Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

From e-voucher to genomic data: Preserving archive specimens as demonstrated with medically important mosquitoes (Diptera: Culicidae) and kissing bugs (Hemiptera: Reduviidae)

  • Silvia Andrade Justi ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliations Walter Reed Biosystematics Unit, Smithsonian Institution Museum Support Center, Suitland, MD, United States of America, Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America, Department of Entomology, Smithsonian Institution National Museum of Natural History, Washington, DC, United States of America

  • John Soghigian,

    Roles Conceptualization, Data curation, Formal analysis, Methodology, Resources, Validation, Visualization, Writing – review & editing

    Affiliation Department of Entomology, North Carolina State University, Raleigh, NC, United States of America

  • David B. Pecor,

    Roles Data curation, Methodology, Writing – review & editing

    Affiliations Walter Reed Biosystematics Unit, Smithsonian Institution Museum Support Center, Suitland, MD, United States of America, Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America, Department of Entomology, Smithsonian Institution National Museum of Natural History, Washington, DC, United States of America

  • Laura Caicedo-Quiroga,

    Roles Methodology, Resources, Writing – review & editing

    Affiliations Walter Reed Biosystematics Unit, Smithsonian Institution Museum Support Center, Suitland, MD, United States of America, Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America, Department of Entomology, Smithsonian Institution National Museum of Natural History, Washington, DC, United States of America

  • Wiriya Rutvisuttinunt,

    Roles Methodology, Resources, Writing – review & editing

    Affiliation Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America

  • Tao Li,

    Roles Methodology, Resources

    Affiliation Viral Diseases Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America

  • Lori Stevens,

    Roles Funding acquisition, Resources, Writing – review & editing

    Affiliation Department of Biology, University of Vermont, Burlington, VT, United States of America

  • Patricia L. Dorn,

    Roles Funding acquisition, Resources, Writing – review & editing

    Affiliation Department of Biological Sciences, Loyola University New Orleans, New Orleans, LA, United States of America

  • Brian Wiegmann,

    Roles Funding acquisition, Supervision

    Affiliation Department of Entomology, North Carolina State University, Raleigh, NC, United States of America

  • Yvonne-Marie Linton

    Roles Funding acquisition, Resources, Supervision, Writing – review & editing

    Affiliations Walter Reed Biosystematics Unit, Smithsonian Institution Museum Support Center, Suitland, MD, United States of America, Entomology Branch, Walter Reed Army Institute of Research, Silver Spring, MD, United States of America, Department of Entomology, Smithsonian Institution National Museum of Natural History, Washington, DC, United States of America


Scientific collections such as the U.S. National Museum (USNM) are critical to filling knowledge gaps in molecular systematics studies. The global taxonomic impediment has resulted in a reduction of expert taxonomists generating new collections of rare or understudied taxa and these large historic collections may be the only reliable source of material for some taxa. Integrated systematics studies using both morphological examinations and DNA sequencing are often required for resolving many taxonomic issues but as DNA methods often require partial or complete destruction of a sample, there are many factors to consider before implementing destructive sampling of specimens within scientific collections. We present a methodology for the use of archive specimens that includes two crucial phases: 1) thoroughly documenting specimens destined for destructive sampling—a process called electronic vouchering, and 2) the pipeline used for whole genome sequencing of archived specimens, from extraction of genomic DNA to assembly of putative genomes with basic annotation. The process is presented for eleven specimens from two different insect subfamilies of medical importance to humans: Anophelinae (Diptera: Culicidae)—mosquitoes and Triatominae (Hemiptera: Reduviidae)—kissing bugs. Assembly of whole mitochondrial genome sequences of all 11 specimens along with the results of an ortholog search and BLAST against the NCBI nucleotide database are also presented.


Scientific collections document the diversity of life and preserve specimens to be studied by future generations. With modern advances in DNA sequencing techniques, museum specimens preserved for decades can continue to inform new investigations filling critical gaps in taxonomic knowledge. Since the advent of modern DNA sequencing techniques, there has been much debate over destructive sampling of specimens housed within scientific collections [1]. However, given the potential contributions that rare or understudied taxa can offer, the term “value-added” may be more appropriate [2]. Nonetheless, this distinction will probably depend on an individual curator’s viewpoint. Managers of scientific collections are more likely to put severe limits on destructive sampling of historical specimens [3], whereas evolutionary biologists may see scientific collections as a near endless resource of material ready to be tapped [46]. Thus, a balance must be struck between the value of genetic sampling while maintaining the integrity of these invaluable scientific collections.

