The draft genomes of Crassostrea gasar and Crassostrea rhizophorae: key resources for leveraging oyster cultivation in the Southwest Atlantic

Abstract

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).

Objective

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 [1,2,3,4], 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 [8,9,10]. 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 [14,15,16,17]. 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

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.