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\begin{document}

\title{Dissecting the chromosome-level genome of the Asian Clam (Corbicula
fluminea)}



\author[1]{Tongqing Zhang}%
\author[1]{Jiawen  Yin}%
\author[1]{Shengkai  Tang}%
\author[1]{Daming  Li}%
\author[1]{Xiankun  Gu}%
\author[2]{Shengyu  Zhang}%
\author[3]{Weiguo  Suo}%
\author[4]{Lei Wu}%
\author[5]{Xiaqing Yu}%
\author[1]{Xiaowei  Liu}%
\author[1]{Yanshan  Liu}%
\author[1]{Qicheng  Jiang}%
\author[1]{Muzi  Zhao}%
\author[1]{Yue  Yin}%
\author[1]{Jianlin  Pan}%
\affil[1]{Freshwater Fisheries Research Institute of Jiangsu Province}%
\affil[2]{Hongze Lake Fisheries Administration Committee Office of Jiangsu Province}%
\affil[3]{Fisheries Management Commission of Gehu Lake}%
\affil[4]{Biomarker Technologies Corporation}%
\affil[5]{Nanjing Agricultural University}%


\vspace{-1em}



  
  \date{May 08, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
The Asian Clam (Corbicula fluminea) is a valuable commercial and
medicinal bivalve that is widely distributed in East and Southeast Asia.
As a natural nutrient source, the clam is high in protein, amino acids,
and microelements. In China, C. fluminea plays an important role in the
diversity of freshwater ecosystems. The genome of C. fluminea has not
yet been characterized, therefore, genome-assisted breeding and
improvements cannot yet be implemented. In this work, we present a de
novo chromosome-scale genome assembly of C. fluminea using PacBio and
Hi-C sequencing technologies. The assembled genome comprised 4,728
contigs, with a contig N50 of 521.06 Kb, and 1,215 scaffolds with a
scaffold N50 of 70.62 Mb. More than 1.51 Gb (99.17\%) of genomic
sequences were anchored to 18 chromosomes, of which 1.40 Gb (92.81\%) of
genomic sequences were ordered and oriented. The genome contains 38,841
coding genes, 32,591 (83.91\%) of which were annotated in at least one
functional database. Compared with related species, C. fluminea had 851
expanded gene families and 191 contracted gene families. The expanded
genes were significantly enriched in 9 terms associated with metabolite
synthesis. The phylogenetic tree showed that C. fluminea diverged from
the ancestors of marine bivalves \textasciitilde{}492.00 million years
ago (Mya). Additionally, we identified two MITF genes in C. fluminea and
several core genes involved in vitamin B6 metabolic pathways. The
high-quality and chromosomal Asian Clam genome will be a valuable
resource for a range of development and breeding studies of C. fluminea
in future research.%
\end{abstract}%



\sloppy


\textbf{Keywords}

\emph{Corbicula fluminea} , genome assembly, PacBio, Hi-C

\textbf{1 Introduction}

\emph{Corbicula fluminea} belongs to the family Corbiculidae,
genus\emph{Corbicula} (Ishibashi et al., 2003; Korniushin, 2004). The
clam has a round base and triangular double shells (Figure 1). The
surface of the shells is glossy, and the shell color varies with the
living environment (Alyakrinskaya, 2005). Shells are brown, yellow,
green, or black and are characterized by circular growth lines (Qiu,
Shi, \& Komaru, 2001). There are three main teeth in the left shell, one
in the front, one in the back and one in the side (Thorp \& James,
1991). The clam grows rapidly and takes only 73--91 days for sexual
maturation (Tao, Deng, \& Li, 2016; Gu, \& Wang, 2001). Due to this, it
has strong reproductive capacity and diffusion ability (Sun, 1995).
\emph{C. fluminea} is native to East Asia, therefore it is also called
the Asian Clam. This species is a native shellfish in China, where it
displays strong environmental adaptability. As a representative of
freshwater macrobenthic invertebrates, clams are an important component
of freshwater ecosystems, and this species has an important impact on
the diversity of freshwater ecosystems (Ding, Deng, \& Cao, 2014; Xiao,
2015). As a bivalve that has successfully undergone radiation in
freshwater habitats (Sirirat, Joong, \& Foighil, 2000), the clam is
widely distributed in lakes and rivers in China. It is also found in
Korea, Japan, and Southeast Asian countries (Jiang, \& Zhao, 1997),
whereas in America and Europe, it is considered as an alien species
(Keogh, \& Simons, 2019; Kondakov, Palatov, \& Bolotov, 2018).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{FIGURE 1 The Asian Clam (\emph{Corbicula fluminea})}

The meat of \emph{C. fluminea} is delicious and nutritious, and thus the
clam is considered as a delicacy. It is rich in protein, amino acids,
and microelements, and its nutritional value has been well studied
(Zhao, \& Liu, 2010; Zhuang, \& Song, 2009). According to the Compendium
of Materia Medica, the Asian Clam has medicinal applications of
detumescence, dehumidification, sobering up, and benefits to the liver.
Modern research has found that the extracts of the clam can protect
against liver damage and reduce blood lipids (Chin, Chien, \& Gow, 2010;
Peng, et al., 2008). The proteins in the clam can be hydrolyzed into
peptides and amino acids using proteases (Wu, \& Sun, 2007). Using
modern enzymolysis technology for hydrolysis, various natural products
that have hepato-protective, anti-inflammatory, antitumor, antioxidant,
and anti-hypertensive properties can be obtained. At present, the
utilization of Asian Clam resources mainly involves fresh, dried, and
canned food. As a traditional Chinese medicine, the Asian Clam is
underdeveloped. For example, Japan and South Korea import tens of
thousands of tons of clams to China every year for deep processing into
health care drugs such as sobering agents and liver-protecting drugs
(Zhang, 1996). Compared with Japan and South Korea, the deep processing
ability for the Asian Clam in China is relatively backward, resulting in
its economic and medicinal value not being fully exploited (Wang, \&
Liu, 2010).

Deciphering the genome of \emph{C. fluminea} is the most basic step in
our research program. The acquisition of a high-quality genome may
provide more detailed insights into the value of \emph{C. fluminea} .
During the past decade, whole-genome sequencing has been widely
performed on a number of bivalves due to the rapid development of
third-generation sequencing (Sun, et al., 2017; Yan, et al., 2019). The
lack of a complete genome was the motivation for a de novo genome
sequencing of \emph{C. fluminea} . In this study, we utilized PacBio and
Hi-C technology to successfully assemble a chromosomal-level genome (2n
= 36). The effective genome information, including chromosomes, genes,
and repeat sequence distributions, is displayed in a circle diagram
(Figure 2). This genome provides the foundation for a range of
development and breeding studies of \emph{C. fluminea} in future
research.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{FIGURE 2 Genome landscape of \emph{Corbicula fluminea}. From
outer to inner circles: a: marker distribution on 18 chromosomes at the
Mb scale; b: LARD distribution on each chromosome; c: PLE distribution
on each chromosome; d: gene distribution on each chromosome; e: GC
content within a 1-Mb sliding window; yellow lines in the inner circle
indicate gene blocks on the forward direction strand, and red lines
indicate gene blocks on the reverse direction strand.}

\textbf{2 Materials and methods}

\textbf{2.1 Sampling, library construction, and sequencing}

Fresh Asian Clam (\emph{Corbicula fluminea} ) samples were collected
from Hongze Lake, Jiangsu, China. Healthy, disease-free, and tender
individuals of \emph{C. fluminea} were selected for genome sequencing.
After shelling and removing the intestines by hand, the remaining body
tissues were immediately transferred into liquid nitrogen and sequenced
by Biomarker Technologies Corporation, Beijing. High-quality genomic DNA
was extracted from the Asian Clam using a DNeasyRBlood \& Tissue Kit
(Qiagen, Hilden, Germany). The DNA quality was measured with Qubit 3.0
(Invitrogen, Carlsbad, CA, USA) and was checked using 1\% agarose gel
electrophoresis.

