- Research article
- Open Access
Close ecological relationship among species facilitated horizontal transfer of retrotransposons
© The Author(s). 2016
Received: 26 April 2016
Accepted: 27 September 2016
Published: 7 October 2016
Horizontal transfer (HT) of genetic materials is increasingly being found in both animals and plants and mainly concerns transposable elements (TEs). Many crustaceans have big genome sizes and are thus likely to harbor high TE contents. Their habitat might offer them ample opportunities to exchange genetic materials with organisms that are ecologically close but taxonomically distant to them.
In this study, we analyzed the transcriptome of Pacific white shrimp (Litopenaeus vannamei), an important economic crustacean, to explore traces of HT events. From a collection of newly assembled transcripts, we identified 395 high reliable TE transcripts, most of which were retrotransposon transcripts. One hundred fifty-seven of those transcripts showed highest similarity to sequences from non-arthropod organisms, including ray-finned fishes, mollusks and putative parasites. In total, 16 already known L. vannamei TE families are likely to be involved in horizontal transfer events. Phylogenetic analyses of 10 L. vannamei TE families and their homologues (protein sequences) revealed that L. vannamei TE families were generally more close to sequences from aquatic species. Furthermore, TEs from other aquatic species also tend to group together, although they are often distantly related in taxonomy. Sequences from parasites and microorganisms were also widely present, indicating their possible important roles in HT events. Expression profile analyses of transcripts in two NCBI BioProjects revealed that transcripts involved in HT events are likely to play important roles in antiviral immunity. More specifically, those transcripts might act as inhibitors of antiviral immunity.
Close ecological relationship, especially predation, might greatly facilitate HT events among aquatic species. This could be achieved through exchange of parasites and microorganisms, or through direct DNA flow. The occurrence of HT events may be largely incidental, but the effects could be beneficial for recipients.
Horizontal transfer (HT) of genetic materials between reproductively isolated species is an important mechanism in the evolution of prokaryotic genomes [1–3]. Recent studies showed that HT events are also widespread in animals and plants and mainly concern transposable elements (TEs) [4–12]. TEs are usually grouped into two distinct classes: class I elements (retrotransposons) and class II elements (DNA transposons) . Retrotransposons, which integrate into new sites via a copy and paste mechanism, are often the major components in the genomes of many eukaryotic species, especially those with large genomes . Retrotransposons constitute over 50 % of the genomes in many plants . In mammals, LINE-1 (L1) retrotransposons’ activity alone generated at least 20 % of the genome . The horizontally transferred TEs are also mainly retrotransposons [6, 11]. However, unlike retroviruses, retrotransposons do not encode an envelope protein and hence require a vector between species to transpose horizontally. The vector discussed here is often thought to be parasites, which have ample opportunities to exchange genetic material with their hosts as the result of an intimate, long-term physical association . In eukaryotes, the underlying mechanisms are largely unknown, but the proximity of species is almost indispensable in all HT events and may consequently increase the likelihood of HT. If HT also plays an important role in eukaryotic evolution, we may expect to find more evidence of HT events among species that are distantly related in taxonomy yet live in the same habitat.
The ancient crustaceans are a great model to investigate horizontal TE transfer (HTT) in eukaryotes. Many of them have big genome sizes and are thus likely to harbor high TE contents . Decapod crustaceans, for instance, have genome sizes range from 1.05 Gb to 40 Gb (for human, the value is around 3 Gb). They have ample opportunities to intimately connect with fishes, mollusks and other animas that also inhabit in fresh or salty water. Furthermore, this connection is much less disturbed by geographical isolation when compared to land animals. Therefore, crustaceans may at least have some sequences that show higher similarity to other aquatic animals than land arthropods. However, one big drawback is that the whole genome sequencing projects of most crustaceans are not finished yet. Even though, next generation sequencing has made available more comprehensive transcriptome sequences for many crustaceans [18–20]. And HTTs detected in transcriptome are of particular importance: they are still active and may still have impact on genome evolution.
