinki, and the ethanol-preserved samples were obtained from Roger Vila’s Butterfly Diversity and Evolution lab (Institute of Evolutionary Biology, CSIC, Barcelona, Spain).

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Table 1. Lycaena helle samples used in the study.

https://doi.org/10.1371/journal.pone.0151651.t001

For the method validation, we used 53 samples of the grasshopper Oedaleus decorus, including 49 samples of both fresh and museum collection specimens for constructing genomic libraries for the capture (see Table 2), and four fresh specimens spanning the species’ distribution (Switzerland, Spain, Hungary, Russia) for generating the RAD probes. The museum samples were on average 64-years-old, with the oldest sample dating back to 1908 and were provided by the National History Museum of London (UK), the Natural History Museum of Bern (Switzerland), the Zoological Museum of Lausanne (Switzerland), the ETH Entomological Collection (Switzerland), the Natural History Museum of Basel (Switzerland), the Natural History Museum of Geneva (Switzerland), and the Natural History Museum of Zurich (Switzerland). The four fresh grasshopper samples were provided by G. Heckel (University of Bern).

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Table 2. Summary of Oedaleus decorus samples used in the study.

https://doi.org/10.1371/journal.pone.0151651.t002

DNA extraction

DNA was extracted from insect legs for all the samples. As museum specimens are usually characterized by low-content of degraded DNA, the isolation protocol was optimized accordingly. The samples were extracted using QIAamp DNA Micro kit (Qiagen, Hombrechtikon, Switzerland) in a laboratory dedicated to low-DNA content samples at the University of Lausanne, Switzerland. For these samples, DNA recovery was improved by prolonged sample grinding, overnight incubation in the lysis buffer for 14h and final DNA elution in 20 μl of the buffer with gradual column centrifugation. Extraction of fresh samples was performed using DNeasy Blood & Tissue Kit (Qiagen). DNA extraction and library preparation using museum specimens was performed using consumables dedicated to the museum specimens only. Benches were thoroughly cleaned with bleach and filter tips were used at all stages of lab work.

RAD probes preparation

The probe precursors were prepared using a double-digestion RAD protocol [8, 23], with further modifications.

Total genomic DNA was digested at 37°C for 3 hours in a 9 μl reaction, containing 6 μl of DNA, 1x CutSmart buffer (New England Biololabs—NEB, Ipswich, MA, USA), 1 U MseI (NEB) and 2 U of SbfI-HF (NEB). The reaction products were purified using AMPure XP (Beckman Coulter, Brea, USA), with a ratio of 2:1 with the sample, according to the manufacturer’s instructions, and resuspended in 10 μl of 10 mM Tris buffer. Subsequently, adapters were ligated to the purified restriction-digested DNA in a 20 μl reaction containing 10 μl of the insert, 0.5 μM of RAD-P1 adapter, 0.5 μM of universal RAD-P2 adapter, 1x T4 ligase buffer, and 400 U of T4 DNA ligase (NEB). Adapter sequences are shown in Table 3; single strand adapter oligonucleotides are annealed before use by heating to 95°C and gradual cooling. Ligation was performed at 16°C for 3 hours. The reaction products were purified using an AMPure XP ratio 1:1 with the sample, and resuspended in 10 mM Tris buffer. The ligation product was size-selected using the Pippin Prep electrophoresis platform (Sage Science, Beverly, USA) with a peak at 270 bp and ‘tight’ size selection range.

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Table 3. Oligonucleotides used in the protocol.

x = barcode sequence in the adapters; barcode sequences can be designed using published scripts [24], available at: https://bioinf.eva.mpg.de/multiplex/; I = inosine in the region complementary to the barcode in blocking oligonucleotides sequences.

https://doi.org/10.1371/journal.pone.0151651.t003

The resulting template was amplified by PCR in a 10 μl mix consisting of 1x Q5 buffer, 0.2 mM of each dNTP, 0.6 μM of each primer (Table 3), and 0.2 U Q5 hot-start polymerase (NEB). The thermocycler program included initial denaturation for 30 sec at 98°C; 30 PCR cycles of 20 sec at 98°C, 30 sec at 60°C, and 40 sec at 72°C; followed by a final extension for 10 min at 72°C. In order to obtain sufficient amount of material for the probes, the reaction had to be run in replicates. The necessary number of replicates has to be determined empirically to reach in total 500–1000 ng of the amplified product required for each capture. DNA amounts were assessed using a Qubit fluorometer (Waltham, MA, USA). Success of size selection and of PCR reactions was confirmed by running them on a Fragment Analyzer (see S1 Fig; Advanced Analytical, Ankeny, IA, USA; see S1 Fig). Afterwards, the PCR products were pooled and purified using AMPure XP, with a ratio of 1:1 with the amplified DNA volume.

