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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Genet.</journal-id>
<journal-title>Frontiers in Genetics</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Genet.</abbrev-journal-title>
<issn pub-type="epub">1664-8021</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">741243</article-id>
<article-id pub-id-type="doi">10.3389/fgene.2021.741243</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Genetics</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>SMRT Sequencing of the Full-Length Transcriptome of the <italic>Coelomactra antiquata</italic>
</article-title>
<alt-title alt-title-type="left-running-head">Deng et&#x20;al.</alt-title>
<alt-title alt-title-type="right-running-head">Full-Length Transcriptome for <italic>Coelomactra antiquata</italic>
</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Deng</surname>
<given-names>Aiping</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="fn" rid="fn1">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1407057/overview"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Jinpeng</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="fn" rid="fn1">
<sup>&#x2020;</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Yao</surname>
<given-names>Zebin</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Afriyie</surname>
<given-names>Gyamfua</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Chen</surname>
<given-names>Ziyang</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Guo</surname>
<given-names>Yusong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Luo</surname>
<given-names>Jie</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Wang</surname>
<given-names>Zhongduo</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1277124/overview"/>
</contrib>
</contrib-group>
<aff id="aff1">
<label>
<sup>1</sup>
</label>College of Fisheries, Guangdong Ocean University, <addr-line>Zhanjiang</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<label>
<sup>2</sup>
</label>Guangdong Provincial Key Laboratory of Aquaculture in South China Sea for Aquatic Economic Animal of Guangdong Higher Education Institutes, Fisheries College, Guangdong Ocean University, <addr-line>Zhanjiang</addr-line>, <country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/345185/overview">Youri I Pavlov</ext-link>, University of Nebraska Medical Center, United&#x20;States</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1301751/overview">Natalie Saini</ext-link>, Medical University of South Carolina, United&#x20;States</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1457470/overview">Lefei Yi</ext-link>, Jiangsu Ocean Universiity, China</p>
</fn>
<corresp id="c001">&#x2a;Correspondence: Jie Luo, <email>luoj@gdou.edu.cn</email>; Zhongduo Wang, <email>wangzd@gdou.edu.cn</email>
</corresp>
<fn fn-type="equal" id="fn1">
<label>
<sup>&#x2020;</sup>
</label>
<p>These authors have contributed equally to this&#x20;work</p>
</fn>
<fn fn-type="other">
<p>This article was submitted to Genomic Assay Technology, a section of the journal Frontiers in Genetics</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>14</day>
<month>10</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="collection">
<year>2021</year>
</pub-date>
<volume>12</volume>
<elocation-id>741243</elocation-id>
<history>
<date date-type="received">
<day>14</day>
<month>07</month>
<year>2021</year>
</date>
<date date-type="accepted">
<day>24</day>
<month>09</month>
<year>2021</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2021 Deng, Li, Yao, Afriyie, Chen, Guo, Luo and Wang.</copyright-statement>
<copyright-year>2021</copyright-year>
<copyright-holder>Deng, Li, Yao, Afriyie, Chen, Guo, Luo and Wang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these&#x20;terms.</p>
</license>
</permissions>
<abstract>
<p>