In this study, we particularly focused on Pacific white shrimp, Litopenaeus vannamei. This species has a genome size approximately 70 % of the human genome and is likely to harbor high TE content . Due to its high commercial value, extensive efforts have been made on its transcriptomics to better understand its immunity, growth and development [18, 22]. We identified hundreds of reliable TE fragments from an up-to-date transcriptome assembly of L. vannamei and showed that many of them are involved in HTT events.
Results and discussion
Overview of TE transcripts in L. vannamei transcriptome
Classification of 395 TE transcripts in L. vannamei transcriptome
Family/consensus sequence (number of transcripts)
BEL-1_LVa-I (6), BEL-2_LVa (1)
Gypsy-12_LVa-I (3), Gypsy-14_LVa-I (3), Gypsy-16_LVa (3), Gypsy-17_LVa (4), Gypsy-18_LVa (1), Gypsy-1_LVa-I (1), Gypsy-3_LVa-I (1), Gypsy-4_LVa-I (7), Gypsy-5_LVa-I (1)
Nimb-N2_LVa (1), Nimb-2_LVa (6), Nimb-1_LVa (30)
Penelope-1_LVa (31), Penelope-2_LVa (5), Penelope-3_LVa (8), Penelope-4_LVa (3), Penelope-5_LVa (2), Penelope-8_LVa (1)
RTE-1_LVa (2), RTE-2_LVa (20), RTE-3_LVa (66), Gypsy-3_LVa-LTR (1)
L. vannamei TE transcripts showed high similarity to nucleotide sequences from distantly related aquatic species
Taxa of TE transcripts’ top hits in querying against NCBI BLAST Nucleotide database
Total (number of transcripts)
Total (number of transcripts)
Top hits of 16 L. vannamei TE families in querying against chromosome and HTGS databases
Query coverage (%)
Gypsy-18_LVa, Gypsy-5_LVa-I , Nimb-1_LVa, Nimb-2_LVa, Penelope-1_LVa, Penelope-3_LVa, Penelope-8_LVa have no significant hit (E-value < 1e-10)
Phylogenetic incongruence of TEs are closely linked with ecological relationships among species
Protein sequences of 10 L. vannamei TE families used for blastp search
Peptidase_A17 super family
pepsin_retropepsin_like super family
EEP super family
RT_like super family
GIY-YIG_SF super family
GIY-YIG_SF super family
GIY-YIG_SF super family
RT_like super family
Terms used to distinguish species
NCBI taxonomy terms
Amphibia, Testudines, Annelida, Crocodylia
Mollusca, Actinopterygii, Echinodermata, Brachiopoda, Enteropneusta, Tunicata, Porifera, Rotifera, Choanoflagellida, Placozoa, Rhizaria, Cyclostomata, Coelacanthiformes, Cnidaria, Euglenozoa, Priapulida, Apusozoa, Heterolobosea, Dipnoi, Chondrichthyes, Cephalochordata
Viridiplantae (exclude Embryophyta), Haptophyceae, Stramenopiles
Arthropoda (exclude Penaeidae)
Squamata, Mammalia, Aves
Bacteria, Fungi, Viruses
Nematoda, Platyhelminthes, Amoebozoa, Jakobida, Alveolata
Overall speaking, organisms with close ecological relationships tend to group together, even being distantly related in taxonomy. When referring to ecological relationships, we should not overlook the fact that L. vannamei and other aquatic species formed a huge food web in water. Therefore, predation among species might greatly facilitate HTTs, either through exchange of parasites and microorganisms, or through direct flow of DNA. After all, naked DNA and RNA can circulate in animal bodily fluids . The huge amounts of TEs may also ensure their success of passing through a digestive system and other barriers.
It has been proposed that HTTs among plants might provide an escape route from silencing and elimination and are thus essential for TEs’ survival in plants . Yet on the other hand, the acquisition of foreign genes by horizontal transfer may enhance the evolutionary potential of the recipient lineage . Although the expansions of TEs look like selfish and parasitic, TEs are actually important drivers of genome evolution: they can provide raw material for novel genes and contribute to regulation and generation of allelic diversity [14, 32, 33]. In this study, the frequent exchange of TEs between L. vannamei and other aquatic species may also provide some evolutionary advantages for them.
