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PacBio single molecule long-read sequencing provides insight into the complexity and diversity of the Pinctada fucata martensii transcriptome


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- sequencing provides insight into the complexity and diversity of the Pinctada fucata martensii transcriptome.
- To date, the molecular mechanisms of the growth of this species remain poorly understood.
- The transcriptome sequencing has been considered to understanding of the complexity of mechanisms of the growth of P.
- Alternative splicing analysis of the 16,388 transcripts was performed after accounting for redundancy, and 9097 gene loci were detected, including 1607 new gene loci and 14,946 newly discovered transcripts.
- Conclusions: Our results significantly improve existing gene models and genome annotations, optimise the genome structure, and in-depth understanding of the complexity and diversity of the differential growth patterns of P.
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- Full list of author information is available at the end of the article Zhang et al.
- BMC Genomics .
- Pincata fucata martensii is one of the most common oysters used for the production of seawater pearls, food and drugs.
- It is also one of the most useful animals for studying biominerals, hence it is often used as a model system to investigate the molecular basis of biominerali- sation [1, 2].
- Thus, a comprehen- sive understanding of the mechanisms of growth and development is required to improve pearl production..
- In the past few years, substan- tial effort has been invested in genetic and genomic re- search related to P.
- The results may permit reannotation of the transcriptome, improve whole-genome annotation, optimise the gen- ome structure, and provide a valuable genetic resource for further studies of pearl oysters growth..
- Zhang et al.
- Due to the limitations of the short read sequencing, an- notation of the selected reference genome may not be sufficiently accurate, hence it is necessary to optimise the genetic structure of the original annotation.
- The positions of 11,235 genes in the genome was optimised by the PacBio technique (Additional file 1:.
- Herein, 641 fusion genes were identified in the PacBio li- brary, and were validated using transcriptome datasets..
- The number of intra-chromosomal fusion transcripts was much lower than that of inter-chromosomal fusion genes in the circos map (Fig.
- Meanwhile, length distribution of the encoded protein sequence for each complete ORF region was mapped, and the results are shown in Fig.
- Alternative poly- adenylation regulates gene expression and enhances the complexity of the transcriptome.
- A total of 2318 AS transcripts were predicted from the PacBio sequence data using AStalavista ana- lysis, of which 177 AS transcripts were not annotated in the published version of the P.
- The location of AS transcripts in the genome was described for all but 177 AS transcripts..
- In total, 4386 transcripts were annotated in the COG database, 5160 were annotated in GO, 7067 were annotated in KEGG, 9337 were annotated in KOG, 11,371 were anno- tated in Pfam, 8204 were annotated in Swiss-Prot, 11,879 were annotated in eggNOG, and 13,309 were annotated in NR.
- In the three main categories, metabolic process (BP) (4663), catalytic activity (MF) (4198) and cell part (CC) (2308) were the most enriched subcategories, re- spectively.
- In the transcriptome database, 1028 gene are.
- not annotated in the genome.
- To assess the presence of these unannotated genes, we conducted BLAST analyses, 516 were found in the blastx search against Swiss-Prot proteins, 986 in NR, 245 in COG database,309 in GO, 416 in KEGG, 578 in KOG,804 in eggNOG and 781 in Pfam (Additional file 6:.
- Identification of lncRNAs was clas- sified based on their position in the reference genome and annotation information.
- Interestingly, the data showed that the number of the five basic types of AS models (except for A3SS in L groups) was much higher than for S groups.
- 2 CIRCOS visualisation of the distribution of different data at the genome-wide level.
- b: Gene density of the reference genome.
- d: Transcript density in the genome.
- 0.01), among which 99 were up- regulated and 129 were down-regulated in the pair- wise groups (Additional file 10: Table S10).
- Differ- ences in expression levels of transcripts in the pairwise comparisons are shown in a volcano plot (Fig.
- PacBio sequencing can optimize genome structure Due to the limitations of short read sequencing, annota- tion of the reference genome is often not sufficiently ac- curate.
- In our present work, a hybrid sequencing approach was used to optimise the genetic structure of the original annotation.
- Additionally, 1607 gene loci were newly discovered in the P.
- PacBio sequencing reveals complexity and diversity in the P.
- a: Distribution of the number of poly (A) sites per gene.
- b: GO classification of the putative functions of unique transcripts.
- c: Distribution of homologous species annotated in the NR database.
- Each point in the volcano plot represents a transcript, the abscissa represents the logarithm of the difference in expression of a transcript in the two samples, and the ordinate represents the negative logarithm of the statistical significance of the change in the expression of the transcript.
- The ordinate represents the KEGG metabolic pathway, and the abscissa represents the number of transcripts annotated to the pathway and the percentage of transcripts in the annotation.
- The number of transcripts in the transcriptome is shown in the heat map.
- In the present work, 12,025 complete ORF sequences were obtained..
- In the present work, 228 DETs were identified between L and S groups, of which 99 DETs were up-regulated and 129 down-regulated in L groups.
- We analysed the mRNA expression profiles of six growth and development-related genes, and four of the six genes were up-regulated in S groups.
- Cell division cycle 16-like protein is one of the components of the anaphase-promoting complex involved in cell division [49].
- Fatty acid-binding protein is an intracellular fatty acid carrier protein that plays an important role in the utilisation of fatty acids in cells [50].
- In the present work, in addition to growth and development-related genes, some genes related to immune processes (PB.1364.11, PB.4993.4 and PB.3677.1) were differentially expressed between L and S groups.
- The results improve existing genome annotations, opti- mise the genome structure, and in-depth revealing the complexity and diversity of the differential growth pat- terns of P.
- martensii were obtained from the Daya Bay Marine Biology Research Stations of the Chinese Acad- emy of Sciences (Shenzhen, Guangdong, P.R.
- Fragmentation was treated with divalent cations under elevated tempera- tures in the NEBNext First Strand Synthesis Reaction Buffer (5.
- After adenylation of the 3′ ends of the DNA fragments, hybridization was per- formed by ligating NEBNext adaptor with a hairpin loop structure.
- The size selection of the full-length cDNA and for building libraries of differently sized cDNA were per- formed by BluePippin® (Sage Science, Beverly, MA, USA)..
- The quality of the libraries was assessed using the Agilent Bioanalyzer 2100 system..
- The criteria for fusion candidates were (1) each tran- script must be mapped to two or more distinct protein- encoding loci in the genome.
- (2) each mapped locus must align at least 5% of the transcript.
- The nucleotide sequences of the primers for PCR..
- MH designed this study, contributed to the original concept of the project and modified the paper.
- The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript..
- The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive in National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, under accession number CRA002619 that are publicly accessible at http://bigd.big.ac.cn/gsa..
- Data supporting the manuscript are also available in the supplementary information.
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- Supporting data for “ the pearl oyster Pinctada fucata martensii genome and multi-omic analyses provide insights into biomineralization

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