Insect field collections represent considerable time and expense, and field biologists and taxonomists with the requisite expertise to build these collections are themselves becoming rare. Thus, access to rare or understudied taxa may only be possible through archive collections. With recent advancements in DNA sequencing, cryptic species—i.e., lineages that are morphologically indistinguishable but evolutionarily independent [7]—are playing a crucial role in our understanding of the evolutionary histories of medically important arthropod species. When assessment of reproductive isolation to determine species status is not feasible, researchers often defer to the phylogenetic species concept [8] to better understand the specific status of lineages of interest.

The development of extraction methods that solubilize and recover DNA from collection specimens—with little or no physical damage—was the first significant step in realizing the potential utility of historical samples in large-scale, molecular insect diversification studies [9,10]. However, only in the past decade random short read sequencing strategies (such as Illumina®) began to become more accessible. Before then, the most common and available method of DNA sequencing was Sanger sequencing [11], which relies on prior sequence knowledge, long DNA fragments, and the intact presence of the target gene region. These requirements of high-quality, un-sheared DNA for PCR amplification and Sanger sequencing served as a unsurmountable obstacle for air-dried, pinned insects, sometimes kept in environmentally unstable conditions [12], where the specimens’ DNA was likely to be highly degraded.

With the development of short read fragment sequencing platforms, such as Illumina®, obtaining partial genomes from degraded samples has become more straightforward, mainly because specific primer binding sites are not required, short fragmented DNA can be used for library construction, and a priori sequence knowledge is irrelevant. Here we document the combination of mostly non-destructive DNA extraction techniques and specific Illumina® library preparations that were fundamental in recovering both whole mitochondrial genomes and nuclear sequences from small and large collection specimens. Additionally, we describe the value of our e-vouchering pipeline to preserve and make available key taxonomic characters that may be destroyed through whole specimen sacrifice or otherwise damaged as a result of immersion in liquids (such as DNA extraction buffers). These methods enable the maximum value added from collection specimens.

The primary purpose of this work is to make available an accessible combination of methods that will allow any researchers, including those with limited expertise in genome sequencing and analysis, to be able to include archive specimens in their molecular systematics studies. The methodology described herein using archive specimens, can also be successfully applied to fresh specimens, and results in comparable data across all specimens.

Materials and methods


Specimens examined during this study belong to two groups of medically important arthropod families: the Anophelinae (Diptera: Culicidae) [mosquitoes] and Triatominae (Hemiptera: Reduviidae) [kissing bugs]. These subfamilies are very different in size and physical reactions to the DNA extraction methodologies described below. Specimens were sourced from the U.S. National Culicidae and Heteroptera collections (Smithsonian Institution–National Museum of Natural History, Washington DC (USNM)).

Since mosquitoes are irreparably damaged by immersion in buffers (i.e., there is loss of scales and setae of vital taxonomic value), even mostly “non-destructive” DNA extraction protocols [3] are unusable for such samples. Thus, mosquito specimens chosen for DNA extraction and sequencing had to meet the four following criteria: (1) samples filled a critical taxonomic knowledge gap; (2) samples were in poor physical condition (i.e., of limited value to future taxonomic studies); (3) samples were part of a series of at least four specimens from the same collection event, with at least three other exemplars of the same sex; and (4) samples were from, or close to, the type locality.

Because the DNA extraction approach used here is non-destructive for Triatominae specimens, samples were randomly chosen from other ongoing studies.

A total of eleven specimens, five mosquitoes and six kissing bugs, collected between 1935 and 1998 were examined (Table 1).

Table 1. Specimens used in this study, their voucher number at USNM and the year of collection.

Geographic origins are listed verbatim from the archive specimen labels.


