Current Zoology 61 (3): 488–504, 2015

Applications of next-generation sequencing to the study of biological invasions

1, * 1 1 2 Marc RIUS , Steve BOURNE , Harry Guy HORNSBY , Mark A. CHAPMAN 1 Ocean and Earth Science, National Oceanography Centre Southampton, University of Southampton, SO14 3ZH, UK 2 Centre for Biological Sciences, Life Sciences Building 85, University of Southampton, Southampton, SO17 1BJ, UK

Abstract Through the widespread implementation of next-generation sequencing (NGS), analyses of the whole genome (the entire DNA content) and the whole transcriptome (the genes being expressed) are becoming commonplace. NGS enables the analysis of a vast amount of previously unattainable genetic information. Despite this potential, NGS has yet to be widely imple- mented in genetic studies of biological invasions. The study of the genomic causes and consequences of biological invasions al- lows a deeper understanding of the molecular mechanisms underpinning the invasion process. In this review, we present a brief introduction to NGS followed by a synthesis of current research in the genomics and transcriptomics of adaptation and coloniza- tion. We then highlight research opportunities in the field, including: (1) assembling genomes and transcriptomes of non-model organisms, (2) identifying genomic regions and candidate genes underlying evolutionary processes, and (3) studying the adaptive role of gene expression variation. In particular, because introduced species face a broad range of physiological and biotic chal- lenges when colonizing novel and variable environments, transcriptomics will enable the study of gene regulatory pathways that may be responsible for acclimation or adaptation. To conclude, we identify a number of research approaches that will aid our fu- ture understanding of biological invasions [Current Zoology 61 (3): 488–504, 2015]. Keywords Exotic species, Genomics, Genotype-environment interactions, Invasive species, Invasion genetics, Invasion route, Non-indigenous species, Non-native species

1 Introduction 2007; Le Roux and Wieczorek, 2009; Fitzpatrick et al., 2012). Despite the progress reported in the literature on Biological invasions can be seen as model systems to invasion genetics during the last two decades, recent understand fundamental ecological and evolutionary reviews have concluded that much work is still needed processes, such as the establishment of species ranges, to fully develop this research field (Geller et al., 2010; the onset of speciation, the effects of inbreeding and hy- Bock et al., 2015; Rius et al., 2015). For example, most bridization, or the study of mechanisms shaping adap- studies restrict their approach to the description of ge- tive evolution (Lee, 2002; Sax et al., 2005). The inves- netic patterns, and do not proceed to the analysis of the tigation of biological invasions sheds light on basic underlying adaptive processes. In addition, these studies processes that typically occur over long periods of time, have mainly been carried out using a small number of but in a contemporary timeframe. Thus, the introduc- loci (e.g. allozymes, DNA sequencing and microsatel- tions of non-indigenous species are unique opportunities lites) and have yet to embrace the potential of adopting to observe otherwise unattainable ‘experiments’ (Sax et next-generation sequencing (NGS) technologies to al., 2007). study biological invasions (see below). Adopting NGS The importance of studying the genetics of biological has the potential to greatly enhance aspects of invasion invasions has been recognized for decades (Baker and genetics, permitting study of the genomic processes Stebbins, 1965; Gray, 1986), with the term ‘invasion involved in colonization and adaptation. Further, NGS genetics’ appearing in the literature in the late 1990’s is expected to facilitate the comparison of whole ge- (e.g. Villablanca et al., 1998; Holland, 2000). It is there- nomes between populations and/or species (Kirk et al., fore well-established that genetics offer versatile tools 2013), which will considerably advance our knowledge to investigate the fundamentals of biological invasions of invasive species. (Lee, 2002; Darling and Blum, 2007; Weinig et al., In this review, we present a brief introduction to

Received Dec. 19, 2014; accepted Mar. 26, 2015.  Corresponding author. E-mail: [email protected] © 2015 Current Zoology

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NGS, with the aim to familiarize invasion biologists higher throughput of NGS makes this technology a much with the most common concepts and latest techniques. more cost-effective option to sequence large portions of Subsequently, we discuss current applications of NGS the genome, or identify and genotype genetic markers. to invasion genetics and end with some future perspec- Although the study of genome-scale processes has tives. traditionally been limited to model organisms, the emer- 2 The Emergence of Next-Generation gence of NGS means that any organism can now be stu- died (Metzker, 2010). This is important because (1) Sequencing model organisms may not reflect the biology of their First-generation, or Sanger sequencing (Sanger et al., close relatives, or display only a fraction of the diversity 1977), was widely used during the 1980s and 1990s, but of living organisms and traits (Huey et al., 2005; Tagu et has been superseded in terms of output by NGS, with al., 2014), and (2) NGS can be employed to understand the daily throughput of NGS being 100–100,000 times how broadly influential ecological and evolutionary pro- higher than Sanger sequencing (Kircher and Kelso, cesses are. In addition, the application of NGS increases 2010; Metzker, 2010). However, NGS, with a few ex- the feasibility of comparisons of multiple genomes ceptions, produces shorter reads than Sanger sequencing, (1000 Genomes Project Consortium, 2012; Huang et al., which leads to complications with respect to assembling 2012; Hester et al., 2013). NGS was, at first appearance, these short reads into loci, genes, and whole genomes a technology that was only accessible to a few research (Pop and Salzberg, 2008). In addition, NGS has a con- groups, due to its high cost (Wetterstrand, 2014). How- siderably higher error rate than Sanger sequencing ever, increasing interest and application of NGS has led (Kircher and Kelso, 2010). Counteracting these disad- to a rapid and dramatic decrease in its costs (Fig. 1). vantages is the ability of NGS to sequence at many fold NGS is nowadays broadly accessible and allows ques- coverage, and with the use of bioinformatic tools to tions to be answered that were previously prohibitively detect errors (McElroy et al., 2014), reduce the likelih- expensive or impractical (Mardis, 2011). Important bi- ood that errors populate the final assembly. Importantly, ological advances as a result of the implementation of the application of NGS does not require whole genomes NGS have been reported in the fields of population ge- or whole transcriptomes to be sequenced and assembled. nomics (Luikart et al., 2003), adaptive evolution (Stap- Instead, methods have been developed to carry out ge- ley et al., 2010; Turner et al., 2010), phylogenetics nomic analyses without a reference genome, including (Lemmon and Lemmon, 2013), conservation biology restriction site-associated DNA sequencing (RAD-Seq) (Angeloni et al., 2012), microbial metagenomics (La- (Baird et al., 2008) or genotyping-by-sequencing (GBS) serson et al., 2011), environmental monitoring (Ficetola (Elshire et al., 2011). Taken together, the substantially et al., 2008; Bohmann et al., 2014) and crop genetics

Fig. 1 Cost of next-generation sequencing and publication rate of studies on invasion genetics Sequencing costs (note log-scale axis) were obtained from the National Human Genome Research Institute (NHGRI) (http://www.genome.gov/ sequencingcosts/) with average sequencing cost per year calculated from the NHGRI project. Publication rate was obtained through Web of Science search on 21st March 2015 with keywords (population) and (genomics, transcriptomics or genetics) and (invas* species), excluding (non-invasive). The initial search was sorted using only the research fields ‘Zoology’, ‘Evolutionary Biology’, ‘Conservation Biology’, ‘Plant Science’, ‘Marine and Freshwater Biology’, ‘Genetics Heredity’ and ‘Environmental Sciences Ecology’, with further curation removing duplicates and unsuitable records.

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(Varshney et al., 2009), among many more. Although nome sequencing in non-model organisms (Ellegren, studies in biological invasions have not broadly em- 2014). braced the possibility of using NGS, the recent increase Comparisons between low-error Sanger sequenced in numbers of ‘invasion genetics’ studies is concurrent genome regions and the equivalent NGS-sequenced with a decrease of NGS cost (see Fig. 1 for details), regions are useful to provide a measure of error rate, in which suggests that NGS will soon become a widely particular in repetitive or duplicated regions of the ge- used technology in the study of invasion genetics. nome (e.g. Alkan et al., 2011). Multiple assembly pro- grams are available, but general consensus on the ‘best’ 3 Sequencing the Genomes and assemblers has yet to be reached. This has sparked Transcriptomes of Non-Model community-guided comparative projects. For example, Organisms GAGE (Genome Assembly Gold-standard Evaluations) (Salzberg et al., 2012), dnGASP (de novo Genome As- 3.1 Genome sequencing and assembly sembly Assessment Project) (CNAG, 2014) and the As- Whole genome studies employing NGS start with the semblathon (Earl et al., 2011; Bradnam et al., 2013) many-fold coverage sequencing of an organism’s DNA, have assessed and compared homology-based and de followed by various steps of assembly (Fig. 2A). The novo assembly programs. Results from these projects quality of a genome assembly is important as it provides have highlighted the value of using a large range of me- a measure of the degree to which the sequence has been trics to fully assess assembly quality (Salzberg et al., correctly assembled and the sequences are reliable. As- 2012; Bradnam et al., 2013). sembly quality can be assessed using different statistics, Some recent applications of whole genome (de novo) which offer a measure of genome completeness and sequencing highlight the exciting research that is now contiguity (Miller et al., 2010; Yandell and Ence, 2012; achievable. For example, the giant panda became the Li et al., 2015). Excellent reviews are available on de first mammal to be sequenced using solely NGS plat- novo genome assembly (Baker, 2012) and use of ge- forms (Li et al., 2010b), and the ambitious Genome 10K

Fig. 2 A typical pipeline for shotgun genome sequencing (A) and RNA-Seq and differential expression analysis (B) The steps of the pathway are shown in red and the processes in blue. The data generated from this can be compared to protein and pathway databas- es in order to gain insight into the roles of expressed genes. Adapted from Oshlack et al. (2010).

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Project aims to sequence 10,000 vertebrate species nome or transcriptome), and mapping program(s) used, (G10KCOS, 2009). Otto et al. (2014) revealed possible is important. For example, mapping reads back to a ge- pathways of adaptation of the malaria parasite in chim- nome will tend to give a lower coverage for loci con- panzees, and Hester et al. (2013) sequenced the genome taining several introns because a greater proportion of of a malaria parasite, finding large genomic regions that reads will span multiple exon-exon boundaries, which were not present in the reference genome and hence not may prevent successful mapping (Oshlack, 2010). This identified using homology-based assembly. Multiple can be alleviated by either utilizing exon junction libra- NGS platforms may be utilized, as in Peng et al. (2014), ries (Marioni et al., 2008; Pickrell et al., 2010), a pro- where data from three different platforms were analyzed gressive mapping strategy (Cloonan et al., 2009; Pick- to assess the genomic basis of weediness in horseweed. rell et al., 2010) or via de novo transcriptome construc- Furthermore, it is possible to simultaneously sequence tion (Oshlack, 2010). genomes of different species, such as the metagenomic The short length of the NGS reads means that these sequencing (i.e. the study of many genomes) of micro- may not map uniquely if they are derived from one of a bial communities (Laserson et al., 2011; Peng et al., pair of highly similar duplicate genes, although to a 2011). certain degree this can be mitigated via using longer 3.2 Transcriptome Sequencing and Assembly NGS reads (De Wit et al., 2012). NGS has the potential The field of transcriptomics is the study and charac- to sequence every expressed gene that is present in a terization of the RNA transcripts expressed by the ge- sample, and as a result it is possible that some contami- nome in a particular tissue at a certain time (Wang et al., nant DNA and RNA, perhaps from parasites or bacteria, 2009). Investigating the entire transcriptome of an or- will be included in the analysis (e.g. Ioannidis et al., ganism can be done via two approaches. Some species 2014). This can be resolved by using DNase treatment have available fully sequenced genomes, and thus tran- prior to library construction (Haas et al., 2013) or simp- scripts can be mapped directly to them. Alternatively, ly by removing any putative contaminant contigs after the genome of a congeneric or sister species can be used. assembly based on sequence homology. Despite these Sequence divergence, however, may confound this ap- caveats, de novo transcriptome assembly has become an proach (Fraser et al., 2011), and thus caution is needed. invaluable tool for the study of non-model organisms. Another approach to study transcriptomes is to se- 3.3 Population genomic analyses quence and assemble the focal species’ transcriptome de Genomes are not homogeneous entities; each region novo (Haas et al., 2013). This has been done for a wide can exhibit different levels of polymorphism and diffe- variety of species in recent years (e.g. Fraser et al., 2011; rentiation, and in some cases this is related to the effects Garg et al., 2011; Zhang et al., 2012; Chapman et al., of natural selection (Nosil et al., 2009). Lewontin and 2013; de Carvalho et al., 2013; Guggisberg et al., 2013; Krakaeur (1973) noted that all loci should be similarly Tang, 2014; Uliano-Silva, 2014). affected by demographic processes, whereas selection There is many software available for de novo tran- only affects a subset of the loci. This characteristic sig- scriptome assembly (Zerbino and Birney, 2008; Simp- nature of selection is generally studied using population son et al., 2009; Grabherr et al., 2011; Luo et al., 2012; genomic approaches. Genotyping many loci throughout Schulz et al., 2012), which utilize different approaches genomes (directly from NGS or based on marker poly- to assemble the data, often with large differences be- morphism; see below) in numerous individuals and/or tween the final assemblies (Zhao et al., 2011). Such com- populations allows the separation of locus-specific ef- parisons between assembly programs, although rarely fects from genome-wide effects (Black et al., 2001). done, can be a useful guide as to which programs per- Sampling numerous loci and testing for outliers identi- form best, however may only be useful on a case-by- fies those that are under selection, with further analyses case basis. carried out to elucidate evolutionary processes (Luikart To compare across individuals, the assembled tran- et al., 2003). Adopting this approach to understand the scriptome, or a previously sequenced genome, is used as loci underlying adaptation and / or invasiveness is par- the reference for mapping back reads obtained from ticularly instructive in invasion biology. multiple individuals. Several programs are available to The method for identifying regions with locus- map RNA reads to a reference (Cloonan et al., 2008; Li specific signatures of selection follows four steps (Lui- et al., 2008; Mortazavi et al., 2008; Langmead et al., kart et al., 2003): (1) sampling a representative number 2009) and consideration of the available reference (ge- of individuals, (2) genotyping a large number of loci,

