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BMC Genom Data
BMC Genom Data
BMC Genomic Data
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BioMed Central London

39227788
1262
10.1186/s12863-024-01262-6
Data Note
The draft genomes of Crassostrea gasar and Crassostrea rhizophorae: key resources for leveraging oyster cultivation in the Southwest Atlantic
http://orcid.org/0000-0002-3011-010X
Lima Nicholas Costa Barroso 1
http://orcid.org/0000-0002-7124-2715
de Almeida Luiz Gonzaga Paula 2
http://orcid.org/0000-0002-3885-7718
Bainy Afonso Celso Dias 3
http://orcid.org/0000-0001-5724-6106
Gerber Alexandra Lehmkuhl 2
http://orcid.org/0000-0001-9519-0545
de Campos Guimarães Ana Paula 2
http://orcid.org/0000-0003-0363-5821
Solé-Cava Antonio Mateo 4
http://orcid.org/0000-0002-0969-7022
de Melo Claudio Manoel Rodrigues 2
http://orcid.org/0000-0002-6322-4672
Lazoski Cristiano 5
http://orcid.org/0000-0003-1808-1390
Zacchi Flávia Lucena 6
http://orcid.org/0000-0002-5359-504X
Henning Frederico 4
http://orcid.org/0000-0001-9654-1829
Soares Leticia Maria Monteiro 4
http://orcid.org/0009-0001-0323-4422
Soares Rafaela Guilherme 4
http://orcid.org/0000-0002-4632-2086
Ribeiro Vasconcelos Ana Tereza atrv@lncc.br

2
1 https://ror.org/03srtnf24 grid.8395.7 0000 0001 2160 0329 Departamento de Bioquímica e Biologia Molecular, Centro de Ciências, Universidade Federal do Ceará (UFC), Fortaleza, CE 60020-181 Brasil
2 https://ror.org/0498ekt05 grid.452576.7 0000 0004 0602 9007 Laboratório de Bioinformática, Laboratório Nacional de Computação Científica, Av. Getúlio Vargas, 333 - Quitandinha, Petrópolis, RJ 25651-076 Brasil
3 https://ror.org/041akq887 grid.411237.2 0000 0001 2188 7235 Laboratório de Biomarcadores de Contaminação Aquática e Imunoquímica, Departamento de Bioquímica, Universidade Federal de Santa Catarina (UFSC), Florianópolis, SC 88037-000 Brasil
4 https://ror.org/03490as77 grid.8536.8 0000 0001 2294 473X Centro Nacional para a Identificação Molecular do Pescado (CENIMP), Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ 21941-590 Brasil
5 https://ror.org/03490as77 grid.8536.8 0000 0001 2294 473X Laboratório de Biodiversidade Genômica (LABIG), Departamento de Genética, Instituto de Biologia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ 21941-902 Brasil
6 https://ror.org/041akq887 grid.411237.2 0000 0001 2188 7235 Laboratório de Moluscos Marinhos (LMM), Departamento de Aquicultura, Universidade Federal de Santa Catarina (UFSC), Florianópolis, SC 88061-600 Brasil
3 9 2024
3 9 2024
2024
25 8120 6 2024
21 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Objectives

The two oyster species studied hold considerable economic importance for artisanal harvest (Crassostrea rhizophorae) and aquaculture (Crassostrea gasar). Their draft genomes will play an important role in the application of genomic methods such as RNAseq, population-based genomic scans aiming at addressing expression responses to pollution stress, adaptation to salinity and temperature variation, and will also permit investigating the genetic bases and enable marker-assisted selection of economically important traits like shell and mantle coloration and resistance to temperature and disease.

Data description

The draft assembly size of Crassostrea gasar is 506 Mbp, and of Crassostrea rhizophorae is 584 Mbp with scaffolds N50 of 11,3 Mbp and 4,9 Mbp, respectively. The general masked bases by RepeatMasker in both genomes were highly similar using different datasets. The masked bases varied from 9.41% in C. gasar to 10.05% in C. rhizophorae and 42.85% in C. gasar to 44.44% in C. rhizophorae using Dfam and RepeatModeler datasets, respectively. Functional annotation with eggNog resulted in 34,693 annotated proteins in C. rhizophorae and 26,328 in C. gasar. BUSCO analysis shows that almost 99% of genes (5,295) are complete in relation to the mollusk orthologous genes dataset (mollusca_odb10).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-024-01262-6.