E-vouchers were generated for all mosquito samples destined for DNA extraction. These e-vouchers consist of a habitus image and images displaying the diagnostic characters for each species, as stated in the original description and subsequent taxonomic reviews. Original descriptions and taxonomic reviews were compiled for all Anophelinae specimens and reviewed for diagnostic morphological characters to capture during imaging [1323]. Mosquito specimens were photographed prior to extraction at the NMNH Scanning Electron Microscopy Imaging Lab, using an Olympus DSX100 camera, and are available as electronic vouchers. Specimens were assigned a unique USNM catalog number along with a unique 2D barcode label. All images, sequence results and subsequent records are linked to the original museum specimen using this unique accession number. After DNA extraction, the Triatominae specimens were returned to the USNM collections and are available for further study. DNA extracts are vouchered in the NMNH biorepository.

DNA extraction, sequencing and QC

DNA was extracted using the same protocol for both Anophelinae and Triatominae, since it is an established method for extraction of archive insect DNA, and eliminates the need for sample maceration. The method is largely non-destructive for larger specimens without scales, including Triatominae, and involves whole-specimen incubation in digestion buffer under gentle agitation for 16–20 hours. Culicidae were placed in 1.5ml tubes and Triatominae in 5 ml tubes with sufficient buffer for full immersion. After incubation, the ~4ml digestion solution for each Triatominae was divided into four separate 1.5ml tubes, which were processed independently for the DNA precipitation, using Sodium chloride and Glycoblue coprecipitant (ThermoFisher Scientific, Waltham, MA, 15 mg/mL diluted to 10ul/ml of digestion buffer) as previously described [3]. The detailed protocol, based on Gilbert et al. [4] is available at

DNA was quantified for all samples using the High Sensitivity kit for Qubit Fluorometric Quantification (ThermoFisher Scientific, Waltham, MA). In cases where the DNA concentration was too low to quantify, the DNA solution was concentrated by evaporation using a Savant SpeedVac Plus Centrifuge Vacuum Concentrator and re-quantified.

Illumina© library prep was performed using KAPA HyperPlus Kits, (Roche, Pleasanton, CA). Since archive specimens exhibit highly fragmented DNA as verified by Agilent TapeStation 4200 Automated Electrophoresis (Agilent Technologies, Blacksburg, VA), the library prep began at the DNA end repair and A-tailing steps following the manufacturer’s protocol. Adapter ligation and PCR amplification conditions followed the manufacturer’s recommendations based on the quantity of DNA available. Subsequent quality control and fragment distribution were again assessed with the TapeStation 4200 (Agilent Technologies) and AMPure XP beads (Beckman Coulter, Sykesville, MD) clean-up was performed to remove adapter dimers and other impurities, when necessary. The detailed protocol, based on the KAPA HyperPlus technical datasheet is available at

Sequencing was performed either using the HiSeq® Illumina® platform (PE 2x150) at Omega Bioservices (Norcross, GA, USA) [Triatominae], or NovaSeq® Illumina® platform (PE 2x150) [Culicidae] at the Walter Reed Army Institute of Research (WRAIR). Sequencing at Omega Bioservices was done as fee for service, for which 10 gigabases (Gb) of raw reads per sample were purchased. For the NovaSeq platform, the samples were run on a S4 flow cell with XP workflow as part of a bigger project, designed to maximize the overall raw reads output. Read quality was assessed using fastqc [24] and adapter trimming and sequence filtering was performed using Trimmomatic [7].

Reference guided assembly

The mitochondrial genome was recovered using reference guided assembly. Reference sequences used for the mitochondrial genomes were, respectively: Triatoma dimidiata (GenBank NC_002609) and Anopheles cruzii (GenBank NC_024740). However, this is a highly customizable step in the pipeline described herein, and any available genomic region can be used as reference.

Trimmed and filtered paired reads, were mapped to their respective reference genome using bowtie2 [25], and writing only the mapped reads to the sequence alignment map (SAM) file output. Consensus sequences of the mapped reads were generated using Unipro UGENE v. 35 [26].

De novo assembly and annotation of nuclear genome and ortholog search

Paired sequence files output by Trimmomatic were used as input for the GATB-Minia Pipeline ( for whole genome assembly. Assembly statistics were calculated using abyss-fac [27] and annotation was performed using AUGUSTUS [28], with the provided training sets for Triatomine and Anophelinae, Rhodnius and Aedes, respectively. Outputs were processed using (

Orthograph [29] databases were constructed using the OrthoDB v. 10.1 [30] set of single-copy orthologs (SCO) for Hemiptera and Diptera, following a custom pipeline (available at Coding sequences output from AUGUSTUS were used as input for the search of SCO. All identified SCO were blasted against the NCBI database, using BLASTn (nucleotide query, nucleotide database) and BLASTp (amino acid query, amino acid database) [31] and the first hit was recorded. Hits were then compared to NCBI taxonomy database, and the number of arthropod orthologs was recorded for each specimen.