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(3) conducting statistical tests to identify outlier loci and (GWSS) (Storz, 2005), genome-wide association scans (4) either estimating neutral demographic parameters (GWAS) (Hirschhorn and Daly, 2005) and genetic-envi- (without using outlier loci), and/or focusing on outlier ronment association scans (GEAS) (Thomas, 2010). loci to investigate the selective forces acting upon them. GWSS compare divergence throughout the genome, Loci involved in adaptive population divergence, along identifying markers with substantial divergence from with tightly linked markers, are expected to display sig- the neutral genome average, whereas GWAS link mark- nificantly greater divergence than the genome-wide er genotypes with quantitative trait variation (Brachi et average divergence. Analyzing large numbers of mark- al., 2011). Finally, GEAS incorporate the impact of en- ers (typically single nucleotide polymorphisms, SNPs; vironment on the trait variation (Bierne et al., 2011). Angeloni et al., 2012; Kumar et al., 2012) throughout These genome-wide analyses show great potential in the genome means that some markers will be from re- revealing the evolutionary processes affecting invasive gions of the genome underlying differential adaptation. populations (Puzey and Vallejo-Marin, 2014). By comparing patterns of polymorphism and between- As mentioned above NGS can be used to identify population divergence throughout the genome, one can polymorphic SNP markers and then a genotyping array identify ‘genomic outliers’. Applying NGS to all of the can be used to assay dozens to hundreds of individuals. individuals of interest could be used to simultaneously Such high-density arrays (Ramos et al., 2009; Kumar et identify and score genotypes, or alternatively a few in- al., 2012), for example, Illumina’s Infinium assay, can dividuals could be sequenced and markers identified for accurately genotype any number of SNPs (up to a mil- creating a genotyping assay (see details of several dis- lion in one assay) across many individuals (Adler et al., covery methods in Davey et al., 2011; Chapman, 2015). 2013). The high-throughput nature and density of SNPs A popular method to rapidly genotype dozens to analyzed with these arrays allows processes like quan- hundreds of individuals is to use NGS to sequence just a titative trait locus (QTL) mapping or linkage disequili- portion of the genome. This ‘reduced-representation’ brium studies (Lien et al., 2011) to be performed in a sequencing allows thousands of loci throughout the ge- more extensive and time-efficient manner. nome to be sequenced by first restriction digesting the Considerations do however need to be taken into ac- DNA and then sequencing the ends of the digested count when identifying markers putatively under selec- fragments using GBS or RAD-Seq. Samples are typi- tion. For example, the fidelity of identifying adaptive cally individually tagged and then sequenced en masse loci could be compromised in populations that have (Smith et al., 2010). The opportunity exists however to recently undergone a genetic bottleneck (Poh et al., pool populations of individuals prior to NGS (Pool-Seq; 2014), although this depends on the tests employed Futschik and Schlötterer, 2010), which greatly increases (Narum and Hess, 2011). Furthermore, the obtainment cost-effectiveness when dealing with large numbers of method of SNPs can also cause an ascertainment bias, samples without drastically compromising fidelity. The affecting interpretation of population genomic patterns NGS of pooled DNA affords a reliable method for ge- (Lachance and Tishkoff, 2013). Inferences of neutrality nome-wide analysis of large numbers of samples (Fut- and identification of candidate genes under selection schik and Schlötterer, 2010), and tools are available can only be reliably made when levels of ascertainment specifically for analyzing pooled data (Bansal, 2010; bias are low (Kelley et al., 2006). In addition, the most Kofler et al., 2011). PoPoolation2 (Kofler et al., 2011) is widely-used statistic for identifying markers under se- a statistical approach that demonstrates an 80‒85% SNP lection (Beaumont, 2005), the fixation index FST discovery rate compared to individual sequencing, and a (Wright, 1951), is not an absolute measure of diver- low false discovery rate. However, analytical limitations gence (Jost, 2008), but instead a relative measure of the do exist (see Schlötterer et al., 2014), mainly because a amount of within-species polymorphism (Charlesworth, reduced representation of the genome may lead to data 1998; Cruickshank and Hahn, 2014). This means that biases. For example, RAD-seq can underestimate (Ar- outlier loci based on FST alone are prone to false posi- nold et al., 2013) and overestimate (Cutler and Jensen, tives when intraspecific diversity is low (e.g. in centro- 2010) the genetic diversity of both individual and pool- meric regions where recombination is suppressed; Be- ed samples respectively. gun and Aquadro, 1992; Turner and Hahn, 2010). In a Many methods exist for scrutinizing the effects of recent review, Cruickshank and Hahn (2014) demon- selection on the genome (Angeloni et al., 2012). Three strate that genomic regions of ‘high divergence’ (based popular approaches are genome-wide selection scans on FST) are regions of the genome with low intraspecific

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polymorphism. FST-like statistics are a relative measure de novo (Haas et al., 2013). of differentiation, while an absolute measure of diver- There are currently two mainstream NGS methods gence between populations (e.g. dXY) is independent of that can be used to generate gene expression profiles: levels of diversity within the populations. Therefore, Tag-seq and RNA-Seq. The former is the most eco- Cruickshank and Hahn (2014) suggest that dXY should nomical option, results in the generation of short reads be combined with relative measures to more fully un- and requires mapping tags to a reference genome. As derstand heterogeneous genomic divergence. noted above, shorter reads are more likely to suffer from In addition to methodological limitations, there are problems associated with ambiguous mapping (Ekblom possible pitfalls in the analysis of population genomic and Galindo, 2010; Cullum, 2011). In addition, a refer- data. For instance, the susceptibility to report type I and ence genome or transcriptome is necessary for this pro- II errors can add to the difficulty of interpretation when cedure and hence it cannot be carried out for non-model selection affects a large number of loci, and when many species unless one is willing to invest in this initial cost. evolutionary processes are present (Bierne et al., 2013). The second method, RNA-Seq allows for a more com- Bioinformatically, the high numbers of SNPs can cause prehensive transcriptome analysis, something that was algorithms to grind to a halt under the computational previously untenable due to technological shortcomings burden induced (Lange et al., 2014). Duforet-Frebourg (Gracey, 2007). This method utilizes short reads (up to a et al. (2014) proposed an approach based on Bayesian few hundred bases in length depending on the sequencer factor models that is able to infer population structure used) that are assembled de novo, though may be mapp- (i.e. the input populations are not defined as belonging ed to a reference genome or transcriptome if available to specific groups) and identify outlier loci. However, (Fig. 2B) (see further details in De Wit et al., 2012). The this approach is still susceptible to the large computa- protocol for using transcriptomic data to quantify gene tional burden. Caution must also be observed when in- expression/differential expression is reviewed in Osh- terpreting results of selective regions influenced by the lack et al. (2010) but in brief, the mapped reads are as- environment as GWSS merely identify correlations, and sembled into ‘expression summaries’, which are norma- follow-up work is essential to investigate the interplay lized to account for size differences between libraries present (Coop et al., 2010). from different individuals (Chu et al., 2014), and then 3.4 Transcriptome analyses exposed to statistical testing to quantify the size of any Using transcriptomics, gene expression changes can expression changes. From the aligned sequences, it is be quantified and related to evolutionary and ecological also possible to carry out population genetic analyses, processes (Stapley et al., 2010). Additionally, transcrip- test for selection (Hodgins et al., 2015), or isolate tome data can be used for SNP discovery and tests for SNPs/microsatellite markers for further population ge- selection in the same way that whole genome or reduced netic or genetic mapping studies (Sun et al., 2012). representation sequencing might be (De Wit et al., 2012). There are a large number of options for analyzing Until the advent of NGS, gene expression profiling transcriptomic data obtained via NGS (both homology- was typically carried out using microarrays. One major based and de novo; Anders and Huber, 2010; Hardcastle drawback from this, however, is that it relies on se- and Kelly, 2010; Robinson et al., 2010) and the appro- quence homology among individuals, and significant priate choice depends on the question under investiga- sequence divergence may prevent RNA from the study tion. This can range from the role of alternative splicing species effectively hybridizing with the DNA probes on (Wang et al., 2010), the detection of novel transcripts the array, giving rise to false detection of expression (Trapnell et al., 2010), and estimates of transcript ab- divergence. undance (Li et al., 2010a). RNA-Seq data typically NGS allows the transcriptome sequencing of mul- conforms to a Poisson distribution, however analyses tiple individuals and does not require the construction of based on these will often fail to capture biological va- a microarray, therefore circumventing the issue of se- riability (Langmead et al., 2010; Oshlack, 2010). As a quence divergence causing false positives. Furthermore, result, a variety of methods have been proposed based whilst transcriptomic studies are still relatively expen- on negative binomial distributions (Robinson et al., sive, they are becoming more cost effective as sequenc- 2010), common dispersal models (Robinson and Smyth, ing costs reduce (see Fig. 1). Finally, this technology 2008), and Bayesian techniques (Hardcastle and Kelly, can be applied to the analysis of essentially any species 2010). However, many of these can only be applied to of interest by sequencing and assembling transcriptomes simple experimental set ups, and more complex designs

494 Current Zoology Vol. 61 No. 3 such as time course experiments require data transfor- ability to de novo genotype non-model organisms (Dheilly mation (Oshlack, 2010). et al., 2014), the detection of multi-locus selective The importance of studying gene expression varia- sweeps (Messer and Petrov, 2013) still requires a refer- tion can be best appreciated through the relatively large ence genome for homology-based mapping (Ellegren et effects brought about by expression changes in just one al., 2012; Poelstra et al., 2014). or a few loci or over very short timeframes (Fisher and Quantifying variation in gene expression can illumi- Oleksiak, 2007; Zhang et al., 2012). This highlights nate a variety of ecological and evolutionary phenome- how differential expression analysis could speed up the na (Akashi, 2001; Guggisberg et al., 2013), as well as time it takes to identify the causative genetic changes inform more applied research such as disease control / underlying major evolutionary differences. resistance and progression (Barakat et al., 2009). Com- 4 Next-Generation Sequencing and parative approaches highlighting differences in gene expression between two or more conditions allow re- the Study of Evolutionary and searchers to investigate the role of gene regulation on Ecological Processes immediate acclimation to various environmental condi- tions (Fraser et al., 2011) and the evolution of novel The application of NGS permits comparisons of ge- adaptations or phenotypes (Lockwood et al., 2010; nomes and transcriptomes among individuals, popula- Guggisberg et al., 2013; Wang et al., 2013; Ye et al., tions and meta-populations, allowing a better under- 2014). Gene expression measurements also allow us to standing of underlying ecological and evolutionary pro- investigate differential expression across taxa, or to cesses. For example, analyses of NGS data enable re- identify genes for further investigation. For example, searchers to examine patterns of selection (Angeloni et NGS studies have uncovered genes that encode heat al., 2012), and identify candidate genes underpinning shock proteins that play a role in adaptation to thermal adaptation (Stapley et al., 2010). This is important as stress (Lockwood et al., 2010; Ronges et al., 2012; Chu loci under selection are candidates for variation in fit- et al., 2014). These genes have been some of the first to ness and may be indicative of specific selective forces be identified as being involved in temperature adapta- involved (Nielsen, 2005). NGS approaches allow popu- tion and stress response (Ananthan et al., 1986) and lation demographic and genetic processes to be sepa- NGS has further shown that the role of these genes in rated from adaptive processes (Luikart et al., 2003; Kirk their response to heat is near universal. and Freeland, 2011; Angeloni et al., 2012). For example, Being able to sequence the transcriptome de novo al- Milano et al. (2014) studied evolutionary processes in lows large-scale comparisons of the gene space of non- the European hake, identifying intra-basin divergent model organisms (Romiguier et al., 2014). Whilst popu- outlier loci suggestive of selection, and putatively neu- lation comparisons between introduced and native tral loci that confirmed the genetic differentiation be- ranges have been made in the past using microarrays tween basins. Another demonstration of the usefulness (Guggisberg et al., 2013), NGS provides an opportunity of this approach was published by Evans et al. (2014), for more detailed comparisons across the whole tran- who used a whole-genome selection scan in tandem with scriptome. Finally, NGS allows the investigation of association studies to identify adaptation along a latitu- processes that cannot be studied effectively with pre- dinal gradient in the black cottonwood tree Populus tricho- vious methods. One such important mechanism is alter- carpa. Overall, there is a clear advantage to applying native splicing, as the majority of genes can be spliced these approaches to introduced species in order to assess in multiple ways (Kim et al., 2007). RNA-Seq has al- patterns of origin, selection and the genomics of adaptation. lowed the investigation of alternative splicing in closely Genome-wide coverage using NGS allows the detec- related mice subspecies revealing 6.5% of genes ex- tion of processes affecting more than one locus, like pressed in the testes show alternative splicing (Harr and genomic regions that show evidence of selective sweeps Turner, 2010), which demonstrates a mechanism that (Sabeti et al., 2002; Schlötterer, 2003; Boitard et al., can contribute to phenotypic and transcriptomic diffe- 2012). Statistical methods are available to improve the rentiation between closely related species. This me- detection of selective sweeps, such as the pooled-sam- chanism is unexplored in non-indigenous species, and ple specific method introduced by Boitard et al. (2012), NGS methods may prove to be the best tool for com- which was able to utilize coverage as low as 1X per paring alternative splicing in native and introduced individual for genotyping. Although NGS possesses the populations.

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5 Applications to the Study of indigenous species have done so in a non-invasive con- text, for example by simply developing genomic and Biological Invasions transcriptomic resources that will assist future studies. We reviewed the existing literature and revealed that Genetic studies have historically focused on under- studies using NGS technology to investigate invasive standing the genetic mechanisms that underlie biologi- species started in 2008 and only in the last few years cal invasions, and the ability of invasive species to cope has there been an increase in both the publication rate with rapid environmental changes (Gray, 1986; Good- and the diversity of research approaches used (Table 1). win et al., 1999; Ellstrand and Schierenbeck, 2000; Dlu- The majority of studies that have used NGS on non- gosch and Parker, 2008). Introduced species are often

Table 1 Studies that use next-generation sequencing to study invasive species, including the objective and whether these are undertaken in the context of biological invasions (‘Inv.’) or not (‘Non.’)