Illumina libraries were constructed according to the manufacturer's
standard PCR-free protocol (Illumina) and sequenced on an Illumina HiSeq
X Ten platform (Illumina, Inc., San Diego, CA, USA) using the paired-end
150 (PE150) strategy. Approximately 30 \selectlanguage{greek}μ\selectlanguage{english}g of genomic DNA was used to
construct PacBio libraries by shearing into \textasciitilde{}20 kb
targeted size fragments with BluePippin (Sage Science, Beverly, MA,
USA). Then, the qualified libraries were prepared for single-molecule
real-time (SMRT) genome sequencing using S/P2-C2 sequencing chemistry on
the PacBio Sequel II platform (PacBio, Pacific Biosciences, USA). The
Hi-C libraries were cross-linked in situ using formaldehyde with a final
concentration of 2\% and homogenized with tissue lysis by the
restriction enzyme \emph{Hind} III. The libraries for Hi-C with insert
sizes of 300--700 bp were sequenced on an Illumina HiSeq X Ten platform
(Illumina, SanDiego, CA, USA). All processes were performed at Biomarker
Technologies Corporation following the standard protocols.

\textbf{2.2 Genome estimation and de novo assembly}

Initially, Illumina data were filtered and corrected by Fastp (version
0.19.3) (Chen, Zhou, Chen, \& Gu, 2018), followed by applying the data
to estimate the genomic features. Beforehand, Illumina reads were
randomly selected and aligned to the Nucleotide Sequence Database (NT)
using BLAST (version 2.2.31) (Altschul, Gish, Miller, Myers, \& Lipman,
1990) with the parameter of E-value = 1e\textsuperscript{-05} for
confirming whether contamination existed. In this study, we plotted the
21-mer depth distribution (k =21) to estimate the genome size,
heterozygosity, and repeats using Jellyfish (version 2) (Mar\selectlanguage{ngerman}çais, \&
Kingsford, 2011). Genome size estimation was implemented by the formula
G = N21-mer (total number of k-mers) /D 21-mer (k-mer depth of the main
peak). Repetitive sequences were accumulated from where the depth of a
k-mer was more than two times that of the main peak, and heterozygous
sequences were estimated at where the depth was half of the main peak.

Using the long single molecular reads from PacBio, the pipelines of
workflow were as follows in the genome assemblies. First, the clean data
from PacBio were subjected to error correction using Canu (version 1.5)
(Koren et al., 2017) with the parameter of error correct coverage = 60.
Subsequently, the outputs were piped into the workflow of SMARTdenovo
(version 1.0) (Schmidt, Vogel, \& Denton, 2017), and the genomic contigs
were automatically generated with the parameters of J=5000, A=1000, and
r=0.95. Finally, the preliminary assembly was polished three times by
Racon (version 1.32) (Vaser, Sovi\selectlanguage{polish}ć, \selectlanguage{english}Nagaranjan, \& \selectlanguage{polish}Š\selectlanguage{english}iki\selectlanguage{polish}ć, \selectlanguage{english}2017),
resulting in the first correction being successfully realized.
Recognizing the relatively high error rate of the third-generation
sequencing platform, Illumina reads specifically for genome estimation
had been prepared for the second correction. This was implemented by
Pilon (version 1.22) (Walker et al., 2014), and the error correction was
again run three times. Each of the tools used for genome assembly was
well-founded for the assembly process of \emph{C. fluminea} .

\textbf{2.3 Hi-C scaffolding}

The contigs generated by the preliminary genome assembly required
filling of gaps and anchoring on the putative chromosomes. The initial
contigs were piped into the Hi-C assembly workflow, and the signals of
chromatin interactions were captured to construct chromosomes. In brief,
the putative Hi-C junctions were aligned by the unique mapped read pairs
using BWA-MEM (version 0.7.10-r789) (Li \& Durbin, 2009). The paired
reads uniquely mapped to the assembly were called the valid interaction
pairs, and they were used for the Hi-C scaffolding. Other invalid reads
included reads of self-ligation and non-ligation; dangling ends were
filtered out using HiC-Pro (version 2.10.0) (Servant, et al., 2015).

The Hi-C reassembly broke the contigs into 50 kb fragments, and the
regions that were mismatched to the initial assembly or could not be
restored were listed as candidate error areas. The genome was subjected
to a final round of error correction, and the gaps were filled during
this round. The reassembled and corrected contigs were divided into
ordered, oriented, and anchored groups by LACHESIS (Burton et al., 2013)
with the parameters CLUSTER\_MIN\_RE\_SITES = 33;
CLUSTER\_MAX\_LINK\_DENSITY = 2; CLUSTER\_NONINFORMATIVE\_RATIO = 2;
ORDER\_MIN\_N\_RES\_IN\_TRUN = 29, and ORDER\_MIN\_N\_RES\_IN\_SHREDS =
29, automatically resulting in putative chromosomes. The gaps generated
during the Hi-C assembly were refilled using LR GapCloser (version 1.1)
(Xu et al., 2019).

\textbf{2.4 Genome quality} \textbf{evaluation and repeats analysis}

The genome of \emph{C. fluminea} was aligned to the actinopterygii
database (odb9) comprising 978 conservative core genes by BUSCO (version
3.0) (Simao, Waterhouse, Ioannidis, Kriventseva, \& Zdobnov, 2015). The
eukaryotic conserved genes for the clam were searched in the database to
evaluate the completeness of the genome. The CEGMA Database comprising
458 conserved core genes of eukaryotes was searched in the same way
using CEGMA (version 2.5) (Parra, Bradnam, \& Korf, 2007). Additionally,
another evaluation was applied to the Illumina short-read alignments to
map to the assembled genome of the clam using BWA-MEM (version
0.7.10-r789) (Li \& Durbin, 2009).

There are two main types of repeats, retrotransposons (Class I in our
analysis) and transposons (Class II in our analysis). We constructed a
specific repeats database for repeat prediction using LTR-FINDER
(version 1.05) (Xu \& Wang, 2007) and RepeatScout (version 1.0.5)
(Price, Jones, \& Pevzner, 2005) followed by the identification and
classification for repeats by PASTEClassifer (version 1.0) (Hoede et
al., 2014). The species-specific repeats library for the clam genome was
successfully generated by aggregating our prediction and Repbase (19.06)
(Bao, Kojima, \& Kohany, 2015). LTR characteristics for the clam were
processed by RepeatMasker (version 4.0.6) (Tarailo-Graovac \& Chen,
2009).

\textbf{2.5 Genome annotation}

We utilized de novo-, homology-, and transcriptome-based methods to
predict protein-coding genes. Five tools employed were Genscan
(verson3.1) (Burge \& Karlin, 1997), Augustus (version 3.1) (Stanke \&
Waack, 2003), GlimmerHMM (version 3.0.4) (Majoros, Pertea, \& Salzberg,
2004), GeneID (version 1.4) (Blanco, Parra, \& Guig\selectlanguage{ngerman}ó, 2007), and SNAP
(version 2006-07-28) (Korf, 2004); these were used for prediction de
novo. Protein sequences from four representative species (\emph{Danio
rerio} , \emph{Crassostrea gigas} , \emph{Crassostrea virginica} ,
and\emph{Mizuhopecten yessoensis} ) were aligned to the Asian Clam
protein sequences to perform homology-based prediction by GeMoMa
(version 1.3.1) (Keilwagen et al., 2016). Transcriptome data were mapped
to the genomic sequences; Hisat (version 2.0.4) (Kim, Langmead, \&
Salzberg, 2015) and Stringtie (version 1.2.3) (Pertea et al., 2015) were
used to assemble and dissect functional genes. TransDecoder (version
2.0) (http://transdecoder.github.io) and GeneMarkS-T (version 5.1)
(Tang, Lomsadze, \& Borodovsky, 2015) were used for transcriptome-based
prediction. Finally, the above methods were integrated into
non-redundant protein-coding gene sets by EVM (version 1.1.1) (Haas et
al., 2008) and PASA (version 2.0.2) (Haas et al., 2003).