In order to identify variable correlations with the quality of the assembly/recovered orthologs, Pearson correlation amongst result variables was calculated using the function ggscatter of the ggpubr R [32] package. The code used is available as supporting information (S1 Appendix).

All the scripts and commands used perform the de novo assembly, annotation and ortholog calling, BLASTn and BLASTp results filtering are publicly available ( The pipeline includes all the analytical steps described on the methods. This includes selection and output of identified by BLAST as belonging to the target group (in this case Arthropods).


DNA extraction and sequencing

Total DNA extracted ranged from 21 to 7379 ng per specimen, which generated 0.6–10 gigabases (Gb) of raw Illumina sequencing results for all five archive mosquitoes and six kissing bug specimens (Table 2). Although complete mitochondrial genomes were uniformly assembled, nuclear genomes varied from two genomes with over 2,000 SCO identified by Orthograph, to specimens with only a few SCO. Details are below. Obtained raw reads are available on GenBank as SRA project PRJNA646392.

Table 2. Amount of total genomic DNA extracted and amount of raw data (i.e., raw reads in gigabases– 109 bp) obtained per specimen.

Reference guided assembly

Regardless of the collection date of the specimen (Triatomines: 1963–1981; Culicids: 1935–1998) or the amount of total DNA recovered, it was possible to assemble and annotate the complete mitochondrial genomes for all 11 archive samples (Table 3). The size of the mitochondrial genomes assembled for the Anophelinae specimens varied between 15,390 bp and 15,430 bp, while the Triatominae mitochondrial genomes assembled ranged between 16,398 and 17,283 bp. All known mitochondrial genes were present in all recovered mitochondrial genomes, and the gene organization recovered was the same as the references.

Table 3. Recovered mitochondrial genome sizes and their respective GenBank accession numbers.

De novo assembly

De novo assembly of the sequences for each specimen was performed without previous filtering for contaminants, in order to be able to allow Orthograph [29] to assemble the genomes based on the constructed reference database alone. Direct assemblies of trimmed and filtered reads were assessed for contiguity using abyss-fac [27]. Both the highest and the lowest N50 (i.e., shortest contig of 50% of the assembly length) were observed on the Triatominae samples: 631-bp (T. dimidiata USNMENT01241973) and 2619-bp (T. dimidiata USNMENT01239008). Anophelinae samples also showed extremes: N50 ranged between 640-bp (An. albitarsis F USNMENT01222377) and 2189-bp (An. maverlius USNMENT01241739).

Unlike the N50 values, the highest and the lowest sum of the sequence sizes were found for the Anophelinae samples. Interestingly, the lowest sum of contig lengths was observed for An. maverlius, the sample with the highest N50 (see Table 4 for more details on the assembly statistics).

Table 4. Summary statics for the genome assembly of each specimen: Total contigs in the assembly (N), contiguity of the assembly (N50), sum of contig lengths (SUM).

Ortholog search, BLAST and QC

The constructed Orthograph databases comprise 1,709 and 3,612 SCO for Hemiptera and Diptera, herein used as reference for Triatominae and Anophelinae specimens, respectively. Out of these, only five to 253 SCO were recovered for the six Triatominae specimens, compared to one to 3,470 SCO for the five Culicidae specimens (Table 5). BLAST results for the recovered SCO showed that not all orthologs identified by Orthograph correspond to known Arthropod sequences. BLASTp results always returned a higher number of hits than BLASTn, likely due to the size difference between nr and nt databases.

Table 5. Comparison between the number of SCO identified by Orthograph, the total number of BLASTn hits, the total number of BLASTn hits that correspond to known Arthropod sequences, the total number of BLASTp hits, the total number of BLASTp hits that correspond to known Arthropod sequences.

Pearson correlations were reciprocally calculated for the following variables: specimen age (calculated based on collection date), total DNA extracted (ng), number of SCO identified by Orthograph, total contigs in the assembly (N), contiguity of the assembly (N50), sum of contig lengths (SUM), number of arthropod hits with BLASTn, number of arthropod hits with BLASTp.