Year Objective Context Species Lead Author 2008 eDNA Inv. Rana catesbeiana Ficetola 2008 Marker Development Non. Gasterosteus aculeatus Baird 2010 De novo Transcriptome Assembly; Gene Expression Non. Agrilus planipennis Mittapalli 2010 Marker Development; Gene Expression; Adaptation Genomics/Genetics Non. Aphis glycines Bai 2010 Population Genomics Inv. Gasterosterus aculeatus Hohenlohe 2010 Transcriptomic Resources Database Construction Non. Eucalyptus grandis Mizrachi 2011 cpDNA Sequencing; Method Comparison Inv. Jacobea vulgaris Doorduin 2011 De novo Genome and Transcriptome Assembly Non. Linepithema humile Smith 2011 De novo Transcriptome Assembly; Gene Expression Non. Bactrocera dorsalis Shen 2011 De novo Transcriptome Assembly; Invasion Genetics Inv. Phragmites australis He 2011 Species-Diagnostic Resource Development Inv. Multiple trout Hohenlohe 2012 cpDNA Sequencing Non. Ageratina adenophora Nie 2012 De novo Genome and Transcriptome Assembly; Gene Expression Non. Crassostrea gigas Zhang 2012 De novo Transcriptome Assembly Non. Bemisia tabaci Su 2012 De novo Transcriptome Assembly Non. Pomacea canaliculata Sun 2012 De novo Transcriptome Assembly; Gene Expression Non. Eriocheir sinensis He 2012 De novo Transcriptome Assembly; Gene Expression; Invasion Genetics Inv. Mikania micrantha Huang 2012 Gene Expression Non. Poaceae species Davidson 2012 Population Genomics Non. Drosophila melanogaster Fabian 2012 Population Genomics Non. Helianthus species Renaut 2012 Species-Diagnostic Resource Development Inv. Multiple trout Amish 2012 Species-Diagnostic Resource Development Non. Rodentia sp. Galan 2012 Transcriptomic Resources Database Construction; Method Comparison Inv. Multiple Compositae Lai 2013 cpDNA Sequencing Non. Camellia species Yang 2013 cpDNA Sequencing Non. Catharanthus roseus Ku 2013 De novo Genome Assembly; Population Genomics Non. Sus scrofa Li 2013 De novo Genome Assembly Non. Botryllus schlosseri Voskoboynik 2013 De novo Transcriptome Assembly Non. Corbicula fluminea Chen 2013 De novo Transcriptome Assembly Non. Centaurea solstitialis Dlugosch 2013 De novo Transcriptome Assembly Non. Multiple Compositae Hodgins 2013 De novo Transcriptome Assembly Inv. Brachypodium sylvaticum Fox 2013 De novo Transcriptome Assembly Non. Aedes albopictus Peolchau 2013 De novo Transcriptome Assembly; Marker Development Non. Spartina sp. De Carvalho 2013 eDNA Inv. Multiple species Pochon Continued Table1

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Year Objective Context Species Lead Author

2013 Gene Expression Non. Aedes aegypti Akbari 2013 Gene Expression Inv. Crassostrea gigas Clark 2013 Genome and Transcriptome Assembly; Evolutionary History Inv. Drosophila suzukii Ometto 2013 Invasion Genetics Inv. Bemisia tabaci Wang 2013 Marker Development Non. Frangula alnus De Kort 2013 Marker Development Non. Cichlasoma urophthalmus Harrison 2013 Marker Development Non. Python molurus bivittatus Hunter 2013 Marker Development Non. Anguilla anguilla Pujolar 2013 Marker Development Non. Sisymbrium austriacum chrysanthum Vandepitte 2013 Marker Development Non. Spartina alterniflora Guo 2013 Marker Development Non. Pterois volitans and P. mi l es Schultz 2013 MicroRNA Resource Development Non. Aedes albopictus Gu 2013 mtDNA Sequencing Non. Multiple ascidians Rubinstein 2013 mtDNA Sequencing; mtDNA Gene Expression Inv. Bemisia tabaci Wang 2013 Population Genomics Inv. Myodes glareolus White 2013 Species-Diagnostic Resource Development; Invasion Genetics Inv. Oncorhynchus mykiss and O. clarki lewisi Hohenlohe 2013 Transcriptomic Resources Database Construction Non. Lonicera japonica He 2014 Adaptation Genetics Inv. Bemisia tabaci Ye 2014 Adaptation Genetics Inv. Castanea mollissima Miller 2014 Adaptation Genetics; Genetic Diversity Non. Youngia japonica Peng 2014 Bacterial Diversity Non. Scirtothrips dorsalis Dickey 2014 cpDNA Sequencing Non. Praxelis (Eupatorium catarium Veldkamp) Zhang 2014 cpDNA Sequencing Non. Centaurea diffusa Turner 2014 De novo Genome Assembly Non. Bactrocera tryoni Gilchrist 2014 De novo Genome Assembly Non. Apis mellifera Elsik 2014 De novo Transcriptome Assembly Non. Forficula auricularia Roulin 2014 De novo Transcriptome Assembly Non. Undaria pinnatifida Shan 2014 De novo Transcriptome Assembly Non. Bactrocera dorsalis Wei 2014 De novo Transcriptome Assembly; Gene Expression Non. Perna viridis Leung 2014 De novo Transcriptome Assembly; Gene Expression Inv. Limnoperna fortunei Uliano-Silva 2014 De novo Transcriptome Assembly; Gene Expression Inv. Octodonta nipae Tang 2014 De novo Transcriptome Assembly; Gene Expression Inv. Halyomorpha halys Ioannidis 2014 eDNA Inv. Multiple Actinopterygii Mahon 2014 Gene Expression Non. Soybean mosaic virs on Aphis glycines Cassone 2014 Gene Expression Non. Halyomorpha halys Sparks 2014 Gene Expression Non. Lissorhoptrus oryzophilus Yuan 2014 Gene Expression Non. Bemisia tabaci Xie 2014 Genetic Diversity Non. Multiple non-model species Romiguier 2014 Genetic Variability Inv. Bemisia tabaci Fang 2014 Invasion Genetics Inv. Multiple microbial taxa Manzari Effect of Trioxys pallidus on Chromaphis 2014 Marker Development Non. Anderson juglandicola 2014 Marker Development Non. Callosobruchus chinensis Duan Continued Table1

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Year Objective Context Species Lead Author

2014 Marker Development Non. Watersipora species Mackie 2014 Marker Development Non. Ageratine adenophora Nie 2014 Marker Development Non. Heracleum persicum Rijal 2014 Marker Development; Invasion Genetics Inv. Pueraria montana var. lobata Hoffberg 2014 mtDNA Sequencing Non. Pterois volitans Del Río-Portilla 2014 mtDNA Sequencing Non. Ponticola kessleri Kalchhauser 2014 mtDNA Sequencing Non. Apis mellifera scutellata Gibson 2014 mtDNA Sequencing Non. Corvus splendens Krzeminska 2014 mtDNA Sequencing Non. Zootermopsis nevadensis Qian 2014 Phylogeographic Construction Inv. Astyanax mexicanus Coghill 2014 Population Genomics Inv. Candidatus Ishikawaella capsulata Brown 2014 Population Genomics Non. Alnus glutinosa De Kort 2014 Population Genomics Inv. Dendroctonus ponderosae Janes 2014 Population Genomics Inv. Mimulus guttatus Puzey 2014 Population Genomics Inv. Cardiocondyla obscurior Shrader 2014 Population Genomics Non. Gasterosteus aculeatus Terekhanova 2014 Population Genomics Inv. Sisymbrium austriacum chrysanthum Vandepitte 2014 Sequencing of Gut Contents Non. Multiple microbial taxa Do 2014 Sequencing of Gut Contents Non. Anolis sagrei Kartzinel 2014 Sequencing of Gut Contents Inv. Hypopthalmicthys molitrix Ye 2014 Species-Diagnostic Resource Development Inv. Hypophthalmichthys nobilis and H. molitrix Lamer 2014 Species-Diagnostic Resource Development Inv. Hypophthalmichthys nobilis and H. molitrix Lance 2015 Adaptation genetics Non. Multiple Helianthus species Baute 2015 De novo Genome Assembly Non. Apis cerana Park 2015 De novo Transcriptome Assembly Non. Procambarus clarkii Manfrin 2015 De novo Transcriptome Assembly Non. Caulerpa taxifolia Ranjan 2015 De novo Transcriptome Assembly Non. Arundo donax Barrero 2015 Gene Expression Non. Eriocheir sinensis Li 2015 Gene Expression Non. Apis mellifera Jasper 2015 Gene Expression Non. Chrysomya megacephala Wang 2015 Marker Development Non. Perccottus glenii King 2015 Marker Development Non. Achatina (=Lissachatina) fulica Morrison 2015 Marker Development; Invasion Genetics Inv. Phytolacca americana Bentley 2015 Marker Development; Invasion Genetics Inv. Hypophthalmichthys nobilis and H. molitrix Miller 2015 MicroRNA Resource Development Non. Bursaphelenchus xylophilus Ding 2015 mtDNA Sequencing; Invasion Genetics Inv. Didemnum vexillum Smith 2015 Population Genomics Non. Poecilia reticulata Fraser 2015 Population Genomics Non. Oncorhynchus nerka Lemay 2015 Sequencing of Gut Contents Non. Harmonia axyridis preying upon Acyrthosiphon pisum Paula 2015 Species-Diagnostic Resource Development Inv. Oncorhynchus mykiss and O. clarki lewisi Hand 2015 Species-Diagnostic Resource Development; Inv. Triturus cristatus and T. carnifex Meilink Invasion Genetics More details of the studied phylum, ecosystem type and full reference details are available in Appendix 1 (Online material).

498 Current Zoology Vol. 61 No. 3 forced to adapt to environments to which they have not (e.g. plant toxins) in other invasive species (Mittapalli et previously been exposed (Blackburn et al., 2011). Con- al., 2010; Ye et al., 2014). Although these studies have sequently, the study of gene regulation of non-indi- generated useful data, they are mostly descriptive (e.g. genous species and how this facilitates species adapta- the construction of a reference transcriptome for future tion to stressful and changing environments is extreme- studies; Mizrachi et al., 2010) and have yet to be ap- ly relevant (Lockwood et al., 2010; Ronges et al., 2012; plied to fully comprehend biological invasions. Zhang et al., 2012). Multiple studies that identify com- Comparative transcriptomics offers one method by mon gene expression changes may shed light on how which to explore the molecular mechanisms behind introduced species successfully adapt to novel environ- biological invasions and has been previously studied to ments and overcome ecological challenges. For exam- some extent using microarrays (Guggisberg et al., 2013; ple, several studies of invasive species have identified a Hodgins et al., 2013). A comparative approach has also consistent up-regulation of cytochrome P450, a gene been adopted in NGS studies of native species. An exa- involved in detoxification, relative to non-invasive popu- mple of this can be seen in recent work undertaken by lations (Mittapalli et al., 2010; Guggisberg et al., 2013; Gleason and Burton (2015) investigating the transcrip- Ioannidis et al., 2014; Ye et al., 2014). In a study on the tomic response to heat in two differentially adapted Canadian thistle Cirsium arvense, many of the loci dif- populations of the marine snail Chlorostoma funebralis. ferentially expressed between native and introduced They uncovered differential expression patterns in a populations were putatively involved in response to number of genes, but most notably they found a signifi- stress, such as genes that oversee transcription and RNA cant up-regulation of Heat Shock Protein 70 in the high- stability (Guggisberg et al., 2013). Interestingly, genes er temperature-adapted population. Indeed, thermal stress encoding for cytochrome P450 were also overexpressed, is a major challenge faced by non-indigenous species suggesting a key role for them as contributors to the and can cause large-scale macromolecular damage, sti- success of introduced species. mulating a molecular repair response (Mariman, 2009). NGS has significantly increased the accessibility of Using microarrays, Lockwood et al. (2010) were able to sequencing and now genomics can be effectively com- demonstrate key expression differences between a na- bined with field experiments (Savolainen et al., 2013) tive and invasive mussel species (Mytilus trossulus and and QTL mapping (Stinchcombe and Hoekstra, 2008; Mytilus galloprovincialis, respectively) at different tem- Lee et al., 2014). SNP analyses have demonstrated how peratures. Whilst many genes exhibited differential ex- introduced species adapt to new environments (Zayed pression, the gene encoding Heat Shock Protein 70 was and Whitfield, 2008; López Herráez et al., 2009; Smith significantly up-regulated in M. galloprovincialis at et al., 2011) and could be used in the future to test cor- high temperature. In contrast, the response of M. tros- relations between SNPs and climate, as in Yoder et al. sulus was to up-regulate genes associated with proteo- (2014). lysis, suggesting that proteins had become damaged Recent examples of the broad adoption of transcrip- beyond repair. These studies give a glimpse at what tomics include the transcriptome sequencing and as- could be achieved if NGS was fully adopted in invasive sembly of the invasive snail Pomacea canaliculata (Sun species. There is a particular need for this to be ad- et al., 2012), stink bug Halyomorpha halys (Ioannidis et dressed, as climate change will significantly affect the al., 2014) and tree Eucalyptus grandis (Mizrachi et al., likelihood and pathways of biological invasions (Hell- 2010). These studies provided novel tools to understand mann et al., 2008; Rahel and Olden, 2008; Early and mechanisms underlying the invasion process and hig- Sax, 2014; Rius et al., 2014), and understanding how hlighted the limited application of NGS in invasion bi- range-shifting species respond to changing temperature ology. Ioannidis et al. (2014) created a de novo tran- regimes is of vital importance for future predictions of scriptome to provide a resource for future work, but in range shifts and invasion risk. In addition, common doing so identified an interesting pattern of differential garden experiments can be used to confirm whether expression between adult and juvenile individuals. gene regulation changes are governed by rapid genetic Twenty-five genes were up-regulated in adults compared adaptation or phenotypic plasticity (Guggisberg et al., to juveniles, and encoded proteins with homology to 2013), which provides insight into the relative impor- cytochrome P450 and glutathione S-transferase, which tance of these two mechanisms (Davidson et al., 2011; have been shown to play a role in detoxification of a Fierst, 2011). wide range of chemical compounds such as xenobiotics The presence of non-indigenous species can also be