<italic>Coelomactra antiquata</italic> is an important aquatic economic shellfish with high medicinal value. However, because <italic>C. antiquata</italic> has no reference genome, a lot of molecular biology research cannot be carried out, so the analysis of its transcripts is an important step to study the regulatory genes of various substances in <italic>C. antiquata</italic>. In the present study, we conducted the first full-length transcriptome analysis of <italic>C. antiquata</italic> by using PacBio single-molecule real-time (SMRT) sequencing technology. The results identified a total of 39,209 unigenes with an average length of 2,732 bp, 23,338 CDSs, 251 AS events, 9,881 lncRNAs, 20,106 SSRs, and 2,316&#xa0;TFs. Subsequently, 59.22% (23,220) of the unigenes were successfully annotated, of which 23,164, 18,711, 15,840, 13,534, and 13,474 unigenes could be annotated using NR, Swiss-prot, KOG, GO, and KEGG databases, respectively. This study lays the foundation for the follow-up research of molecular biology and provides a reference for studying the more medicinal value of <italic>C. antiquata</italic>.</p>
</abstract>
<kwd-group>
<kwd>
<italic>Coelomactra antiquata</italic>
</kwd>
<kwd>full-length transcriptome</kwd>
<kwd>SMRT sequencing</kwd>
<kwd>RNA-seq</kwd>
<kwd>function annotation</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>The <italic>Coelomactra antiquata</italic> is a bivalve marine creature that lives in the bottom sand (<xref ref-type="bibr" rid="B18">Kong and Li, 2009</xref>). As a wide temperature shellfish, it is predominantly distributed in the western Pacific Ocean, the Indian Peninsula, Japan, and the coast of China. In China, <italic>C. antiquata</italic> is distributed from Liaoning province in the north and Guangxi Zhuang autonomous region in the south (<xref ref-type="bibr" rid="B19">Kong et&#x20;al., 2007</xref>). The meat of the <italic>C. antiquata</italic> is tender, delicious, and nutritious, making it a remarkable species with high economic value (<xref ref-type="bibr" rid="B24">Liu et&#x20;al., 2006</xref>). However, due to excessive fishing in recent decades, the natural population of <italic>C. antiquata</italic> has gradually decreased (<xref ref-type="bibr" rid="B13">He et&#x20;al., 2021</xref>). Fortunately, the artificial breeding of <italic>C. antiquata</italic> has gradually matured in recent years of continuous attempts (<xref ref-type="bibr" rid="B23">Liu et&#x20;al., 2012</xref>; <xref ref-type="bibr" rid="B8">Chen., 2018a</xref>; <xref ref-type="bibr" rid="B7">Chen., 2018b</xref>).</p>
<p>In addition to research on mitochondrial genomes (<xref ref-type="bibr" rid="B28">Meng et&#x20;al., 2012</xref>, <xref ref-type="bibr" rid="B27">2013</xref>; <xref ref-type="bibr" rid="B32">Shen et&#x20;al., 2016</xref>), the content of previous research mainly focused on its morphological research (<xref ref-type="bibr" rid="B19">Kong et&#x20;al., 2007</xref>), population genetic comparison (<xref ref-type="bibr" rid="B17">Kong and Li, 2007</xref>), and organizational composition research of <italic>C. antiquata</italic> (<xref ref-type="bibr" rid="B37">Wu et&#x20;al., 2019</xref>). As well, there was some research on the possible role of this bivalve in disease treatment. For example, treating diabetic mice with different doses of <italic>C. antiquata</italic> extract can reduce the blood glucose concentration of diabetic mice and increase the antioxidant activity of serum (<xref ref-type="bibr" rid="B36">Wen et&#x20;al., 2015</xref>). Also, using a dose of 30&#xa0;mg/kg of <italic>C. antiquata</italic> polysaccharides on human carcinoma of esophagus cells transplanted in nude mice, its inhibitory rate of 28.85% was recorded (<xref ref-type="bibr" rid="B39">Yang et&#x20;al., 2015</xref>).</p>
<p>According to the above description, there have been many reports on the genetic, morphological, and disease treatment effects of <italic>C. antiquata</italic>, but there are few studies on the transcriptome level. Transcriptome sequencing (RNA-seq) is a technology that uses high-throughput sequencing technology to sequence and analyze all or part of mRNA, small RNA, and no-codingRNA in cells or tissues. RNA-seq can identify genes involved in a variety of biological processes and obtain relevant transcripts in biological processes (<xref ref-type="bibr" rid="B11">De Klerk et&#x20;al., 2014</xref>; <xref ref-type="bibr" rid="B6">Chen et&#x20;al., 2020</xref>). With the continuous development of nucleic acid sequencing technology and the advent of third-generation sequencing technology, the full-length transcriptome can be obtained more simply and accurately (<xref ref-type="bibr" rid="B31">Schadt et&#x20;al., 2010</xref>). Compared with the first-generation and second-generation sequencing technologies, the third-generation sequencing technology can directly obtain the full-length transcript sequence