Variables that were found to be positively and significantly correlated (p<0.05) were the number of SCO identified by Orthograph, N, N50, SUM, number of arthropod hits with BLASTn, number of arthropod hits with BLASTp (Table 6). No variables were found to be negatively correlated and, the age of the specimen was not significantly correlated to any of the other variables.

Table 6. Pearson correlations calculated between the variables.

When the recovered orthologs were blasted against the NCBI nucleotide (nt) and protein (nr) databases, with only the first hit being recorded, there was an almost perfect positive correlation between the number of orthologs recovered and the number of arthropod hits (Table 6). Lower quality assemblies (i.e., low number SCO recovered), usually yielded a higher percentage of contaminant microorganism blast hits [data available at].


In this study we describe a successful combination of established wet laboratory protocols, with a bioinformatics pipeline that allowed for the recovery of complete mitochondrial genome sequences from all insect specimens, which had been previously collected 20–84 years ago (the latter, An. crucians B). This is remarkable considering the raw sequence data differed by over an order of magnitude and there was a large range in the number of nuclear SCO recovered (with only one SCO for one specimen). Despite being developed specifically for archive specimens, the Walter Reed Biosystematics Unit standard operating procedure (SOP) described here can also be applied to freshly collected, frozen, dried or ethanol-stored specimens.

The results presented highlight the advantage of the non-targeted short read DNA sequencing by reflecting the quality and availability of the starting material, avoiding long and laborious experimental hours in the search for genomic regions that might just be too degraded to be recovered by Sanger Sequencing [22] or by targeted enrichment approaches [21]. Specimen age (calculated based on collection date) also appears irrelevant to the results obtained, increasing the utility of archive insect collections for genetic studies. Personal field-to-lab observation by the authors lead to the hypothesis that the preserving procedure to which each specimen is subjected prior to being vouchered and deposited in the collection, rather than age, are likely very important factors.

It is incredible to observe that, regardless of specimen age or insect size, this methodology allowed for the assembly of complete mitochondrial genomes for all the dry, pinned specimens studied, compounded with the advantage of extracting nuclear SCO, and possibly other nuclear genes of interest, depending on the reference sequence used.

Illustrating the lack of correlation between the age of the specimen and the quality of the sequencing results, the highest number of SCO was recovered from a specimen 42 years old (An. ininii– 3,367 arthropod SCO), a better result than for the most recent specimen (An. albitarsis F– 2,188 arthropod SCO). Specimen size, and consequently amount of DNA extracted, was not a predictor of assembly contiguity or recovery of orthologs. For the much larger triatomines, only 0.05–13.3% of all arthropod SCO from the Hemiptera database were recovered; while for the mosquitoes between 0.02–93.2% of all arthropod SCO from the Diptera database were recovered. In addition, multicopy genes (e.g., nuclear ribosomal genes), which were not identified here, may be useful for phylogenetic analysis. While the mitochondrial genome was used here as an example for the reference guided assembly, in future applications, any genomic region can be used as reference. It is important to note that, in this case, results will depend on sequenced genomic DNA availability.

The BLAST results, however, highlight the need for caution with the Orthograph [29] recovered SCO. While Orthograph has been widely used to identify putative orthologous groups [3336], it also can infer putative homology even from distantly related species. As such, contaminants that are not cleaned from assemblies may be misidentified as orthologs in the organism of interest (e.g., see blast results for the T. dimidiata s.l. specimens). Particularly in the case of museum specimens, where contaminant DNA (e.g., fungi) could be as abundant as target DNA, care should be taken to verify the identity of contigs in assemblies or Orthograph putative orthologs, against a larger database of nucleotide or protein sequences, such as with NCBI BLAST.

The use of insect archive specimens for phylogenetic studies is by no means a new endeavor. In fact, researchers have been including such specimens in systematics or evolutionary studies for almost two decades now [5,3743]. While preserving the integrity of the specimen is essential, in most cases a whole leg was used for DNA extraction, which may or may not have impacted downstream taxonomic character availability, but also may not have provided sufficient starting quantities of DNA for downstream analyses. Use of a specimen portion highlights the importance of e-vouchering, even when the specimen is kept virtually intact.