RIUS M et al.: Next-generation sequencing and biological invasions 499 monitored through the investigation of environmental changes that take place during single and multiple inva- DNA (eDNA), which are fragments of DNA left in the sion events. environment by local species (Ficetola et al., 2008; 6 Concluding Remarks Piaggio et al., 2014) that can be used to identify the species present without observing the organism (Hebert Our synopsis shows that during a period of constant- et al., 2003). An eDNA barcoding approach, which ly decreasing cost of NGS, studies in invasion genetics commonly targets the mtDNA cytochrome c oxidase have continually increased. Despite this, NGS is still subunit I gene for animals (Marescaux and Van Doninck, underutilized on a large scale in invasion biology, there 2013; Mahon et al., 2014), allows the detection of in- are only a few comparisons between introduced and conspicuous, rare, or cryptic introduced species (Boh- native populations, and even fewer investigations into mann et al., 2014). This is particularly important when the genetic basis of adaptation in non-indigenous spe- phenotypic plasticity renders morphological assessment cies. With a comprehensive implementation of genomic difficult (Marescaux and Van Doninck, 2013). NGS has and transcriptomic approaches, it is expected that more revolutionized DNA barcoding (Shokralla et al., 2012; in-depth analyses of mechanisms involved in the inva- Shokralla et al., 2014) by decreasing the cost of se- sion process will be inferred across taxa / ecosystems. quencing, but more importantly has removed the expen- For example, it is known that hybridization may have sive and time-consuming need to clone amplified eDNA important effects on the fitness of some species (see into bacteria before sequencing hundreds of thousands Rius and Darling, 2014), but more research is needed to of clones (Valentini et al., 2009). Applied to eDNA, understand why and how hybridization might have a NGS has detected the presence of invasive species in positive or negative influence on colonization success. the bait trade (Mahon et al., 2014) and American bull- NGS will allow the comparison of the genomic archi- frogs in French ponds (Ficetola et al., 2008). DNA bar- tecture of introduced and native ranges and reveal coding is now able to detect and quantify species com- possible links between phenotypic plasticity and rapid position and biodiversity (Thomsen et al., 2012; Kelly evolutionary change. Furthermore, revealing the ge- et al., 2014), creating the potential for eDNA samples to nomic architecture of divergence between native and be used to assess the ecological consequences of the non-indigenous species will allow a better understand- presence of non-indigenous species on a much quicker ing of the link between genotypes and phenotypes, with timeframe than conventional surveying. Monitoring a particular focus on the association between fitness and eDNA can be essential for the management of invasive adaptation. We expect that manipulative experiments species, as the earlier the invasion of a species is de- and studies of closely related species will proliferate in tected, the cheaper and quicker it is to mitigate (Hulme, the future, as NGS is employed in a range of evolutio- 2006). nary and ecological questions concerning, hybridization Finally, genetic data allows the inference of the most and invasiveness. Future work should not only advance likely origins and colonization histories of introduced fundamental knowledge, but also enhance our predicta- species (Dlugosch and Parker, 2008; Estoup and Guil- bility of biological invasions. lemaud, 2010), which is vital for understanding the in- vasion process and predicting future invasions (Hulme Acknowledgements We are grateful to Dr. Lee Ann Rollins et al., 2008). In addition, revealing invasion routes en- (Guest Editor) for the invitation to write this review. We thank hances our understanding of how species ranges shift as three anonymous reviewers for insightful comments that con- a result of climate change (Buckley, 2008), elucidating siderably improved the paper. whether or not a species is truly invasive (i.e., human References assisted transport outside the native range, naturaliza- tion and natural spread). The further-developing field of 1000 Genomes Project Consortium, 2012. An integrated map of genomics will provide a more studious view of invasion genetic variation from 1,092 human genomes. Nature 491: 56– routes, allowing more precise inferences to be calcu- 65. Adler AJ, Wiley GB, Gaffney PM, 2013. Infinium assay for large- lated from genomic data and propelling the field beyond scale SNP genotyping applications. J. Vis. Exp. 81: e50683. the limits of genetic marker methods (Kirk et al., 2013). Akashi H, 2001. Gene expression and molecular evolution. Curr. Furthermore, employing outlier (sequence or expres- Opin. Genet. Develop. 11: 660–666. sion-based) detection methods to these investigations Alkan C, Sajjadian S, Eichler EE, 2011. Limitations of next-gene- could help elucidating the genomic and transcriptomic ration genome sequence assembly. Nat. Methods 8: 61–65.

500 Current Zoology Vol. 61 No. 3

Ananthan J, Goldberg AL, Voellmy R, 1986. Abnormal proteins ment of invasive species, invaded landscapes and communities. serve as eukaryotic stress signals and trigger the activation of J. Appl. Ecol. 45: 397–402. heat shock genes. Science 232: 522–524. Chapman MA, 2015. Transcriptome sequencing and marker deve- Anders S, Huber W, 2010. Differential expression analysis for lopment for four underutilized legumes. Appl. Plant Sci. 3: sequence count data. Genome Biol. 11: R106. 1400111. Angeloni F, Wagemaker N, Vergeer P, Ouborg J, 2012. Genomic Chapman MA, Hiscock SJ, Filatov DA, 2013. Genomic diver- toolboxes for conservation biologists. Evol. Appl. 5: 130–143. gence during speciation driven by adaptation to altitude. Mol. Arnold B, Corbett-Detig RB, Hartl D, Bomblies K, 2013. Biol. Evol. 30: 2553–2567. RADseq underestimates diversity and introduces genealogical Charlesworth B, 1998. Measures of divergence between popu- biases due to nonrandom haplotype sampling. Mol. Ecol. 22: lations and the effect of forces that reduce variability. Mol. 3179–3190. Biol. Evol. 15: 538–543. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL et al., Chu ND, Miller LP, Kaluziak ST, Trussell GC, Vollmer SV, 2014. 2008. Rapid SNP discovery and genetic mapping using sequ- Thermal stress and predation risk trigger distinct transcri- enced RAD markers. PLoS ONE 3: e3376. ptomic responses in the intertidal snail Nucella lapillus. Mol. Baker HG, Stebbins GL, 1965. Genetics of Colonizing Species. Ecol. 23: 6104–6113. New York: Academic Press. Cloonan N, Forrest ARR, Kolle G, Gardiner BBA, Faulkner GJ et Baker M, 2012. De novo genome assembly: What every biologist al., 2008. Stem cell transcriptome profiling via massive-scale should know. Nat. Methods 9: 333–337. mRNA sequencing. Nat. Meth. 5: 613–619. Bansal V, 2010. A statistical method for the detection of variants Cloonan N, Xu Q, Faulkner GJ, Taylor DF, Tang DT et al., 2009. from next-generation resequencing of DNA pools. Bioinfor- RNA-MATE: A recursive mapping strategy for high-throu- matics 26: i318–324. ghput RNA-sequencing data. Bioinformatics 25: 2615–2616. Barakat A, DiLoreto DS, Zhang Y, Smith C, Baier K et al., 2009. CNAG, 2014. http://cnag.bsc.es/. Comparison of the transcriptomes of American chestnut Cas- Coop G, Witonsky D, Di Rienzo A, Pritchard JK, 2010. Using tanea dentata and Chinese chestnut Castanea mollissima in environmental correlations to identify loci underlying local response to the chestnut blight infection. BMC Plant Biol. 9: adaptation. Genetics 185: 1411–1423. 51. Cruickshank TE, Hahn MW, 2014. Reanalysis suggests that geno-

Beaumont MA, 2005. Adaptation and speciation: What can FST mic islands of speciation are due to reduced diversity, not tell us? Trends Ecol. Evol. 20: 435–440. reduced gene flow. Mol. Ecol. 23: 3133–3157. Begun DJ, Aquadro CF, 1992. Levels of naturally occurring DNA Cullum R, Alder O, Hoodless PA, 2011. The next generation: polymorphism correlate with recombination rates in D. mela- Using new sequencing technologies to analyse gene regulation. nogaster. Nature 356: 519–520. Respirology 16: 210–222. Bierne N, Roze D, Welch JJ, 2013. Pervasive selection or is it…? Cutler DJ, Jensen JD, 2010. To pool, or not to pool? Genetics 186:

Why are FST outliers sometimes so frequent? Mol. Ecol. 22: 41–43. 2061–2064. Darling JA, Blum MJ, 2007. DNA-based methods for monitoring Bierne N, Welch J, Loire E, Bonhomme F, David P, 2011. The invasive species: A review and prospectus. Biol. Invasions 9: coupling hypothesis: Why genome scans may fail to map local 751–765. adaptation genes. Mol. Ecol. 20: 2044–2072. Davey JW, Hohenlohe PA, Etter PD, Boone JQ, Catchen JM et al., Black WC, Baer CF, Antolin MF, DuTeau NM, 2001. Population 2011. Genome-wide genetic marker discovery and genotyping genomics: Genome-wide sampling of insect populations. Annu. using next-generation sequencing. Nat. Rev. Genet. 12: 499– Rev. Entom. 46: 441–469. 510. Blackburn TM, Pysek P, Bacher S, Carlton JT, Duncan RP et al., Davidson AM, Jennions M, Nicotra AB, 2011. Do invasive 2011. A proposed unified framework for biological invasions. species show higher phenotypic plasticity than native species Trends Ecol. Evol. 26: 333–339. and, if so, is it adaptive? A meta-analysis. Ecol. Lett. 14: 419– Bock DG, Caseys C, Cousens RD, Hahn MA, Heredia SM et al., 431. 2015. What we still don’t know about invasion genetics. Mol. de Carvalho JF, Poulain J, Da Silva C, Wincker P, Michon- Ecol. 24: 2277–2297. Coudouel S et al., 2013. Transcriptome de novo assembly from Bohmann K, Evans A, Gilbert MT, Carvalho GR, Creer S et al., next-generation sequencing and comparative analyses in the 2014. Environmental DNA for wildlife biology and biodi- hexaploid salt marsh species Spartina maritima and Spartina versity monitoring. Trends Ecol. Evol. 29: 358–367. alterniflora (Poaceae). Heredity 110: 181–193. Boitard S, Schlötterer C, Nolte V, Pandey RV, Futschik A, 2012. De Wit P, Pespeni MH, Ladner JT, Barshis DJ, Seneca F et al., Detecting selective sweeps from pooled next-generation se- 2012. The simple fool's guide to population genomics via quencing samples. Mol. Biol. Evol. 29: 2177–2186. RNA-Seq: An introduction to high-throughput sequencing data Brachi B, Morris GP, Borevitz JO, 2011. Genome-wide associa- analysis. Mol. Ecol. Res. 12: 1058–1067. tion studies in plants: The missing heritability is in the field. Dheilly NM, Adema C, Raftos DA, Gourbal B, Grunau C et al., Genome Biol. 12: 232– 239. 2014. No more non-model species: The promise of next gen- Bradnam KR, Fass JN, Alexandrov A, Baranay P, Bechner M et eration sequencing for comparative immunology. Developm. al., 2013. Assemblathon 2: Evaluating de novo methods of geno- Comp. Immun. 45: 56–66. me assembly in three vertebrate species. GigaScience 2: 10. Dlugosch KM, Parker IM, 2008. Founding events in species Buckley YM, 2008. The role of research for integrated manage- invasions: Genetic variation, adaptive evolution, and the role