The other genome features, including pseudogenes and non-coding RNAs,
were identified by referring to the miRbase database (version 21.0)
(Griffiths-Jones, Grocock, Van Dongen, Bateman, \& Enright, 2006) and
Rfam (version 13.0) (Daub, Eberhardt, Tate, \& Burge, 2015). In the
process of searching for putative pseudogenes, candidates were assessed
based on the premature stop codons or frameshift mutations in the gene
structure using GenBlastA (version 1.0.4) (She, et al., 2011). The
identification of transfer RNA (tRNA) was performed by tRNAscan-SE
(version 1.3.1) (Lowe \& Eddy, 1997). MicroRNA and ribosomal RNA (rRNA)
were identified by Infernal (version 1.1) (Nawrocki \& Eddy, 2013).

The protein-coding genes were subject to functional annotation by
aligning to the EuKaryotic Orthologous Groups (KOG) (Tatusov et al.,
2003), Kyoto Encyclopedia of Genes and Genomes (KEGG) (Kanehisa \& Goto,
2000), TrEMBL (Boeckmann et al., 2003), Swiss-Prot (Boeckmann et al.,
2003), and Non-redundant (Nr) databases (Marchler et al., 2011) using
BLAST (version 2.2.31) (Altschul, Gish, Miller, Myers, \& Lipman, 1990)
with a maximal E-value of 1e\selectlanguage{english}-05. Kyoto Encyclopedia of Genes and Genomes
(KEGG) pathway annotations and Gene ontology (GO) (Consortium, 2004)
terms were assigned to identify gene functions using Blast2GO (version
4.1) (Conesa et al., 2005).

\textbf{2.6 Gene family identification}

Protein data from \emph{C. fluminea} and other representative species,
including \emph{Capitella teleta} , \emph{Lingula anatina} ,
\emph{Octopus vulgaris} , \emph{Lottia gigantea} , \emph{Crassostrea
gigas} ,\emph{Crassostrea virginica} , \emph{Pinctada imbricata}
,\emph{Mizuhopecten yessoensis} , \emph{Mytilus coruscus} ,
and\emph{Bathymodiolus platifrons} , were retrieved in the corresponding
databases and aligned using BLAST (version 2.2.31) (Altschul, Gish,
Miller, Myers, \& Lipman, 1990) with a maximum e-value of 1e\selectlanguage{english}-5. Proteins
with sequence lengths \textgreater{}100 amino acids were searched
against the Pfam (https://pfam.xfam.org) database by Pfam scan
(El-Gebali, et al., 2018). The ortholog groups for gene families were
generally clustered using OrthoMCL (version 2.0.9) (Li, Stoeckert, \&
Roos, 2003). Four selected shellfish (\emph{C. gigas} , \emph{L.
gigantea} , \emph{B. platifrons} , and \emph{C. virginica} ) and
\emph{C. fluminea} were grouped together to conduct the analysis for
gene family characteristics.

\textbf{2.7 Phylogenetic and gene family evolutionary analyses}

The single-copy orthologs from all involved species were statistically
analyzed using the longest transcripts for each gene. The single-copy
orthologous genes shared by the above 11 species (including \emph{C.
fluminea} ) were aligned using MUSCLE (version 3.8.31) (Edgar, 2004).
The super-alignment of nucleotide sequences provided a reference tree
topology using PhyML (version 3.3) (Guindon et al., 2010). The
divergence times among species were roughly estimated by the MCMCTree
program of the PAML package (version 4.7a) (Yang, 2007) with the
approximate likelihood calculation method. We utilized molecular clock
data from the TimeTree (http://www.timetree.org/) (Kumar, Stecher,
Suleski, \& Hedges, 2017) database as the calibration times.

According to divergence times and phylogenetic relationships, CAFE
(version 4.2) (De Bie, Cristianini, Demuth, \& Hahn, 2006) was used to
analyze gene family evolution. The gene family expansion and contraction
were analyzed by comparing the differences between the ancestor and
involved species. The extended family genes for \emph{C. fluminea} were
extracted and aligned to the functional enrichment on GO and KEGG to
detect their functions.

\textbf{2.8 Transcriptome sequencing}

Specimens of mature \emph{Meretrix meretrix} and \emph{Ruditapes
philippinarum} were collected from coastal area in Nantong, Jiangsu.
They and \emph{Corbicula fluminea} were dissected and fixed in RNAlater.
The RNA was extracted using TRIzol (Thermo Fisher, USA), and the
libraries were generated using a NEBNext(r) Ultra\selectlanguage{english} RNA Library Prep Kit
for Illumina(r) (NEB, USA) following the instruction manual. Three
biological repeats were set for each species. All samples were sequenced
on an Illumina HiSeq X Ten platform (Illumina, Inc., San Diego, CA,
USA). The clean reads for \emph{C. fluminea} were mapped to the Asian
Clam genome using Tophat (version 2.0) (Ghosh, \& Chan, 2016), and only
reads with a perfect match or one mismatch were used for further
analysis. For the transcriptome data of \emph{M. meretrix} and \emph{R.
philippinarum,} a de novo transcriptome assembly was conducted by
Trinity (version 2.1.1) (Haas et al., 2013), and CD-HIT (Fu, Niu, Zhu,
Wu, \& Li, 2012) was used to reduce sequence redundancy. Finally, we
presented Kallisto (Bray, Pimentel, Melsted, \& Pachter, 2016), an
RNA-seq quantification program that mapped clean reads to known
transcripts for quantification and standardization. Gene expression
levels were estimated by TPM.

\textbf{3 Results and discussion}

\textbf{3.1 Statistics of sequencing data}

More than 252.77 Gb of clean data for survey analysis (Illumina) were
generated, and the data covered the depth of 154.13X for the Asian Clam
genome (Table 1). Two single-molecule real-time (SMRT) cells were
processed, and approximately 15.03 million PacBio reads (\selectlanguage{english}~293.72 Gb)
were generated (Table 1). The max subread for PacBio offline was 286.39
kb; the N50 and mean length of subreads were 31.18 kb and 19.54 kb,
respectively. The valid subreads were mainly distributed from 500 bp to
40,000 bp (Supporting Information Figure S1). After the Hi-C data
processing by filtering of low-quality reads, we obtained approximately
780.87 million clean reads (\textasciitilde{}233.26 Gb) from two
libraries that were used for chromosomal construction (Table 1).
Additionally, approximately 8 Gb clean data for transcriptome sequencing
was performed for subsequent gene prediction analysis (Table 1).

\textbf{TABLE 1 Statistics of the sequencing data}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Types\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Sequencing platform\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Library size\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Number of library\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Clean data (Gb)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Coverage (\selectlanguage{ngerman}×) \selectlanguage{english}+\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Illumina HiSeq X\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
350 bp\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
252.77\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
154.13\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
PacBio\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
PacBio Sequel II\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
20 kb\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
293.72\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
193.40\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Hi-C\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Illumina HiSeq X\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
300--700 bp\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
233.26\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
142.23\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Transcriptome\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Illumina HiSeq X\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
350 bp\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8.18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{3.2 K-mer analysis and genome assembly}

Before k-mer analysis, Illumina reads from the survey analysis were
mapped to the Nucleotide Sequence Database (NT); 86.38\% of reads were
successfully matched, indicating that the data were credible for further
analysis. The k-mer number of 187,447,882,456 was screened out by
filtering of abnormal k-mers. The k-mer depth of 115 was the main peak
in the plot (Supporting Information Figure S2), and the genome size was
calculated as \textasciitilde{} 1.64 Gb according to the k-mer formula.
The k-mer depths of 58 and 230 represented beginning locations in the
computation of heterozygous and repetitive sequences, respectively.
Finally, the clam genome was estimated to have a heterozygosity rate of
2.41\% and a repeat ratio of 64.55\%. It was deemed to be a large
complex genome with high heterozygosity and a high level of repetition.