An aliquot of the resulting library was sequenced and the rest of the library was converted into probes by removing adapter sequences by enzymatic restriction followed by biotinylation. The probe precursors were incubated at 37°C for 3 hours in a 50 μl reaction containing 30 μl of DNA, 1x CutSmart buffer (NEB), 5 U of MseI (NEB) and 10 U of SbfI-HF (NEB), replicated as required by the amount of the amplified product. The reaction was ended with 20 min enzyme inactivation at 65°C for 20 min and the resulting fragments were purified using AMPure XP:reaction volume ratio of 1.5:1. Purified fragments were biotin nick-labelled using BioNick DNA Labeling System (Thermo Fisher, Waltham, MA, USA) according to the supplier’s instructions and purified using AMPure XP:reaction volume ratio of 1.5:1. The resulting fragments will thereafter be referred to as probes.

Shotgun library preparation

Shotgun libraries were prepared from the fresh and museum specimens based on a published protocol for degraded DNA samples [15], modified in order to incorporate adapter design of Meyer & Kircher [24]. The approach used for library preparation, utilizing barcoded P1 adapter and 12 indexed P2 PCR primers, allows a high sample multiplexing on a single sequencing lane (see Table 3 [24]).

For L. helle, DNA from each individual was divided in two aliquots. One aliquot was kept intact (i.e. high molecular weight DNA in the fresh sample and naturally degraded DNA in museum specimens) and the second was sonicated using Covaris focused ultrasonicator (Woburn, MA, USA) with a peak at 300 bp. Both aliquots were processed in parallel during subsequent steps of libraries preparation. All the libraries from O. decorus were prepared without sonication, based on the test results obtained from the L. helle libraries.

DNA samples were first 5’-phosphorylated in order to allow adapter ligation in the next steps of the protocol. 8 μl of DNA was denatured at 95°C for 10 minutes and quickly chilled on ice. The 10 μl reaction consisting of denatured DNA, 1x PNK buffer and 10U of T4 polynucleotide kinase (NEB) was incubated at 37°C for 30min and heat-inactivated at 65°C for 20 min. The DNA was then purified using an AMPure:reaction volume ratio of 2:1 and resuspended in 10 μl of 10 mM Tris buffer.

A guanidine tailing reaction of the 3’-terminus was performed after heat denaturation of DNA at 95°C for 10 minutes and quickly chilling on ice. The reaction composed of 1x buffer 4 (NEB), 0.25 mM cobalt chloride (NEB), 4 mM GTP (Life Technologies), 10 U TdT (NEB) and 10 μl of denatured DNA in 20 μl reaction volume was incubated at 37°C for 30 min and heat-inactivated at 70°C for 10 min.

The second DNA strand was synthesized using Klenow Fragment (3’ → 5’ exo-), with a primer consisting of the Illumina P2 sequence and a poly-C sequence homologous to the poly-G tail (see Table 3) added to the DNA strand in the previous reaction. A 10 μl reaction mix consisting of 1 μl of NEBuffer 4 (10x), 0.6 μl of dNTP mix (25 mM each), 1 μl of the P2 oligonucleotide (15 mM), 5.4 μl of water, and 2 μl of Klenow Fragment (3’ → 5’ exo-; NEB, 5 U/μl) was added to the 20 μl of the TdT reaction mix, incubated at 23°C for 3 h, and heat-inactivated at 75°C for 20 min. The double stranded product was then blunt-ended by adding a mix consisting of 0.5 μl of NEBuffer 4 (10x), 0.35 μl of BSA (10 mg/ml), 0.2 μl of T4 DNA polymerase (NEB, 3 U/μl) and 3.95 μl of water, and incubated at 12°C for 15 min. The resulting product was purified using AMPure XP:reaction ratio of 2:1 and resuspended in 10 μl of 10 mM Tris buffer.