RIUS M et al.: Next-generation sequencing and biological invasions 501

of multiple introductions. Mol. Ecol. 17: 431–449. snail Chlorostoma funebralis. Mol. Ecol. 24: 610–627. Duforet-Frebourg N, Bazin E, Blum MG, 2014. Genome scans for Goodwin BJ, McAllister AJ, Fahrig L, 1999. Predicting in- detecting footprints of local adaptation using a Bayesian factor vasiveness of plant species based on biological information. model. Mol. Biol. Evol. 31: 2483–2495. Conserv. Biol. 13: 422–426. Earl D, Bradnam K, St John J, Darling A, Lin D et al., 2011. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA et al., Assemblathon 1: A competitive assessment of de novo short 2011. Full-length transcriptome assembly from RNA-Seq data read assembly methods. Genome Res. 21: 2224–2241. without a reference genome. Nat. Biotechnol. 29: 644–652. Early R, Sax DF, 2014. Climatic niche shifts between species' Gracey AY, 2007. Interpreting physiological responses to environ- native and naturalized ranges raise concern for ecological fore- mental change through gene expression profiling. J. Exp. Biol. casts during invasions and climate change. Global Ecol. Bio- 210: 1584–1592. geogr. 23: 1356–1365. Gray AJ, 1986. Do invading species have definable genetic Ekblom R, Galindo J, 2011. Applications of next generation se- characteristics? Phil. Trans. R. Soc. Lond. 314: 655–674. quencing in molecular ecology of non-model organisms. Here- Guggisberg A, Lai Z, Huang J, Rieseberg LH, 2013. Transcrip- dity 107: 1–15. tome divergence between introduced and native populations of Ellegren H, 2014. Genome sequencing and population genomics Canada thistle Cirsium arvense. New Phyt. 199: 595–608. in non-model organisms. Trends Ecol. Evol. 29: 51–63. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD et Ellegren H, Smeds L, Burri R, Olason PI, Backstrom N et al., al., 2013. De novo transcript sequence reconstruction from 2012. The genomic landscape of species divergence in Fice- RNA-seq using the Trinity platform for reference generation dula flycatchers. Nature 491: 756–760. and analysis. Nat. Protoc. 8: 1494–1512. Ellstrand NC, Schierenbeck KA, 2000. Hybridization as a stimu- Hardcastle TJ, Kelly KA, 2010. baySeq: Empirical Bayesian lus for the evolution of invasiveness in plants? Proc. Nat. Acad. methods for identifying differential expression in sequence Sci. USA 97: 7043–7050. count data. BMC Bioinformatics. 11: 422. Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K et al., Harr B, Turner LM, 2010. Genome-wide analysis of alternative 2011. A robust, simple genotyping-by-sequencing (GBS) app- splicing evolution among Mus subspecies. Mol. Ecol. 19: 228– roach for high diversity species. PLoS ONE 6: e19379. 239. Estoup A, Guillemaud T, 2010. Reconstructing routes of invasion Hebert PD, Cywinska A, Ball SL, deWaard JR, 2003. Biological using genetic data: Why, how and so what? Mol. Ecol. 19: identifications through DNA barcodes. Proc. Roy. Soc. 270: 4113–4130. 313–321. Evans LM, Slavov GT, Rodgers-Melnick E, Martin J, Ranjan P et Hellmann JJ, Byers JE, Bierwagen BG, Dukes JS, 2008. Five al., 2014. Population genomics of Populus trichocarpa identi- potential consequences of climate change for invasive species. fies signatures of selection and adaptive trait associations. Nat. Conserv. Biol. 22: 534–543. Genet. 46: 1089–1096. Hester J, Chan ER, Menard D, Mercereau-Puijalon O, Barnwell J Ficetola GF, Miaud C, Pompanon F, Taberlet P, 2008. Species et al., 2013. De novo assembly of a field isolate genome detection using environmental DNA from water samples. Biol. reveals novel Plasmodium vivax erythrocyte invasion genes. Lett. 4: 423–425. PLoS Negl. Trop. Dis. 7: e2569. Fierst JL, 2011. A history of phenotypic plasticity accelerates ada- Hirschhorn JN, Daly MJ, 2005. Genome-wide association studies ptation to a new environment. J. Evol. Biol. 24: 1992–2001. for common diseases and complex traits. Nat. Rev. Genet. 6: Fisher MA, Oleksiak MF, 2007. Convergence and divergence in 95–108. gene expression among natural populations exposed to pollu- Hodgins KA, Bock DG, Hahn MA, Heredia SM, Turner KG et al., tion. BMC Genom. 8: 108. 2015. Comparative genomics in the Asteraceae reveals little Fitzpatrick BM, Fordyce JA, Niemiller ML, Reynolds RG, 2012. evidence for parallel evolutionary change in invasive taxa. What can DNA tell us about biological invasions? Biol. Inva- Mol. Ecol.: DOI: 10.1111/mec.13026. sions 14: 245–253. Hodgins KA, Lai Z, Nurkowski K, Huang J, Rieseberg LH, 2013. Fraser BA, Weadick CJ, Janowitz L, Rodd FH, Hughes KA, 2011. The molecular basis of invasiveness: Differences in gene ex- Sequencing and characterisation of the guppy Poecilia reticu- pression of native and introduced common ragweed Ambrosia lata transcriptome. BMC Genom. 12: 202–215. artemisiifolia in stressful and benign environments. Mol. Ecol. Futschik A, Schlötterer C, 2010. The next generation of molecular 22: 2496–2510. markers from massively parallel sequencing of pooled DNA Holland BS, 2000. Genetics of marine bioinvasions. Hydrobio- samples. Genetics 186: 207–218. logia 420: 63–71. G10KCOS, 2009. Genome 10K: A proposal to obtain whole- Huang X, Kurata N, Wei X, Wang ZX, Wang A et al., 2012. A genome sequence for 10,000 vertebrate species. J. Heredity map of rice genome variation reveals the origin of cultivated 100: 659–674. rice. Nature 490: 497–501. Garg R, Patel RK, Tyagi AK, Jain M, 2011. De novo assembly of Huey RB, Gilchrist GW, Hendry AP, 2005. Using invasive species chickpea transcriptome using short reads for gene discovery to study evolution: Case studies with Drosophila and salmon. and marker identification. DNA Res. 18: 53–63. In: Sax DF, Stachowicz JJ, Gaines SD eds. Species Invasions: Geller JB, Darling JA, Carlton JT, 2010. Genetic perspectives on Insights into Ecology, Evolution, and Biogeography. Sunder- marine biological invasions. Annu. Rev. Mar. Sci. 2: 367–393. land, Massachusetts: Sinauer Associates Inc., 139–164. Gleason LU, Burton RS, 2015. RNA-seq reveals regional dif- Hulme PE, 2006. Beyond control: Wider implications for the ferences in transcriptome response to heat stress in the marine management of biological invasions. J. Appl. Ecol. 43: 835–

502 Current Zoology Vol. 61 No. 3

847. in systematics and phylogenetics. Annu. Rev. Ecol. Evol. Syst. Hulme PE, Bacher S, Kenis M, Klotz S, Kühn I et al., 2008. 44: 99–121. Grasping at the routes of biological invasions: A framework Lewontin RC, Krakaeur J, 1973. Distribution of gene frequency for integrating pathways into policy. J. Appl. Ecol. 45: 403– as a test of the theory of the selective neutrality of polymor- 414. phisms. Genetics 74: 175–195. Ioannidis P, Lu Y, Kumar N, Creasy T, Daugherty S et al., 2014. Li B, Fillmore N, Bai Y, Collins M, Thomson JA et al., 2014. Rapid transcriptome sequencing of an invasive pest, the brown Evaluation of de novo transcriptome assemblies from RNA- marmorated stink bug Halyomorpha halys. BMC Genom. 15: Seq data. Genome Biol. 15: 553. 738. Li B, Ruotti V, Stewart RM, Thomson JA, Dewey CN, 2010a.

Jost L, 2008. GST and its relatives do not measure differentiation. RNA-Seq gene expression estimation with read mapping Mol. Ecol. 17: 4015–4026. uncertainty. Bioinformatics 26: 493–500. Kelley JL, Madeoy J, Calhoun JC, Swanson W, Akey JM, 2006. Li H, Ruan J, Durbin R, 2008. Mapping short DNA sequencing Genomic signatures of positive selection in humans and the reads and calling variants using mapping quality scores. Geno- limits of outlier approaches. Genome Res. 16: 980–989. me Res. 18: 1851–1858. Kelly RP, Port JA, Yamahara KM, Crowder LB, 2014. Using Li R, Fan W, Tian G, Zhu H, He L et al., 2010b. The sequence and environmental DNA to census marine fishes in a large meso- de novo assembly of the giant panda genome. Nature 463: cosm. PLoS ONE 9: e86175. 311–317. Kim E, Magen A, Ast G, 2007. Different levels of alternative Lien S, Gidskehaug L, Moen T, Hayes BJ, Berg PR et al., 2011. A splicing among eukaryotes. Nucl. Acid. Res. 35: 125–131. dense SNP-based linkage map for Atlantic salmon Salmo salar Kircher M, Kelso J, 2010. High-throughput DNA sequencing: reveals extended chromosome homeologies and striking diffe- Concepts and limitations. Bioessays 32: 524–536. rences in sex-specific recombination patterns. BMC Genom. Kirk H, Dorn S, Mazzi D, 2013. Molecular genetics and genomics 12: 615. generate new insights into invertebrate pest invasions. Evol. Lockwood BL, Sanders JG, Somero GN, 2010. Transcriptomic Appl. 6: 842–856. responses to heat stress in invasive and native blue mussels Kirk H, Freeland JR, 2011. Applications and implications of (genus Mytilus): Molecular correlates of invasive success. J. neutral versus non-neutral markers in molecular ecology. Int. J. Exp. Biol. 213: 3548–3558. Mol. Sci. 12: 3966–3988. López Herráez D, Bauchet M, Tang K, Theunert C, Pugach I et al., Kofler R, Pandey RV, Schlötterer C, 2011. PoPoolation2: ide- 2009. Genetic variation and recent positive selection in world- ntifying differentiation between populations using sequencing wide human populations: Evidence from nearly 1 million of pooled DNA samples (Pool-Seq). Bioinformatics 27: 3435– SNPs. PLoS ONE 4: e7888. 3436. Luikart G, England PR, Tallmon D, Jordan S, Taberlet P, 2003. Kumar S, Banks TW, Cloutier S, 2012. SNP Discovery through The power and promise of population genomics: From geno- next-generation sequencing and its applications. Int. J. Plant typing to genome typing. Nat. Rev. Genet. 4: 981–994. Genomics 2012: 831460. Luo R, Liu B, Xie Y, Li Z, Huang W et al., 2012. SOAPdenovo2: Lachance J, Tishkoff SA, 2013. SNP ascertainment bias in An empirically improved memory-efficient short-read de novo population genetic analyses: Why it is important, and how to assembler. GigaScience 1: 18. correct it. Bioessays 35: 780–786. Mahon AR, Nathan LR, Jerde CL, 2014. Meta-genomic survei- Lange K, Papp JC, Sinsheimer JS, Sobel EM, 2014. Next gene- llance of invasive species in the bait trade. Conserv. Genet. ration statistical genetics: Modeling, penalization, and optimi- Res. 6: 563–567. zation in high-dimensional data. Annu. Rev. Stat. Appl. 1: 279– Mardis ER, 2011. A decade's perspective on DNA sequencing 300. technology. Nature 470: 198–203. Langmead B, Hansen KD, Leek JT, 2010. Cloud-scale RNA- Marescaux J, Van Doninck K, 2013. Using DNA barcoding to sequencing differential expression analysis with Myrna. Geno- differentiate invasive Dreissena species (Mollusca, Bivalvia). me Biol. 11: R83. ZooKeys 365: 235–244. Langmead B, Trapnell C, Pop M, Salzberg SL, 2009. Ultrafast Mariman EC, 2009. 2DE-proteomics meta-data indicate the and memory-efficient alignment of short DNA sequences to existence of distinct cellular stress-responsive mechanisms. the human genome. Genome Biol. 10: R25. Exp. Rev. Proteom. 6: 337–339. Laserson J, Jojic V, Koller D, 2011. Genovo: De Novo Assembly Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y, 2008. for Metagenomes. J. Comput. Biol. 18: 429–443. RNA-seq: An assessment of technical reproducibility and Le Roux J, Wieczorek AM, 2009. Molecular systematics and comparison with gene expression arrays. Genome Res. 18: population genetics of biological invasions: Towards a better 1509–1517. understanding of invasive species management. Ann. Appl. McElroy K, Thomas T, Luciani F, 2014. Deep sequencing of Biol. 154: 1–17. evolving pathogen populations: Applications, errors, and bio- Lee CE, 2002. Evolutionary genetics of invasive species. Trends informatic solutions. Microb. Inform. Exp. 4: 1. Ecol. Evol. 17: 386–391. Messer PW, Petrov DA, 2013. Population genomics of rapid Lee YW, Gould BA, Stinchcombe JR, 2014. Identifying the genes adaptation by soft selective sweeps. Trends Ecol. Evol. 28: underlying quantitative traits: A rationale for the QTN pro- 659–669. gramme. AoB Plants 6: plu004. Metzker ML, 2010. Sequencing technologies: The next generation. Lemmon EM, Lemmon AR, 2013. High-throughput genomic data Nat. Rev. Genet. 11: 31–46.

RIUS M et al.: Next-generation sequencing and biological invasions 503

Milano I, Babbucci M, Cariani A, Atanassova M, Bekkevold D et on aquatic invasive species. Conserv. Biol. 22: 521–533. al., 2014. Outlier SNP markers reveal fine-scale genetic struc- Ramos AM, Crooijmans RP, Affara NA, Amaral AJ, Archibald AL turing across European hake populations Merluccius merlu- et al., 2009. Design of a high density SNP genotyping assay in ccius. Mol. Ecol. 23: 118–135. the pig using SNPs identified and characterized by next Miller JR, Koren S, Sutton G, 2010. Assembly algorithms for generation sequencing technology. PLoS ONE 4: e6524. next-generation sequencing data. Genomics 95: 315–327. Rius M, Clusella-Trullas S, McQuaid CD, Navarro RA, Griffiths Mittapalli O, Bai X, Mamidala P, Rajarapu SP, Bonello P et al., CL et al., 2014. Range expansions across ecoregions: Interac- 2010. Tissue-specific transcriptomics of the exotic invasive tions of climate change, physiology and genetic diversity. insect pest emerald ash borer Agrilus planipennis. PLoS ONE Global Ecol. Biogeogr. 23: 76–88. 5: e13708. Rius M, Darling JA, 2014. How important is intraspecific genetic Mizrachi E, Hefer CA, Ranik M, Joubert F, Myburg AA, 2010. De admixture to the success of colonising populations? Trends novo assembled expressed gene catalog of a fast-growing Ecol. Evol. 29: 233–242. Eucalyptus tree produced by Illumina mRNA-Seq. BMC Rius M, Turon X, Bernardi G, Volckaert FAM, Viard F, 2015. Genom. 11: 681–692. Marine invasion genetics: From spatio-temporal patterns to Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B, 2008. evolutionary outcomes. Biol. Invasions 17: 869–885. Mapping and quantifying mammalian transcriptomes by Robinson MD, McCarthy DJ, Smyth GK, 2010. edgeR: A Bio- RNA-Seq. Nat. Methods 5: 621–628. conductor package for differential expression analysis of digi-