The initial filtered PacBio subreads were subjected to error correction
by Canu, resulting in 15,031,088 subreads generated for subsequent
assembly. The number of contigs obtained by Canu and SMARTdenovo with
the polish by Racon and Pilon was 4,347. The analysis finally resulted
in an Asian Clam genome of 1.52 Gb with a contig N50 of 603.64 Kb. The
size of the Asian Clam genome for PacBio assembly was slightly smaller
than that estimated by k-mer analysis (1.64 Gb), which was in line with
the regularity. It indicates that we have captured and assembled most of
the sequences of Asian Clam genome. The accuracy of the sequence needs
to be verified by Hi-C technology.

\textbf{3.3 Chromosome construction by Hi-C}

More than 571.60 million read pairs (73.20\%) of total Hi-C data were
mapped to the initial genomic assembly. We utilized a total of 116.65
million valid interaction pairs for Hi-C scaffolding; invalid
interaction pairs, including reads of dangling end pairs, re-ligation
pairs, self-cycle pairs, and dumped pairs, were filtered out (Supporting
Information Table S1).

The initial assembled contigs were broken and reassembled using unique
mapped read pairs for Hi-C. The areas that could be restored as
candidate areas had already been corrected. After Hi-C assembly and
manual adjustment, we obtained 4,728 corrected contigs (Table 2)
assigned to 18 pseudochromosomes. The final assembly presented a
high-quality Asian Clam genome that reached 1.52 Gb in length and was
characterized by a contig N50 of 521.06 Kb and a scaffold N50 of 70.62
Mb (Table 2). There were 1.51 Gb of genomic sequences accounting for
99.17\% of total contig sequences on 18 chromosomes comprising 4,621
contigs (97.74\%) (Figure 3). Additionally, 1.40 Gb (92.81\%) of genomic
sequences were anchored with a defined order and orientation in a Hi-C
interaction heat map (Supporting Information Table S2). The scaffolding
process for the Asian Clam genome showed a high level of efficiency
(genomic sequences more than 99\%, contigs more than 97\%) and deserved
to be considered as a high-quality and chromosomal-level genome.

\textbf{TABLE 2} \textbf{Statistics and characteristics of the genome
for \emph{Corbicula fluminea}}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Characteristics & Number & Size & Percentage\tabularnewline
Estimate of genome size & & 1.64 Gb &\tabularnewline
Final assembly genome size & & 1.52 Gb &\tabularnewline
Contig number and N50 & 4,728 & 521.06 Kb &\tabularnewline
Maximum contig & & 3.17 Mb &\tabularnewline
Scaffold number and N50 & 1,215 & 70.62 Mb &\tabularnewline
Maximum scaffold & & 144.27 Mb &\tabularnewline
Heterozygosity rate & & & 2.41\%\tabularnewline
Total repetitive sequences & & 1.06 Gb & 69.66\%\tabularnewline
Total protein-coding genes & 38,841 & 0.54 Gb &\tabularnewline
Annotated protein-coding genes & 32,591 & & 83.91\%\tabularnewline
MicroRNA & 45 & &\tabularnewline
Ribosomal RNA (rRNA) & 420 & &\tabularnewline
Transfer RNA (tRNA) & 3,707 & &\tabularnewline
\bottomrule
\end{longtable}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{FIGURE 3 The genome-wide Hi-C heatmap of \emph{Corbicula
fluminea}. LG 1--18 are the abbreviations of Lachesis Groups 1--18
representing the 18 pseudochromosomes.}

\textbf{3.4 Evaluation and repeat annotation}

We finally obtained a data set that contained 889 complete BUSCO groups
(90.90\%) and 423 (92.36\%) CEGMA groups. The mapping ratio for Illumina
data was up to 97.45\%. The BUSCO, CEGMA, and the mapping ratio for
Illumina directly supported the high-quality Asian Clam genome that we
assembled. More detailed information may be found in Supporting
Information Table S3. More than 1.06 Gb of genomic sequences were
identified and marked as repeats, representing 69.66\% of the total
genomic sequences (Table 2). Approximately 608.85 Mb (57.54\%) of LARDs
was the predominant repeat type. Other types of repeats with high
proportions were TIRs (10.46\%), PLEs (12.38\%), and LINEs (7.07\%)
(Supporting Information Table S4).

\textbf{3.5 Gene prediction and gene annotation}

A consensus of the results of all three methods for protein-coding genes
prediction was reached, and the final number of non-redundant
protein-coding genes was 38,841, with a total length of 0.54 Gb (Table
2, Supporting Information Table S5). More than 32,591 protein-coding
genes (83.91\%) were annotated in at least one functional database
(Table 3). All genes for each database are annotated in Supporting
Information Table S6. Additionally, the Asian Clam gene sets comprised
260,971 exons, and the average gene length was \textasciitilde{} 13.97
kb. The Asian Clam genome contained 3,048 pseudogenes, 45 microRNAs, 420
rRNAs, and 3,707 tRNAs (Table 2, Supporting Information Table S7).

\textbf{TABLE 3 Statistics of gene annotation to different databases}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Annotation database & Annotated number & Percentage (\%) & 100[?]Protein
length\textless{}300 & Protein length[?]300\tabularnewline
GO\_Annotation & 7,489 & 19.28 & 2,243 & 5,119\tabularnewline
KEGG\_Annotation & 12,757 & 32.84 & 3,466 & 9,144\tabularnewline
KOG\_Annotation & 18,233 & 46.94 & 4,642 & 13,426\tabularnewline
TrEMBL\_Annotation & 32,280 & 83.11 & 10,097 & 21,841\tabularnewline
Nr\_Annotation & 32,382 & 83.37 & 10,170 & 21,858\tabularnewline
All\_Annotated & 32,591 & 83.91 & 10,275 & 21,957\tabularnewline
\bottomrule
\end{longtable}

\textbf{3.6 Gene family identification}

Gene family analysis identified a total of 71,331 gene families among
five kinds of shellfish (Supporting Information Table S8), and we
discovered 23,063 gene families clustered by 38,841 protein-coding genes
in the Asian Clam genome. We compared the \emph{C. fluminea} genome to
those of \emph{C. gigas} , \emph{L. gigantea} , \emph{B. platifrons} ,
and\emph{C. virginica} and discovered that 16,170 gene families were
specific to \emph{C. fluminea} (Figure 4A). Additionally, we
statistically analyzed several gene features, including single-copy
orthologs, multiple copy orthologs, other orthologs, and unique genes
(Figure 4B). The five shellfish shared 146 single-copy orthologs, and
the Asian Clam genome contained 25,878 unique genes (Supporting
Information Table S9).