without assembling, which can truly reflect the transcriptome information of the sequenced species (<xref ref-type="bibr" rid="B21">Li et&#x20;al., 2008</xref>; <xref ref-type="bibr" rid="B2">Bleidorn, 2016</xref>; <xref ref-type="bibr" rid="B15">Jia et&#x20;al., 2020</xref>). This study used PacBio&#x2019;s single-molecule real-time (SMRT) sequencing technology to generate comprehensive full-length transcriptome of <italic>C. antiquata</italic>. We then systematically carried out structural analysis and functional annotation of those full-length transcriptomes to obtain a large amount of usable sequence information. From this sequence information, we can see that many transcripts of <italic>C. antiquata</italic> have signal transduction, synthesis, and metabolism functions, indicating that there may be many biologically active substances in <italic>C. antiquata</italic> that participate in the life processes. This study will provide data for follow-up study of certain functional genes, molecular biology research, and exploration of possible biomedical functions of <italic>C. antiquata</italic>.</p>
</sec>
<sec sec-type="materials|methods" id="s2">
<title>Materials and Methods</title>
<sec id="s2-1">
<title>Sample Collection for Iso-Seq</title>
<p>One <italic>C. antiquata</italic> (Shell length: 87&#xa0;mm, shell width: 65&#xa0;mm, shell height: 40.5&#xa0;g) sampled from Leizhou in Guangdong Province. Tissues including blood, mantle, adductor muscle, lip, foot, gill, inlet pipe, outlet pipe, kidney, intestine, liver, and gonad were rapidly collected, immediately frozen in liquid nitrogen, and then stored at &#x2212;80&#xb0;C for preservation until RNA extraction.</p>
</sec>
<sec id="s2-2">
<title>RNA Extraction</title>
<p>Total RNA was separately extracted from these tissue samples (<xref ref-type="bibr" rid="B15">Jia et&#x20;al., 2020</xref>; <xref ref-type="bibr" rid="B42">Zheng et&#x20;al., 2020</xref>). The purity, concentration, and absorption peak of the extracted RNA were measured using a NanoDrop 2,000 spectrophotometer (Thermo Fisher Scientific Inc., United&#x20;States). Agarose gel electrophoresis was mainly used to detect the genomic contamination, purity of samples, and the Agilent 2,100 was used to determine the RIN value accurately detecting the integrity of RNA. When the test results met the requirements, RNA samples from 12 tissue were mixed together for the following library preparation.</p>
</sec>
<sec id="s2-3">
<title>Library Preparation and SMRT Sequencing</title>
<p>The Clonetech SMARTerTM PCR cDNA Synthesis Kit was used to reverse transcribe the pooled total RNA into cDNA. Afterwards, polymerase chain reaction (PCR) was employed to amplify the cDNA and using primers with Oligo dT. The amplified cDNA was purified with PB magnetic beads. After purification, all full-length cDNAs were end-repaired and connected with SMRT dumbbell adaptors. Exonuclease digestion was implemented to remove sequences that failed to ligate to the adapters. The resulting sequences were purified again. Finally, a SMRTbell library was constructed. Prior to sequencing, the accurate quantification of the libraries was assessed by Qubit 3.0 and the size of the libraries were detected by Agilent 2,100. Then the full-length transcriptome was sequenced with PacBio sequencer.</p>
</sec>
<sec id="s2-4">
<title>Sequencing Data Processing</title>
<p>The raw sequencing data were processed using the SMRTlink (<xref ref-type="bibr" rid="B14">Hon et&#x20;al., 2020</xref>) software with the parameters: --min_passes 3; --min_length 50; --max_length 15,000. The high-quality sequencing reads produced by a single molecule in the sequencing process are called polymerase read, and the polymerase reads remove the sequencing adapters to form subreads. A circular consensus sequence (CCS) was obtained from the subreads. The CCS sequence was checked to see whether it contained 5&#x2032;primer, 3&#x2032;primer, and polyA. Their positional relationships were assessed and later divided the CCS sequence into three categories: the full-length sequence (FL), the full-length non-concatemer sequence (FLNC), and the full-length non-chimeric sequence with polyA. ICE of SMRTlink software was used to cluster FLNC sequences and obtain a set of cluster consensus sequences. Further the sequences were polished by Arrow algorithm (<xref ref-type="bibr" rid="B4">Cao et&#x20;al., 2020</xref>) and obtained the FLNC polished high quality consensus Sequences. Finally, CD-HIT (<xref ref-type="bibr" rid="B22">Li &#x26; Godzik, 2006</xref>) software (parameters: -c 0.99; -G 0; -aL 0.90; -AL 100; -aS 0.99; -AS 30) was used to perform clustering and de-redundancy. The unigenes from high quality full-length transcripts were used for subsequent analysis.</p>
