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Article

De novo Transcriptome Generation and Annotation for Two Korean Endemic Land Snails, Aegista chejuensis and Aegista quelpartensis, Using Illumina Paired-End Sequencing Technology

by
Se Won Kang
1,†,
Bharat Bhusan Patnaik
1,2,†,
Hee-Ju Hwang
1,
So Young Park
1,
Tae Hun Wang
1,
Eun Bi Park
1,
Jong Min Chung
1,
Dae Kwon Song
1,
Hongray Howrelia Patnaik
1,
Jae Bong Lee
3,
Changmu Kim
4,
Soonok Kim
4,
Hong Seog Park
5,
Jun Sang Lee
6,
Yeon Soo Han
7 and
Yong Seok Lee
1,*
1
Department of Life Science and Biotechnology, College of Natural Sciences, Soonchunhyang University, 22 Soonchunhyangro, Shinchang-myeon, Asan, Chungcheongnam-do 31538, Korea
2
Trident School of Biotech Sciences, Trident Academy of Creative Technology (TACT), Chandaka Industrial Estate, Chandrasekharpur, Bhubaneswar, Odisha 751024, India
3
Korea Zoonosis Research Institute (KOZRI), Chonbuk National University, 820-120 Hana-ro, Iksan, Jeollabuk-do 54528, Korea
4
National Institute of Biological Resources, 42, Hwangyeong-ro, Seo-gu, Incheon 22689, Korea
5
Research Institute, GnC BIO Co., LTD. 621-6 Banseok-dong, Yuseong-gu, Daejeon 34069, Korea
6
Institute of Environmental Research, Kangwon National University, 1 Kangwondaehak-gil, Chuncheon-si, Gangwon-do 243341, Korea
7
College of Agriculture and Life Science, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2016, 17(3), 379; https://doi.org/10.3390/ijms17030379
Received: 7 January 2016 / Revised: 5 March 2016 / Accepted: 9 March 2016 / Published: 15 March 2016
(This article belongs to the Section Biochemistry)
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Versions Notes

Abstract

:
Aegista chejuensis and Aegista quelpartensis (Family-Bradybaenidae) are endemic to Korea, and are considered vulnerable due to declines in their population. The limited genetic resources for these species restricts the ability to prioritize conservation efforts. We sequenced the transcriptomes of these species using Illumina paired-end technology. Approximately 257 and 240 million reads were obtained and assembled into 198,531 and 230,497 unigenes for A. chejuensis and A. quelpartensis, respectively. The average and N50 unigene lengths were 735.4 and 1073 bp, respectively, for A. chejuensis, and 705.6 and 1001 bp, respectively, for A. quelpartensis. In total, 68,484 (34.5%) and 77,745 (33.73%) unigenes for A. chejuensis and A. quelpartensis, respectively, were annotated to databases. Gene Ontology terms were assigned to 23,778 (11.98%) and 26,396 (11.45) unigenes, for A. chejuensis and A. quelpartensis, respectively, while 5050 and 5838 unigenes were mapped to 117 and 124 pathways in the Kyoto Encyclopedia of Genes and Genomes database. In addition, we identified and annotated 9542 and 10,395 putative simple sequence repeats (SSRs) in unigenes from A. chejuensis and A. quelpartensis, respectively. We designed a list of PCR primers flanking the putative SSR regions. These microsatellites may be utilized for future phylogenetics and conservation initiatives.

Graphical Abstract

1. Introduction

Mollusca represents the second most speciose phyla of all animals that inhabit marine, freshwater, and terrestrial habitats. Molluscs are highly diverse ecologically, and include economic aquaculture species, environmental biomarker species, destructive pests, and disease vectors. Eighty percent of mollusc species belong to the class Gastropoda, which is one of the most successful animal groups on earth. Gastropods live in a wide range of habitats: marine, freshwater, inland aquatic (including salt lakes), and terrestrial ecosystems. Terrestrial molluscs have a number of striking adaptations in their physiology, sense organs, reproduction, and development, which allow them to occupy a diverse range of niches. In fact, land snails are recognized as model systems for investigating shell morphological evolution as a result of adaptation to particular habitats [1,2], as well as reproductive and defensive behaviors [3].
The land gastropods of the family Bradybaenidae are distributed across a wide range of habitats in East Asia. This taxonomic family comprises land snails, terrestrial pulmonate gastropod molluscs, which are medium to small in size and belong to the superfamily Helicoidea. Based on karyotyping, one study documented the conservation of chromosome number in 36 species in the family Bradybaenidae [4]. The Bradybaenid snails of East Asia that belong to the genus Aegista are very closely related, and include 75 species and 112 subspecies [5]. The subgenera Aegista and Plectotropis are distributed mainly in Japan, China, and the Korean mainland [6], while the subgenera Coelorus and Neoaegista are endemic to Japan [7,8]. The Bradybaenidae snails of Korea have been classified into 24 species. Jeju Island contains a small but interesting assortment of these snails, including eight species out of which three belong to the genus Aegista [9]. The Aegista species identified on Jeju Island include Aegista chejuensis, Aegista chosenica [10], and Aegista quelpartensis. A. chejuensis and A. quelpartensis have been found restricted to Jeju Island with no reports from any other parts of the country. Aegista species of land snails are recorded from lowland broadleaf and evergreen forests, and are usually found under fallen trees or leaf litter or attached to the stems and leaves of plants [2].
A. chejuensis and A. quelpartensis, reported from under shrubs and stone piles on Jeju Island, are listed as Korean endemic species in the Korean Red List of Threatened Species, 2014. Moreover, A. chejuensis is classified as a vulnerable species owing to a decline in its numbers over recent years, due mainly to predation by natural enemies and habitat destruction as a result of forest development. With no regional conservation measures, these endemic species face the prospect of extinction in their natural habitat. Given the vulnerable status of this species, it is critical to identify genetic markers that can be used for phylogenetics and genetic mapping. Currently, genomic information for these species is scarce (for example, there are no NCBI entries), which limits our understanding of their phylogeography and our ability to identify immediate conservation priorities.
Next-generation sequencing (NGS) technologies have been used to sequence the transcriptomes of non-model organisms, thus providing genetic resources for use in functional genomics, phylogeographic/phylogenetic research, and conservation genomics [11,12]. A 454/Roche NGS platform produces long read lengths, while the Solexa/Illumina NGS platform produces shorter reads which are useful in downstream applications such as sequence annotation [13,14]. Most recent transcriptomic analyses conducted for molluscan species have used the Illumina HiSeq platform, owing to its efficiency and relatively low cost [15,16,17]. For instance, among snails, the de novo transcriptome of the pond snail Radix balthica has been studied using the Illumina platform [14]. A large-scale transcriptomic dataset for the freshwater snail Oncomelania hupensis, which acts as the intermediate host of Schistosoma japonicum, was also created with the Illumina sequencer [18]. Similarly, this technology was used to study transcriptome-wide expression analysis in four populations of the marine snail Tegula atra along the Chilean coast [19]. Despite the progress for marine and freshwater snails, transcriptomics research in land snails has been limited, with the exception of the Illumina transcript libraries derived from central nervous system, hepatopancreas, and foot muscle of the terrestrial snail pest Theba pisana [20]. The partial genomes of two land snail species from family Bradybaenidae, Aegista diversifamilia and Dolicheulota formosensis, have yielded a large amount of genetic data which may facilitate research on the evolutionary processes in gastropods [21].
In this study, we investigated the whole-body transcriptomes of the Korean endemic land snails A. chejuensis and A. quelpartensis using the Illumina NGS platform. We provide new genomic sequence information for these species and discuss its application in molecular taxonomy, functional genomics, and conservation genetics study.