HTT involved transcripts might play important roles in antiviral immunity
Transcripts that showed similar expression pattern to HTT transcripts under VP28 stimulation
Methyl-CpG-binding domain protein 1
Transporter of various types of lipids in hemolymph
Signal transduction in Rho pathway
LIM and calponin domains-containing protein
Actomyosin structure organization
Open rectifier potassium channel protein
Background potassium channel
Proto-oncogene tyrosine-protein kinase ROS
Epithelial cell differentiation
Linear gramicidin synthase subunit B
Antibiotic biosynthetic process
Although the number of presumptive horizontally transferred genes is increasing, the exact role of HT/HTT in the evolution of unicellular eukaryotes is still blurry. Our knowledge about the underlying mechanism is even more limited. In this study, we found that in L. vannamei, an ancient crustacean, a considerable number of transcripts are also involved in HTT events. Nearly all of the HTT transcripts are transcripts of retrotransposons, which is in accordance with previous findings. Phylogenetic analyses revealed that L. vannamei TEs are often most close to TEs from aquatic species. Furthermore, TEs from other aquatic species, the taxonomic relationship among which are often very far away, also tend to group together. We suggest that HTT events might frequently occur among species that have close ecological relationships, the underlying impetus of which might be predation among those species. Through analyses of expression profile, we found that TE/HTT transcripts are more likely to play important roles in antiviral immunity, and they might actually act as inhibitors of antiviral immunity.
Identification of transcripts derived from TEs
A new transcriptome assembly of L. vannamei was downloaded from http://oaktrust.library.tamu.edu/handle/1969.1/152151, which contains 110,474 contigs with an N50 of 2701 bases . Each assembled contig was viewed as a transcript, regardless of alternative transcripts that share the same precursors. To exclude artifacts  and possible contaminations in sampling, these transcripts were conducted local blastn search against whole collection of L. vannamei sequences downloaded from NCBI. Fifty-six thousand six hundred eight transcripts with higher similarities to already existed L. vannamei nucleotide sequences or expressed sequence tags (ESTs) were selected for further analysis (for more details, see Additional file 13). To isolate TE related transcripts, we conducted a local BLAST based two-step searching of similar domains/sequences. First, the fifty-six thousand six hundred transcripts were conducted blastx search against cdd_delta , which contains 26,482 conserved domain sequences downloaded from ftp://ftp.ncbi.nlm.nih.gov/blast/db/. 813 transcripts were identified as TE-related because each of them has at least one hit that is TE-related (has the character string ‘transposon’ in sequence description). Second, to exclude transcripts that are actually transcripts of single/low copy genes that happened to contain TE-related domain(s), two further sequence searches were conducted for the above 813 transcripts: (i) blastx again cdd_delta again, and (ii) tblastx against a database contains 45,725 repetitive sequences downloaded from Repbase Update (http://www.girinst.org/, relase 20.09) . The criteria here were as follows: for a given query transcript, the E-value of the top hit in tblastx should be lower than 1e-5 and also lower than that in blastx top hit. Finally, 395 transcripts were identified as transcripts of TEs, with very high reliability.
Characterization of superfamilies and families of TE derived transcripts
The 395 TE derived transcripts were conducted tblastx to determine their superfamilies and blastn to determine their families. The database used here is the same as the one described above which contains 45,725 repetitive sequences. Briefly, a transcript was thought belonging to the same superfamily as its top hit in tblastx results; to determine its family classification, the top hit in blastn results should come from L. vannamei and meet an E-value cut-off at 1e-20. Therefore, 376 transcripts had their superfamilies determined while only 230 transcripts could be identified as transcripts of already known L. vannamei TE families. In total, 31 families were identified and only two were not consistent with identified superfamilies.