</sec>
<sec id="s2-5">
<title>Function Annotation</title>
<p>To obtain basic annotations information, non-redundant transcripts were annotated against six different databases, namely, Non-supervised Orthologous Groups (NR), EuKaryotic Orthologous Groups (KOG), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Swiss-Prot, and Pfam databases. DIAMOND (<xref ref-type="bibr" rid="B3">Buchfink et&#x20;al., 2014</xref>) software (parameters: --more-sensitive; -k 10; -e 1e-5) was used for NR, Swiss-Prot, KOG, GO, KEGG databases analyses, and the Hmmer package (<xref ref-type="bibr" rid="B29">Nguyen et&#x20;al., 2016</xref>) with default parameters utilized for Pfam database analyses.</p>
</sec>
<sec id="s2-6">
<title>Structure Analysis</title>
<sec id="s2-6-1">
<title>CDS, LncRNA, and TFs Prediction</title>
<p>TransDecoder software (<xref ref-type="bibr" rid="B12">Haas et&#x20;al., 2013</xref>) (parameters: -G universal; -S; -m 100) was used to predict the coding sequences (CDS) of transcripts. Transcripts longer than 200 nucleotides (nts) were used for the long noncoding RNA (lncRNA) prediction. Four methods, Coding Potential Calculator 2 (CPC2) (<xref ref-type="bibr" rid="B16">Kang et&#x20;al., 2017</xref>) (default parameters), Coding-Non-Coding Index (CNCI) (<xref ref-type="bibr" rid="B33">Sun et&#x20;al., 2013</xref>) (parameters: -m pl), Coding Potential Assessment Tool (CPAT) (<xref ref-type="bibr" rid="B35">Wang et&#x20;al., 2013</xref>) (default parameters), and PLEK (<xref ref-type="bibr" rid="B20">Li et&#x20;al., 2014</xref>) (parameters: -minlength 200), were integrated to identify lncRNA in the transcripts and depict the intersection of the results predicted by the four methods. For the TF (transcription factor) analysis, we used DIAMOND (<xref ref-type="bibr" rid="B3">Buchfink et&#x20;al., 2014</xref>) software to align the sequences to the AnimalTFDB (animal transcription factor database) for TFs prediction.</p>
</sec>
<sec id="s2-6-2">
<title>Simple Sequence Repeat Analysis</title>
<p>With the default parameters of MISA 1.0 (<xref ref-type="bibr" rid="B1">Beier et&#x20;al., 2017</xref>), all SSRs present within the transcriptome sequences were identified and count the regional distribution of some SSRs. In the process of identification, the minimum value of repeat number varied with different repeat units per unit sizes and their minimum number of repetitions were: 1&#x2013;10, 2&#x2013;6, 3&#x2013;5, 4&#x2013;5, 5-5, and 6&#x2013;5. For instance, 1&#x2013;10 indicates that a single nucleotide must be repeated at least 10&#x20;times to be detected. The SSR were divided into seven types: Mono-, Di-, Tri-, Tetra-, Penta-, Hexa-, and compound&#x20;SSR.</p>
</sec>
<sec id="s2-6-3">
<title>Alternative Splice Prediction</title>
<p>In this study, IsoSeq_AS_de_novo software (<xref ref-type="bibr" rid="B30">Palareti et&#x20;al., 2016</xref>) with default parameters was used to perform Alternative splice analysis of the non-redundant sequences, and this software used a method that does not require reference sequences to detect AS isoforms.</p>
</sec>
</sec>
</sec>
<sec sec-type="results" id="s3">
<title>Result</title>
<sec id="s3-1">
<title>SMRT Sequencing Data Analysis</title>
<p>By using the PacBio Sequel II sequencing platform, we obtained 956,679 of polymerase read (about 112.26&#xa0;Gb). In a total of 87,338,730 subreads, an average length of the subread was 1,173&#x20;bp and an N50 length of 2,347 bp. After self-correction among subreads, 660,201 CCS reads were gained in which a mean of the CCS read length was 2,416 bp. The amount of CCSs for each transcript ranges from 2 to 24,103, with an average of 9, and the average accuracy of the obtained CCS data was 0.99951. By detecting the sequences, 5,057,806 CCSs were identified as full-length reads and 495,198 were identified as FLNC reads with an average length was 2,423&#x20;bp and an N50 length of 3,057 bp. Then, 41,056 polished transcripts were obtained. Finally, the redundant reads were removed by CD-HIT and 39,209 unigenes with a mean length of 2,732&#x20;bp were obtained (<xref ref-type="table" rid="T1">Table&#x20;1</xref>). The length distribution of unigenes is as shown in <xref ref-type="fig" rid="F1">Figure&#x20;1</xref>. The majority of unigenes were between 1,000 and 4,000&#x20;bp (28,643, 73.05%), and the longest unigenes was about&#x20;8,000.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Description of full-length sequencing in <italic>C. antiquata</italic>.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Type</th>