2. Results and Discussion

2.1. Illumina Sequence Analysis and Assembly

We obtained the whole transcriptomes of the Bradybaenidae land snails A. chejuensis and A. quelpartensis using the Illumina HiSeq 2500 sequencing platform. The transcriptome assembly and analysis workflow is shown in Figure 1. Each sequencing lane generated 2 × 50-nt independent reads from either end of a cDNA fragment. We obtained 256,655,870 (32,338,639,620 nt) and 239,242,058 (30,144,499,308 nt) raw reads for A. chejuensis and A. quelpartensis, respectively. After pre-processing the raw reads which included trimming adapter sequences, we recovered 99.83% and 99.79% of the sequencing reads for the A. chejuensis and A. quelpartensis transcriptomes, respectively (Table S1). A total of 253,220,985 (31,397,895,789 nt) and 235,525,993 (29,188,212,057 nt) clean reads remained, with average fragment lengths of 124 and 123.9 bp for A. chejuensis and A. quelpartensis, respectively. An overview of the transcriptome sequencing, assembling, and clustering results is presented in Table 1.
Clean reads were assembled using a Trinity de novo program (default sequence length: >200 nt) for contiguous, overlapping sequences (contigs). A total of 375,118 (229,108,084 nt) and 463,438 (269,776,350 nt) contig sequences were assembled for A. chejuensis and A. quelpartensis, respectively. The mean length and the N50 length of contigs in A. chejuensis were 610.8 and 788 bp, whereas in A. quelpartensis they were 582.1 and 719 bp, respectively. The longest contigs were 34,543 and 26,467 bp for A. chejuensis and A. quelpartensis, respectively. A size distribution analysis of the contigs in A. chejuensis (Figure 2A) revealed 74,114 contigs (19.8%) ranging from 501 to 1000 bp, 33,700 contigs (8.98%) ranging from 1001 to 2000 bp, and 16,728 contigs (4.46%) over 2001 bp in length. A. quelpartensis had approximately 88,765 contigs (19.15%) ranging from 501 to 1000 bp, 38,194 contigs (8.24%) ranging from 1001 to 2000 bp, and 17,932 contigs (3.87%) over 2001 bp in length. The previously reported Japanese scallop (Mizuhopecten yessoensis) transcriptome, which was created using the Illumina sequencing platform, generated contigs ranging from 100 to 29,088 bp in length, with an average of 436 bp [22]. In this study, we obtained a longer average contig length, with the longest in A. chejuensis being 34,543 bp. The average contig length in our assembly exceeded those of the non-model snail species R. balthica (536 bp average contig length) [14], the blood cockle Anadara trapezia (505 bp) [23], and the Chlamys farreri mantle transcriptome (249 bp) [24]. Generally, larger values for contig number, N50 length, average contig length, and maximum contig length are associated with superior assembly performance (although there are some exceptions) [25,26]. We calculated N50 by adding long contigs to short contigs until the summed length exceeded 50% of the total length of all contigs. The Trinity de novo assembler used in this study has been shown to outperform other top assemblers and is widely used across a variety of taxa [27,28,29,30].
Next, the unigenes (sequences not being extended on either side) were obtained by mapping the paired-end reads to contigs and using TGICL to form a single set of non-redundant unigenes. TGICL effectively removes redundancy and retains long, high-quality transcripts which are essential parameters for obtaining rich genetic information [31,32,33]. The analysis yielded 198,531 (145,998,300-nt) unigene sequences with average and N50 lengths of 735.4 and 1073 bp, respectively, for A. chejuensis. For A. quelpartensis, the clustering assembly yielded 230,497 unigene sequences with average and N50 lengths of 705.6 and 1001 bp, respectively. Of the assembled unigenes for A. chejuensis, 84,737 (42.68%) were longer than 500 bp, and 50,428 (25.4%) were longer than 1000 bp. In case of A. quelpartensis, 93,717 (40.66%) unigenes were longer than 500 bp, and 40,519 (17.58%) were longer than 1000 bp. The size distribution of assembled unigenes is shown in Figure 2B. The average length and N50 length of unigenes obtained in this study is greater than average length (453 bp) and N50 length (492 bp) of unigenes in the comprehensive transcriptome dataset for Echinolittorina snails [15]. Overall, based on the comparison of transcriptomes, we find a more effective de novo assembly in A. quelpartensis as it generated a larger number of transcripts from fewer raw read sequences.