Keywords

Mollusca
Crassostrea
Gasar
Rhizophorae
Genome
issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcObjective

The two oyster species whose draft genomes we publish here, Crassostrea rhizophorae and Crassostrea gasar hold considerable economic importance for artisanal harvest and aquaculture. Their draft genomes will play an important role in answering different questions. C. rhizophorae grows across a wide range of environments despite varying degrees of environmental stress and is commonly used as a sentinel and bioindicator species in environmental monitoring studies. Using RNAseq, population genomic analysis, and RAD markers, we are comparing C. rhizophorae oyster samples from heavily polluted and pristine areas in Rio de Janeiro, Paraná, and Santa Catarina States. This comparison aims to elucidate metabolic pathways, identify loci under selection, and gain insights into the adaptation mechanisms of these oysters to pollution, ultimately designing an effective biomonitoring system. Efficient use of reduced genomic representation methods for population genomics requires genome sequences to locate associated markers. Crassostrea gasar is particularly suitable for cultivation and exhibits traits of economic importance, such as shell and mantle coloration and resistance to temperature and salinity variations. These traits must be artificially selected to improve yield and market value. The genomes produced will help identify the genetic bases of these important traits. Through population genomics, transcriptomics, and forward-genetics, we can effectively assist in their artificial selection via Marker Assisted Selection (MAS) to improve aquaculture production of the species. Therefore, by making these data available, we aim to collaborate on genomics studies across oysters.

Data description

The specimens of Crassostrea rhizophorae used for PacBio CLR, PacBio HiFi, MinIon (Oxford Nanopore Technologies), and Illumina sequencing were sampled from natural outbred population at Praia da Boa Viagem (Niterói, RJ, Brazil), Praia da Caieira da Barra do Sul (Florianópolis, SC, Brazil) and Rio Bücheller (Florianópolis, SC, Brazil) (Reads summary in Table 1—Data Set 1 (Table 1)). The specimens of Crassostrea gasar used for PacBio CLR, HiFi, and Illumina sequencing originated from the stock maintained at the Laboratory of Marine Mollusk at the Federal University of Santa Catarina (UFSC) (Reads summary in Table 1—Data Set 1 (Table 1)). Specimens were dissected live for mantle tissues.

A schematic of the assembly, gene prediction, and annotation process for both genomes is shown in Fig. 1. We used the genomes and proteins of C. angulata, C. gigas, and C. virginica for comparison with C. gasar and C. rhizophorae draft assembly and predicted genes. Assembly was performed with Hifiasm v0.19.5 and ntLink v1.3.9 [1–4], with scaffold gap-filling done using GapFiller v1-11 [5]. After assembly and gap-filling, the final drafts were checked for completeness and basic assembly statistics using BUSCO v5.4.7 and Quast v5.2.0 [6, 7]. Repeat identification and masking for all five genomes was carried out with RepeatMasker v4.1.6, RepeatModeler v2.0.5, and Dfam v3.8 [8–10]. Gene prediction was performed with the BRAKER pipeline v3.0.3, employing AUGUSTUS and GeneMarker-ET based on RNA-seq [11]. Functional annotation of predicted genes used eggNOG v2.1.10 and Diamond v2.1.9 [12, 13].Fig. 1 Pipeline for the assembly and annotation of the draft genomes of C. gasar and C. rhizophorae

The draft assembly size of C. gasar was 506 Mbp, and C. rhizophorae was 584 Mbp, with scaffold N50 sizes of 11.3 Mbp and 4.9 Mbp, respectively (Table 1—Data Set 1 (Table 2)). BUSCO analysis showed that nearly 99% of genes (5,295) in the mollusk orthologous genes dataset (mollusca_odb10) are complete (Table 1—Data Set 1 (Table 3)). The number of repetitive sequences across all analyzed genomes was similar [14–17]. Using the Dfam dataset and the RepeatModeler generated dataset; masked bases ranged from 7.86% in C. angulata to 10.05% in C. rhizophorae, and from 42.85% in C. gasar to 47.47% in C. angulata, respectively (Table 1—Data Set 1 (Table 4)).

