Abstract
Cobia (Rachycentron canadum) is a marine teleost species with great productive potential worldwide. However, the genomic information currently available for this species in public databases is limited. Such lack of information hinders gene expression assessments that might bring forward novel insights into the physiology, ecology, evolution, and genetics of this potential aquaculture species. In this study, we report the first de novo transcriptome assembly of R. canadum liver, improving the availability of novel gene sequences for this species. Illumina sequencing of liver transcripts generated 1,761,965,794 raw reads, which were filtered into 1,652,319,304 high-quality reads. De novo assembly resulted in 101,789 unigenes and 163,096 isoforms, with an average length of 950.61 and 1,617.34 nt, respectively. Moreover, we found that 126,013 of these transcripts bear potentially coding sequences, and 125,993 of these elements (77.3%) correspond to functionally annotated genes found in six different databases. We also identified 701 putative ncRNA and 35,414 putative lncRNA. Interestingly, homologues for 410 of these putative lncRNAs have already been observed in previous analyses with Danio rerio, Lates calcarifer, Seriola lalandi dorsalis, Seriola dumerili, or Echeneis naucrates. Finally, we identified 7894 microsatellites related to cobia’s putative lncRNAs. Thus, the information derived from the transcriptome assembly described herein will likely assist future nutrigenomics and breeding programs involving this important fish farming species.
This is a preview of subscription content, log in via an institution to check access.
Availability of Data and Material
Sequencing raw data were deposited in the Sequence Read Archive (SRA) repository of the National Center for Biotechnology Information (NCBI), under accession numbers SRR13009897, SRR13009896, SRR13009895, SRR13009894, SRR13009893, SRR13009892, SRR13009891, SRR13009890, SRR13009889, SRR13009888, SRR13009887, and SRR13009886, associated to the BioProject numbers PRJNA675281 and BioSamples numbers SAMN16708758, SAMN16708759, SAMN16708760, SAMN16708761, SAMN16708762, SAMN16708763, SAMN16708764, SAMN16708765, SAMN16708766, SAMN16708767, SAMN16708768, and SAMN16708769. The Transcriptome Shotgun Assembly (TSA) project has been deposited at DDBJ/EMBL/GenBank under accession number GIWT00000000. The version described in this paper is the first version, GIWT00000000.1. Supplementary Table S1 is available from the Figshare repository (https://doi.org/10.6084/m9.figshare.14522781.v2). Additional data derived from this study (including all intermediate data) are also available from the Open Science Framework (OSF) repository (https://doi.org/10.17605/OSF.IO/BV3WA). Details about the software and databases used are available in Supplementary Table ST1–S11.
References
Andrew SC, Primmer CR, Debes PV, Erkinaro J, Verta JP (2021) The Atlantic salmon whole blood transcriptome and how it relates to major locus maturation genotypes and other tissues. Mar Genomics. https://doi.org/10.1016/j.margen.2020.100809
Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc. Accessed 21 Apr 2021
Arnold CR, Kaiser JB, Holt GJ (2002) Spawning of cobia Rachycentron canadum in captivity. J World Aquac Soc. https://doi.org/10.1111/j.1749-7345.2002.tb00496.x
Beier S, Thiel T, Münch T, Scholz U, Mascher M (2017) MISA-web: a web server for microsatellite prediction. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx198
Benetti DD, Orhun MR, Sardenberg B, O'Hanlon B, Welch A, Hoenig R, Zink I, Rivera JA, Denlinger B, Bacoat D, Palmer K (2008) Advances in hatchery and grow-out technology of cobia Rachycentron canadum (Linnaeus). Aquac Res. https://doi.org/10.1111/j.1365-2109.2008.01922.x
Boratyn GM et al (2013) BLAST: a more efficient report with usability improvements. Nucleic Acids Res. https://doi.org/10.1093/nar/gkt282
Camargo AP et al (2020) RNAsamba: neural network-based assessment of the protein-coding potential of RNA sequences. NAR Genom Bioinform. https://doi.org/10.1093/nargab/lqz024
Calduch-Giner JA, Bermejo-Nogales A, Benedito-Palos L, Estensoro I, Ballester-Lozano G, Sitjà-Bobadilla A, Pérez-Sánchez J (2013) Deep sequencing for de novo construction of a marine fish (Sparus aurata) transcriptome database with a large coverage of protein-coding transcripts. BMC Genomics. https://doi.org/10.1186/1471-2164-14-178
Chen S, Zhou Y, Chen Y, Gu J (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty560
Ewels P, Magnusson M, Lundin S, Käller M (2016) MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. https://doi.org/10.1093/bioinformatics/btw354
Fan G, Cao Y, Wang Z (2018) Regulation of long noncoding RNAs responsive to phytoplasma infection in Paulownia tomentosa. Int J Genomics. https://doi.org/10.1155/2018/3174352
FAO (2020) The state of world fisheries and aquaculture 2020. Sustainability in action. http://www.fao.org/documents/card/en/c/ca9229en. Accessed 21 Apr 2021
Fox SE, Christie MR Marine M, Priest HD, Mockler TC, Blouin MS (2014) Sequencing and characterization of the anadromous steelhead (Oncorhynchus mykiss) transcriptome. Mar Genomics. https://doi.org/10.1016/j.margen.2013.12.001
Fraser TW, Davies SJ (2009) Nutritional requirements of cobia, Rachycentron canadum (Linnaeus): a review. Aquac Res. https://doi.org/10.1111/j.1365-2109.2009.02215.x
Glencross BD, De Santis C, Bicskei B, Taggart JB, Bron JE, Betancor MB, Tocher DR (2015) A comparative analysis of the response of the hepatic transcriptome to dietary docosahexaenoic acid in Atlantic salmon (Salmo salar) post-smolts. BMC Genomics. https://doi.org/10.1186/s12864-015-1810-z
Gui D et al (2013) De novo assembly of the Indo-Pacific humpback dolphin leucocyte transcriptome to identify putative genes involved in the aquatic adaptation and immune response. PLoS One. https://doi.org/10.1371/journal.pone.0072417
Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, Couger MB, Eccles D, Li B, Lieber M, MacManes MD (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc. https://doi.org/10.1038/nprot.2013.084
Hart AJ, Ginzburg S, Xu M, Fisher CR, Rahmatpour N, Mitton JB, Paul R, Wegrzyn JL (2020) EnTAP: bringing faster and smarter functional annotation to non-model eukaryotic transcriptomes. Mol Ecol Resour. https://doi.org/10.1111/1755-0998.13106
Herkenhoff ME et al (2018) Fishing into the MicroRNA transcriptome. Front Genet. https://doi.org/10.3389/fgene.2018.00088
Hu X et al (2018) ZFLNC: a comprehensive and well-annotated database for zebrafish lncRNA. Database. https://doi.org/10.1093/database/bay114
Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, Rattei T, Mende DR, Sunagawa S, Kuhn M, Jensen LJ (2016) eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. https://doi.org/10.1093/nar/gkv1248.