2.2. Sequence Annotation and Homology Characteristics

The assembled unigene sequences of A. chejuensis and A. quelpartensis were aligned with protein and nucleotide databases using BLASTX and BLASTN analysis, respectively, at an E-value cutoff of ≤1.0 × 10−5. Table 2 reports the sequence-based annotation profiles of unigenes obtained against protein databases such as Protostome DB (PANM-DB), Clusters of Orthologous Groups of proteins (COG), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and nucleotide database in Unigene DB. While PANM, Unigene, and COG DB were used as sequence annotation databases, GO, KEGG, and InterProScan analysis were used for enrichment analysis. The results indicated that, out of 198,531 and 230,497 unigenes for A. chejuensis and A. quelpartensis, a total of 68,484 (34.5%) and 77,745 (33.73%) unigenes, respectively, were annotated to the public databases. However, 65%–66% of unigenes were not annotated based on BLAST searches with protein sequences from the public databases. Most of these unigenes were relatively short sequences and, understandably, may lack conserved protein motifs and domains. Moreover, in addition to protein-coding genes, the transcriptome may also contain incompletely spliced introns, orphaned untranslated regions (UTRs), non-coding genes, and random transcriptional noise. Consequently, these are valid sequences, even though they were not annotated as proteins. Out of the public databases used for sequence annotation in this study, PANM-DB recorded the largest number of annotated hits, with 61,483 (30.97%) and 69,549 (30.17%) unigenes for A. chejuensis and A. quelpartensis, respectively. This is not surprising, since the database contains only protostome protein sequences (Mollusca, Arthropoda and Nematoda). The database is more efficient than the NCBI non-redundant (nr) and Molluscs DB in terms of speed and quality of annotation [34]. The BLASTX annotation results for A. chejuensis and A. quelpartensis unigene sequences against PANM-DB are shown in Tables S2 and S3, respectively. We were able to map GO terms to 23,778 and 26,396 unigenes and KEGG pathways to 2246 and 2537 unigenes of A. chejuensis and A. quelpartensis, respectively. A size-based analysis of the BLAST annotated unigenes showed that longer unigenes were more likely to find matches in the reference public databases.
We also clarified the homologous matches of assembled unigene sequences in the PANM, Unigene, and COG databases using BLASTX with a cutoff E-value of 10−5 (Figure 3). Among the assembled unigenes annotated to the three public databases, 15,792 and 5780 unigenes in A. chejuensis had homologous sequences in PANM-DB and COG DB and PANM-DB and Unigene DB, respectively. A total of 12,398 unigenes were annotated by all three databases (Figure 3A). In the case of A. quelpartensis unigenes, 17,308 and 6827 unigenes were annotated concurrently by PANM-DB and COG DB, and by PANM-DB and Unigene DB, respectively. A total of 13,827 unigenes had homologous matches in all the three databases (Figure 3B). We found that most of the unigenes annotated to the functional COG DB also had homologous matches to protein sequences in PANM-DB. A. quelpartensis had more unigenes that showed homology to proteins in all three databases, which may reflect the larger number of transcripts available for this species. We also analyzed the assembled unigenes of both the species that annotated to all three databases (data not shown). The unigenes of A. quelpartensis and not A. chejuensis show homologous matches to G-type lysozyme and peroxiredoxin I in all three databases. Furthermore, homologous matches to both snail yolk ferritin and snail soma ferritin were noticed in the case of A. quelpartensis unigenes, while A. chejuensis unigenes found a homologous match to only snail soma ferritin. The annotated information for unigenes showing homologous matches in all three databases for both species includes catalase, β tubulin, importin β1, argonaute-2, kinesin light and heavy chain, troponin and tropomyosin, hedgehog, calmodulin, cathepsin, ubiquitin family, heat shock protein 70, C-type lectin, etc. These are specific examples and in no way represent the only results of the transcriptome annotation.
We annotated unigenes using a BLAST search of PANM-DB, and assessed the E-values of alignments, sequence identity, similarity distribution, and the ratio of unigene hits to non-hits. In the case of A. chejuensis unigenes annotated to PANM-DB, the top hit E-value ranged from 1.0 × 10−50 to 1.0 × 10−5 (39,249 unigenes, 63.84%), followed by 1.0 × 10−100 to 1.0 × 10−50 (8482 unigenes, 13.8%), and 0 (7308 unigenes, 11.89%) (Figure 4A). We found that 40% of the mapped sequences had identities between 40%‒60% to matches in PANM-DB, while 23% of sequences had identities of 60%–80% (Figure 4B). Regarding the similarity distribution of annotated unigenes, most sequences (66.36%) had similarity higher than 60%, and 23.22% of sequences had similarity higher than 80% (Figure 4C). The hit percentage of unigenes increased in direct proportion to the length of the annotated unigenes, with approximately 85% of sequences longer than 2001 bp having hits to matching proteins in PANM-DB (Figure 4D).
The characteristics of the homology search for A. quelpartensis unigenes annotated against PANM-DB are shown in Figure 5. The E-value distribution of the top matches in PANM-DB showed that 68.16% of unigenes displayed evidence indicating a high degree of homology (1.0 × 10−50–1.0 × 10−5) to known genes, whereas 12.49% and 10.10% of sequences had E-values ranging from 1.0 × 10−100 to 1.0 × 10−50 and 0, respectively (Figure 5A). Forty-two percent of sequences showed identity of 40%–60%, and 22% of sequences showed identity of 60%–80% to matches in PANM-DB (Figure 5B). A large proportion of the unigenes (44.09%) showed similarity of 60%–80%, while 21.50% of unigenes showed similarity greater than 80% to the top annotated sequences in PANM-DB (Figure 5C). As with A. chejuensis, the number of annotated hits of A. quelpartensis unigenes increased with length; BLAST hits were obtained for approximately 85% of sequences longer than 2001 bp (Figure 5D). The species distribution showed that the greatest number of matches for A. chejuensis (47.37% of PANM-DB annotated unigenes) and A. quelpartensis unigenes (42.53% of PANM-DB annotated unigenes) were with Aplysia californica genes for which the genomic resources have previously been characterized in detail [35,36]. There were also top hit matches of unigene sequences to known proteins of the marine gastropod mollusc Lottia gigantea and the Pacific oyster Crassostrea gigas. The top hit species distributions for A. chejuensis and A. quelpartensis are shown in Figure 6A,B, respectively.