Evidence of HTTs and identification of L. vannamei TE families involved in HTTs
A Biopython  module, Bio.Blast.NCBIWWW, was used to query the NCBI BLAST Nucleotide (nt) database over the Internet using the 395 TE derived transcripts. All hits with E-value lower than 1e-5 were screened for their taxa. To effectively distinguish the organisms in the hits, 17 taxa were selected (as shown in Table 2). Their frequencies as top hit were counted. Since penaeidae shrimps are very close in evolution , they were excluded from the taxon arthropoda, that is to say hits from penaeidae family were filtered (mainly L. vannamei, Penaeus monodon and Marsupenaeus japonicus). Transcripts that showed highest sequence similarity to distantly related taxa, which meant the top hits were not from arthropods, were believed to be involved in HTTs. If the corresponding families of those transcripts were from L. vannamei, then they will be isolated. In total, 16 L. vannamei TE families were possibly involved in HTTs, representing 83 transcripts.
Presence of HTT-involved L. vannamei TE families’ homologues in other species
The Bio.Blast.NCBIWWW module was also used for the 16 L. vannamei TE families to conducted homology search against the NCBI BLAST chromosome and HTGS (high throughput genomic sequences) databases, respectively. The threshold of E-value was set to be 1e-10. For a given TE family, its best hit in searching against the two databases were extracted, the taxon and organism of which was also screened as described above.
Of the 16 L. vannamei TE families, 10 have coding regions (CDS) being annotated. Therefore, the longest protein sequence (in case there are more than one CDS) of each TE family was extracted and combined. The conserved domains within these protein sequences were predicated by the NCBI online tool CDD search (http://0-www.ncbi.nlm.nih.gov.brum.beds.ac.uk/Structure/cdd/wrpsb.cgi) and the results are displayed in Table 4. These protein sequences were used to conduct blastp search against NCBI BLAST Protein (nr) database. The threshold of E-value was set to be 1e-20; however, the actual E-value of all significant hits was 0. To remove redundancies, hits of one given query sequence were selected in the following way: hits with query length coverage less than 60 % were abandoned; the organisms of remaining hits were screened and only the top hit from the same organism was selected for further analyses (Additional file 14). The selected protein sequences were all downloaded from NCBI using Batch Entrez. All sequences, including queries, were aligned with MUSCLE . We used FastTree  and RAxML  to construct phylogenetic trees from the multiple alignments (Additional file 15). FastTree trees were built using the defaulted JTT + CAT model and gamma approximation on substitution rates. RAxML trees were built using LG model (selected by automatic test of all models), gamma approximation on substitution rates and 100 bootstraps. Approximately unbiased (AU) tests of RAxML tree topologies were carried out using CONSEL .
Identification of differentially expressed transcripts
Naturally, at the threshold of 1, all transcripts will be included; while at the threshold of 10, only 20 % or fewer transcripts will be included (see Fig. 4).
To predict transcripts of functional genes (other than TEs) that showed similar expression pattern to HTT transcripts, we developed One-Class SVM models  implemented in Scikt-learn , a Python module for machine learning. The defaulted RBF kernel was chosen. HTT transcripts with max fold change above four (in order to get more than 50 samples) in either BioProject were selected as training data. Transcripts that predicted to be positive were collected and used to conduct blastx search against NCBI BLAST Protein (nr) database.
We are thankful for constructive comments provided by anonymous reviewers.
This work was supported by the Agricultural Science and Technology Achievement Transformation Fund Project of Ministry of Science and Technology of the People’s Republic of China (No. 2012GB2E200361), the Northwest A&F University Experimental Demonstration Station (Base) and Innovation of Science and Technology Achievement Transformation Project (No. XNY2013-4), the Open Fund of Key Laboratory of Experimental Marine Biology, Chinese Academy of Sciences (No. KF2015No11) and the Overall Plan of Scientific and Technical Innovation Projects of Shaanxi Province (No. 2015KTTSNY01-01).
Availability of data and materials
All data generated or analyzed during this study are included in this published article and its supplementary information files.