While the expectation of a contiguous, close to complete genome as the standard of genomic projects, low-coverage genomes are sufficient for both deep and shallow phylogenomic studies [4446]. Moreover, it is worth noting that even partial genomes may offer data for a wide range of future projects regardless of method of capture, unlike sequence capture (AHE/UCE) [38] where the data captured is limited to the probes, which can be extremely limited in their taxonomic coverage/utility.

Finally, financial savings on labor might just be greater than experiment-related savings provided by the use of these less costly approaches, with the added bonus of additional data. Even though the study of archive specimens is often associated with systematics and evolutionary biology, the usefulness of such specimens, especially for disease vectors and agricultural pests, can go beyond basic science and help understand other important factors, such as the evolution of insecticide resistance [39].

Scientific collections offer a unique resource for investigating the evolutionary history of extant and extinct taxa. Rare or cryptic taxa, not typically available among freshly collected specimens, can fill critical gaps in knowledge. However, there is a trade-off to having scientific collections available to inform future investigations. The approach outlined herein attempts to balance the need to apply modern DNA sequencing tools to understudied/rare taxa and the need to preserve voucher specimens. Whenever possible, non-destructive methods should be used for sampling scientific collections. When destructive sampling is unavoidable, samples should be part of a related series and careful attention should be paid to document any sacrificed specimen. E-vouchers provide the opportunity for morphology to be examined retrospectively in the context of sequencing and phylogenetic analysis results, even when the physical specimen is lost or damaged in the process.


Regardless of the fact that this study is based upon DNA extracted from Arthropods, the pipeline described is applicable to any starting DNA, provided that taxa-specific steps (e.g., DNA extraction methodology, orthograph database and BLAST filters) are adjusted accordingly.

Additionally, the variation of the results observed, even for the specimens with comparable sequencing depths, leads to the conclusion that the starting DNA (i.e., physical availability of the genome regions in the sample) is the single most important factor for the recovery of genome coverage. The quality of the starting DNA will be a direct result of handling and storage of the specimen, from the field to its final destiny.

Supporting information

S1 Appendix. R code and results for the pairwise correlation comparison of the variables studied.



We thank Thomas Henry (Curator, USNM Hemiptera collection), James E. Pecor (WRBU Museum Specialist for the US National Mosquito Collection (USNM)) and Randall T. Schuh (Curator, AMNH Hemiptera collection) for facilitating access to, and approving experimental sampling on the specimens. S.A.J. is a National Research Council Research Associate at the Walter Reed Biosystematics Unit and Walter Reed Army Institute of Research. J.S. is a Postdoctoral Fellow under NSF DEB #1754376. The material published reflects the views of the authors and should not be misconstrued to represent those of the U.S. Department of the Army, the U.S. Department of Defense, The National Instituted of Health or the National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