\textbf{3.7 Phylogenetic analysis and gene family evolution}

The phylogenetic position of \emph{C. fluminea} and other representative
species was estimated based on single-copy orthologs. Three time points
for the most recent common ancestor (MRCA) were estimated by TimeTree.
The differentiation time of \emph{C. gigas} and \emph{C. virginica} was
72.9 (63.2 - 82.7) million years ago (Mya) (Plazzi \& Passamonti, 2010);
that of \emph{B. platifrons} and \emph{M. coruscus} was 387 (308 - 481)
Mya (Peterson, Cotton, Gehling, \& Pisani, 2008); that of \emph{C.
fluminea} and six marine bivalves was 492 (472 - 516) Mya (St\selectlanguage{ngerman}öger, et
al., 2013; Huang, et al., 2018). We utilized these time of MRCA to
calibrate the phylogenetic tree, resulting in the phylogenetic tree
constructed by seven bivalves and four other marine species (Figure 4C).
As shown, all marine bivalves were clustered together, especially those
belong to the same family. The phylogenetic tree showed that \emph{C.
fluminea} and the closest genetic relatives\emph{,} the ancestors of
marine bivalves (family Mytilidae represented by \emph{B. platifrons}and
\emph{M. coruscus} ; family Ostreidae represented by \emph{C. gigas}and
\emph{C. virginica;} family Pteriidae represented by \emph{P.
imbricata;} family Pectinidae represented by \emph{M. yessoensis} )
diverged \textasciitilde{} 492.00 million years ago. This evidence
suggests that \emph{C. fluminea} is a kind of bivalve, which is
obviously different from marine bivalves. It is suggested that bivalves
were divided into two groups, and they survive in freshwater and
seawater respectively for a long time (million years). Some references
showed that Heterodonta living in the freshwaters originated in the
Paleozoic (Moore \& Raymond, 1969; Cope \& Veliger, 1995) and Veneroida
diversified during the Mesozoic and Cenozoic eras (Stanley, 1968). The
ancestors of \emph{C. fluminea} may have invaded and migrated to
freshwater from the ocean since millions of years ago, and they have
evolved to fill various freshwater habitat.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{FIGURE 4 The comparative genomic analysis of \emph{Corbicula
fluminea} and other species. (A) Venn diagram of gene families
between\emph{Corbicula fluminea} and \emph{Crassostrea gigas},
\emph{Lottia gigantea}, \emph{Bathymodiolus platifrons}, and
\emph{Crassostrea virginica}. (B) Distribution of multiple-copy
orthologs, other orthologs, single-copy orthologs, and unique genes in
\emph{Corbicula fluminea} and the above four species. (C) Phylogenetic
tree, divergence time, and profiles of gene families that underwent
expansion and contraction in 11 species.}

Combining the phylogenetic relationships, gene family evolution was
calculated by comparing the differences between ancestors and \emph{C.
fluminea} . This analysis resulted in 851 gene families being
significantly expanded (P \textless{} 0.05) and 191 gene families being
significantly contracted (P \textless{} 0.05) in the Asian Clam genome
(Figure 4C, Supporting Information Table S10). The 851 expanded gene
families were clustered by 9,967 functional genes (Supporting
Information Table S11). The functional enrichment analysis on GO and
KEGG of those expanded genes identified 325 significantly enriched
(q-value \textless{} 0.01) GO terms (Supporting Information Table S12)
and 19 significantly enriched (q-value \textless{} 0.01) KEGG pathways
(Supporting Information Table S13, Supporting Information Figure S3).
Among 19 pathways, 9 pathways related to metabolite synthesis were given
attention, such as taurine and hypotaurine metabolism, drug metabolism,
O-Glycan biosynthesis and so on (Table 4). These results are helpful for
us to study and understand the characteristics of \emph{C. fluminea.}

\textbf{TABLE 4 Significantly enriched KEGG pathways for expanded family
genes in \emph{Corbicula fluminea}}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
KEGG ID\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
KEGG pathways\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Adjusted \emph{P}-value\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Number of genes\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Enrichment factor\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00430\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Taurine and hypotaurine metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.58E-05\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
59\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.48\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00983\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Drug metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9.74E-04\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
61\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.90\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00512\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Mucin type O-Glycan biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.001810\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
39\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.40\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00604\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Glycosphingolipid biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.002917\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3.33\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00910\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Nitrogen metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.003432\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
26\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.86\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00590\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Arachidonic acid metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.007314\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
47\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.96\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00513\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Various types of N-glycan biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.008318\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
56\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.81\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00603\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Glycosphingolipid biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.008975\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
35\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.16\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00600\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Sphingolipid metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.009744\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
32\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.77\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{3.8 MITF gene family analysis}

In shellfish, it has been reported that microphthalmia-associated
transcription factor (MITF) plays an important role in immune defense
and shell color formation (Zhang, et al., 2017; Zhang, et al., 2018).
The MITF gene family consists of three subfamilies, namely TFEB, TFEC,
and TFE3, in the Pfam database (Zhao, Zhao, Zhou, \& Mattei, 1993). In
this study, we detected two genes from the Asian Clam genome, namely
EVM0008002 and EVM0031201, which were identified as MITF genes. Most
species in the phylogenetic analysis possessed one or two MITF genes,
while \emph{L. gigantea} was apparently missing MITF genes (Supporting
Information Table S14). This finding implies that \emph{L. gigantea} may
have lost some functions of immune defense or shell color due to the
deletion of MITF genes. Additionally, there were five and seven MITF
genes in \emph{C. virginica} and \emph{L. anatina} , respectively
(Supporting Information Table S14). Combined with the gene family
expansion and contraction analysis, we found the MITF gene family
expanded in \emph{C. virginica} and \emph{L. anatina} and contracted
in\emph{L. gigantea} . Proteins from all species were aligned to a
super-alignment matrix with the guidance of protein alignment, and the
genic tree comprising MITF family genes from all involved species was
successfully constructed using MUSCLE (Figure 5). Firstly, MITF family
genes originated from the same species showed the most recent genetic
relationships. Secondly, like family Mytilidae represented by \emph{B.
platifrons} and \emph{M. coruscus} , family Ostreidae represented
by\emph{C. gigas} and \emph{C. virginica} , those from the same families
were clustered together. Additionally, the clustering relationships of
MITF family genes were also similar to those shown by the phylogenetic
tree of single-copy orthologs. This finding indirectly corroborates the
reliability of the phylogenetic relationship analysis. Meanwhile, we
observed that \emph{C. fluminea} had a greater genetic distance from
other marine bivalves via the MITF and phylogenetic tree analyses. It is
probably because the divergence of \emph{Corbiucula} from marine
bivalves, and the migration from marine to freshwater is an ancient
event.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{FIGURE 5 The genic tree and gene structure analysis of the MITF
gene family in \emph{Corbicula fluminea} and other species.}