Barcoded P1 adapters (see Table 3) were ligated to the 5’-phosphorylated end of the double-stranded product in a 20 μl reaction consisting of 10 μl of the double-stranded DNA, 1 μl of the 25 uM adapters, 1x T4 DNA ligase buffer, and 400 U of T4 DNA ligase (NEB). Adapters have to be annealed before use in the RAD probes protocol. The reaction was incubated at 16°C overnight. The resulting product was purified using an AMPure:reaction ratio of 1:1 and resuspended in 20 μl of 10 mM Tris buffer. Ligated P1 adapters were filled-in in a 40 μl reaction consisting of 20 μl of purified ligation product, 1x ThermoPol reaction buffer (NEB), 12 U of Bst polymerase (NEB), and dNTPs (0.25 mM each), and incubated at 37°C for 20 min.

The resulting template was amplified by PCR adding 15 μl of a mix consisting of 5 μl of Q5 reaction buffer (5x), 0.2 μl of dNTPs (25 mM each), 2.5 μl of the PCR primer mix (5 μM each), and 0.5 U of Q5 Hot Start High-Fidelity DNA polymerase (NEB) to the 10 μl of the template. The program started with 20 sec at 98°C, followed by 25 cycles of 10 sec at 98°C, 20 sec at 60°C, and 25 sec at 72°C, followed by a final extension for 2 min at 72°C. Success of each PCR reaction was checked using gel electrophoresis, and the resulting products were purified using AMPure XP:reaction ratio of 0.7:1. Samples were then pooled in equimolar ratios.

In solution hybridization capture, library reamplification and sequencing

The hybridization capture and library enrichment steps described below are based on previously published protocols [13, 25] with some modifications. The hybridization mix consisted of 6x SSC, 50 mM EDTA, 1% SDS, 2x Denhardt’s solution, 2 μM of each blocking oligonucleotide (to prevent hybridization of adapter sequences; see Table 3), 500 to 1000 ng of the probes and 500 to 1000 ng of the shotgun libraries, in a total volume of 40 μl. On account of grasshopper larger genome size and preliminary results indicating low signal-noise ratio in the butterfly libraries, 500 ng of human Cot-1 DNA (Thermo Fisher Scientific, Switzerland) was added to the O. decorus hybridization mix in order to prevent non-specific hybridizations caused by repetitive sequences. The mix was denatured at 95°C for 10 min and subsequently incubated at 65°C for 48 hours. The probes, hybridized with targeted fragments of the library, were then separated on streptavidin beads (Dynabeads M-280, Life Technologies). 10 μl of the beads solution was washed three times on the magnet with 200 μl of TEN buffer (10 mM Tris-HCl 7.5, 1 mM EDTA, 1 M NaCl) and resuspended in 200 μl of TEN. 40 μl of the hybridization mix was added to the 200 μl of the beads solution and incubated for 30 min at room temperature. After separating the beads with the magnet, the supernatant was removed and the beads were washed four times under different stringency conditions as follows. The beads were resuspended in 200 μl of 65°C 1x SSC/0.1% SDS wash buffer, incubated for 15 min at 65°C, separated on the magnet and the supernatant was removed. The above step was performed again with 1x SSC/0.1% SDS, followed by 0.5x SSC/0.1% SDS and 0.1x SSC/0.1% SDS. Finally, the hybridization-enriched product was washed-off from the probes by adding 30 μl of 80°C water and incubating at 80°C for 10 min.

Enrichment of the captured libraries was performed in a 50 μl PCR reaction containing 1x Q5 reaction buffer (NEB), 0.2 mM dNTPs, 0.5 μM of each PCR primer (the P1 universal primer and one of the 12 P2 indexed primers, see Table 3), 1U of Q5 Hot Start High-Fidelity DNA Polymerase (NEB), and 15 μl of the template. The program started with 20 sec initial denaturation at 98°C; followed by 25 PCR cycles of 10 sec at 98°C, 20 sec at 60°C, and 25 sec at 72°C; and a final extension for 2 min at 72°C. The enriched-captured libraries were purified using an AMPure XP:reaction ratio 1:1 and pooled in equimolar ratios for sequencing (see S2 Fig for a profile example of the re-amplified capture library after AMPure purification).