<th align="center">Total number</th>
<th align="center">Min length</th>
<th align="center">Average length</th>
<th align="center">Max length</th>
<th align="center">N50</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Polymerase read</td>
<td align="center">956,679</td>
<td align="char" char=".">51</td>
<td align="center">117,339</td>
<td align="center">425,391</td>
<td align="center">189,911</td>
</tr>
<tr>
<td align="left">Subread</td>
<td align="center">87,338,730</td>
<td align="char" char=".">51</td>
<td align="center">1,173</td>
<td align="center">274,252</td>
<td align="center">2,347</td>
</tr>
<tr>
<td align="left">CCS</td>
<td align="center">660,201</td>
<td align="char" char=".">48</td>
<td align="center">2,416</td>
<td align="center">14,948</td>
<td align="center">3,136</td>
</tr>
<tr>
<td align="left">FLNC</td>
<td align="center">495,198</td>
<td align="char" char=".">50</td>
<td align="center">2,423</td>
<td align="center">8,687</td>
<td align="center">3,057</td>
</tr>
<tr>
<td align="left">Unigenes</td>
<td align="center">39,209</td>
<td align="char" char=".">66</td>
<td align="center">2,732</td>
<td align="center">8,074</td>
<td align="center">3,324</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Length distribution of unigenes obtained from <italic>C. antiquata</italic> SMRT library.</p>
</caption>
<graphic xlink:href="fgene-12-741243-g001.tif"/>
</fig>
</sec>
<sec id="s3-2">
<title>Functional Annotation</title>
<p>In all, 39,208 transcripts were annotated in the five databases, of which 23,264 (59.33%), 13,534 (34.52%), 13,474 (34.36%), 15,840 (40.40%), and 18,711 (47.72%) transcripts were, respectively, matched to the NR, GO, KEGG, KOG, and Swiss-Prot databases (<xref ref-type="fig" rid="F2">Figure&#x20;2A</xref>). A total of 8,359 (22.84%) transcripts were annotated in all the databases (<xref ref-type="fig" rid="F2">Figure&#x20;2B</xref>). Aligning each transcript with the homologous sequence of the NR library, it was determined which species the sequence with the best comparison result belongs to, and count the number of homologous sequences aligned with each species. According to statistics, the species with the most homology was <italic>Mizuhopecten yessoensis</italic> (6,118 transcripts), followed by <italic>Crassostrea gigas</italic> (4,276), <italic>Crassostrea virginica</italic> (3,471), and <italic>Lottia gigantea</italic> (1,281) (<xref ref-type="fig" rid="F2">Figure&#x20;2C</xref>).</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Functional annotation in <italic>C. antiquata.</italic> <bold>(A)</bold> Statistics of the transcripts annotated in different databases. <bold>(B)</bold> Venn diagram of annotations in NR, GO, KEGG, KOG, and SwissProt databases. <bold>(C)</bold> Distribution of the top 10 species with matched transcripts in the NR database. <bold>(D)</bold> Distribution of GO terms for all annotated transcripts in biological process, cellular component, and molecular function. <bold>(E)</bold> KEGG pathways enriched by transcripts. <bold>(F)</bold> COG categories of the transcripts.</p>
</caption>
<graphic xlink:href="fgene-12-741243-g002.tif"/>
</fig>
<p>According to GO classification statistics of the transcripts, the annotated results included three broad categories: Biological process (22,917 transcripts), Cellular component (26,837) and Molecular function (15,073). The Cellular process (5,413, 39.99%), Cell (4,702, 34.74%), and Binding (6,879, 50.82%) were the most annotated transcripts in the three categories mentioned above (<xref ref-type="fig" rid="F2">Figure&#x20;2D</xref>).</p>