2.3. Functional Prediction Using COG, GO and KEGG

The assembled A. chejuensis and A. quelpartensis unigenes were annotated against the proteins in the COG database. As a platform, the COG analysis classifies gene products into atleast 25 protein families broadly categorized to “metabolism”, “cellular processes and signaling”, “information storage and processing” or “poorly characterized groups”. In this study, a total of 28,197 and 31,148 unigenes from A. chejuensis and A. quelpartensis, respectively, were classified into 25 functional categories (excluding the multi-class category) (Figure 7). For both species, a large proportion of annotated sequences fell into the “general function prediction only” and “signal transduction mechanisms” categories, as well as the multi-class category. For A. chejuensis and A. quelpartensis sequences annotated against the COG database, 20.18% and 19.89% of unigenes, respectively, were grouped into “general function prediction only” category. The groups to which the fewest unigenes were annotated in both species included “defense mechanisms”, “chromatin structure and dynamics”, “coenzyme transport and metabolism”, “nuclear structure”, and “cell motility”. The COG-based functional predictions of annotated A. chejuensis and A. quelpartensis unigenes are shown in Figure 7A,B, respectively. Our results are consistent with the COG-based functional prediction of unigenes in the Littorinid snail Echinolittorina malaccana [15], invasive golden apple snail Pomacea canaliculata, and the mud snail Cipangopaludina cahayensis [37].
Functional analysis with the BLAST2GO suite was conducted on A. chejuensis and A. quelpartensis unigenes to identify associated GO terms and KEGG pathways. GO classification matches a gene to others of known (or predicted) function, but does not provide conclusive evidence of its function. The three main GO terms were “biological process”, “molecular function”, and “cellular component”. Among the 23,778 unigenes annotated for A. chejuensis, 8724, 847, and 792 sequences were classified into “molecular function”, “cellular component”, and “biological process”, respectively (Figure 8A). A total of 6620 unigenes were classified into both the “molecular function” and “biological process” categories, while 5323 sequences were classified into all three categories of GO term annotations. Moreover, 6960 (29.27%) unigene sequences annotated with a single GO term, and the remaining 16,818 (70.73%) sequences were annotated with more than one GO term (Figure 8B). Of the 26,396 unigenes annotated for A. quelpartensis, 9473, 992, and 977 sequences were assigned to the “molecular function”, “biological process”, and “cellular component” categories, respectively (Figure 8C). In addition, 5426 unigenes were classified into all three categories. A total of 7749 (29.36%) unigenes were annotated with a single GO term, while the remaining unigenes (18,647, 70.64%) were annotated with more than one GO term (Figure 8D).
The top represented GO terms under the three functional categories (on level 2) for A. chejuensis are shown in Figure 9A. Within the category of “biological process”, most GO terms were grouped into “cellular process” (GO: 0009987), “metabolic process” (GO: 0008152) and “single-organism process” (GO: 0044699). Within the ‘cellular component’ category, the most highly represented GO terms were “membrane” (GO: 0016020), “cell” (GO: 0005623), and “organelle” (GO: 0043226). Among the 11 “molecular function” subcategories, most unigenes were assigned to “binding” (GO: 0005488), followed by “catalytic activity” (GO: 0003824) and “transporter activity” (GO: 0005215). GO term annotations for unigenes in the categories “response to stimulus” (GO: 0050896), “signaling” (GO: 0023052), and “immune system process” (GO: 0002376) helped to shed light on the adaptations of such endemic land snails. The assignment of GO functional terms to predicted A. quelpartensis unigenes followed a similar trend. The assigned sequences were categorized into 40 subcategories within three main categories, including “biological process” (19), “molecular function” (11), and “cellular component” (10) (Figure 9B). Within the “biological process” category, “cellular process”, “metabolic process” and “single-organism process” represented the most common GO terms. Within the “molecular function” category, “binding” and “catalytic activity” were the main GO assignments; within the “cellular components” category, the main assignments were “cell”, “membrane”, “organelle” and “macromolecular complex” (GO: 0032991). GO-based annotation suggested a diverse functional categorization of the predicted unigenes in A. chejuensis and A. quelpartensis, which is consistent with the results from the sequenced land snail T. pisana [20] and the freshwater snail O. hupensis [18]. In inferring functionality from GO terms, it is important to emphasize that not all GO terms are of equal validity, and that unigene function can be only predicted, not determined with certainty [38]. A GO classification, therefore, is not conclusive evidence of functionality and, in the absence of experimental verification, GO results can only suggest functionality. A large number of false positives are likely in many species as the majority of GO terms are assigned the code “IEA” (inferred from electronic annotation). These are terms that have not been manually curated and hence are of questionable validity. The GO term annotation in this study hence suggests that a gene is grouped to those of known (or predicted) function.
In addition to COG analysis and GO annotation, we mapped the A. chejuensis and A. quelpartensis predicted proteins to reference KEGG pathways for functional categorization and annotation (Figure 10). Annotation based on the KEGG database is an alternative method to assess the involvement of unigenes in significant biological pathways, thus providing insight into intracellular metabolic functions. The assembled unigenes fell into KEGG pathways corresponding to “metabolism”, “genetic information processing”, “environmental information processing”, and “organismal systems”. For A. chejuensis, this process led to the assignment of 5050 assembled unigene sequences to a total of 117 pathways, while 5838 sequences were assigned to 124 pathways for A. quelpartensis. The unigenes identified from A. quelpartensis, but not A. chejuensis, relates to caffeine metabolism; d-alanine, d-glutamine, and d-glutamate metabolism; flavone and flavonol biosynthesis; lysine biosynthesis; and other types of O-glycan biosynthesis pathways. Metabolic pathways were highly represented with 4809 and 5546 unigenes for A. chejuensis and A. quelpartensis, respectively, associated with basic metabolism functions. Among the “signal transduction mechanism” pathways, unigenes from both species were assigned to “mTOR signaling pathway” and “phosphatidylinositol signaling system”. Unigenes from both species were assigned to the immune system “T-cell receptor signaling pathway”. The KEGG pathway resources for land snails of the genus Aegista may help reveal the specific bioprocesses related to its successful adaptation to a particular habitat, as well as possible gene plasticity allowing survival in other environments.

2.4. Protein Domain Identification Using InterProScan Searches

Using InterProScan, we annotated 6826 and 7170 unique protein domains among the assembled unigenes of A. chejuensis and A. quelpartensis, respectively. The most well-represented protein domains identified in A. chejuensis unigenes were the zinc finger, C2H2-like domain (IPR015880), G-protein-coupled receptor, rhodopsin-like family domain (IPR000276), and Ribonuclease H-like domain (IPR012337) (Table 3). Other domains with sequence hits included the ankyrin repeat (IPR002110), protein kinase (IPR002290), RNA recognition motif (IPR000504), WD40 repeat (IPR001680), immunoglobulin-like fold (IPR013783), and C-type lectin domain (IPR001304). The protein domains most highly represented based on A. quelpartensis unigene annotation are shown in Table 4. As with A. chejuensis, the dominant domains included the transcription factor zinc finger domains, protein kinase domains, and RNA recognition motif domains, which are pivotal in cellular regulatory processes.
The zinc finger C2H2-like domains are ubiquitous protein domains responsible for interaction with nucleic acids and protein targets [39]. Generally, multiple clusters of C2H2 zinc finger domains enable highly specific nucleic acid binding, thus assisting in cell fate determination and early developmental processes [40]. Recently, an expansion of C2H2-like zinc finger domains was identified in amphioxus and the California two-spot octopus, Octopus bimaculoides [41]. The C-type lectin, WD-40, protein kinase, catalytic, ankyrin repeat and immunoglobulin-like fold domains were also found to be abundant InterPro domains in the Mytilus galloprovincialis digestive gland transcriptome [28]. Protein kinase domains and WD40 repeat domains are conserved sequences involved in signal transduction functions and apoptosis [42,43]. Immunoglobulin-like fold repeat motifs are characteristic of proteins involved in the immune system and cellular processes, and are mediators of protein–protein interactions [44,45]. The InterProScan protein domain identification complements the functional annotation of unigenes, but is not error-free since it is based on electronic annotation. Hence, it could be considered as a preliminary step to unravel the putative functions of assembled sequences from transcriptome characterization.