The probes precursors (RAD library) for the butterfly libraries were sequenced on one lane of Illumina MiSeq 300 bp single-end. Butterfly capture-enriched libraries were sequenced on one lane of MiSeq 150 bp paired-end, and grasshopper capture-enriched libraries were sequenced on one lane of Illumina HiSeq 100 bp paired-end.

Updated versions of the lab protocol can be found at https://github.com/chiasto/hyRAD.

Data analysis

The hyRAD datasets correspond to target-enriched libraries and cannot be analysed with the usual RAD pipelines [26, 27] Indeed, although they were generated using RAD loci, the obtained sequences are not flanked by the restriction sites and instead may not overlap completely and/or extend before and after the RAD locus. As a result, the analysis pipeline must include the following steps:

1. demultiplexing and cleaning of raw reads;
2. building of reference sequences for each RAD locus;
3. alignment of reads against the obtained references;
4. SNP calling.

All bioinformatic steps of the hyRAD pipeline can be run at https://insidedna.me.

Demultiplexing and data preparation

The obtained reads were demultiplexed using the fastx_barcode_splitter tool from the FASTX-Toolkit package [28]. RAD-seq sequences (probe precursors) were processed by Trim Galore! [29] and cleaned with the fastq-mcf tool from ea-utils package [30] to remove low quality nucleotides and adapter sequences. The PCR duplicates were removed from RAD-seq probes precursors and hyRAD datasets using the MarkDuplicates tool of Picard toolkit [31]. Reads from hyRAD libraries were tested for exogeneous DNA contamination using BLAST against NCBI nucleotide database (50,000 reads for sonicated or non-sonicated fresh or museum DNA samples).

Exploring the methods of reference creation on Lycaena helle libraries

Paired-end reads obtained from the hybridization-capture library for each sample were mapped onto three references: (1) consensus sequences for the clustered RAD-seq reads (RAD-ref), (2) RAD-seq reads extended using hybridization-captured reads (RAD-ref-ext), and (3) contigs assembled from the reads of hybridization-captured samples (assembly-ref). As we had no reference genome available, we focused on checking the numbers of loci/SNPs obtained by mapping the reads on each reference and the overlap of the loci obtained using different methods. Theoretically, we should be able to retrieve all the loci from the sequenced probe precursors (RAD-seq library) in the captured libraries. We thus evaluate the number of captured loci homologous to those retrieved by sequencing the RAD-seq libraries.However, different factors affect signal to noise ratio (i.e. the proportion of fragments homologous with the probe) in the captured libraries and can decrease the numbers of homologous fragments retrieved.

Mapping and SNP calling

Reads from the hyRAD library were cleaned by Trimmomatic with milder parameters than reads used in the de novo assembly (keeping 60–85% of the reads in historical specimens). Read mapping was performed using bowtie2-build for the reference indexing and bowtie2 for mapping [36], PCR duplicates were removed using the MarkDuplicates tool from the Picard toolkit [31] and SNPs were called with FreeBayes using the default parameters [37, 38]. To evaluate the level of DNA damage in museum DNA samples, we used mapDamage2.0 that rescales base quality scores of putatively post-mortem damaged bases [39] in order to minimize presence of post-mortem conversions in the resulting SNPs. Datasets for replicates were merged and analysed for SNPs with FreeBayes. Obtained VCF files were filtered by the vcffilter tool from the vcflib [40] and VCFtools [41]. In the resulting set only biallelic sites with high quality (PHRED>30), minor allele count larger than 1/6 of all, present in at least 50% of the samples and with a minimum depth of 6 were kept, indels were removed. Potential paralogs and multi-copy sites were removed based on a coverage of a standard-deviation three times higher than the mean [42]. VCF format files [43] were converted to SNP-based NEXUS files using PGDSpider converter [44] and to structure data files for every individual using the vcf-consensus tool (see also Fig 2). Updated versions of the bioinformatic pipeline can be found at https://github.com/chiasto/hyRAD.

Genetic structure

In order to check whether data was reflecting genetic structure, we applied fastStructure, a Structure-like algorithm adapted to large SNP genotype data [45] to the six final datasets (RAD-ref, RAD-ref-ext and assembly-ref, both for samples with and without sonication). The analyses were conducted using a simple prior and assuming two groups (k = 2).