Narum SR, Hess JE, 2011. Comparison of FST outlier tests for tal gene expression data. Bioinformatics 26: 139–140. SNP loci under selection. Mol. Ecol. Res. 11: 184–194. Robinson MD, Smyth GK, 2008. Small-sample estimation of Nielsen R, 2005. Molecular signatures of natural selection. Annu. negative binomial dispersion, with applications to SAGE data. Rev. Genet. 39: 197–218. Biostatistics 9: 321–332. Nosil P, Funk DJ, Ortiz-Barrientos D, 2009. Divergent selection Romiguier J, Gayral P, Ballenghien M, Bernard A, Cahais V et al., and heterogeneous genomic divergence. Mol. Ecol. 18: 375– 2014. Comparative population genomics in animals uncovers 402. the determinants of genetic diversity. Nature 515: 261–263. Oshlack A, Robinson MD, Young MD, 2010. From RNA-seq Ronges D, Walsh JP, Sinclair BJ, Stillman JH, 2012. Changes in reads to differential expression results. Genome Biol. 11: 220– extreme cold tolerance, membrane composition and cardiac 229. transcriptome during the first day of thermal acclimation in the Otto TD, Rayner JC, Bohme U, Pain A, Spottiswoode N et al., porcelain crab Petrolisthes cinctipes. J. Exp. Biol. 215: 1824– 2014. Genome sequencing of chimpanzee malaria parasites 1836. reveals possible pathways of adaptation to human hosts. Nat. Sabeti PC, Reich DE, Higgins JM, Levine HZP, Richter DJ et al., Commun. 5: 4754. 2002. Detecting recent positive selection in the human genome Peng Y, Lai Z, Lane T, Nageswara-Rao M, Okada M et al., 2014. from haplotype structure. Nature 419: 832–837. De novo genome assembly of the economically important Salzberg SL, Phillippy AM, Zimin A, Puiu D, Magoc T et al., weed horseweed using integrated data from multiple seque- 2012. GAGE: A critical evaluation of genome assemblies and ncing platforms. Plant Physiol. 166: 1241–1254. assembly algorithms. Genome Res. 22: 557–567. Peng Y, Leung HC, Yiu SM, Chin FY, 2011. Meta-IDBA: A de Sanger F, Nicklen S, Coulson AR, 1977. DNA sequencing with Novo assembler for metagenomic data. Bioinformatics 27: chain-terminating inhibitors. Proc. Nat. Acad. Sci. USA 74: i94–101. 5463–5467. Piaggio AJ, Engeman RM, Hopken MW, Humphrey JS, Keacher Savolainen O, Lascoux M, Merila J, 2013. Ecological genomics KL et al., 2014. Detecting an elusive invasive species: A of local adaptation. Nat. Rev. Genet. 14: 807–820. diagnostic PCR to detect Burmese python in Florida waters Sax DF, Stachowicz JJ, Brown JH, Bruno JF, Dawson MN et al., and an assessment of persistence of environmental DNA. Mol. 2007. Ecological and evolutionary insights from species inva- Ecol. Res. 14: 374–380. sions. Trends Ecol. Evol. 22: 465–471. Pickrell JK, Marioni JC, Pai AA, Degner JF, Engelhardt BE et al., Sax DF, Stachowicz JJ, Gaines SD, 2005. Introduction. In: Sax 2010. Understanding mechanisms underlying human gene DF, Stachowicz JJ, Gaines SD eds. Species Invasions: Insights expression variation with RNA sequencing. Nature 464: 768– into Ecology, Evolution, and Biogeography. Sunderland, 772. Massachusetts: Sinauer Associates Inc., 1–7. Poelstra JW, Vijay N, Bossu CM, Lantz H, Ryll B et al., 2014. Schlötterer C, 2003. Hitchhiking mapping: Functional genomics The genomic landscape underlying phenotypic integrity in the from the population genetics perspective. Trends Genet. 19: face of gene flow in crows. Science 344: 1410–1414. 32–38. Poh Y-P, Domingues VS, Hoekstra HE, Jensen JD, 2014. On the Schlötterer C, Tobler R, Kofler R, Nolte V, 2014. Sequencing prospect of identifying adaptive loci in recently bottlenecked pools of individuals - mining genome- wide polymorphism populations. PLoS ONE 9: e110579. data without big funding. Nat. Rev. Genet. 15: 749–763. Pop M, Salzberg SL, 2008. Bioinformatics challenges of new Schulz MH, Zerbino DR, Vingron M, Birney E, 2012. Oases: sequencing technology. Trends Genet. 24: 142–149. Robust de novo RNA-seq assembly across the dynamic range Puzey J, Vallejo-Marin M, 2014. Genomics of invasion: Diversity of expression levels. Bioinformatics 28: 1086–1092. and selection in introduced populations of monkeyflowers Shokralla S, Gibson JF, Nikbakht H, Janzen DH, Hallwachs W et Mimulus guttatus. Mol. Ecol. 23: 4472–4485. al., 2014. Next-generation DNA barcoding: Using next-gene- Rahel FJ, Olden JD, 2008. Assessing the effects of climate change ration sequencing to enhance and accelerate DNA barcode

504 Current Zoology Vol. 61 No. 3

capture from single specimens. Mol. Ecol. Res. 14: 892–901. 9: e102973. Shokralla S, Spall JL, Gibson JF, Hajibabaei M, 2012. Next-gene- Valentini A, Pompanon F, Taberlet P, 2009. DNA barcoding for ration sequencing technologies for environmental DNA re- ecologists. Trends Ecol. Evol. 24: 110–117. search. Mol. Ecol. 21: 1794–1805. Varshney RK, Nayak SN, May GD, Jackson SA, 2009. Next- Simpson JT, Wong K, Jackman SD, Schein JE, Jones SJ et al., generation sequencing technologies and their implications for 2009. ABySS: A parallel assembler for short read sequence crop genetics and breeding. Trends Biotechnol. 27: 522–530. data. Genome Res. 19: 1117–1123. Villablanca FX, Roderick GK, Palumbi SR, 1998. Invasion gene- Smith CD, Zimin A, Holt C, Abouheif E, Benton R et al., 2011. tics of the Mediterranean fruit fly: Variation in multiple nu- Draft genome of the globally widespread and invasive Argen- clear introns. Mol. Ecol. 7: 547–560. tine ant Linepithema humile. Proc. Nat. Acad. Sci. USA 108: Wang L, Xi Y, Yu J, Dong L, Yen L et al., 2010. A statistical 5673–5678. method for the detection of alternative splicing using RNA-seq. Stapley J, Reger J, Feulner PG, Smadja C, Galindo J et al., 2010. PLoS ONE 5: e8529. Adaptation genomics: The next generation. Trends Ecol. Evol. Wang YL, Wang YJ, Luan JB, Yan GH, Liu SS et al., 2013. 25: 705–712. Analysis of the transcriptional differences between indigenous Stinchcombe JR, Hoekstra HE, 2008. Combining population gen- and invasive whiteflies reveals possible mechanisms of whi- omics and quantitative genetics: Finding the genes underlying tefly invasion. PLoS ONE 8: e62176. ecologically important traits. Heredity 100: 158–170. Wang Z, Gerstein M, Snyder M, 2009. RNA-Seq: A revolutionary Storz JF, 2005. Using genome scans of DNA polymorphism to tool for transcriptomics. Nat. Rev. Genet. 10: 57–63. infer adaptive population divergence. Mol. Ecol. 14: 671–688. Weinig C, Brock MT, Dechaine JA, Welch SM, 2007. Resolving Sun J, Wang M, Wang H, Zhang H, Zhang X et al., 2012. De novo the genetic basis of invasiveness and predicting invasions. assembly of the transcriptome of an invasive snail and its Genetica 129: 205–216. multiple ecological applications. Mol. Ecol. Res. 12: 1133– Wetterstrand KA, 2014. DNA Sequencing Costs: Data from the 1144. NHGRI Genome Sequencing Program. http://www.genome. Tagu D, Colbourne JK, Nègre N, 2014. Genomic data integration gov/sequencingcosts. Accessed December 16, 2014. for ecological and evolutionary traits in non-model organisms. Wright S, 1951. The genetical structure of populations. Ann. BMC Genom. 15: 490. Eugen. 15: 323–354. Tang B, Chen J, Hou Y, Meng E, 2014. Transcriptome immune Yandell M, Ence D, 2012. A beginner's guide to eukaryotic analysis of the invasive beetle Octodonta nipae (Maulik) genome annotation. Nat. Rev. Genet. 13: 329–342. (Coleoptera: Chrysomelidae) parasitized by Tetrastichus bron- Ye XD, Su YL, Zhao QY, Xia WQ, Liu SS et al., 2014. Trans- tispae Ferrière (Hymenoptera: Eulophidae). PLoS ONE 9: criptomic analyses reveal the adaptive features and biological e91482. differences of guts from two invasive whitefly species. BMC Thomas D, 2010. Gene-environment-wide association studies: Genom. 15: 370. Emerging approaches. Nat. Rev. Genet. 11: 259–272. Yoder JB, Stanton-Geddes J, Zhou P, Briskine R, Young ND et al., Thomsen PF, Kielgast J, Iversen LL, Wiuf C, Rasmussen M et al., 2014. Genomic signature of adaptation to climate in Medicago 2012. Monitoring endangered freshwater biodiversity using truncatula. Genetics 196: 1263–1275. environmental DNA. Mol. Ecol. 21: 2565–2573. Zayed A, Whitfield CW, 2008. A genome-wide signature of Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G et al., positive selection in ancient and recent invasive expansions of 2010. Transcript assembly and quantification by RNA-Seq the honey bee Apis mellifera. Proc. Nat. Acad. Sci. USA 105: reveals unannotated transcripts and isoform switching during 3421–3426. cell differentiation. Nat. Biotechnol. 28: 511–515. Zerbino DR, Birney E, 2008. Velvet: Algorithms for de novo short Turner TL, Bourne EC, Von Wettberg EJ, Hu TT, Nuzhdin SV, read assembly using de Bruijn graphs. Genome Res. 18: 821– 2010. Population resequencing reveals local adaptation of 829. Arabidopsis lyrata to serpentine soils. Nat. Genet. 42: 260– Zhang G, Fang X, Guo X, Li L, Luo R et al., 2012. The oyster 263. genome reveals stress adaptation and complexity of shell Turner TL, Hahn MW, 2010. Genomic islands of speciation or formation. Nature 490: 49–54. genomic islands and speciation? Mol. Ecol. 19: 848–850. Zhao QY, Wang Y, Kong YM, Luo D, Li X et al., 2011. Opti- Uliano-Silva M, Americo JA, Brindeiro R, Dondero F, Prosdocimi mizing de novo transcriptome assembly from short-read RNA- F et al., 2014. Gene discovery through transcriptome sequen- Seq data: A comparative study. BMC Bioinformatics 12: S2. cing for the invasive mussel Limnoperna fortunei. PLoS ONE

RIUS M et al.: Next-generation sequencing and biological invasions 505i

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RIUS M et al.: Next-generation sequencing and biological invasions 507iii

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RIUS M et al.: Next-generation sequencing and biological invasions 509 v

vi 510 Current Zoology Vol. 61 No. 3

Reference Akbari OS, Antoshechkin I, Amrhein H, Williams B, Diloreto R et al., 2013. The developmental transcriptome of the mosquito Aedes aegypti, an invasive species and major arbovirus vector. G3 (Bethesda) 3: 1493–1509. Amish SJ, Hohenlohe PA, Painter S, Leary RF, Muhlfeld C et al., 2012. RAD sequencing yields a high success rate for westslope cutthroat and rainbow trout species-diagnostic SNP assays. Mol. Ecol. Resour. 12: 653–660. Andersen J, Mills N, 2014. iMSAT: A novel approach to the development of microsatellite loci using barcoded Illumina libraries. BMC Genomics 15: 858. Bai X, Zhang W, Orantes L, Jun TH, Mittapalli O et al., 2010. Combining next-generation sequencing strategies for rapid molecular resource development from an invasive aphid species Aphis glycines. PloS ONE 5: e11370. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL et al., 2008. Rapid SNP discovery and genetic mapping using sequenced RAD markers. PloS ONE 3: e3376. Barrero RA, Guerrero FD, Moolhuijzen P, Goolsby JA, Tidwell J et al., 2015. Shoot transcriptome of the giant reed Arundo donax. Data in Brief 3: 1–6. Baute GJ, Kane NC, Grassa CJ, Lai Z, Rieseberg LH, 2015. Genome scans reveal candidate domestication and improvement genes in cultivated sunflower, as well as post-domestication introgression with wild relatives. New Phytol. 206: 830–838. Bentley KE, Berryman KR, Hopper M, Hoffberg SL, Myhre KE et al., 2015. Eleven microsatellites in an emerging invader Phytolacca americana (Phytolaccaceae) from its native and introduced ranges. Appl. Plant Sci. 3: 1500002. Brown AM, Huynh LY, Bolender CM, Nelson KG, McCutcheon JP, 2014. Population genomics of a symbiont in the early stages of a pest invasion. Mol. Ecol. 23: 1516–1530. Cardoso AM, Cavalcante JJ, Cantao ME, Thompson CE, Flatschart RB et al., 2012. Metagenomic analysis of the microbiota from the crop of an invasive snail reveals a rich reservoir of novel genes. PLoS ONE 7: e48505. Cassone BJ, Michel AP, Stewart LR, Bansal R, Mian MA et al., 2014. Reduction in fecundity and shifts in cellular processes by a native virus on an invasive insect. Genome Biol. Evol. 6: 873–885. Chen H, Zha J, Liang X, Bu J, Wang M et al., 2013. Sequencing and de novo assembly of the Asian clam Corbicula fluminea transcriptome using the Illumina GAIIx method. PLoS ONE 8: e79516. Chiu JC, Jiang X, Zhao L, Hamm CA, Cridland JM et al., 2013. Genome of Drosophila suzukii, the spotted wing drosophila. G3 (Bethesda) 3: 2257–2271. Clark MS, Thorne MA, Amaral A, Vieira F, Batista FM et al., 2013. Identification of molecular and physiological responses to chronic environmental challenge in an invasive species: The Pacific oyster Crassostrea gigas. Ecol. Evol. 3: 3283–3297. Coghill LM, Darrin Hulsey C, Chaves-Campos J, Garcia de Leon FJ, Johnson SG, 2014. Next generation phylogeography of cave and surface Astyanax mexicanus. Mol. Phylogenet. Evol. 79: 368–374. Davidson RM, Gowda M, Moghe G, Lin H, Vaillancourt B et al., 2012. Comparative transcriptomics of three Poaceae species reveals patterns of gene expression evolution. Plant J. 71: 492–502. De Kort H, Vandepitte K, Mergeay J, Honnay O, 2013. Isolation, characterization and genotyping of single nucleotide polymorphisms in the non-model tree species Frangula alnus (Rhamnaceae). Conservation Genet. Resour. 6: 267–269. De Kort H, Mergeay J, Vander Mijnsbrugge K, Decocq G, Maccherini S et al., 2014. An evaluation of seed zone delineation using phenotypic and population genomic data on black alder Alnus glutinosa. Journal of Applied Ecology 51: 1218–1227. Del Rio-Portilla MA, Vargas-Peralta CE, Machkour-M'Rabet S, Henaut Y, Garcia-De-Leon FJ, 2014. Lionfish Pterois volitans Linnaeus 1758, the complete mitochondrial DNA of an invasive species. Mitochondr. DNA Early Online: 1–2. Dickey AM, Trease AJ, Jara-Cavieres A, Kumar V, Christenson MK et al., 2014. Estimating biological diversity in Scirtothrips dorsalis (Thysanoptera: Thripidae) via next generation sequencing. Fla. Entemol. 97: 362–366. Ding X, Ye J, Wu X, Huang L, Zhu L et al., 2015. Deep sequencing analyses of pine wood nematode Bursaphelenchus xylophilus microRNAs reveal distinct miRNA expression patterns during the pathological process of pine wilt disease. Gene 555: 346–356. Dlugosch KM, Lai Z, Bonin A, Hierro J, Rieseberg LH, 2013. Allele identification for transcriptome-based population genomics in the invasive plant Centaurea solstitialis. G3 (Bethesda) 3: 359–367. Do TH, Nguyen TT, Nguyen TN, Le QG, Nguyen C et al., 2014. Mining biomass-degrading genes through Illumin-based de novo sequencing and metagenomic analysis of free-living bacteria in the gut of the lower termite Coptotermes gestroi harvested in Vietnam. J. Biosci. Bioeng. 118: 665–671. Doorduin L, Gravendeel B, Lammers Y, Ariyurek Y, Chin AWT et al., 2011. The complete chloroplast genome of 17 individuals of pest species Jacobaea vulgaris: SNPs, microsatellites and barcoding markers for population and phylogenetic studies. DNA Res. 18: 93–105. Dray L, Neuhof M, Diamant A, Huchon D, 2014. The complete mitochondrial genome of the devil firefish Pterois miles (Bennett, 1828) (Scorpaenidae). Mitochondr. DNA Early Online: 1–2. Duan CX, Li DD, Sun SL, Wang XM, Zhu ZD, 2014. Rapid development of microsatellite markers for Callosobruchus chinensis using Illumina paired-end sequencing. PLoS ONE 9: e95458. Elsik CG, Worley KC, Bennett AK, Beye M, Camara F et al., 2014. Finding the missing honey bee genes: Lessons learned from a genome upgrade. BMC Genomics 15: 86. Fabian DK, Kapun M, Nolte V, Kofler R, Schmidt PS et al., 2012. Genome-wide patterns of latitudinal differentiation among populations of Drosophila melanogaster from North America. Mol. Ecol. 21: 4748–4769.