2.5. Simple Sequence Repeat (SSR) Identification

SSRs are short sequences of 2–6 bases, and are established molecular markers in gene polymorphism studies and genomics applications. The SSRs in cDNAs are considered more transferable than random genomic SSRs because SSRs in genes are likely to be more conserved across taxa than SSRs from noncoding regions [46]. Therefore, the strength of transcriptome-derived SSRs in facilitating evolutionary analyses is due to the fact that they likely occur in the protein-coding regions of annotated unigenes [47,48,49]. These SSRs can be used to analyze the attributes of functional genes in association with their phenotypes [50]. We identified SSRs in 37,869 and 40,573 unigene sequences longer than 1 kb from A. chejuensis and A. quelpartensis, respectively. A total of 9542 and 10,395 SSRs comprising dinucleotide to hexanucleotide repeats were identified as high-priority markers. Mononucleotide microsatellites may arise from errors in sequencing homopolymeric regions, and thus were not considered for this study. We have provided a list of primer sequences that can be utilized to target the potential polymorphic SSRs in the two species (Tables S4 and S5 for A. chejuensis and A. quelpartensis, respectively). To characterize the markers in more detail and improve their utility for conservation genetics, we provide the PANM-DB annotation of SSR-containing sequences. Some of these microsatellite markers are located in unigene sequences related to innate immunity and defense (such as T-cell receptor, fibrinogen related protein, TRAF3, lectins, tumor necrosis factor etc.), defense processes against oxidative stress and other environmental perturbations (such as acetylcholine receptor, cytochrome P450 etc.), and regulatory binding/interaction processes (including the zinc finger motifs and serine/threonine motifs). This information may be useful for future work related to conservation genetics and population genetics for these two species endemic to Korea.
In both species, dinucleotide repeat motifs were the most common type of marker, followed by trinucleotide and tetranucleotide motifs. In A. chejuensis, dinucleotide repeats with six tandem iterations, trinucleotide repeats with five tandem iterations, and tetranucleotide repeats with four tandem iterations were the most common (Figure 11A). Among the different numbers of tandem repeats, six tandem iterations were more common, followed by five and seven tandem iterations. The majority of pentanucleotide and hexanucleotide repeats showed a maximum of four tandem iterations. In A. quelpartensis, the frequencies of various types of SSRs were similar to those of A. chejuensis. The most frequent repeats were dinucleotides, which accounted for 57.58% of all SSRs, followed by trinucleotides (31.62%) and tetranucleotides (10.10%). Six iterations were most common among dinucleotide repeats, while five iterations were the most common among trinucleotide repeats. The less-common pentanucleotide and hexanucleotide repeats had a maximum of four iterations. SSRs with few tandem iterations were more common than those with many iterations, with the most common class being n = 6. A summary of the classified repeat types in A. quelpartensis unigenes is shown in Figure 11B.
We also identified the most common repeat motif types among SSRs (Figure 12). Among A. chejuensis SSRs, AC/GT motifs (29.99%) were most abundant among dinucleotide repeats, while ATC/ATG motifs (11.30%) and AAAG/CTTT (1.41%) were the most abundant among trinucleotide and tetranucleotide repeats, respectively. A detailed profile of the SSR motif types identified in A. chejuensis is shown in Figure 12A. Among the A. quelpartensis SSRs, AC/GT (29.41%) was also the most abundant dinucleotide repeat type (Figure 12B). The most abundant trinucleotide and tetranucleotide motifs were ATC/ATG (10.91%) and ACAG/CTGT (1.5%), respectively. In the invasive snail P. canaliculata [37] and oyster Crassostrea hongkongensis [16], dinucleotide repeats with AG/CT and AT/AT repeat motifs were the most abundant SSRs. We also identified AT/AT repeat types in both A. chejuensis and A. quelpartensis SSRs. In the snail E. malaccana, the most common dinucleotide motif types in SSRs were AC/GT, followed by AG/CT [15]. SSRs identified from unigenes will facilitate future research on the genetic diversity and conservation of these species.

3. Experimental Section

3.1. Sample Preparation, cDNA Synthesis and Illumina Sequencing

A. chejuensis and A. quelpartensis were collected in living conditions from evergreen Jeju Jeolmul National Recreation Forest in Myeongnim-ro, Jeju-si, Jeju-do Island, Korea in August 2014. Our collection was permitted and assisted by the forest institute. Since these land snails are not on the list of endangered or protected species, no other permission was required. The snails were brought to the laboratory and rinsed in double distilled H2O to remove the mud and other particles attached to the shell. Snails were maintained at room temperature, within built-in enclosures and provided with water and food ad libitum. The snails were carefully removed from their shells with care to remove shell fragments. Whole-body samples of A. chejuensis and A. quelpartensis (n = 10) were frozen in liquid nitrogen for RNA extraction.
Total RNA was extracted from the snap-frozen homogenized samples using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) following the manufacturer’s protocol. RNA samples were treated with RNase-Free DNase I to eliminate genomic DNA. The purity and integrity of the extracted RNA was confirmed using the NanoDrop-2000 Spectrophotometer (Thermo Scientific, Wilmington, DE, USA) and Agilent 2100 Bioanalyzer with a minimum integrity number value of 7.
Oligo(dT) beads were used to elute poly(A) mRNA after RNA extraction. The mRNA was fragmented using fragmentation buffer to obtain short fragments prior to cDNA synthesis. First-strand cDNA synthesis was carried out using random-hexamer primers, with the short fragments as templates. Second-strand cDNA synthesis was carried out using buffer, dNTPs, RNaseH, and DNA polymerase I. The synthesized double-stranded cDNA was purified using the QiaQuick PCR extraction kit (Qiagen Inc., Valencia, CA, USA) and resolved with EB buffer for end-repair and A-tailing. Subsequently, sequencing adapters were attached to the fragments. The fragments were purified by agarose gel electrophoresis and enriched by PCR amplification. The cDNA library was sequenced by GnC Bio Company, Daejeon, Korea, using the Illumina HiSeq 2500, according to the manufacturer’s instructions. The transcriptome datasets of A. chejuensis and A. quelpartensis are available from the NCBI Sequence Read Archive (SRA) under the accession numbers SRP064881 (Project number PRJNA298949) and SRP064882 (Project number PRJNA298950), respectively. The datasets with the assembled contig information can be downloaded on or after 16 October 2016 (release date) [51].