In both genomes, over 90% of proteins had hits in the NR database using Diamond, with 99% being mollusk proteins (Table 1—Data Set 1 (Table 5)). Approximately 80% of hits related to mollusks had query and subject coverage above 90%. Functional annotation with eggNOG identified 34,693 and 26,328 proteins for C. rhizophorae and C. gasar, respectively (Table 1—Data Set 1 (Table 6)).

These results demonstrate that the draft genomes of C. gasar and C. rhizophorae represent each species and are sufficiently contiguous to describe genes and repetitive elements, making them suitable references for further research [18, 19]. These data will be used in transcriptome analyses of 3RAD analyses, among other studies (Table 1). Table 1 Overview of data files/data sets

Label	Name of data file/data set	File types
(file extension)	Data repository and identifier (DOI or accession number)	
Data file 1	Figure_1	portable document format (.pdf)	https://doi.org/10.5281/zenodo.12103998 [20]	
Data File 2	gasar_annotation	general transfer format (.gtf)	https://doi.org/10.5281/zenodo.12103998 [20]	
Data File 3	rhizophorae_annotation	general transfer format (.gtf)	https://doi.org/10.5281/zenodo.12103998 [20]	
Data File 4	Crassostrea gasar draft genome	genbank format (.gbk)	https://identifiers.org/ncbi/nucleotide:JBEEQF000000000.1 [18]	
Data File 5	Crassostrea rhizophorae draft genome	genbank format (.gbk)	https://identifiers.org/ncbi/nucleotide:JBEOLP000000000.1 [19]	
Data set 1	Tables	spreadsheets (.xlsx)	https://doi.org/10.5281/zenodo.12103998 [20]	

Limitations

Integrating data from different sequencing platforms and individuals posed significant challenges in producing the draft genomes. We explored using Illumina-generated reads alongside PacBio data to form contigs and scaffolds during assembly. Despite trying various methods, we consistently encountered more fragmented assemblies when combining both data types. Therefore, we decided to use Illumina reads to fill gaps within scaffolds generated solely from PacBio reads.

Supplementary Information

Supplementary Material 1.

Abbreviations

CLR Pacific Biosciences Continuous Long Reads

DNA Deoxyribonucleic Acid

MAS Marker Assisted Selection

HMW-DNA High molecular weight DNA

NCBI National Center for Biotechnology Information

RNA Ribonucleic Acid

RNA-seq RNA Sequencing

RPM Revolutions Per Minute

UFSC Universidade Federal de Santa Catarina

Authors’ contributions

F.H., A.M.S.C., L.M.M.S., R.G.S., F.L.Z., A.C.D.B., C.M.R.M. collected the samples. C.L. and C.M.R.M. identified the samples. F.H. extracted nucleic acids. A.P.C.G. and A.L.G. sequenced the genomes, C.L. performed ONT analyses. L.G.P.A. and N.C.B.L. processed the genomes and the analysis. L.G.P.A. prepared all figures and tables. N.C.B.L., L.G.P.A. and A.T.R.V prepared the manuscript. All authors reviewed the manuscript.

Funding

This study was developed in the frameworks of the Pensa Rio project from Carlos Chagas Filho Foundation for Research Support of the State of Rio de Janeiro (FAPERJ) E-26/010/003027/2014. A.T.R.V. was supported by grants from the National Council for Scientific and Technological Development (CNPq) (307145/2021–2) and FAPERJ (E-26/201.046/2022). C.L. was supported by grants from FAPERJ (210.579/2014). F.H. was supported by grants from CNPq (315816/2021–0) and FAPERJ (E-26/201.458/2021). A.C.D.B. was supported by grants from the CNPq (311725/2021–0). A.M.S.C. was supported by grants from CNPq (303300/2019–1) and FAPERJ (E-26/201.019/2022).

Availability of data and materials

The draft genomes and the raw reads used in this study are publicly available in NCBI, Bioproject accession PRJNA1117898 (https://identifiers.org/ncbi/bioproject:PRJNA1117898). Crassostrea gasar draft genome and Crassostrea rhizophorae draft genome are available at https://identifiers.org/ncbi/nucleotide:JBEEQF000000000.1 [18] and https://identifiers.org/ncbi/nucleotide:JBEOLP000000000.1 [19], respectively.

Tables and Figure are available at: https://doi.org/10.5281/zenodo.12103998 [20]. Please see Table 1 for details and links to the data.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

All authors consent to this text for publication.