RIUS M et al.: Next-generation sequencing and biological invasions 511vii

Fang YW, Liu LY, Zhang HL, Jiang DF, Chu D, 2014. Competitive ability and fitness differences between two introduced populations of the invasive whitefly Bemisia tabaci Q in China. PLoS ONE 9: e100423. Ferreira de Carvalho J, Poulain J, Da Silva C, Wincker P, Michon-Coudouel S et al., 2013. Transcriptome de novo assembly from next-generation sequencing and comparative analyses in the hexaploid salt marsh species Spartina maritima and Spartina alterniflora (Poaceae). Heredity 110: 181–193. Ficetola GF, Miaud C, Pompanon F, Taberlet P, 2008. Species detection using environmental DNA from water samples. Biol. Lett. 4: 423–425. Fox SE, Preece J, Kimbrel JA, Marchini GL, Sage A et al., 2013. Sequencing and de novo transcriptome assembly of Brachypodium sylvaticum (Poaceae). Appl. Plant Sci. 1: 1200011. Fraser BA, Kunstner A, Reznick DN, Dreyer C, Weigel D, 2015. Population genomics of natural and experimental populations of guppies Poecilia reticulata. Mol. Ecol. 24: 389–408. Fridjonsson O, Olafsson K, Tompsett S, Bjornsdottir S, Consuegra S et al., 2011. Detection and mapping of mtDNA SNPs in Atlantic salmon using high throughput DNA sequencing. BMC Genomics 12: 179. Galan M, Pages M, Cosson JF, 2012. Next-generation sequencing for rodent barcoding: Species identification from fresh, degraded and environmental samples. PLoS ONE 7: e48374. Galindo J, Grahame JW, Butlin RK, 2010. An EST-based genome scan using 454 sequencing in the marine snail Littorina saxatilis. J. Evol. Biol. 23: 2004–2016. Gibson JD, Hunt GJ, 2014. The complete mitochondrial genome of the invasive Africanized honey beeApis mellifera scutellata (Insecta: Hymenoptera: Apidae). Mitochondr. DNA Early Online: 1–2. Gilchrist A, Shearman D, Frommer M, Raphael K, Deshpande N et al., 2014. The draft genome of the pest tephritid fruit fly Bactrocera tryoni: resources for the genomic analysis of hybridising species. BMC Genomics 15: 1153. Gill N, Buti M, Kane N, Bellec A, Helmstetter N et al., 2014. Sequence-based analysis of structural organization and composition of the cultivated sunflower (Helianthus annuus L.) Genome. Biology 3: 295–319. Gu J, Hu W, Wu J, Zheng P, Chen M et al., 2013. miRNA Genes of an invasive vector mosquitoAedes albopictus. PLoS ONE 8: e67638. Guo W, Huang Y, He Z, Yan Y, Zhou R et al., 2013. Development and characterization of microsatellite loci for smooth cordgrass Spartina alterniflora (Poaceae). Appl. Plant Sci. 1: apps.1200211. Hand B, Hether T, Kovac R, Muhlfeld C, Amish SJ et al., 2015. Genomics and introgression: Discovery and mapping of thousands of species-diagnostic SNPs using RAD sequencing. Current Zoology 61: 146–154. Harrison E, Love CN, Jones KL, Lance SL, Trexler JC et al., 2013. Isolation and characterization of 18 novel polymorphic microsatellite markers from the Mayan cichlid Cichlasoma urophthalmus. Conservation Genet. Resour. 5: 703–705. He L, Wang Q, Jin X, Wang Y, Chen L et al., 2012. Transcriptome profiling of testis during sexual maturation stages in Eriocheir sinensis using Illumina sequencing. PLoS ONE 7: e33735. He L, Xu X, Li Y, Li C, Zhu Y et al., 2013. Transcriptome analysis of buds and leaves using 454 pyrosequencing to discover genes associated with the biosynthesis of active ingredients in Lonicera japonica Thunb. PLoS ONE 8: e62922. He R, Kim MJ, Nelson W, Balbuena TS, Kim R et al., 2012. Next-generation sequencing-based transcriptomic and proteomic analysis of the common reedPhragmites australis (Poaceae) reveals genes involved in invasiveness and rhizome specificity. Am. J. Bot. 99: 232–247. Hodgins KA, Lai Z, Oliveira LO, Still DW, Scascitelli M et al., 2014. Genomics of Compositae crops: Reference transcriptome assemblies and evidence of hybridization with wild relatives. Mol. Ecol. Resour. 14: 166–177. Hoffberg SL, Bentley KE, Lee JB, Myhre KE, Iwao K et al., 2014. Characterization of 15 microsatellite loci in kudzu (Pueraria montana var. lobata) from the native and introduced ranges. Conservation Genet. Resour. Advance Online Publication: DOI 10.1007/s12686- 12014-10381-12687. Hohenlohe PA, Bassham S, Etter PD, Stiffler N, Johnson EA et al., 2010. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet. 6: e1000862. Hohenlohe PA, Amish SJ, Catchen JM, Allendorf FW, Luikart G, 2011. Next-generation RAD sequencing identifies thousands of SNPs for assessing hybridization between rainbow and westslope cutthroat trout. Mol. Ecol. Resour. 11 (Suppl. 1): 117–122. Hohenlohe PA, Day MD, Amish SJ, Miller MR, Kamps-Hughes N et al., 2013. Genomic patterns of introgression in rainbow and westslope cutthroat trout illuminated by overlapping paired-end RAD sequencing. Mol. Ecol. 22: 3002–3013. Huang Y, Li Y, Burt DW, Chen H, Zhang Y et al., 2013. The duck genome and transcriptome provide insight into an avian influenza virus reservoir species. Nat. Genet. 45: 776–783. Huang YL, Fang XT, Lu L, Yan YB, Chen SF et al., 2012. Transcriptome analysis of an invasive weed Mikania micrantha. Biol. Plantarum 56: 111–116. Hunter ME, Hart KM, 2013. Rapid microsatellite marker development using next generation pyrosequencing to inform invasive Burmese python Python molurus bivittatusManagement. Int. J. Mol. Sci. 14: 4793–4804. Ioannidis P, Lu Y, Kumar N, Creasy T, Daugherty S et al., 2014. Rapid transcriptome sequencing of an invasive pest, the brown marmorated stink bug Halyomorpha halys. BMC Genomics 15: 738. Janes JK, Li Y, Keeling CI, Yuen MM, Boone CK et al., 2014. How the mountain pine beetle Dendroctonus ponderosae breached the Canadian Rocky Mountains. Mol. Biol. Evol. 31: 1803–1815. Jasper WC, Linksvayer TA, Atallah J, Friedman D, Chiu JC et al., 2015. Large-scale coding sequence change underlies the evolution of

viii512 Current Zoology Vol. 61 No. 3

postdevelopmental novelty in honey bees. Mol. Biol. Evol. 32: 334–346. Jones JC, Fan S, Franchini P, Schartl M, Meyer A, 2013. The evolutionary history of Xiphophorus fish and their sexually selected sword: A genome-wide approach using restriction site-associated DNA sequencing. Mol. Ecol. 22: 2986–3001. Kalchhauser I, Kutschera VE, Burkhardt-Holm P, 2014. The complete mitochondrial genome of the invasive Ponto-Caspian goby Ponticola kessleri obtained from high-throughput sequencing using the Ion Torrent Personal Genome Machine. Mitochondr. DNA Early Online: 1–3. Kartzinel TR, Pringle RM, 2015. Molecular detection of invertebrate prey in vertebrate diets: Trophic ecology of Caribbean island lizards. Mol. Ecol. Resour. Early Publication: doi: 10.1111/1755–0998.12366. King TL, Eackles MS, Reshetnikov AN, 2015. Rapid isolation of microsatellite DNAs and identification of polymorphic mitochondrial DNA regions in the fish rotan Perccottus glenii invading European Russia. Conservation Genet. Resour. Advance Online Publication: DOI 10.1007/s12686-12015-10430-x. Krzeminska U, Wilson R, Rahman S, Song BK, Gan HM et al., 2014. The complete mitochondrial genome of the invasive house crow Corvus splendens (Passeriformes: Corvidae). Mitochondr. DNA Early Online: 1–2. Ku C, Chung W-C, Chen L-L, Kuo C-H, 2013. The complete plastid genome sequence of Madagascar periwinkle Catharanthus roseus (L.) G. Don: Plastid genome evolution, molecular marker identification, and phylogenetic implications in asterids. PLoS ONE 8: e68518. Kulathinal RJ, Stevison LS, Noor MA, 2009. The genomics of speciation in Drosophila: Diversity, divergence, and introgression estimated using low-coverage genome sequencing. PLoS Genet. 5: e1000550. Lai Z, Kane NC, Kozik A, Hodgins KA, Dlugosch KM et al., 2012. Genomics of compositae weeds: EST libraries, microarrays, and evidence of introgression. Am. J. Bot. 99: 209–218. Lamer JT, Sass GG, Boone JQ, Arbieva ZH, Green SJ et al., 2014. Restriction site-associated DNA sequencing generates high-quality single nucleotide polymorphisms for assessing hybridization between bighead and silver carp in the United States and China. Mol. Ecol. Resour. 14: 79–86. Lance RF, Farrington HL, Edwards CE, Guan X, Carr MR et al., 2014. Development and testing of new genetic markers for the detection of invasive bighead and silver carp (Hypophthalmichthys nobilis and H. molitrix) DNA in environmental water samples from North America. bioRxiv doi: http://dx.doi.org/10.1101/003624. Laporte M, Shao Z, Berrebi P, Laabir M, Abadie E et al., 2014. Isolation of 12 microsatellite markers following a pyrosequencing procedure and cross-priming in two invasive cryptic species Alexandrium catenella (group IV) and A. tamarense (group III) (Dinophyceae). Mar. Pollut. Bull. 83: 302–305. Lemay MA, Russello MA, 2015. Genetic evidence for ecological divergence in kokanee salmon. Mol. Ecol. 24: 798–811. Leung PT, Ip JC, Mak SS, Qiu JW, Lam PK et al., 2014. De novo transcriptome analysis of Perna viridis highlights tissue-specific patterns for environmental studies. BMC Genomics 15: 804. Li M, Tian S, Jin L, Zhou G, Li Y et al., 2013. Genomic analyses identify distinct patterns of selection in domesticated pigs and Tibetan wild boars. Nat. Genet. 45: 1431–1438. Li S-L, Vasemägi A, Matos-Máravi P, Ramula S, 2013. Development and testing of microsatellite loci for the invasive herb Lupinus polyphyllus through 454 pyrosequencing. Mol. Ecol. Resour. 13: 760–762. Li Y, Hui M, Cui Z, Liu Y, Song C et al., 2015. Comparative transcriptomic analysis provides insights into the molecular basis of the metamorphosis and nutrition metabolism change from zoeae to megalopae in Eriocheir sinensis. Comp. Biochem. Phys. D 13: 1–9. Livaja M, Wang Y, Wieckhorst S, Haseneyer G, Seidel M et al., 2013. BSTA: A targeted approach combines bulked segregant analysis with next- generation sequencing and de novo transcriptome assembly for SNP discovery in sunflower. BMC Genomics 14: 628. Mackie JA, Wostenberg D, Doan M, Craig SF, Darling JA, 2014. High-throughput Illumina sequencing and microsatellite design in Watersipora (Bryozoa), a complex of invasive species. Conservation Genet. Resour. 6: 1053–1055. Mahon AR, Nathan LR, Jerde CL, 2014. Meta-genomic surveillance of invasive species in the bait trade. Conservation Genet. Resour. 6: 563–567. Manfrin C, Tom M, De Moro G, Gerdol M, Giulianini PG et al., 2015. The eyestalk transcriptome of red swamp crayfish Procambarus clarkii. Gene 557: 28–34. Manzari C, Fosso B, Marzano M, Annese A, Caprioli R et al., 2014. The influence of invasive jellyfish blooms on the aquatic microbiome in a coastal lagoon (Varano, SE Italy) detected by an Illumina-based deep sequencing strategy. Biol. Invasions 17: 923–940. Meilink WRM, Arntzen JW, van Delft JJCW, Wielstra B, 2015. Genetic pollution of a threatened native crested newt species through hybridization with an invasive congener in the Netherlands. Biol. Cons. 184: 145–153. Miller AC, Woeste KE, Anagnostakis SL, Jacobs DF, 2014. Exploration of a rare population of Chinese chestnut in North America: Stand dynamics, health and genetic relationships. AoB Plants 6: plu065. Miller JJ, Eackles MS, Stauffer JR, King TL, 2015. Next-generation genomic shotgun sequencing indicates greater genetic variability in the mitochondria of Hypophthalmichthys molitrix relative to H. nobilis from the Mississippi River, USA and provides tools for research and detection. Conservation Genet. Resour. 7: 9–11. Mittapalli O, Bai X, Mamidala P, Rajarapu SP, Bonello P et al., 2010. Tissue-specific transcriptomics of the exotic invasive insect pest emerald ash borer Agrilus planipennis. PLoS ONE 5: e13708. Mizrachi E, Hefer CA, Ranik M, Joubert F, Myburg AA, 2010. De novo assembled expressed gene catalog of a fast-growing Eucalyptus tree produced by Illumina mRNA-Seq. BMC Genomics 11: 681. Morrison C, Springmann M, Iwanowicz D, Wade C, 2015. Development of ten microsatellite loci in the invasive giant African land snail