Overlap between assembly references

To evaluate the level of overlap among the three assembly references for L. helle (RAD-ref, RAD-ref-ext, and assembly-ref) we used the OrthoMCL [46] pipeline for orthology detection. Most of the pipeline was run with the default parameters, except for Blastall and MCL clustering steps. Here, we used more stringent parameter values (e-value of 0.0001 and MCL was run with an inflation parameter of 2.0) in order to reduce chances of detecting false orthology groups. As a result, we obtained clusters of contigs being contributed by the three assembly references. We then counted how many of these clusters–presumably corresponding to homologous loci–were shared among the available reference assembly approaches. Eventually, to reveal the number of RAD loci present in the references, reads of the raw RAD library were mapped on RAD-ref and assembly-ref using bowtie2 [36] and levels of mapping were compared.

Proof of concept: application of hyRAD to Oedaleus decorus

The utility of the method was further validated using 49 museum and fresh samples of a Palearctic grasshopper species for which a marked east-west spatial genetic structure was identified previously [22]. The catalog was built based on eight specimens from the captured library that showed the largest number of reads and spanned the species’ distribution area (Switzerland, Italy, Spain, Russia), using the method that yielded the highest number of contigs and produced consistent genetic structure in L. helle (assembly-ref, i.e., denovo reference built using SOAPdenovo, see Results and Discussion section and Table 4). The generated contigs were blasted against GenBank databases for bacterial, fungal and technical sequences with a minimum E-value threshold of 0.1. Endogeneous contigs of each samples were assembled to generate the final reference using Geneious V9.0.2 [47]. Filters were subsequently applied to keep only high quality and informative SNPs as for the L. helle libraries. The final VCF matrix was converted into Structure format using PGDSpider V2.0.9.0 [44] and population structure was inferred using fastStructure [45] using a simple prior and assuming two groups (k = 2). Quantum GIS V2.4.0 was used to present the geographic distribution of the genetic clusters [48].

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Table 4. Data on obtained references for L. helle (RAD-ref, RAD-ref-ext, assembly-ref) and O. decorus (assembly-ref).

https://doi.org/10.1371/journal.pone.0151651.t004

Sequencing and data quality

Comparison of references obtained for L. helle libraries

Proportion of single-hit alignments and SNP numbers.

Consensus clustering of the RAD-based reference within individuals produced the largest number of clusters with 2x and 3x coverage with a clustering identity threshold of 0.91, compared to other threshold values. Consensus clustering among individuals produced the best results with a clustering identity threshold of 0.71 (see S3 Fig).

The highest number, length and the total length of reference contigs were obtained using de novo assembly with SOAPdenovo (assembly-ref; Table 4). Both RAD-based assemblies produced an order of magnitude lower number of contigs. The extension performed on the obtained RAD reference followed by trimming of adapter sequences resulted in references with an average shorter length (lower N50) than the starting contigs—whereas priceTI extended a large number of probes, this did not reflect in a substantially higher average loci length because of further trimming of obtained contigs.

The highest levels of single-hit alignments for most of the samples, except the oldest ones, for both preparation methods (sonicated and not sonicated) were obtained when mapped on the sequenced RAD loci extended using PriceTI (RAD-ref-ext). This method was followed by de novo assembly using reads from the hybridization-capture library from a single fresh specimen (assembly-ref) and mapping on the RAD loci (RAD-ref); although the difference between the last two approaches was not large (Fig 3). Only for the oldest samples as well as in the non-sonicated fresh sample, de novo reference provided slightly better results.

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Fig 3. Percentage of the captured reads showing unique mapping events for different types of DNA preparations and bioinformatic pipelines.

https://doi.org/10.1371/journal.pone.0151651.g003

In terms of the number of SNPs retained after coverage, paralogs and among-samples overlap filtering, the RAD-ref pipeline detected the highest numbers of loci, regardless of sample age and preparation (Fig 4). No clear correlation with sample age could be observed, although all the methods provided the lowest SNP numbers in the fresh samples–most likely an effect of small genetic distance between the reference sample and the fresh specimens (as the fresh sample was used both for the reference and the aligned sample, only heterozygote sites account for SNPs here). A higher number of SNPs was detected in the sonicated library only for the fresh specimens.