  1. 1. Graves GR, Braun MJ. Museums: Storehouses of DNA? Science (80). 1992;255: 1335–1336. pmid:1542783
  2. 2. Payne RB, Sorenson MD. Museum collections as sources of genetic data. Bonner Zool Beiträge. 2003;51: 97–104.
  3. 3. Mandrioli M. Insect collections and DNA analyses: how to manage collections? Museum Manag Curatorsh. 2008;23: 193–199.
  4. 4. Holmes MW, Hammond TT, Wogan GOU, Walsh RE, LaBarbera K, Wommack EA, et al. Natural history collections as windows on evolutionary processes. Mol Ecol. 2016;25: 864–881. pmid:26757135
  5. 5. Wandeler P, Hoeck PEA, Keller LF. Back to the future: museum specimens in population genetics. Trends Ecol Evol. 2007;22: 634–42. pmid:17988758
  6. 6. Yeates DK, Zwick A, Mikheyev AS. Museums are biobanks: unlocking the genetic potential of the three billion specimens in the world’s biological collections. Curr Opin Insect Sci. 2016;18: 83–88. pmid:27939715
  7. 7. Mayr E. Populations, species, and evolution. Cambridge MBP of HUP, editor. 1970.
  8. 8. Mishler B, Brandon R. Individuality, pluralism, and the phylogenetic species concept. Biol Philos. 1987;2: 397–414.
  9. 9. Gilbert MTP, Moore W, Melchior L, Worobey M. DNA Extraction from Dry Museum Beetles without Conferring External Morphological Damage. PLoS One. 2007; 1–4. pmid:17342206
  10. 10. Thomsen PF, Elias S, Gilbert MTP, Haile J, Munch K, Froese DG, et al. Non-Destructive Sampling of Ancient Insect DNA. 2009;4. pmid:19337382
  11. 11. Sanger F, Nicklen S. DNA sequencing with chain-terminating. 1977;74: 5463–5467.
  12. 12. Dale C, Justi S, Galvao C. Tropical insect collections and DNA extraction, using Rhodnius Stål 1859 (Hemiptera: Heteroptera: Reduviidae: Triatominae). Zootaxa. 2013;3694: 398–399. Available: pmid:26312300
  13. 13. Beltrán-Aguilar A, Ibáñez-Bernal S, Mendoza- Palmero F, Sandoval-Ruiz CA, Hernández-Xoliot RA. Taxonomía y distribución de los anofelinos en el estado de Veracruz, México (Diptera: Culicidae, Anophelinae). Acta Zoológica Mex. 2011;27: 601–755.
  14. 14. Brochero HHL, Li C, Wilkerson RC. A newly recognized species in the Anopheles (Nyssorhynchus) albitarsis complex (Diptera: Culicidae) from Puerto Carreno, Colombia. Am J Trop Med Hyg. 2007;76: 1113–1117. pmid:17556620
  15. 15. Yamaguti S, LaCasse WJ. Mosquito fauna of North America. Part I. Genus Anopheles. Off Surg Headquarters, 8th Army, US Army, United States. 1950.
  16. 16. Faran ME. Mosquito studies (Diptera, Cilicidae). XXXIV. A revision of the albimanus section of the subgenus Nyssorhynchus of Anopheles. Contrib Am Entomol Inst. 1980;15: 215.
  17. 17. Floch H, Abonnenc E. Anophèles de la Guyane Française. Arch l’Institut Pasteur la Guyane du Territ l’Inini. 1951;136: 1–91.
  18. 18. Harrison BA, Byrd BD, Sither CB, Whitt PB. The Mosquitoes of the Mid-Atlantic Region: An Identification Guide. Mosquito a. Western Carolina University, Cullowhee, NC; 2016.
  19. 19. Lee D, Debenham ML. The Culicidae of the Australasian region. Volume 6, Nomenclature, synonymy, literature, distribution, biology and relation to disease: Genera Armigeres, Bironella and Coquillettidia. In: Commonwealth Department of Health S of PH and TMMS, editor. Canberra: Australian Govt. Publishing Service; 1988. Available:
  20. 20. Reinert JF, Kaiser PE, Seawright JA. Analysis of the Anopheles (Anopheles) quadrimaculatus complex of sibling species (Diptera: Culicidae) using morphological, cytological, molecular, genetic, biochemical, and ecological techniques in an integrated approach. J Am Mosq Control Assoc. 1997;13 Suppl: 1–102. Available:
  21. 21. Ruiz-Lopez F, Wilkerson RC, Conn JE, McKeon SN, Levin DM, Quiñones ML, et al. DNA barcoding reveals both known and novel taxa in the Albitarsis Group (Anopheles: Nyssorhynchus) of Neotropical malaria vectors. Parasit Vectors. 2012;5: 44. pmid:22353437
  22. 22. Senevet G, E. A. Quelques anophélinés de la Guyane Française. Arch l’Institut Pasteur d’Algérie. 1938;16: 486‒512.
  23. 23. Soesilo R, van Slooten J. Miscellaneous notes on anopheline mosquitoes in the Dutch East Indies. Meded van den D der Volksgezondbeid Ned. 1931;20: 124–128.
  24. 24. Bioinformatics B. FastQC. 2010. Available:
  25. 25. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9: 357–359. pmid:22388286
  26. 26. Okonechnikov K, Golosova O, Fursov M. Unipro UGENE: a unified bioinformatics toolkit. Bioinformatics. 2012;28: 1166–1167. pmid:22368248
  27. 27. Jackman SD, Vandervalk BP, Mohamadi H, Chu J, Yeo S, Hammond SA, et al. ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter. Genome Res. 2017;27: 768–777. pmid:28232478
  28. 28. Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: A b initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34: 435–439. pmid:16845043
  29. 29. Petersen M, Meusemann K, Donath A, Dowling D, Liu S, Peters RS, et al. Orthograph: A versatile tool for mapping coding nucleotide sequences to clusters of orthologous genes. BMC Bioinformatics. 2017;18: 1–10. pmid:28049414
  30. 30. Kriventseva E V, Kuznetsov D, Tegenfeldt F, Manni M, Dias R, Simão FA, et al. OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucleic Acids Res. 2019;47: D807–D811. pmid:30395283
  31. 31. Altschul S, Gish W, Miller W, Myers E, Lipman D. Basic local alignment search tool. J Mol Biol. 1990;215: 403–410. pmid:2231712
  32. 32. R Development Core Team. R. 2008.
  33. 33. Peters RS, Krogmann L, Mayer C, Donath A, Gunkel S, Meusemann K, et al. Evolutionary History of the Hymenoptera. Curr Biol. 2017;27: 1013–1018. pmid:28343967
  34. 34. Gillung JP, Winterton SL, Bayless KM, Khouri Z, Borowiec ML, Yeates D, et al. Anchored phylogenomics unravels the evolution of spider flies (Diptera, Acroceridae) and reveals discordance between nucleotides and amino acids. Mol Phylogenet Evol. 2018;128: 233–245. pmid:30110663
  35. 35. Kawahara AY, Plotkin D, Espeland M, Meusemann K, Toussaint EFA, Donath A, et al. Phylogenomics reveals the evolutionary timing and pattern of butterflies and moths. Proc Natl Acad Sci. 2019;116: 22657–22663. pmid:31636187
  36. 36. Song H, Béthoux O, Shin S, Donath A, Letsch H, Liu S, et al. Phylogenomic analysis sheds light on the evolutionary pathways towards acoustic communication in Orthoptera. Nat Commun. 2020;11: 4939. pmid:33009390
  37. 37. Prosser SWJ, Dewaard JR, Miller SE, Hebert PDN. DNA barcodes from century-old type specimens using next-generation sequencing. Mol Ecol Resour. 2016;16: 487–497. pmid:26426290
  38. 38. Blaimer BB, Lloyd MW, Guillory WX, Brady SG. Sequence capture and phylogenetic utility of genomic ultraconserved elements obtained from pinned insect specimens. PLoS One. 2016;11: 1–20. pmid:27556533
  39. 39. Hartley CJ, Newcomb RD, Russell RJ, Yong CG, Stevens JR, Yeates DK, et al. Amplification of DNA from preserved specimens shows blowflies were preadapted for the rapid evolution of insecticide resistance. Proc Natl Acad Sci U S A. 2006;103: 8757–8762. pmid:16723400
  40. 40. Mitchell A. Collecting in collections: A PCR strategy and primer set for DNA barcoding of decades-old dried museum specimens. Mol Ecol Resour. 2015;15: 1102–1111. pmid:25644663
  41. 41. Staats M, Erkens RHJ, van de Vossenberg B, Wieringa JJ, Kraaijeveld K, Stielow B, et al. Genomic Treasure Troves: Complete Genome Sequencing of Herbarium and Insect Museum Specimens. PLoS One. 2013;8. pmid:23922691
  42. 42. Miller JA, Beentjes KK, Van Helsdingen P, Ijland S. Which specimens from a museum collection will yield DNA barcodes? A time series study of spiders in alcohol. Zookeys. 2013;365: 245–261. pmid:24453561
  43. 43. Harper GL, Maclean N, Goulson D. Analysis of museum specimens suggests extreme genetic drift in the adonis blue butterfly (Polyommatus bellargus). Biol J Linn Soc. 2006;88: 447–452.
  44. 44. Zhang G, Li B, Li C, Gilbert MTP, Jarvis ED, Wang J. Comparative genomic data of the Avian Phylogenomics Project. Gigascience. 2014;3: 26. pmid:25671091
  45. 45. Zhang F, Ding Y, Zhu C, Zhou X, Orr MC, Scheu S, et al. Phylogenomics from low‐coverage whole‐genome sequencing. Matschiner M, editor. Methods Ecol Evol. 2019;10: 507–517.
  46. 46. Allio R, Scornavacca C, Nabholz B, Clamens A-L, Sperling FA, Condamine FL. Whole Genome Shotgun Phylogenomics Resolves the Pattern and Timing of Swallowtail Butterfly Evolution. Hahn M, editor. Syst Biol. 2020;69: 38–60. pmid:31062850