\textbf{3.9 Vitamin B6 metabolic pathway analysis}

Vitamin B6 (VB6) usually exists in food as pyridoxal, pyridoxine, or
pyridoxamine, each of which is converted into pyridoxal phosphate by the
mammalian liver (Ramos, et al., 2017). VB6 is a component of co-enzymes
in the human body that are involved in a variety of metabolic reactions,
especially those closely related to amino acid metabolism (Ueland,
McCann, Midttun, \& Ulvik, 2017; Bird, 2018). According to relevant
studies, vitamin B1 (thiamine), vitamin B2 (riboflavin), and vitamin B3
(nicotinic acid) are found in relatively high levels in \emph{C.
fluminea} , whereas the VB6 content is very low (Wang, \& Liu, 2010).
Combined with the transcriptome data, we analyzed the expression of
genes involved in VB6 synthesis in \emph{C. fluminea, M. meretrix,}
and\emph{R. philippinarum.} We searched and discovered 10 existing
enzymes (\emph{PdxST} , \emph{PNPO} , \emph{PDXP} , \emph{PDXK} ,
\emph{PDIA1} ,\emph{PDIA3} , \emph{PDIA4} , \emph{PDIA5} , \emph{PDIA6}
, and\emph{TXNDC10} ) of the VB6 pathway (Figure 6A)\emph{.} Ribulose
5-phosphate is converted to pyridoxal 5-phosphate by
\emph{PdxST}(K06215), and two genes (EVM0000630 and EVM0035945) in
\emph{C. fluminea} were responsible for the translation of
\emph{PdxST.}Pyridoxal 5-phosphate is converted to pyridoxal by
\emph{PDXP} (K13248), and EVM0004517 was responsible for the translation
of \emph{PDXP} . Pyridoxal can be transformed into 4-Pyridoxate or
4-Pyridoxolactone, but these pathways were blocked due to \emph{C.
fluminea} having lost the relevant enzymes or genes. For all genes on
the VB6 pathways of \emph{C. fluminea,} genes \emph{PNPO} , \emph{PDXK}
, \emph{PDXP} , \emph{PDIA1} , and \emph{PDIA4} had one copy; genes
\emph{PdxST} , \emph{PDIA3} ,\emph{PDIA5} , and \emph{TXNDC10} had two
copies; the \emph{PDIA6} gene had three copies (Supporting Information
Table S15). We suspect that the number of genes involved in the VB6
pathway is low in \emph{C. fluminea} , and this may lead to the low
content of VB6. Additionally, we checked the expression of homologous
genes in \emph{C. fluminea, M. meretrix,} and \emph{R. philippinarum.}
The expression levels of\emph{PdxST} , \emph{PDIA1} , \emph{PDIA3} , and
\emph{PDIA4} were higher in \emph{C. fluminea} than in \emph{M.
meretrix} and \emph{R. philippinarum} (Figure 6B)\emph{.} The low
expression of the \emph{PDXP}gene may inhibit pyridoxal synthesis
downstream, and it may be one of the reasons for the low expression of
VB6. However, more evidence will be needed for further validation in the
future.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{FIGURE 6 Genes involved in vitamin B6 metabolism and their
expression levels in metabolic pathways. (A) Genes and metabolites
involved in the vitamin B6 pathway. (B) Expression levels of genes
related to vitamin B6 synthesis in \emph{Corbicula
fluminea},\emph{Meretrix meretrix}, and \emph{Ruditapes philippinarum}.
C represents \emph{Corbicula fluminea}; M represents \emph{Meretrix
meretrix}; R represents \emph{Ruditapes philippinarum.}}

\textbf{4 Conclusion}

In this study, we assembled a chromosome-level Asian Clam genome using a
combination of PacBio and Hi-C technology. The results suggested the
high quality of the genome in several ways. The data filled the gap in
our knowledge of \emph{C. fluminea} and provide a reference for future
research. We assembled 1.52 Gb of genome data distributed across 18
chromosomes, with a contig N50 of 521.06 Kb and a scaffold N50 of 70.62
Mb. We assigned 99.17\% of contig sequences to the 18 chromosomes, thus
ensuring the integrity of genetic information for each chromosome as
much as possible. Additionally, the comparative genomics studies offer
evidences for the evolution and characteristics of \emph{C. fluminea} ,
and showed that \emph{C. fluminea} and its closest relatives\emph{,} the
ancestors of marine bivalves, diverged \textasciitilde{} 492.00 million
years ago. MITF gene family analysis identified two genes in the Asian
Clam genome. The genic tree comprising all MITF family genes from
involved species showed that the relationship of MITF family genes was
similar to that shown by the phylogenetic tree. All lines of evidence
suggest that \emph{C. fluminea} is clearly different from other marine
bivalves, and its migration from marine to freshwater is an ancient
event. In addition, we examined expanded gene families and found 9
significantly enriched pathways associated with metabolite synthesis by
KEGG analysis. The vitamin B6 metabolic pathway revealed relatively few
genes involved in pyridoxal, pyridoxine, or pyridoxamine synthesis,
providing reference for explaining the lower content of VB6 in \emph{C.
fluminea} . The genomic information presented in our analysis will help
to better understand, develop, and improve \emph{C. fluminea} as well as
establish a strong foundation for genome-assisted breeding programs in
the future.

\textbf{Acknowledgements}

We appreciate the help from Jiangsu Marine Fisheries Research Institute
during sample collection, and the bioinformatic analysis achieved by
Biomarker Technologies Corporation. This work was supported by the major
project of hydro bios resources in Jiangsu Province (ZYHB16-3), the
monitoring of fishery resources in fishery waters of Jiangsu Province in
2018 (ZYHJ201801), the identification of \emph{Corbicula} species and
genetic conservation unit in Jiangsu inland waters (SZL201901).

\textbf{Data Accessibility}

Raw sequencing reads for PacBio and Illumina are available at GenBank as
BioProject PRJNA657911. Raw sequencing data (Illumina, PacBio, and Hi-C
data) have been deposited in the SRA (Sequence Read Archive) database as
SUB7507164. The data including assembly and annotation that supported
the findings of this study have been deposited in the in the FigShare
database,doi.org/10.6084/m9.figshare.12805886.v1
(https://doi.org/10.6084/m9.figshare.12805886.v1).

\textbf{Author Contributions}

J.P., T.Z. and J.Y. designed and managed the project. T.Z. and J.Y.
interpreted the data and drafted the manuscript. S.T., D.L. and X.G.
prepared the materials. S.Z., W.S., X.L. and Y.L. preformed the DNA
extraction, RNA extraction and libraries construction. J.Y., L.W., and
X.Y. performed the bioinformatic analysis. All authors contributed to
the final manuscript editing.

\textbf{Conflict of interest}

The authors declare no conflict of interest exists.

\textbf{TABLES}

\textbf{TABLE 1 Statistics of the sequencing data}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Types\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Sequencing platform\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Library size\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Number of library\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Clean data (Gb)\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Coverage (\selectlanguage{ngerman}×) \selectlanguage{english}+\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Illumina HiSeq X\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
350 bp\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
252.77\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
154.13\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
PacBio\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
PacBio Sequel II\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
20 kb\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
293.72\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
193.40\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Hi-C\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Illumina HiSeq X\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
300--700 bp\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
233.26\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
142.23\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Transcriptome\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Illumina HiSeq X\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
350 bp\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8.18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{TABLE 2} \textbf{Statistics and characteristics of the genome
for \emph{Corbicula fluminea}}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
Characteristics & Number & Size & Percentage\tabularnewline
Estimate of genome size & & 1.64 Gb &\tabularnewline
Final assembly genome size & & 1.52 Gb &\tabularnewline
Contig number and N50 & 4,728 & 521.06 Kb &\tabularnewline
Maximum contig & & 3.17 Mb &\tabularnewline
Scaffold number and N50 & 1,215 & 70.62 Mb &\tabularnewline
Maximum scaffold & & 144.27 Mb &\tabularnewline
Heterozygosity rate & & & 2.41\%\tabularnewline
Total repetitive sequences & & 1.06 Gb & 69.66\%\tabularnewline
Total protein-coding genes & 38,841 & 0.54 Gb &\tabularnewline
Annotated protein-coding genes & 32,591 & & 83.91\%\tabularnewline
MicroRNA & 45 & &\tabularnewline
Ribosomal RNA (rRNA) & 420 & &\tabularnewline
Transfer RNA (tRNA) & 3,707 & &\tabularnewline
\bottomrule
\end{longtable}

\textbf{TABLE 3 Statistics of gene annotation to different databases}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
Annotation database & Annotated number & Percentage (\%) & 100[?]Protein
length\textless{}300 & Protein length[?]300\tabularnewline
GO\_Annotation & 7,489 & 19.28 & 2,243 & 5,119\tabularnewline
KEGG\_Annotation & 12,757 & 32.84 & 3,466 & 9,144\tabularnewline
KOG\_Annotation & 18,233 & 46.94 & 4,642 & 13,426\tabularnewline
TrEMBL\_Annotation & 32,280 & 83.11 & 10,097 & 21,841\tabularnewline
Nr\_Annotation & 32,382 & 83.37 & 10,170 & 21,858\tabularnewline
All\_Annotated & 32,591 & 83.91 & 10,275 & 21,957\tabularnewline
\bottomrule
\end{longtable}