RIUS M et al.: Next-generation sequencing and biological invasions 513ix

Achatina (=Lissachatina) fulica Bowdich, 1822. Conservation Genet. Resour. 7: 201–202. Mu X, Hou G, Song H, Xu P, Luo D et al., 2015. Transcriptome analysis between invasive Pomacea canaliculata and indigenous Cipangopaludina cahayensis reveals genomic divergence and diagnostic microsatellite/SSR markers. BMC Genet. 16: 12. Nie X, Lv S, Zhang Y, Du X, Wang L et al., 2012. Complete chloroplast genome sequence of a major invasive species, crofton weed Ageratina adenophora. PLoS ONE 7: e36869. Nie XJ, Liu PX, Deng PC, Lu YZ, Biradar SS et al., 2014. Large-scale identification of microsatellites for a major invasive weed Ageratina adenophora using the Illumina sequencing technology. Weed Res. 54: 133–139. Novy A, Flory SL, Honig JA, Bonos S, Hartman JM, 2012. Characterization of polymorphic microsatellites for the invasive grass Microstegium vimineum (Poaceae). Am. J. Bot. 99: e56–58. Nystedt B, Street NR, Wetterbom A, Zuccolo A, Lin YC et al., 2013. The Norway spruce genome sequence and conifer genome evolution. Nature 497: 579–584. Ometto L, Cestaro A, Ramasamy S, Grassi A, Revadi S et al., 2013. Linking genomics and ecology to investigate the complex evolution of an invasive Drosophila pest. Genome Biol. Evol. 5: 745–757. Palti Y, Gao G, Miller MR, Vallejo RL, Wheeler PA et al., 2014. A resource of single-nucleotide polymorphisms for rainbow trout generated by restriction-site associated DNA sequencing of doubled haploids. Mol. Ecol. Resour. 14: 588–596. Park D, Jung J, Choi B-S, Jayakodi M, Lee J et al., 2015. Uncovering the novel characteristics of Asian honey bee Apis cerana by whole genome sequencing. BMC Genomics 16: 1. Paula DP, Linard B, Andow DA, Sujii ER, Pires CS et al., 2014. Detection and decay rates of prey and prey symbionts in the gut of a predator through metagenomics. Mol. Ecol. Resour. Advance Online Publication: DOI 10.1111/1755–0998.12364. Pegadaraju V, Nipper R, Hulke B, Qi L, Schultz Q, 2013. De novo sequencing of sunflower genome for SNP discovery using RAD (Restriction site Associated DNA) approach. BMC Genomics 14: 556. Peng Y, Gao X, Li R, Cao G, 2014. Transcriptome sequencing and De Novo analysis of Youngia japonica using the illumina platform. PLoS ONE 9: e90636. Pochon X, Bott NJ, Smith KF, Wood SA, 2013. Evaluating detection limits of next-generation sequencing for the surveillance and monitoring of international marine pests. PloS ONE 8: e73935. Poelchau MF, Reynolds JA, Denlinger DL, Elsik CG, Armbruster PA, 2013. Transcriptome sequencing as a platform to elucidate molecular components of the diapause response in the Asian tiger mosquito Aedes albopictus. Physiol. Entomol. 38: 173–181. Poux C, Gothland M, Holl A-C, Spilmont N, Arnaud J-F, 2015. Highly polymorphic microsatellite markers in two species, the invasive shore crabs Hemigrapsus sanguineus and Hemigrapsus takanoi (Decapoda, Varunidae). Conservation Genet. Resour. Advance Online Publication: DOI 10.1007/s12686-12015-10426-12686. Prentis PJ, Woolfit M, Thomas-Hall SR, Ortiz-Barrientos D, Pavasovic A et al., 2010. Massively parallel sequencing and analysis of expressed sequence tags in a successful invasive plant. Ann. Bot. 106: 1009–1017. Pujolar JM, Jacobsen MW, Frydenberg J, Als TD, Larsen PF et al., 2013. A resource of genome-wide single-nucleotide polymorphisms generated by RAD tag sequencing in the critically endangered European eel. Mol. Ecol. Resour. 13: 706–714. Pukk L, Kisand V, Ahmad F, Gross R, Vasemägi R, 2014. Double-restriction-site-associated DNA (dRAD) approach for fast microsatellite marker development in Eurasian perch (Perca fluviatilis L.). Conservation Genet. Resour. 6: 183–184. Pukk L, Ahmad F, Hasan S, Kisand V, Gross R et al., 2015. Less is more: Extreme genome complexity reduction with ddRAD using Ion Torrent semiconductor technology. Mol. Ecol. Resour. Advance Online Publication: DOI 10.1111/1755–0998.12392. Puritz JB, Hollenbeck CM, Gold JR, 2014. dDocent: A RADseq, variant-calling pipeline designed for population genomics of non-model organisms. PeerJ 2: e431. Puzey J, Vallejo-Marin M, 2014. Genomics of invasion: Diversity and selection in introduced populations of monkeyflowers Mimulus guttatus. Mol. Ecol. 23: 4472–4485. Qian ZQ, 2014. The complete mitogenome of the dampwood termite Zootermopsis nevadensis (Insecta: Isoptera: Termopsidae). Mitochondr. DNA Early Online: 1–2. Ranjan A, Townsley BT, Ichihashi Y, Sinha NR, Chitwood DH, 2015. An intracellular transcriptomic atlas of the giant coenocyte Caulerpa taxifolia. PLoS Genet. 11: e1004900. Rašić G, Filipović I, Weeks AR, Hoffmann AA, 2014. Genome-wide SNPs lead to strong signals of geographic structure and relatedness patterns in the major arbovirus vector Aedes aegypti. BMC Genomics 15: 275. Renaut S, Grassa CJ, Moyers BT, Kane NC, Rieseberg LH, 2012. The population genomics of sunflowers and genomic determinants of protein evolution revealed by RNAseq. Biology 1: 575–596. Rijal DP, Falahati-Anbaran M, Alm T, Alsos IG, 2014. Microsatellite markers for Heracleum persicum (Apiaceae) and allied taxa: Application of next-generation sequencing to develop genetic resources for invasive species management. Plant Mol. Biol. Rep. DOI: 10.1007/s11105-014-0841-y. Robinson NA, Hall NE, Ross EM, Cooke IR, Shiel BP et al., 2014. The complete mitochondrial genome of Haliotis laevigata (Gastropoda: Haliotidae) using MiSeq and HiSeq sequencing. Mitochondr. DNA Early Online: 1–2. Romiguier J, Gayral P, Ballenghien M, Bernard A, Cahais V et al., 2014. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 515: 261–263. Roulin AC, Wu M, Pichon S, Arbore R, Kuhn-Buhlmann S et al., 2014. De novo transcriptome hybrid assembly and validation in the European earwig (Dermaptera, Forficula auricularia). PLoS ONE 9: e94098.

x 514 Current Zoology Vol. 61 No. 3

Rubinstein ND, Feldstein T, Shenkar N, Botero-Castro F, Griggio F et al., 2013. Deep sequencing of mixed total DNA without barcodes allows efficient assembly of highly plastic ascidian mitochondrial genomes. Genome Biol. Evol. 5: 1185–1199. Schrader L, Kim JW, Ence D, Zimin A, Klein A et al., 2014. Transposable element islands facilitate adaptation to novel environments in an invasive species. Nat. Commun. 5: 5495. Schultz TF, Fitzpatrick CK, Wilson Freshwater D, Morris JA, 2013. Characterization of 18 polymorphic microsatellite loci from invasive lionfish (Pterois volitans and P. miles). Conservation Genet. Resour. 5: 599–601. Shan TF, Pang SJ, Li J, Li X, 2014. De novo transcriptome analysis of the gametophyte of Undaria pinnatifida (Phaeophyceae). J. Appl. Phycol. Advance Online Publication: DOI 10.1007/s10811-10014-10393-10814. Shen G-M, Dou W, Niu J-Z, Jiang H-B, Yang W-J et al., 2011. Transcriptome analysis of the oriental fruit fly Bactrocera dorsalis. PLoS ONE 6: e29127. Smith CD, Zimin A, Holt C, Abouheif E, Benton R et al., 2011. Draft genome of the globally widespread and invasive Argentine ant Linepithema humile. Proc. Natl. Acad. Sci. USA 108: 5673–5678. Smith KF, Abbott CL, Saito Y, Fidler AE, 2015. Comparison of whole mitochondrial genome sequences from two clades of the invasive ascidianDidemnum vexillum. Mar. Genomics 19: 75–83. Sparks ME, Shelby KS, Kuhar D, Gundersen-Rindal DE, 2014. Transcriptome of the invasive brown marmorated stink bug Halyomorpha halys (Stal) (Heteroptera: Pentatomidae). PLoS ONE 9: e111646. Su YL, Li JM, Li M, Luan JB, Ye XD et al., 2012. Transcriptomic analysis of the salivary glands of an invasive whitefly. PLoS ONE 7: e39303. Sun J, Wang M, Wang H, Zhang H, Zhang X et al., 2012. De novo assembly of the transcriptome of an invasive snail and its multiple ecological applications. Mol. Ecol. Resour. 12: 1133–1144. Tang B, Chen J, Hou Y, Meng E, 2014. Transcriptome immune analysis of the invasive beetle Octodonta nipae (Maulik) (Coleoptera: Chrysomelidae) parasitized by Tetrastichus brontispae Ferriere (Hymenoptera: Eulophidae). PLoS ONE 9: e91482. Terekhanova NV, Logacheva MD, Penin AA, Neretina TV, Barmintseva AE et al., 2014. Fast evolution from precast bricks: Genomics of young freshwater populations of threespine stickleback Gasterosteus aculeatus. PLoS Genet. 10: e1004696. Turner KG, Grassa CJ, 2014. Complete plastid genome assembly of invasive plant Centaurea diffusa. bioRxiv doi: 10.1101/123456. Uliano-Silva M, Americo JA, Brindeiro R, Dondero F, Prosdocimi F et al., 2014. Gene discovery through transcriptome sequencing for the invasive mussel Limnoperna fortunei. PLoS ONE 9: e102973. Umbers KDL, Jennions MD, Keogh JS, 2012. Twenty-five new polymorphic microsatellites for the eastern mosquitofish Gambusia holbrooki (Actinopterygii : Poeciliidae), an invasive species in Australia. Aust. J. Zool. 60: 235–237. Valles SM, Oi DH, Yu F, Tan XX, Buss EA, 2012. Metatranscriptomics and pyrosequencing facilitate discovery of potential viral natural enemies of the invasive Caribbean crazy ant Nylanderia pubens. PLoS ONE 7: e31828. Vandepitte K, Honnay O, Mergeay J, Breyne P, Roldan-Ruiz I et al., 2013. SNP discovery using Paired-End RAD-tag sequencing on pooled genomic DNA of Sisymbrium austriacum (Brassicaceae). Mol. Ecol. Resour. 13: 269–275. Vandepitte K, de Meyer T, Helsen K, van Acker K, Roldan-Ruiz I et al., 2014. Rapid genetic adaptation precedes the spread of an exotic plant species. Mol. Ecol. 23: 2157–2164. Voskoboynik A, Neff NF, Sahoo D, Newman AM, Pushkarev D et al., 2013. The genome sequence of the colonial chordate Botryllus schlosseri. eLife 2: e00569. Wang X, Xiong M, Lei C, Zhu F, 2015. The developmental transcriptome of the synanthropic fly Chrysomya megacephala and insights into olfactory proteins. BMC Genomics 16: 20. Wang YL, Wang YJ, Luan JB, Yan GH, Liu SS et al., 2013. Analysis of the transcriptional differences between indigenous and invasive whiteflies reveals possible mechanisms of whitefly invasion. PLoS ONE 8: e62176. Wei D, Li HM, Yang WJ, Wei DD, Dou W et al., 2015. Transcriptome profiling of the testis reveals genes involved in spermatogenesis and marker discovery in the oriental fruit fly Bactrocera dorsalis. Insect Mol. Biol. 24: 41–57. White TA, Perkins SE, Heckel G, Searle JB, 2013. Adaptive evolution during an ongoing range expansion: The invasive bank vole Myodes glareolus in Ireland. Mol. Ecol. 22: 2971–2985. Wurm Y, Wang J, Riba-Grognuz O, Corona M, Nygaard S et al., 2011. The genome of the fire ant Solenopsis invicta. Proc. Natl. Acad. Sci. USA 108: 5679–5684. Xie W, Guo L, Jiao X, Yang N, Yang X et al., 2014. Transcriptomic dissection of sexual differences in Bemisia tabaci, an invasive agricultural pest worldwide. Sci. Rep. 4: 4088. Yang JB, Yang SX, Li HT, Yang J, Li DZ, 2013. Comparative chloroplast genomes of camellia species. PLoS ONE 8: e73053. Ye L, Amberg J, Chapman D, Gaikowski M, Liu WT, 2014. Fish gut microbiota analysis differentiates physiology and behavior of invasive Asian carp and indigenous American fish. ISME J. 8: 541–551. Ye X-D, Su Y-L, Zhao Q-Y, Xia W-Q, Liu S-S et al., 2014. Transcriptomic analyses reveal the adaptive features and biological differences of guts from two invasive whitefly species. BMC Genomics 15: 370. Yuan X, Zhou W-W, Zhou Y, Liu S, Lu F et al., 2014. Composition and expression of heat shock proteins in an invasive pest, the rice water weevil (Coleoptera: Curculionidae). Fla. Entemol. 97: 611–619. Zhang G, Fang X, Guo X, Li L, Luo R et al., 2012. The oyster genome reveals stress adaptation and complexity of shell formation. Nature 490: 49–54. Zhang Y, Li L, Yan TL, Liu Q, 2014. Complete chloroplast genome sequences of Praxelis (Eupatorium catarium Veldkamp), an important invasive species. Gene 549: 58–69.