<p>In KEGG pathways, the transcripts were assigned to five main categories: Cellular processes (3,845 transcripts), Environmental information processing (2,604), Genetic information processing (2,192), Metabolism (4,291), and Organismal systems (5,359). Signal transduction (2,129, 15.80%) was the largest group of transcripts, followed by Transport and catabolism (1,498, 11.12%) (<xref ref-type="fig" rid="F2">Figure&#x20;2E</xref>).</p>
<p>The KOG classifications of the transcripts obtained clusters of 26 functional categories (<xref ref-type="fig" rid="F2">Figure&#x20;2F</xref>). A total of 2,543 (16.05%) transcripts were annotated in General function prediction only, which is the most among functional categories. Next was the Signal transduction mechanisms (2,159, 13.63%).</p>
</sec>
<sec id="s3-3">
<title>Structure Analysis</title>
<sec id="s3-3-1">
<title>CDS Prediction</title>
<p>The number and length of 5&#x2032;UTR, 3&#x2032;UTR, and CDS were identified by transdecoder software. In total, 15,555 transcripts were predicted in the 5&#x2032;UTR, 20,550 in the 3&#x2032;UTR, and 23,338 in the CDS. As shown in <xref ref-type="fig" rid="F3">Figure&#x20;3</xref>, most of the CDS, 19,643 transcripts (84.16%) lengths, were less than 2,000&#xa0;nt, 14.13% ranged from 2,000 to 4,000&#xa0;nt (3,299 transcripts), and only 396 transcripts representing 1.69% were over 4,000&#xa0;nt.</p>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Length distribution of CDSs.</p>
</caption>
<graphic xlink:href="fgene-12-741243-g003.tif"/>
</fig>
</sec>
<sec id="s3-3-2">
<title>Identification of lncRNAs</title>
<p>We identified 14,493, 13,827, 14,027, and 11,074 lncRNAs by CNCI, CPAT, CPC2, and PLEK, respectively. The results of these four methods were integrated and 9,881 lncRNA transcripts were predicted totally (<xref ref-type="fig" rid="F4">Figure&#x20;4A</xref>). By comparing the length distribution density of lncRNA and original mRNAs, it was found that there were more lncRNAs with lengths between 1,000 and 2,000&#xa0;nt than mRNAs, and the longest predicted lncRNA does not exceed 8,000&#xa0;nt (<xref ref-type="fig" rid="F4">Figure&#x20;4B</xref>).</p>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Long non-coding RNA (lncRNA) prediction. <bold>(A)</bold> Venn diagram of lncRNAs predicted by CNCI, CPAT, CPC2, and PLEK methods. <bold>(B)</bold> Length distribution of identified lncRNAs and mRNA in <italic>C. antiquata</italic>.</p>
</caption>
<graphic xlink:href="fgene-12-741243-g004.tif"/>
</fig>
</sec>
<sec id="s3-3-3">
<title>SSR Analysis</title>
<p>SSR analysis of the transcriptome revealed a total of 20,106 SSRs using MISA 1.0 software. Upon careful scrutiny of the obtained SSRs, the most predominant was the mono-nucleotide repeats (9,443), which accounted for 46.96%, followed by the di-nucleotide repeats (5,932), representing 29.50%, and tri-nucleotide repeats (3,379), accounted for 16.80%. However, tetra-nucleotide, penta-nucleotide, and hexa-nucleotide repeats accounted for a very small number, 4.92, 1.15, and 0.64%, respectively. Besides, the number of repetitions for most SSRs were 5-8 and 9&#x2013;12 (<xref ref-type="fig" rid="F5">Figure&#x20;5</xref>). Since some transcripts cannot predict the CDS, the total number of SSRs that can be counted in different regions was 9,804. Among the 9,804 SSRs, the number in the 3&#x2032;UTR was the most (8,024), followed by the CDS (1,354), and the 5&#x2032;UTR (426) was the least (<xref ref-type="table" rid="T2">Table&#x20;2</xref>).</p>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Summary of SSR types in the <italic>C. antiquata</italic> transcriptome.</p>
</caption>
<graphic xlink:href="fgene-12-741243-g005.tif"/>
</fig>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Regional distribution of some SSRs in the full-length transcriptome of <italic>C. antiquata.</italic>
</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Type</th>
<th align="center">Number</th>
<th align="center">Ratio (%)</th>
<th align="center">5&#x2032;UTR</th>
<th align="center">CDS</th>
<th align="center">3&#x2032;UTR</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Mono-</td>
<td align="char" char=".">4,179</td>
<td align="char" char=".">42.63</td>
<td align="char" char=".">172</td>
<td align="char" char=".">50</td>
<td align="char" char=".">3,957</td>
</tr>
<tr>
<td align="left">Di-</td>
<td align="char" char=".">2,696</td>
<td align="char" char=".">27.50</td>
<td align="char" char=".">117</td>
<td align="char" char=".">38</td>
<td align="char" char=".">2,541</td>
</tr>
<tr>
<td align="left">Tri-</td>