\textbf{TABLE 4 Significantly enriched KEGG pathways for expanded family
genes in \emph{Corbicula fluminea}}\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
KEGG ID\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
KEGG pathways\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Adjusted \emph{P}-value\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Number of genes\strut
\end{minipage} & \begin{minipage}[t]{0.19\columnwidth}\raggedright\strut
Enrichment factor\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00430\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Taurine and hypotaurine metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.58E-05\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
59\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.48\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00983\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Drug metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
9.74E-04\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
61\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.90\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00512\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Mucin type O-Glycan biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.001810\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
39\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.40\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00604\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Glycosphingolipid biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.002917\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
3.33\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00910\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Nitrogen metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.003432\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
26\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.86\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00590\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Arachidonic acid metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.007314\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
47\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.96\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00513\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Various types of N-glycan biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.008318\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
56\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.81\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00603\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Glycosphingolipid biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.008975\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
35\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
2.16\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
ko00600\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
Sphingolipid metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
0.009744\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
32\strut
\end{minipage} & \begin{minipage}[t]{0.17\columnwidth}\raggedright\strut
1.77\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Figure legends}

\textbf{FIGURE 1 The Asian Clam (\emph{Corbicula fluminea})}

\textbf{FIGURE 2 Genome landscape of \emph{Corbicula fluminea}. From
outer to inner circles: a: marker distribution on 18 chromosomes at the
Mb scale; b: LARD distribution on each chromosome; c: PLE distribution
on each chromosome; d: gene distribution on each chromosome; e: GC
content within a 1-Mb sliding window; yellow lines in the inner circle
indicate gene blocks on the forward direction strand, and red lines
indicate gene blocks on the reverse direction strand.}

\textbf{FIGURE 3 The genome-wide Hi-C heatmap of \emph{Corbicula
fluminea}. LG 1--18 are the abbreviations of Lachesis Groups 1--18
representing the 18 pseudochromosomes.}

\textbf{FIGURE 4 The comparative genomic analysis of \emph{Corbicula
fluminea} and other species. (A) Venn diagram of gene families
between\emph{Corbicula fluminea} and \emph{Crassostrea gigas},
\emph{Lottia gigantea}, \emph{Bathymodiolus platifrons}, and
\emph{Crassostrea virginica}. (B) Distribution of multiple-copy
orthologs, other orthologs, single-copy orthologs, and unique genes in
\emph{Corbicula fluminea} and the above four species. (C) Phylogenetic
tree, divergence time, and profiles of gene families that underwent
expansion and contraction in 11 species.}

\textbf{FIGURE 5 The genic tree and gene structure analysis of the MITF
gene family in \emph{Corbicula fluminea} and other species.}

\textbf{FIGURE 6 Genes involved in vitamin B6 metabolism and their
expression levels in metabolic pathways. (A) Genes and metabolites
involved in the vitamin B6 pathway. (B) Expression levels of genes
related to vitamin B6 synthesis in \emph{Corbicula
fluminea},\emph{Meretrix meretrix}, and \emph{Ruditapes philippinarum}.
C represents \emph{Corbicula fluminea}; M represents \emph{Meretrix
meretrix}; R represents \emph{Ruditapes philippinarum.}}

\textbf{Additional files}

\textbf{Supporting Information Figure S1 The number of subreads
distributed in different lengths.}

\textbf{Supporting Information Figure S2 Frequency distribution of the
21-mer graph analysis used to estimate the features of \emph{Corbicula
fluminea} genome.}

\textbf{Supporting Information Figure S3 KEGG enrichment analysis for
expanded family genes in \emph{Corbicula fluminea.}}

\textbf{Supporting Information Table S1 Statistics of the different
types of Hi-C reads.}

\textbf{Supporting Information Table S2 Summary of the Hi-C assembly.}

\textbf{Supporting Information Table S3 Summary of the assessment of
genome assembly.}

\textbf{Supporting Information Table S4 Statistics of the repeated
sequences.}

\textbf{Supporting Information Table S5 Summary of the gene prediction
results.}

\textbf{Supporting Information Table S6 Integrated lists of gene
annotation for \emph{Corbicula fluminea} genome.}

\textbf{Supporting Information Table S7 The family consisting of miRNAs,
rRNAs and tRNAs in \emph{Corbicula fluminea.}}

\textbf{Supporting Information Table S8 The identification of gene
families among five shellfish.}

\textbf{Supporting Information Table S9 Summary of gene features in five
shellfish.}

\textbf{Supporting Information Table S10 The expanded and contracted
gene families in \emph{Corbicula fluminea.}}

\textbf{Supporting Information Table S11 The expanded family genes
in\emph{Corbicula fluminea.}}

\textbf{Supporting Information Table S12 Significantly enriched GO terms
for expanded family genes in \emph{Corbicula fluminea}.}

\textbf{Supporting Information Table S13 Significantly enriched KEGG
pathways for expanded family genes in \emph{Corbicula fluminea.}}

\textbf{Supporting Information Table S14 MITF genes
between\emph{Corbicula fluminea} and other representative species.}

\textbf{Supporting Information Table S15 Genes involved in vitamin B6
synthesis pathway in \emph{Corbicula fluminea}, \emph{Meretrix
meretrix}and \emph{Ruditapes philippinarum.}}

\textbf{References}

Altschul, S. F., Gish, W., Miller, W., Myers, E. W., \& Lipman, D. J.
(1990). Basic local alignment search tool. \emph{Journal of molecular
biology} , \emph{215} (3), 403-410. doi:10.1016/S0022-2836(05)80360-2

Alyakrinskaya, I. O. (2005). Functional significance and weight
properties of the shell in some mollusks. \emph{Biology Bulletin}
,\emph{32} (4), 397-418. doi:10.1007/s10525-005-0118-y

Bao, W., Kojima, K. K., \& Kohany, O. (2015). Repbase Update, a database
of repetitive elements in eukaryotic genomes. \emph{Mobile Dna}
,\emph{6} (1), 11. doi:10.1186/s13100-015-0041-9

Bird, R. P. (2018). The Emerging Role of Vitamin B6 in Inflammation and
Carcinogenesis. \emph{Adv Food Nutr Res, 3} (83), 151-194.
doi:10.1016/bs.afnr.2017.11.004

Blanco, E., Parra, G., \& Guig\selectlanguage{ngerman}ó, R. (2007). Using geneid to identify
genes. \emph{Current protocols in bioinformatics} , \emph{18} (1),
4.3.1-4.3.28. doi:10.1002/0471250953.bi0403s00

Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M., Estreicher, A.,
Gasteiger, E., . . . Schneider, M. (2003). The SWISS-PROT protein
knowledgebase and its supplement TrEMBL in 2003. \emph{Nucleic acids
research} , \emph{31} (1), 365-370. doi:10.1093/nar/gkg095

Bray, N. L., Pimentel, H., Melsted, P., \& Pachter, L. (2016).
Near-optimal probabilistic RNA-seq quantification. \emph{Nat Biotechnol,
34} (5), 525-527. doi:10.1038/nbt.3519

Burge, C., \& Karlin, S. (1997). Prediction of complete gene structures
in human genomic DNA. \emph{Journal of molecular biology} ,\emph{268}
(1), 78-94. doi:10.1006/jmbi.1997.0951

Burton, J. N., Adey, A., Patwardhan, R. P., Qiu, R., Kitzman, J. O., \&
Shendure, J. (2013). Chromosome-scale scaffolding of de novo genome
assemblies based on chromatin interactions. \emph{Nature Biotechnology}
,\emph{31} (12), 1119-1125. doi:10.1038/nbt.2727

Chen, S., Zhou, Y., Chen, Y., \& Gu, J. (2018). fastp: an ultra-fast
all-in-one FASTQ preprocessor. \emph{Bioinformatics} , \emph{34} (17),
884-890. doi:10.1093/bioinformatics/bty560

Chin, L. H., Chien, C. H., \& Gow, C. Y. (2010). Hepatoprotection by
freshwater clam extract against ccl4-induced hepatic damage in
rats.\emph{American Journal of Chinese Medicine} , \emph{38} (5),
881-894. doi:10.1142/S0192415X10008329

Conesa, A., Götz, S., García-Gómez, J. M., Terol, J., Talón, M., \&
Robles, M. (2005). Blast2GO: a universal tool for annotation,
visualization and analysis in functional genomics
research.\emph{Bioinformatics} , \emph{21} (18), 3674-3676.
doi:10.1093/bioinformatics/bti610

Consortium, G. O. (2004). The Gene Ontology (GO) database and
informatics resource. \emph{Nucleic acids research} , \emph{32}
(Database issue), D258-D261. doi:10.1093/nar/gkh036

Cope, J., Veliger, C. (1995). The early evolution of the
Bivalvia.\emph{Origin and, evolutionary, radiation of the Mollusca}
,\emph{123} (34), 342-335.