Competing interests

The authors declare no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Cheng H Concepcion GT Feng X Zhang H Li H Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm Nat Methods 2021 18 170 175 10.1038/s41592-020-01056-5 33526886
Cheng H, Concepcion GT, Feng X, Zhang H, Li H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat Methods. 2021;18:170–5.33526886 10.1038/s41592-020-01056-5
2. Cheng H Jarvis ED Fedrigo O Koepfli K-P Urban L Gemmell NJ Haplotype-resolved assembly of diploid genomes without parental data Nat Biotechnol 2022 40 1332 1335 10.1038/s41587-022-01261-x 35332338
Cheng H, Jarvis ED, Fedrigo O, Koepfli K-P, Urban L, Gemmell NJ, et al. Haplotype-resolved assembly of diploid genomes without parental data. Nat Biotechnol. 2022;40:1332–5.35332338 10.1038/s41587-022-01261-x
3. Coombe L Warren RL Wong J Nikolic V Birol I ntLink: A Toolkit for De Novo Genome Assembly Scaffolding and Mapping Using Long Reads Curr Protoc 2023 3 e733 10.1002/cpz1.733 37039735
Coombe L, Warren RL, Wong J, Nikolic V, Birol I. ntLink: A Toolkit for De Novo Genome Assembly Scaffolding and Mapping Using Long Reads. Curr Protoc. 2023;3:e733.37039735 10.1002/cpz1.733
4. Coombe L Li JX Lo T Wong J Nikolic V Warren RL LongStitch: high-quality genome assembly correction and scaffolding using long reads BMC Bioinformatics 2021 22 534 10.1186/s12859-021-04451-7 34717540
Coombe L, Li JX, Lo T, Wong J, Nikolic V, Warren RL, et al. LongStitch: high-quality genome assembly correction and scaffolding using long reads. BMC Bioinformatics. 2021;22:534.34717540 10.1186/s12859-021-04451-7
5. Nadalin F Vezzi F Policriti A GapFiller: a de novo assembly approach to fill the gap within paired reads BMC Bioinformatics. 2012 13 Suppl 14 Suppl 14 S8 10.1186/1471-2105-13-S14-S8 23095524
Nadalin F, Vezzi F, Policriti A. GapFiller: a de novo assembly approach to fill the gap within paired reads. BMC Bioinformatics. 2012;13 Suppl 14(Suppl 14):S8.23095524 10.1186/1471-2105-13-S14-S8
6. Manni M Berkeley MR Seppey M Simão FA Zdobnov EM BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes Mol Biol Evol 2021 38 4647 4654 10.1093/molbev/msab199 34320186
Manni M, Berkeley MR, Seppey M, Simão FA, Zdobnov EM. BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes. Mol Biol Evol. 2021;38:4647–54.34320186 10.1093/molbev/msab199
7. Mikheenko A Prjibelski A Saveliev V Antipov D Gurevich A Versatile genome assembly evaluation with QUAST-LG Bioinformatics 2018 34 i142 i150 10.1093/bioinformatics/bty266 29949969
Mikheenko A, Prjibelski A, Saveliev V, Antipov D, Gurevich A. Versatile genome assembly evaluation with QUAST-LG. Bioinformatics. 2018;34:i142–50.29949969 10.1093/bioinformatics/bty266
8. Smit AFA, Hubley R, Green P. RepeatMasker Open-4.0. 2013--2015. 2015. http://www.repeatmasker.org.
9. Flynn JM Hubley R Goubert C Rosen J Clark AG Feschotte C RepeatModeler2 for automated genomic discovery of transposable element families Proc Natl Acad Sci USA 2020 117 9451 9457 10.1073/pnas.1921046117 32300014
Flynn JM, Hubley R, Goubert C, Rosen J, Clark AG, Feschotte C, et al. RepeatModeler2 for automated genomic discovery of transposable element families. Proc Natl Acad Sci USA. 2020;117:9451–7.32300014 10.1073/pnas.1921046117
10. Storer J Hubley R Rosen J Wheeler TJ Smit AF The Dfam community resource of transposable element families, sequence models, and genome annotations Mob DNA 2021 12 2 10.1186/s13100-020-00230-y 33436076