<td align="char" char=".">2,234</td>
<td align="char" char=".">22.79</td>
<td align="char" char=".">93</td>
<td align="char" char=".">1,245</td>
<td align="char" char=".">896</td>
</tr>
<tr>
<td align="left">Tetra-</td>
<td align="char" char=".">501</td>
<td align="char" char=".">5.11</td>
<td align="char" char=".">19</td>
<td align="char" char=".">4</td>
<td align="char" char=".">478</td>
</tr>
<tr>
<td align="left">Penta-</td>
<td align="char" char=".">116</td>
<td align="char" char=".">1.18</td>
<td align="char" char=".">24</td>
<td align="char" char=".">1</td>
<td align="char" char=".">91</td>
</tr>
<tr>
<td align="left">Hexa-</td>
<td align="char" char=".">78</td>
<td align="char" char=".">0.80</td>
<td align="char" char=".">1</td>
<td align="char" char=".">16</td>
<td align="char" char=".">61</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s3-3-4">
<title>Transcription Factor Prediction</title>
<p>TF is a key factor in regulating gene expression in animals. In this study, 2,316&#xa0;TFs from 59&#xa0;TF families were identified by DIAMOND software. List the top 20&#xa0;TF families in <xref ref-type="fig" rid="F6">Figure&#x20;6</xref>, the BHLH family (369, 15.93%) was the most represented, followed by the zf-C2H2 family (278, 12.00%).</p>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Identification of TFs in<italic>C. antiquata</italic>. The number and family of TFs were predicted by SMRT.</p>
</caption>
<graphic xlink:href="fgene-12-741243-g006.tif"/>
</fig>
</sec>
</sec>
<sec id="s3-4">
<title>Alternative Splice Prediction</title>
<p>A total of 251 AS events were detected via the IsoSeq_AS_de_novo in all unigenes obtained by SMRT sequencing. Due to the lack of an available <italic>C. antiquata</italic> reference genome, it is necessary to further characterize the types of AS events in future studies.</p>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<title>Discussion</title>
<p>PacBio RNA-seq has fast sequencing speed, high accuracy, and long readings. Because of the advantages of PacBio RNA-seq, it has been widely used in various species research (<xref ref-type="bibr" rid="B26">Ma et&#x20;al., 2019</xref>; <xref ref-type="bibr" rid="B38">Xue et&#x20;al., 2019</xref>; <xref ref-type="bibr" rid="B25">Luo et&#x20;al., 2020</xref>; <xref ref-type="bibr" rid="B42">Zheng et&#x20;al., 2020</xref>; <xref ref-type="bibr" rid="B10">Chen et&#x20;al., 2021</xref>). Because <italic>C. antiquata</italic> is an important economic shellfish with high medicinal potential, there are many studies now. The present study provides the first full-length transcriptome resource for <italic>C. antiquata</italic> using PacBio single-molecule long-read sequencing technology. By processing and analyzing the sequenced data, a total of 39,209 unigenes were finally identified, with an average length of 2,732 bp. In previous studies, some researchers used the Illumina platform to sequence the transcriptome, and obtained the second-generation transcriptome data of the <italic>C. antiquata</italic> (<xref ref-type="bibr" rid="B40">Yi et&#x20;al., 2019</xref>). The second-generation sequencing identified 214,732 unigenes with a mean length of 616.2 bp. Compared with the results measured by second-generation sequencing, the total number of transcripts obtained by the SMRT sequencing technology in this study is larger and the average length is longer.</p>
<p>In addition, the unigenes obtained were functionally annotated in databases, and 59.22% of the unigenes were successfully annotated. The percentage is not very high, and the possible reason is that there are few studies on molecular biology of this shellfish in the past, the data collected in the database is incomplete, and the genomic information of <italic>C. antiquata</italic> has not yet been referenced. According to the NR annotation situation, <italic>Mizuhopecten yessoensis</italic> has the most homologous sequences annotated, which reflects the high affinity between <italic>C. antiquata</italic> and <italic>Mizuhopecten yessoensis</italic>. It also provides a valuable data basis for the detailed comparison of gene expression between the two species in the future. Among the function statistics against KOG, KEGG, and GO database, the number of transcripts annotated in KOG was the largest. More transcripts are involved in intracellular signal transduction and play a role in the endocrine system to participate in the metabolism of various substances, which proves that there may be many biologically active substances in <italic>C. antiquata</italic> that can be excavated and used in biomedicine.</p>