XW carried out the collection and analysis of data, wrote Python scripts and wrote the manuscript; XL participated in the design of the study. Both authors read and approve the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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- Zhaxybayeva O, Doolittle WF. Lateral gene transfer. Curr Biol. 2011;21(7):R242–6.View ArticlePubMedGoogle Scholar
- Ochman H, Lawrence JG, Groisman EA. Lateral gene transfer and the nature of bacterial innovation. Nature. 2000;405(6784):299–304.View ArticlePubMedGoogle Scholar
- Skippington E, Ragan MA. Lateral genetic transfer and the construction of genetic exchange communities. FEMS Microbiol Rev. 2011;35(5SI):707–35.View ArticlePubMedGoogle Scholar
- Chapman JA, Kirkness EF, Simakov O, Hampson SE, Mitros T, Weinmaier T, Rattei T, Balasubramanian PG, Borman J, Busam D, et al. The dynamic genome of Hydra. Nature. 2010;464(7288):592–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Danchin EGJ, Rosso MN, Vieira P, de Almeida-Engler J, Coutinho PM, Henrissat B, Abad P. Multiple lateral gene transfers and duplications have promoted plant parasitism ability in nematodes. P Natl Acad Sci USA. 2010;107(41):17651–6.View ArticleGoogle Scholar
- El Baidouri M, Carpentier MC, Cooke R, Gao D, Lasserre E, Llauro C, Mirouze M, Picault N, Jackson SA, Panaud O. Widespread and frequent horizontal transfers of transposable elements in plants. Genome Res. 2014;24(5):831–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Gladyshev EA, Meselson M, Arkhipova IR. Massive horizontal gene transfer in bdelloid rotifers. Science. 2008;320(5880):1210–3.View ArticlePubMedGoogle Scholar
- Graham LA, Lougheed SC, Ewart KV, Davies PL. Lateral Transfer of a Lectin-Like Antifreeze Protein Gene in Fishes. PLoS ONE. 2008;3(7):e2616.View ArticlePubMedPubMed CentralGoogle Scholar
- Hotopp JCD, Clark ME, Oliveira DCSG, Foster JM, Fischer P, Munoz Torres MC, Giebel JD, Kumar N, Ishmael N, Wang S, et al. Widespread lateral gene transfer from intracellular bacteria to multicellular eukaryotes. Science. 2007;317(5845):1753–6.View ArticleGoogle Scholar
- Rot C, Goldfarb I, Ilan M, Huchon D. Putative cross-kingdom horizontal gene transfer in sponge (Porifera) mitochondria. BMC Evol Biol. 2006;6(71).Google Scholar
- Walsh AM, Kortschak RD, Gardner MG, Bertozzi T, Adelson DL. Widespread horizontal transfer of retrotransposons. P Natl Acad Sci USA. 2013;110(3):1012–6.View ArticleGoogle Scholar
- Wijayawardena BK, Minchella DJ, DeWoody JA. Hosts, parasites, and horizontal gene transfer. Trends Parasitol. 2013;29(7):329–38.View ArticlePubMedGoogle Scholar
- Wicker T, Sabot F, Hua-Van A, Bennetzen JL, Capy P, Chalhoub B, Flavell A, Leroy P, Morgante M, Panaud O, et al. A unified classification system for eukaryotic transposable elements. Nat Rev Genet. 2007;8(12):973–82.View ArticlePubMedGoogle Scholar
- Kazazian HH. Mobile Elements: Drivers of Genome Evolution. Science. 2004;303(5664):1626–32.View ArticlePubMedGoogle Scholar
- Kumar A, Jeffrey B. Plant retrotransposons. Annu Rev Genet. 1999;33:479–532.View ArticlePubMedGoogle Scholar
- Boissinot S, Chevret P, Furano AV. L1 (LINE-1) retrotransposon evolution and amplification in recent human history. Mol Biol Evol. 2000;17(6):915–28.View ArticlePubMedGoogle Scholar