3.2. De novo Transcriptome Assembly

The raw reads generated by Illumina sequencing were transformed by base calling and preprocessed to remove adapter fragments, ambiguous reads (i.e., reads more than 5% unknown nucleotides) and low-quality sequences (Phred quality score <20 bases). Overlapping high-quality reads were used to create longer contiguous fragments (contigs) using the Trinity short reads assembler [52]. Assembly was carried out using the default Trinity options and a minimum length of 200 nt. The contig N50 value was computed using the Trinity script: % $TRINITY_HOME/util/TrinityStats.pl Trinity.fasta. Next, we used the TIGR Gene Indices Clustering Tools (TGICL) [53] a sequence clustering software, to cluster contigs into unigenes. The unigene sequences constitute expressed assembled sequences, but are not characterized sufficiently to be represented as a gene. For TGICL, we used the default parameters: n (number of sequences in a clustering search slice)—1000, p (minimum percent identity for overlaps)—94, I (minimum overlap length)—30, and v (maximum length of unmatched overhangs)—90.

3.3. Functional Annotation

The assembled unigenes profile was determined by a sequence-based annotation against Protostome database (PANM-DB) using BLASTX alignment (E-value ≤ 1 × 10−5). PANM-DB is a database for the analysis of molluscan NGS data, and contains protein sequences from Arthropoda, Nematoda, and Mollusca [34]. PANM-DB is linked to the amino acid BLAST web-interface of the Malacological Society of Korea [54]. Additional databases used for unigene annotation included the Unigene (BLASTN; E-value ≤ 1 × 10−5), Clusters of Orthologous Groups (COG), and Gene Ontology (GO) DB. The best-aligned results were used to determine the sequence direction and coding sequence (CDS) of the unigenes. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations were assigned using the Blast2GO program [55]. WEGO software [56] was used to suggest the GO functional classification. The protein domain hits for assembled unigenes were recorded based on an InterProScan search in the Blast2GO interface [57].

3.4. SSR Motifs Detection

Simple sequence motifs (SSR) were identified using the program MicroSAtellite (MISA) [58] on unigenes of A. chejuensis and A. quelpartensis longer than 1 kb. The parameters defined for SSR analysis were as follows: di-, tri-, tetra-, penta-, and hexa-nucleotide repeats with a minimum of six, five, four, four, and four repeat numbers, respectively. Owing to the possibility of homopolymer generation during Illumina sequencing, we excluded mononucleotide repeats from our analysis. The BatchPrimer 3 program [59] was used to design the primers flanking the SSR motifs for polymorphism analysis. Primers were designed based on the following criteria: dinucleotides with six or more iterations and tri-/tetranucleotides with a minimum of four iterations. The primer characteristics were as follows: primer length 18–23 bases with an optimum size of 21 bases, product size of 100–300 bases, Tm (melting temperature) ranging from 50 to 70 °C, and primer GC% of 30%–70%. We also predicted unigene function based on homology to PANM-DB sequences (E-value cut-off of 10−5).

4. Conclusions

Our study represents the first transcriptome analysis for the land snails A. chejuensis and A. quelpartensis, which are endemic to Korea and are listed in the Red List of Threatened Species. Our results include novel genetic resources which may prove valuable in future research on the adaptive physiology and phylogeography of these species. Our genomic data were generated using Illumina HiSeq 2500 de novo transcriptome assembly, and functions were predicted using BLAST searches against public databases. Furthermore, SSRs identified from unigenes will facilitate the assessment of genetic diversity and conservation of these species in their natural habitat.

Supplementary Materials

Supplementary materials can be found at https://www.mdpi.com/1422-0067/17/3/379/s1.

Acknowledgments

This work was supported by the grant entitled “The Genetic and Genomic Evaluation of Indigenous Biological Resources” funded by the National Institute of Biological Resources (NIBR201503202) and Soonchunhyang University Research Fund.