Storer J, Hubley R, Rosen J, Wheeler TJ, Smit AF. The Dfam community resource of transposable element families, sequence models, and genome annotations. Mob DNA. 2021;12:2.33436076 10.1186/s13100-020-00230-y
11. Hoff KJ Lomsadze A Borodovsky M Stanke M Whole-Genome Annotation with BRAKER Methods Mol Biol 2019 1962 65 95 10.1007/978-1-4939-9173-0_5 31020555
Hoff KJ, Lomsadze A, Borodovsky M, Stanke M. Whole-Genome Annotation with BRAKER. Methods Mol Biol. 2019;1962:65–95.31020555 10.1007/978-1-4939-9173-0_5
12. Cantalapiedra CP Hernández-Plaza A Letunic I Bork P Huerta-Cepas J eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale Mol Biol Evol 2021 38 5825 5829 10.1093/molbev/msab293 34597405
Cantalapiedra CP, Hernández-Plaza A, Letunic I, Bork P, Huerta-Cepas J. eggNOG-mapper v2: Functional Annotation, Orthology Assignments, and Domain Prediction at the Metagenomic Scale. Mol Biol Evol. 2021;38:5825–9.34597405 10.1093/molbev/msab293
13. Buchfink B Reuter K Drost H-G Sensitive protein alignments at tree-of-life scale using DIAMOND Nat Methods 2021 18 366 368 10.1038/s41592-021-01101-x 33828273
Buchfink B, Reuter K, Drost H-G. Sensitive protein alignments at tree-of-life scale using DIAMOND. Nat Methods. 2021;18:366–8.33828273 10.1038/s41592-021-01101-x
14. Peñaloza C Gutierrez AP Eöry L Wang S Guo X Archibald AL A chromosome-level genome assembly for the Pacific oyster Crassostrea gigas Gigascience. 2021 10 giab020 10.1093/gigascience/giab020 33764468
Peñaloza C, Gutierrez AP, Eöry L, Wang S, Guo X, Archibald AL, et al. A chromosome-level genome assembly for the Pacific oyster Crassostrea gigas. Gigascience. 2021;10:giab020.33764468 10.1093/gigascience/giab020
15. Zhang G Fang X Guo X Li L Luo R Xu F The oyster genome reveals stress adaptation and complexity of shell formation Nature 2012 490 49 54 10.1038/nature11413 22992520
Zhang G, Fang X, Guo X, Li L, Luo R, Xu F, et al. The oyster genome reveals stress adaptation and complexity of shell formation. Nature. 2012;490:49–54.22992520 10.1038/nature11413
16. Qi H Cong R Wang Y Li L Zhang G Construction and analysis of the chromosome-level haplotype-resolved genomes of two Crassostrea oyster congeners: Crassostrea angulata and Crassostrea gigas Gigascience. 2022 12 giad077 10.1093/gigascience/giad077 37787064
Qi H, Cong R, Wang Y, Li L, Zhang G. Construction and analysis of the chromosome-level haplotype-resolved genomes of two Crassostrea oyster congeners: Crassostrea angulata and Crassostrea gigas. Gigascience. 2022;12:giad077.37787064 10.1093/gigascience/giad077
17. Puritz JB Guo X Hare M He Y Hillier LW Jin S A second unveiling: Haplotig masking of the eastern oyster genome improves population-level inference Mol Ecol Resour 2024 24 e13801 10.1111/1755-0998.13801 37186213
Puritz JB, Guo X, Hare M, He Y, Hillier LW, Jin S, et al. A second unveiling: Haplotig masking of the eastern oyster genome improves population-level inference. Mol Ecol Resour. 2024;24:e13801.37186213 10.1111/1755-0998.13801
18. Genbank. Crassostrea gasar draft genome. NCBI. 2024. https://identifiers.org/ncbi/nucleotide:JBEEQF000000000.1. Accessed 15 Jul 2024.
19. Genbank. Crassostrea rhizophorae draft genome. NCBI. 2024. https://identifiers.org/ncbi/nucleotide:JBEOLP000000000.1. Accessed 15 Jul 2024.
20. Lima N, Almeida L, Gerber A, Guimarães A, Solé-Cava A, Melo C, et al. The draft genomes of Crassostrea gasar and Crassostrea rhizophorae: key resources for leveraging oyster cultivation in the Southwest Atlantic. Zenodo; 2024.