Kalvari I et al (2018) Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx1038
Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. https://doi.org/10.1093/nar/28.1.27
Kanehisa M (2019) Toward understanding the origin and evolution of cellular organisms. Protein Sci. https://doi.org/10.1002/pro.3715
Kanehisa M, Furumichi M, Sato Y, Ishiguro-Watanabe M, Tanabe M (2021) KEGG: integrating viruses and cellular organisms. Nucleic Acids Res. https://doi.org/10.1093/nar/gkaa970
Kang YJ et al (2017) CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. https://doi.org/10.1093/nar/gkx428
Leaver MJ, Bautista JM, Björnsson BT, Jönsson E, Krey G, Tocher DR, Torstensen BE (2008) Towards fish lipid nutrigenomics: current state and prospects for fin-fish aquaculture. Rev Fish Sci. https://doi.org/10.1080/10641260802325278
Magnanou E, Klopp C, Noirot C, Besseau L, Falcón J (2014) Generation and characterization of the sea bass Dicentrarchus labrax brain and liver transcriptomes. Gene. https://doi.org/10.1016/j.gene.2014.04.032
Marz M et al (2011) Animal snoRNAs and scaRNAs with exceptional structures. RNA Biol. https://doi.org/10.4161/rna.8.6.16603
Menegidio FB, Jabes DL, Costa de Oliveira R, Nunes LR (2018) Dugong: a Docker image, based on Ubuntu Linux, focused on reproducibility and replicability for bioinformatics analyses. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx554
Nawrocki EP, Eddy SR (2013) Infernal 1.1: 100-fold faster RNA homology searches. Bioinformat. https://doi.org/10.1093/bioinformatics/btt509
Nunes AJP (2014) Ensaios com o beijupirá, Rachycentron canadum. Fortaleza: Ministério da Pesca e Aquicultura/CNPQ/UFC. http://www.repositorio.ufc.br/handle/riufc/8655. Accessed 21 Apr 2021
Rasal KD et al (2016) MicroRNA in aquaculture fishes: a way forward with high-throughput sequencing and a computational approach. Rev Fish Biol Fish. https://doi.org/10.1007/s11160-016-9421-6
Seppey M, Manni M, Zdobnov EM (2019) BUSCO: assessing genome assembly and annotation completeness. Methods Mol Biol. https://doi.org/10.1007/978-1-4939-9173-0_14
The Gene Ontology Consortium (2019) The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1055
UniProt Consortium (2019) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. https://doi.org/10.1093/nar/gky1049
Wang L et al (2013) CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model. Nucleic Acids Res. https://doi.org/10.1093/nar/gkt006
Zdobnov EM, Tegenfeldt F, Kuznetsov D, Waterhouse RM, Simao FA, Ioannidis P, Seppey M, Loetscher A, Kriventseva EV (2017) OrthoDB v9. 1: cataloging evolutionary and functional annotations for animal, fungal, plant, archaeal, bacterial and viral orthologs. Nucleic Acids Res. https://doi.org/10.1093/nar/gkw1119.
Funding
This study was financed in part by the São Paulo Research Foundation (FAPESP: 2019/26018–0) and National Council for Scientific and Technological Development (CNPq: 305493/2019–1). D.A.B., B.C.A, and A.S.S. are recipients of scholarship grants from Coordination for the Improvement of Higher Education Personnel (CAPES). A.W.S.H. is recipient of CNPq productivity scholarships (304662/2017–8).
Author information
Authors and Affiliations
Contributions
B.C.A. sampled the specimens. B.C.A., G.S.B., A.W.S.H., and R.G.M. performed the molecular analyses and sequencing. D.A.B., A.S.S., D.L.J., L.R.N., and F.B.M. assembled and evaluated the transcriptome assembly and annotation. All the authors wrote the paper. All the authors read and approved the final version of the manuscript.
Corresponding author
Ethics declarations
Ethics Approval
This study’s experimental procedures were conducted according to the guidelines and approval of the Mogi das Cruzes University Institutional Animal Care and Use Ethics Committee (#008/2017).
Disclaimer
The authors alone are responsible for the content and the writing of the paper.
Conflict of Interest
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Aciole Barbosa, D., Araújo, B.C., Branco, G.S. et al. Transcriptomic Profiling and Microsatellite Identification in Cobia (Rachycentron canadum), Using High-Throughput RNA Sequencing. Mar Biotechnol 24, 255–262 (2022). https://doi.org/10.1007/s10126-021-10081-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10126-021-10081-0