Author Contributions

Se Won Kang, Bharat Bhusan Patnaik, Hee-Ju Hwang, Soonok Kim and Yong Seok Lee designed the experiments. So Young Park, Tae Hun Wang, Eun Bi Park, Jong Min Chung, Dae Kwon Song and Jae Bong Lee performed the experiments. Bharat Bhusan Patnaik, Hee-Ju Hwang, Hongray Howrelia Patnaik and Se Won Kang analyzed the data. Bharat Bhusan Patnaik, Hee-Ju Hwang, Hongray Howrelia Patnaik, and Se Won Kang wrote the paper. Changmu Kim, Soonok Kim, Hong Seog Park, Jun Sang Lee, and Yeon Soo Han contributed reagents/materials/analysis tools. Yong Seok Lee supervised the entire study.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Schematic work-flow of the transcriptome analysis employed in the present study to annotate the unigenes of Korean endemic land snails, Aegista chejuensis and Aegista quelpartensis.
Figure 1. Schematic work-flow of the transcriptome analysis employed in the present study to annotate the unigenes of Korean endemic land snails, Aegista chejuensis and Aegista quelpartensis.
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Figure 2. Size distribution of contigs (A) and unigenes (B) after assembly and clustering of the clean reads obtained from transcriptome sequencing of A. chejuensis and A. quelpartensis.
Figure 2. Size distribution of contigs (A) and unigenes (B) after assembly and clustering of the clean reads obtained from transcriptome sequencing of A. chejuensis and A. quelpartensis.
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Figure 3. Sequence-based annotation of A. chejuensis (A); and A. quelpartensis (B) unigenes against PANM-DB, COG DB (BLASTX) and Unigene DB (BLASTN). The numbers represent the number of unigenes uniquely matched to homologous sequences in one, two or all three databases.
Figure 3. Sequence-based annotation of A. chejuensis (A); and A. quelpartensis (B) unigenes against PANM-DB, COG DB (BLASTX) and Unigene DB (BLASTN). The numbers represent the number of unigenes uniquely matched to homologous sequences in one, two or all three databases.
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Figure 4. PANM-DB homology classification of A. chejuensis unigenes. (A) E-value distribution; (B) Identity distribution; (C) Similarity distribution; (D) Unigene hit or non-hit ratio.
Figure 4. PANM-DB homology classification of A. chejuensis unigenes. (A) E-value distribution; (B) Identity distribution; (C) Similarity distribution; (D) Unigene hit or non-hit ratio.
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Figure 5. PANM-DB homology classification of A. quelpartensis unigenes. (A) E-value distribution; (B) Identity distribution; (C) Similarity distribution; (D) Unigene hit or non-hit ratio.
Figure 5. PANM-DB homology classification of A. quelpartensis unigenes. (A) E-value distribution; (B) Identity distribution; (C) Similarity distribution; (D) Unigene hit or non-hit ratio.
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Figure 6. PANM-DB based top-hit species classification for (A) A. chejuensis; and (B) A. quelpartensis using BLASTX analysis.
Figure 6. PANM-DB based top-hit species classification for (A) A. chejuensis; and (B) A. quelpartensis using BLASTX analysis.
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Figure 7. COG classification of (A) A. chejuensis and (B) A. quelpartensis unigenes. The code descriptions for COG categories are as follows: R, general function prediction only; Multi, more than one classified function; T, signal transduction mechanisms; S, unknown function; O, post-translational modification, protein turnover, and chaperones; K, transcription; Z, cytoskeleton; J, translation, ribosomal structure, and biogenesis; U, intracellular trafficking, secretion, and vesicular transport; G, carbohydrate transport and metabolism; E, amino acid transport and metabolism; A, RNA processing and modification; P, inorganic ion transport and metabolism; L, replication, recombination, and repair; I, lipid transport and metabolism; C, energy production and conversion; Q, secondary metabolites biosynthesis, transport and catabolism; W, extracellular structures; M, cell wall/membrane/envelope biogenesis; D, cell cycle control, cell division, and chromosome portioning; F, nucleotide transport and metabolism; V, defense mechanisms; B, chromatin structure and dynamics; H, co-enzyme transport and metabolism; Y, nuclear structure; N, cell motility.
Figure 7. COG classification of (A) A. chejuensis and (B) A. quelpartensis unigenes. The code descriptions for COG categories are as follows: R, general function prediction only; Multi, more than one classified function; T, signal transduction mechanisms; S, unknown function; O, post-translational modification, protein turnover, and chaperones; K, transcription; Z, cytoskeleton; J, translation, ribosomal structure, and biogenesis; U, intracellular trafficking, secretion, and vesicular transport; G, carbohydrate transport and metabolism; E, amino acid transport and metabolism; A, RNA processing and modification; P, inorganic ion transport and metabolism; L, replication, recombination, and repair; I, lipid transport and metabolism; C, energy production and conversion; Q, secondary metabolites biosynthesis, transport and catabolism; W, extracellular structures; M, cell wall/membrane/envelope biogenesis; D, cell cycle control, cell division, and chromosome portioning; F, nucleotide transport and metabolism; V, defense mechanisms; B, chromatin structure and dynamics; H, co-enzyme transport and metabolism; Y, nuclear structure; N, cell motility.
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Figure 8. The functional prediction of unigenes under GO classification. (A) The distribution of the unigenes of A. chejuensis to GO biological function, cellular component and molecular function; (B) The number of GO terms ascribed to unigenes of A. chejuensis; (C) The GO functional prediction of A. quelpartensis unigenes; (D) The number of GO terms ascribed to unigenes of A. quelpartensis.
Figure 8. The functional prediction of unigenes under GO classification. (A) The distribution of the unigenes of A. chejuensis to GO biological function, cellular component and molecular function; (B) The number of GO terms ascribed to unigenes of A. chejuensis; (C) The GO functional prediction of A. quelpartensis unigenes; (D) The number of GO terms ascribed to unigenes of A. quelpartensis.
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Figure 9. GO functional classification at level 2 for A. chejuensis (A); and A. quelpartensis (B) unigenes.
Figure 9. GO functional classification at level 2 for A. chejuensis (A); and A. quelpartensis (B) unigenes.
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Figure 10. KEGG classification of unigenes in A. chejuensis (A); and A. quelpartensis (B).
Figure 10. KEGG classification of unigenes in A. chejuensis (A); and A. quelpartensis (B).
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Figure 11. Summary of classified SSR repeat types in A. chejuensis (A); and A. quelpartensis (B) transcriptome.
Figure 11. Summary of classified SSR repeat types in A. chejuensis (A); and A. quelpartensis (B) transcriptome.
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Figure 12. Frequency distribution of SSR repeat types in A. chejuensis (A); and A. quelpartensis (B) transcriptome.
Figure 12. Frequency distribution of SSR repeat types in A. chejuensis (A); and A. quelpartensis (B) transcriptome.
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Table
Table 1. Statistics of transcriptome sequencing and assembling for A. chejuensis and A. quelpartensis.
Table 1. Statistics of transcriptome sequencing and assembling for A. chejuensis and A. quelpartensis.
StatisticsA. chejuensisA. quelpartensis
Raw Reads
Number of sequences256,655,870239,242,058
Number of total nucleotides32,338,639,62030,144,499,308
Clean Reads
Number of sequences253,220,985235,525,993
Number of total nucleotides31,397,895,78929,188,212,057
Mean length (bp)124123.9
High-quality reads (%)98.66 (sequences); 97.09 (bases)98.45 (sequences); 96.83 (bases)
Number of reads discarded (%)1.34 (sequences); 2.91 (bases)1.55 (sequences); 3.17 (bases)
Assembled contigs
Number of contigs375,118463,438
Number of total nucleotides229,108,084269,776,350
Mean length (bp)610.8582.1
N50 length (bp)788719
GC% of contig42.0241.53
Largest contig (bp)34,54326,467
Number of contigs ≥500 bp124,882145,244
Assembled unigenes
Number of unigenes198,531230,497
Number of total nucleotides145,998,300162,627,732
Mean length (bp)735.4705.6
N50 length (bp)10731001
GC% of unigene41.9841.40
Length ranges (bp)105–34,543100–29,273
Table
Table 2. Annotation of A. chejuensis and A. quelpartensis unigenes against the public databases.
Table 2. Annotation of A. chejuensis and A. quelpartensis unigenes against the public databases.
DatabasesAll Annotated Unigenes≤300 bp300–1000 bp≥1000 bp
A. chejuensisA. quelpartensisA. chejuensisA. quelpartensisA. chejuensisA. quelpartensisA. chejuensisA. quelpartensis
PANM61,48369,549888211,19127,31832,40125,28325,957
Unigene24,66028,2203857456410,54312,97110,26010,685
COG28,19731,14824433085885911,29716,89516,766
GO23,77826,39627473467867010,73412,36112,195
KEGG2246253719023063487714221430
ALL68,48477,74510,51513,03731,55237,41026,41727,298
Table
Table 3. Top hit InterPro terms from the InterProScan annotations of A. chejuensis unigenes.
Table 3. Top hit InterPro terms from the InterProScan annotations of A. chejuensis unigenes.
DomainDescriptionNumber of Unigenes
IPR015880Zinc finger, C2H2-like domain1655
IPR000276G protein-coupled receptor, rhodopsin-like family338
IPR012337Ribonuclease H-like domain322
IPR002110Ankyrin repeat307
IPR000477Reverse transcriptase domain299
IPR013087Zinc finger C2H2-type/integrase DNA-binding domain290
IPR027417P-loop containing nucleoside triphosphate hydrolase domain254
IPR002290Serine/threonine/dual specificity protein kinase, catalytic domain238
IPR003591Leucine-rich repeat, typical subtype repeat174
IPR002126Cadherin domain171
IPR000504RNA recognition motif domain169
IPR001680WD40 repeat164
IPR000742EGF-like domain162
IPR002048EF-hand domain154
IPR005135Endonuclease/exonuclease/phosphatase domain138
IPR013783Immunoglobulin-like fold domain136
IPR011701Major facilitator superfamily130
IPR002035von Willebrand factor, type A domain124
IPR012336Thioredoxin-like fold domain124
IPR001304C-type lectin domain118
IPR001478PDZ domain114
IPR019734Tetratricopeptide repeat114
IPR001841Zinc finger, RING-type domain111
IPR001245Serine-threonine/tyrosine-protein kinase catalytic domain104
IPR001849Pleckstrin homology domain103
IPR002172Low-density lipoprotein (LDL) receptor class A repeat103
IPR003593AAA+ ATPase domain103
IPR001452SH3 domain100
IPR003599Immunoglobulin subtype domain100
IPR000008C2 domain96
IPR011989Armadillo-like helical domain92
IPR007087Zinc finger, C2H2 domain90
IPR001888Transposase, type 1 family86
IPR000859CUB domain83
IPR001881EGF-like calcium-binding domain82
IPR003961Fibronectin type III domain80
IPR0194277TM GPCR, serpentine receptor class w (Srw) family79
IPR020846Major facilitator superfamily domain77
IPR002557Chitin binding domain75
Table
Table 4. Top hit InterPro terms from the InterProScan annotations of A. quelpartensis unigenes.
Table 4. Top hit InterPro terms from the InterProScan annotations of A. quelpartensis unigenes.
DomainDescriptionNumber of Unigenes
IPR015880Zinc finger, C2H2-like domain1658
IPR000477Reverse transcriptase domain378
IPR012337Ribonuclease H-like domain360
IPR027417P-loop containing nucleoside triphosphate hydrolase domain322
IPR002110Ankyrin repeat322
IPR000276G protein-coupled receptor, rhodopsin-like family280
IPR013087Zinc finger C2H2-type/integrase DNA-binding domain249
IPR000504RNA recognition motif domain223
IPR002290Serine/threonine/dual specificity protein kinase, catalytic domain 217
IPR001680WD40 repeat198
IPR002048EF-hand domain179
IPR005135Endonuclease/exonuclease/phosphatase domain176
IPR013783Immunoglobulin-like fold domain175
IPR003591Leucine-rich repeat, typical subtype repeat165
IPR002126Cadherin domain153
IPR000742EGF-like domain146
IPR011701Major facilitator superfamily145
IPR012336Thioredoxin-like fold domain140
IPR002035von Willebrand factor, type A domain126
IPR011989Armadillo-like helical domain116
IPR001841Zinc finger, RING-type domain109
IPR001888Transposase, type 1 family105
IPR019734Tetratricopeptide repeat104
IPR001304C-type lectin domain104
IPR001245Serine-threonine/tyrosine-protein kinase catalytic domain102
IPR016040NAD(P)-binding domain101
IPR001478PDZ domain101
IPR002347Glucose/ribitol dehydrogenase family100
IPR000008C2 domain98
IPR003593AAA+ ATPase domain97
IPR020846Major facilitator superfamily domain96
IPR001849Pleckstrin homology domain93
IPR029058Alpha/Beta hydrolase fold domain92
IPR001452SH3 domain92
IPR003599Immunoglobulin subtype domain90
IPR015943WD40/YVTN repeat-like-containing domain89
IPR007087Zinc finger, C2H2 domain89
IPR002172Low-density lipoprotein (LDL) receptor class A repeat86
IPR000859CUB domain86
IPR000719Protein kinase domain81