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Fig 4. Mean number of SNPs per sample obtained for different types of DNA preparations and bioinformatic pipelines.

https://doi.org/10.1371/journal.pone.0151651.g004

RAD-sequencing datasets depend on the presence of the restriction sites and therefore any polymorphism in such sites leads to either missing loci or alleles. As our method does not depend on the restriction site presence, combined with the high number of gathered SNPs, this allows obtaining largely filled data matrices. Matrix fullness was >50% in all cases:

• assembly-ref, non-sonicated: 66.1%
• assembly-ref, sonicated: 63.4%
• RAD-ref, non-sonicated: 52.0%
• RAD-ref, sonicated: 69.1%
• RAD-ref-ext, non-sonicated: 63.4%
• RAD-ref-ext, sonicated: 72.5%

Lacking an objective criterion for assessing the ‘best’ performing method for building the catalog, we tested which of the three references created the dataset producing the expected spatial division of genetic structure between Finnish and Romanian L. helle samples. The spatial genetic structure inferred by fastStructure revealed that the eight samples are divided into two clusters of, respectively, seven Finnish vs. one Romanian sample only when using the non-sonicated library mapped to the assembly-ref catalog. This result is in agreement with the hypothesis that when using sonicated DNA, we are at high risk of incorporating contaminant DNA which can blur the signal (Matthias Meyer, Max Planck Institute for Evolutionary Anthropology, Liepzig, DE; personal communication). This is an interesting result as BLAST analyses on the six catalogs did not retrieve differences in the level of known contaminants (see below).

Loci overlap among the assembly methods.

About 15.2% of the reference loci obtained via de novo assembly were shared with those based on the clustering of RAD-seq reads (either RAD-ref or RAD-ref-ext; Fig 5). In contrast, an appreciable fraction of the obtained reference loci (59.7%) were unique to the assembly-ref approach. When mapping raw RAD reads on RAD-ref, 26.3% did not map, 42.4% mapped once and 31.3% mapped more than once. This shows that RAD-ref summarizes ca. ¾ of the reads from the RAD dataset. In contrast, when mapping raw RAD reads on assembly-ref, 78,3% of reads did not map, 21,6% mapped once and 0,1% mapped more than once. This result shows that assembly-ref possibly contains three quarters of all loci that are not homologous to the RAD probes. Such low signal to noise ratio (targeted reads to the number of total reads) is most likely a result of background carryover in the hybridization capture step, a phenomenon which can have many sources., It could result from ‘daisy-chaining’ of the captured fragments [49, 50], where partially complementary DNA molecules hybridize with the other fragments that are already hybridized to the probes. We can however discard this explanation as a primary reason for the background carryover as extending of RAD probes did not produce longer contigs (in RAD-ref-ext assemblies). Another likely reason could be carryover of random DNA fragments with repetitive sequences. The extent of such process can be significantly reduced by adding blocking agents to the hybridization mix (typically Cot-1 as was performed for the O.decorus dataset [51] or salmon sperm DNA), as well as optimizing hybridization temperature and wash stringency to increase capture efficiency. Such developments are desirable because they increase the percentage of reads matching the loci of interest and eventually improve the overall sequencing coverage.

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Fig 5. Number of loci obtained using different bioinformatic approaches, identified using the OrthoMCL [42] pipeline for orthology detection.

https://doi.org/10.1371/journal.pone.0151651.g005

Effects of sample preparation and age on the numbers of SNPs obtained and the exogeneous DNA content

Reads can be mapped on a reference either once with a highest score (i.e., single mapping) or on more than one region of reference with close scores (i.e., multi mapping). The reasons for multi-mapping events can be biological (e.g., paralog sequences) or technical (splitting single loci into more reference loci), nevertheless these mapping events cannot be used for SNP calling and offer another benchmark for the assembly methods used. Differences in the number of single mapping events and in the numbers of SNPs obtained were not substantial between sonicated and non-sonicated samples, and depended on the sample age and the bioinformatic pipeline used (Figs 3 and 4). We expected that museum specimens should perform better without sonication, as the DNA was already visibly fragmented, and sonication of museum specimens may increase the levels of exogenous DNA contamination (by fragmenting intact fungal or bacterial DNA contaminating museum samples; M. Meyer, pers. comm.). In terms of mapping events, whereas the fresh sample usually showed a better ratio of single- to multi-mapping events when sonicated, there was a trend towards a higher percentage of unique mapping events for non-sonicated 30-years old museum specimens, on average 1.3 times higher, irrespective of the reference used (the difference was less clear for 58-years old museum specimens, and depended on the reference used). We would therefore advise not to sonicate DNA obtained from the museum specimens, which significantly cuts down the price and time required for library preparation, except in cases when no signs of degradation are observable on the DNA profile. As levels of DNA degradation of contemporary samples may vary, one may consider that the sonication step should be advisable when working with well-preserved DNA. However, this is still an open question, as whereas BLAST searches did not retrieve higher fractions of contaminants in sonicated vs. non-sonicated libraries, the expected population structure was retrieved was the non-sonicated one mapped on assembly-ref.