<p>We also analyzed the structure of the de-redundancy transcripts. A total of 20,106 SSRs and 9,881 lncRNAs were predicted. SSR is widely used in genetic diversity testing, genetic map construction, Gene expression regulation, etc. (<xref ref-type="bibr" rid="B34">Tranbarger et&#x20;al., 2012</xref>; <xref ref-type="bibr" rid="B9">Chen et&#x20;al., 2014</xref>). Compared with the number of SSRs and SSR types obtained by sequencing of the transcriptome of the <italic>Tegillarca granosa</italic>, the number of SSR present in <italic>C. antiquata</italic> was less, and its SSR di-nucleotide repeats were the most (H. <xref ref-type="bibr" rid="B5">Chen et&#x20;al., 2017</xref>). These differences may be related to the different tissue specificities of the two shellfishes. Besides, this study predicted the number of TFs and the detailed family classification of <italic>C. antiquata,</italic> The BHIH family has the largest number. BHLH TFs are the most widespread category in eukaryotes, and they can participate in various processes in cells, such as regulating carbohydrate response genes (<xref ref-type="bibr" rid="B41">Yu et&#x20;al., 2021</xref>), which may indirectly affect the synthesis of various biologically active substances.</p>
<p>In summary, this study successfully constructed a high-quality full-length transcriptome of <italic>C. antiquata</italic>, and preliminarily analyzed its transcriptome structure and functional characteristics, obtained the relevant annotation function of transcripts in the database, and enriched the genetic information of this species. It has laid a solid foundation for the mining and utilization of later functional genes and other molecular biology research.</p>
</sec>
<sec sec-type="conclusion" id="s5">
<title>Conclusion</title>
<p>We applied PacBio SMRT sequencing platform to obtain a large number of full-length transcriptome data of <italic>C. antiquata</italic> for the first time. The number and mean length of the unigenes from SMRT sequencing were much better than those from Illumina sequencing. And through structural analysis and functional annotation of the obtained full-length transcripts, gene function and gene structure information can be obtained more comprehensively. The acquisition of the full-length transcripts provides molecular biology data for <italic>C. antiquata</italic>, which lacks genomic information. As a species with high medicinal value, in the future, the full-length transcriptome data can be combined with the second-generation sequencing results to conduct further research on the medical effects of its internal substances.</p>
</sec>
</body>
<back>
<sec id="s6">
<title>Data Availability Statement</title>
<p>The datasets provided in this study can be found in the online repository: NCBI SRA database, with the accession number SRR15211944. The repository and accession number can be found at <ext-link ext-link-type="uri" xlink:href="https://www.ncbi.nlm.nih.gov/sra">https://www.ncbi.nlm.nih.gov/sra</ext-link>.</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>AD and JL are the main executives of the experimental design and experimental research of this study, completing the experimental data analysis and writing the first draft of the paper; ZY and ZC participated in some experiments; GA and YG checked and corrected the draft; JL and ZW as the project leader, instructed experimental design, data analysis, reviewed the draft of the paper and approved the final draft. All authors have read and agreed to the final&#x20;text.</p>
</sec>
<sec id="s8">
<title>Funding</title>
<p>This work was supported by the project of Zhanjiang Agricultural and Rural Bureau under Zhancainong (2019) No. 152; Zhanjiang Municipal Bureau of Agriculture and Rural Affairs, the central governments 2019 fishery product oil price subsidy municipal-level overall funding project, research on artificial breeding and breeding technology of Coelomactra antiquata, Zhan Cainong (2019) No.&#x20;152.</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s10">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
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