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MDPI and ACS Style

Kang, S.W.; Patnaik, B.B.; Hwang, H.-J.; Park, S.Y.; Wang, T.H.; Park, E.B.; Chung, J.M.; Song, D.K.; Patnaik, H.H.; Lee, J.B.; et al. De novo Transcriptome Generation and Annotation for Two Korean Endemic Land Snails, Aegista chejuensis and Aegista quelpartensis, Using Illumina Paired-End Sequencing Technology. Int. J. Mol. Sci. 2016, 17, 379. https://doi.org/10.3390/ijms17030379

AMA Style

Kang SW, Patnaik BB, Hwang H-J, Park SY, Wang TH, Park EB, Chung JM, Song DK, Patnaik HH, Lee JB, et al. De novo Transcriptome Generation and Annotation for Two Korean Endemic Land Snails, Aegista chejuensis and Aegista quelpartensis, Using Illumina Paired-End Sequencing Technology. International Journal of Molecular Sciences. 2016; 17(3):379. https://doi.org/10.3390/ijms17030379

Chicago/Turabian Style

Kang, Se Won, Bharat Bhusan Patnaik, Hee-Ju Hwang, So Young Park, Tae Hun Wang, Eun Bi Park, Jong Min Chung, Dae Kwon Song, Hongray Howrelia Patnaik, Jae Bong Lee, and et al. 2016. "De novo Transcriptome Generation and Annotation for Two Korean Endemic Land Snails, Aegista chejuensis and Aegista quelpartensis, Using Illumina Paired-End Sequencing Technology" International Journal of Molecular Sciences 17, no. 3: 379. https://doi.org/10.3390/ijms17030379

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