- Piednoël M, Donnart T, Esnault C, Graça P, Higuet D, Bonnivard E. LTR-Retrotransposons in R. exoculata and Other Crustaceans: The Outstanding Success of GalEa-Like Copia Elements. PLoS ONE. 2013;8(3):e57675.View ArticlePubMedPubMed CentralGoogle Scholar
- Ghaffari N, Sanchez-Flores A, Doan R, Garcia-Orozco KD, Chen PL, Ochoa-Leyva A, Lopez-Zavala AA, Carrasco JS, Hong C, Brieba LG, et al. Novel transcriptome assembly and improved annotation of the whiteleg shrimp (Litopenaeus vannamei), a dominant crustacean in global seafood mariculture. Sci Rep-UK. 2014;4:7081.View ArticleGoogle Scholar
- Li J, Li J, Chen P, Liu P, He Y. Transcriptome analysis of eyestalk and hemocytes in the ridgetail white prawn Exopalaemon carinicauda: assembly, Annotation and Marker Discovery. Mol Biol Rep. 2015;42(1):135–47.View ArticlePubMedGoogle Scholar
- Shen H, Hu Y, Ma Y, Zhou X, Xu Z, Shui Y, Li C, Xu P, Sun X. In-Depth Transcriptome Analysis of the Red Swamp Crayfish Procambarus clarkii. PLoS ONE. 2014;9(10):e110548.View ArticlePubMedPubMed CentralGoogle Scholar
- Chow S, Dougherty WJ, Sandifer PA. Meiotic chromosome complements and nuclear DNA contents of four species of shrimps of the genus Penaeus. J Crustacean Biol. 1990;10(1):29–36.View ArticleGoogle Scholar
- Sookruksawong S, Sun F, Liu Z, Tassanakajon A. RNA-Seq analysis reveals genes associated with resistance to Taura syndrome virus (TSV) in the Pacific white shrimp Litopenaeus vannamei. Dev Comp Immunol. 2013;41(4):523–33.View ArticlePubMedGoogle Scholar
- Pradeep B, Shekar M, Karunasagar I, Karunasagar I. Characterization of variable genomic regions of Indian white spot syndrome virus. Virology. 2008;376(1):24–30.View ArticlePubMedGoogle Scholar
- Thitamadee S, Prachumwat A, Srisala J, Jaroenlak P, Salachan PV, Sritunyalucksana K, Flegel TW, Itsathitphaisarn O. Review of current disease threats for cultivated penaeid shrimp in Asia. Aquaculture. 2016;452:69–87.View ArticleGoogle Scholar
- Koski LB, Golding GB. The Closest BLAST Hit Is Often Not the Nearest Neighbor. J Mol Evol. 2001;52(6):540–2.View ArticlePubMedGoogle Scholar
- Price MN, Dehal PS, Arkin AP. FastTree 2—Approximately Maximum-Likelihood Trees for Large Alignments. PLoS ONE. 2010;5(3):e9490.View ArticlePubMedPubMed CentralGoogle Scholar
- Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30(9):1312–3.View ArticlePubMedPubMed CentralGoogle Scholar
- Hedges SB, Marin J, Suleski M, Paymer M, Kumar S. Tree of Life Reveals Clock-Like Speciation and Diversification. Mol Biol Evol. 2015;32(4):835–45.View ArticlePubMedPubMed CentralGoogle Scholar
- Peterson KJ, Cotton JA, Gehling JG, Pisani D. The Ediacaran emergence of bilaterians: congruence between the genetic and the geological fossil records. Philo Trans R Soc B Biol Sci. 2008;363(1496):1435–43.View ArticleGoogle Scholar
- Boto L. Horizontal gene transfer in the acquisition of novel traits by metazoans. P Roy Soc B-Biol Sci. 2014;281(20132450).Google Scholar
- Stroun M, Lyautey J, Lederrey C, Mulcahy HE, Anker P. Alu repeat sequences are present in increased proportions compared to a unique gene in plasma/serum DNA: evidence for a preferential release from viable cells? Ann NY Acad Sci. 2001;945:258–64.View ArticlePubMedGoogle Scholar
- Abrusan G, Szilagyi A, Zhang Y, Papp B. Turning gold into ‘junk’: transposable elements utilize central proteins of cellular networks. Nucleic Acids Res. 2013;41(5):3190–200.View ArticlePubMedPubMed CentralGoogle Scholar