As one of the main types of post-mortem DNA degradation is deamination of cytosines, highly damaged ancient or museum DNA samples are usually characterized by higher uracil content [52–54]. In classical library preparation protocols, the usage of a proofreading polymerases should stall the chain elongation in the presence of uracil and thus reduce the misincorporation errors in the final dataset. On the other hand this approach might not be optimal for highly degraded ancient DNA samples, where a large fraction of DNA fragments may carry cytosine to uracil misincorporations [53]. Moreover the usage of a proofreading polymerase does not prevent the misincorporations caused by direct deamination of methylated cytosine to thymine, or less common deamination of guanidine to adenine [52, 54]. In the protocol used above [15], the second strand synthesis was performed using Klenow Fragment (3’ → 5’ exo-), lacking proofreading ability, and thus approximately half of the resulting DNA fragments should have cytosine to uracil misincorporation substituted for thymine, amplifiable by proofreading DNA polymerase. We thus opted for a bioinformatic post-processing way of filtering-out such bases. Post-mortem damage in the sequenced samples was assessed by mapDamage2.0, which rescales sequence files by downscaling quality scores of likely post-mortem damaged bases. As some SNPs became filtered by lower quality scores after the rescaling, the number of SNPs is decreased after mapDamage2.0. We expected higher number of discarded SNPs in the oldest samples, because of a higher proportion of DNA damage occurring with time. The proportion of SNPs discarded after applying mapDamage2.0 was the highest among the 58 years old samples (1.46% for RAD-ref; 1.89% for RAD-ref-ext; 2.36% for assembly-ref), although relatively low, given the samples’ age and preservation type (see S1 Table).

It is worth mentioning, that the higher number of SNPs detected in libraries from museum specimens, comparing to the fresh samples, is not an effect of post-mortem damage (an opposite trend was detected with the higest proportion of type II transitions to transitions [55] and transversions present in the fresh specimens; S4 Fig)

Application: spatial genetic structure of Oedaleus decorus

The de novo reference catalog was composed of 408,851 reference contigs. The N50 length was 321 bp and the total length was 119,789,911 bp. Among the total number of contigs, 9% were shown to be of exogeneous origin by the BLAST search, either against fungi and bacteria GenBank databases or against technical sequences. Such a level of contaminants is expected here, as in contrast to the L. helle references, which were built from fresh samples, the O. decorus assembly-ref was based on eight specimens from the captured library—either fresh or pin-mounted—that showed the largest number of reads. A total of 4,783,774 informative sites were retrieved after SNP calling. After removing indels and low quality sites, 125,890 sites were conserved. Keeping only biallelic loci with a minor allele count of at least 6, with data fullness higher or equal to 50% of the samples, we obtained 6,046 loci. Finally, we conserved 2,979 SNPs after the removal of potential paralogous sites. The median depth for each SNP was 10. On average, each of the 49 samples were characterized by 1864 SNPs and each SNP was found in 32 individuals (62.7% of matrix fullness).

The spatial genetic structure inferred by fastStructure revealed two geographically distinct clades in the west and the east of the Palearctic (Fig 6). This result supports the eastern-western split previously highlighted in Oedaleus decorus based on mtDNA amplicons [22]. This demonstrates that hyRAD is a reliable technique to infer spatial genetic structure from both fresh samples and museum samples collected at various time points in the past.

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Fig 6. Spatial genetic structure of O. decorus inferred using fastStructure with k = 2 and simple priors.

Colours denote the two different genetic groups supported by a previous study relying on mtDNA markers [22].

https://doi.org/10.1371/journal.pone.0151651.g006