- Nefedova LN, Kuzmin IV, Makhnovskii PA, Kim AI. Domesticated retroviral GAG gene in Drosophila: New functions for an old gene. Virology. 2014;450–451:196–204.View ArticlePubMedGoogle Scholar
- van Hulten MCW, Witteveldt J, Snippe M, Vlak JM. White spot syndrome virus envelope protein VP28 is involved in the systemic infection of shrimp. Virology. 2001;285(2):228–33.View ArticlePubMedGoogle Scholar
- Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC. Estimating the support of a high-dimensional distribution. Neural Comput. 2001;13(7):1443–71.View ArticlePubMedGoogle Scholar
- Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, et al. Scikit-learn: Machine Learning in Python. J Mach Learn Res. 2011;12:2825–30.Google Scholar
- Liu H, Söderhäll K, Jiravanichpaisal P. Antiviral immunity in crustaceans. Fish Shellfish Immunol. 2009;27(2):79–88.View ArticlePubMedGoogle Scholar
- Wang L, Sun X, Zhou Z, Zhang T, Yi Q, Liu R, Wang M, Song L. The promotion of cytoskeleton integration and redox in the haemocyte of shrimp Litopenaeus vannamei after the successive stimulation of recombinant VP28. Dev Comp Immunol. 2014;45(1):123–32.View ArticlePubMedGoogle Scholar
- Sritunyalucksana K, Wannapapho W, Lo CF, Flegel TW. PmRab7 is a VP28-binding protein involved in white spot syndrome virus infection in shrimp. J Virol. 2006;80(21):10734–42.View ArticlePubMedPubMed CentralGoogle Scholar
- Ongvarrasopone C, Chanasakulniyom M, Sritunyalucksana K, Panyim S. Suppression of PmRab7 by dsRNA inhibits WSSV or YHV infection in shrimp. Mar Biotechnol. 2008;10(4):374–81.View ArticlePubMedGoogle Scholar
- Dillies MA, Rau A, Aubert J, Hennequet-Antier C, Jeanmougin M, Servant N, Keime C, Marot G, Castel D, Estelle J, et al. A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform. 2013;14(6):671–83.View ArticlePubMedGoogle Scholar
- Birney E. Assemblies: the good, the bad, the ugly. Nat Methods. 2011;8(1):59–60.View ArticlePubMedGoogle Scholar
- Marchler-Bauer A, Derbyshire MK, Gonzales NR, Lu S, Chitsaz F, Geer LY, Geer RC, He J, Gwadz M, Hurwitz DI, et al. CDD: NCBI’s conserved domain database. Nucleic Acids Res. 2015;43(D1):D222–6.View ArticlePubMedGoogle Scholar
- Bao W, Kojima KK, Kohany O. Repbase Update, a database of repetitive elements in eukaryotic genomes. Mobile DNA-UK. 2015;6(11):11.View ArticleGoogle Scholar
- Cock PJA, Antao T, Chang JT, Chapman BA, Cox CJ, Dalke A, Friedberg I, Hamelryck T, Kauff F, Wilczynski B, et al. Biopython: freely available Python tools for computational molecular biology and bioinformatics. Bioinformatics. 2009;25(11):1422–3.View ArticlePubMedPubMed CentralGoogle Scholar
- Ma KY, Chan TY, Chu KH. Phylogeny of penaeoid shrimps (Decapoda: Penaeoidea) inferred from nuclear protein-coding genes. Mol Phylogenet Evol. 2009;53(1):45–55.View ArticlePubMedGoogle Scholar
- Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Shimodaira H, Hasegawa M. CONSEL: for assessing the confidence of phylogenetic tree selection. Bioinformatics. 2001;17(12):1246–7.View ArticlePubMedGoogle Scholar
- Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60.View ArticlePubMedPubMed CentralGoogle Scholar
- Anders S, Pyl PT, Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9.View ArticlePubMedGoogle Scholar
- Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009;26(1):139–40.View ArticlePubMedPubMed CentralGoogle Scholar