Daniel, L., Graf, K. S., \& Cummings. (2006). Palaeoheterodont diversity
(mollusca: trigonioida + unionoida): what we know and what we wish we
knew about freshwater mussel evolution. \emph{Zoological Journal of the
Linnean Society, 12} (6), 245-251. doi:10.1111/j.1096-3642.2006.00259.x.

Daub, J., Eberhardt, R. Y., Tate, J. G., \& Burge, S. W. (2015). Rfam:
annotating families of non-coding RNA sequences. In Picardi E. (Eds.),
RNA Bioinformatics. Methods in Molecular Biology (pp. 349-363).\emph{New
York} , NY: Humana Press.

De Bie, T., Cristianini, N., Demuth, J. P., \& Hahn, M. W. (2006). CAFE:
a computational tool for the study of gene family
evolution.\emph{Bioinformatics} , \emph{22} (10), 1269-1271.
doi:10.1093/bioinformatics/btl097

Ding, L. Y., Deng, Y. H, \& Cao, Y. H. (2014). Ecological environment
indicator function of \emph{Corbicula fluminea} . \emph{Contemporary
fisheries} , \emph{8} (1), 78-79. (in Chinese)

Edgar, R. C. (2004). MUSCLE: multiple sequence alignment with high
accuracy and high throughput. \emph{Nucleic acids research} ,\emph{32}
(5), 1792-1797. doi:10.1093/nar/gkh340

El-Gebali, S., Mistry, J., Beteman, A., Eddy, S. R., Luciani, A.,
Potter, S. C., . . . Tosatto, S. C. E. (2018). The Pfam protein families
database in 2019. \emph{Nucleic Acids Research} , \emph{47} (1),
427-432. doi:10.1093/nar/gky995

Fu, L., Niu, B., Zhu, Z., Wu, S., \& Li, W. (2012). CD-HIT: accelerated
for clustering the next-generation sequencing data.\emph{Bioinformatics}
, \emph{28} (23), 3150-3152. doi:10.1093/bioinformatics/bts565

Ghosh, S., \& Chan, C. K. (2016). Analysis of RNA-Seq Data Using TopHat
and Cufflinks. \emph{Methods Mol Biol,} \emph{13} (74), 339-361.
doi:10.1007/978-1-4939-3167-5\_18

Griffiths-Jones, S., Grocock, R. J., Van Dongen, S., Bateman, A., \&
Enright, A. J. (2006). miRBase: microRNA sequences, targets and gene
nomenclature. \emph{Nucleic acids research} , \emph{34} (Database
issue), D140-D144. doi:10.1093/nar/gkj112

Gu, M. Q., \& Wang, Z. (2001). Embryonic development observation and
staging of \emph{Corbicula fluminea} (müller). \emph{Fisheries
information and strategy} , \emph{5} (10), 28-29. (in Chinese)

Guindon, S., Dufayard, J., Lefort, V., Anisimova, M., Hordijk, W., \&
Gascuel, O. (2010). New algorithms and methods to estimate
maximum-likelihood phylogenies: assessing the performance of PhyML
3.0.\emph{Systematic biology} , \emph{59} (3), 307-321.
doi:10.1093/sysbio/syq010

Haas, B. J., Delcher, A. L., Mount, S. M., Wortman, J. R., Smith Jr, R.
K., Hannick, L. I., . . . White, O. (2003). Improving the Arabidopsis
genome annotation using maximal transcript alignment
assemblies.\emph{Nucleic acids research} , \emph{31} (19), 5654-5666.
doi:10.1093/nar/gkg770

Haas, B. J., Papanicolaou, A., Yassour, M., Grabherr, M., Blood, P. D.,
Bowden, J., . . . Regev, A. (2013). De novo transcript sequence
reconstruction from RNA-seq using the Trinity platform for reference
generation and analysis. \emph{Nature Protocols} , \emph{8} (8),
1494-1512. doi:10.1038/nprot.2013.084

Haas, B. J., Salzberg, S. L., Zhu, W., Pertea, M., Allen, J. E., Orvis,
J., . . . Wortman, J. R. (2008). Automated eukaryotic gene structure
annotation using EVidenceModeler and the Program to Assemble Spliced
Alignments. \emph{Genome Biology} , \emph{9} (1), R7.
doi:10.1186/gb-2008-9-1-r7

Hoede, C., Arnoux, S., Moisset, M., Chaumier, T., Inizan, O., Jamilloux,
V., \& Quesneville, H. (2014). PASTEC: an automatic transposable element
classification tool. \emph{Plos One} , \emph{9} (5), e91929.
doi:10.1371/journal.pone.0091929

Huang, X. C., Wu, R. W., An, C. T., Xie, G. L., Su, J. H. . . . Wu, X.
P. (2018). Reclassification of Lamprotula rochechouartii as
Margaritifera rochechouartiicomb. nov. (Bivalvia: Margaritiferidae)
revealed by time-calibrated multi-locus phylogenetic analyses and
mitochondrial phylogenomics of \emph{Unionoida. Mol Phylogenet Evol}
,\emph{62} (120), 297-306. doi:10.1016/j.ympev.2017.12.017.

Ishibashi, R., Ookubo, K., . . . Kawamura, K. (2003). Androgenetic
Reproduction in a Freshwater Diploid Clam \emph{Corbicula
fluminea}(Bivalvia: Corbiculidae). \emph{Zoolog} , \emph{20} (6),
727-732. doi:10.2108/zsj.20.727

Jiang, H. T., \& Zhao, W. P. (1997). Exploitation and culture technology
of \emph{Corbicula fluminea} . \emph{Anhui agriculture} , \emph{8} (10),
26-27. (in Chinese)

Kanehisa, M., \& Goto, S. (2000). KEGG: Kyoto Encyclopedia of Genes and
Genomes. \emph{Nucleic acids research} , \emph{28} (1), 27-30.
doi:10.1093/nar/28.1.27

Keilwagen, J., Wenk, M., Erickson, J. L., Schattat, M. H., Grau, J., \&
Hartung, F. (2016). Using intron position conservation for
homology-based gene prediction. \emph{Nucleic acids research} ,\emph{44}
(9), e89. doi:10.1093/nar/gkw092

Keogh, S. M., \& Simons, A. M. (2019). Molecules and morphology reveal
'new' widespread north american freshwater mussel species (bivalvia:
unionidae). \emph{Molecular Phylogenetics and Evolution} , \emph{138}
(1), 182-192. doi:10.1016/j.ympev.2019.05.029

Kim, D., Langmead, B., \& Salzberg, S. L. (2015). HISAT: a fast spliced
aligner with low memory requirements. \emph{Nature Methods} ,\emph{12}
(4), 357-360. doi:10.1038/nmeth.3317

Kondakov, A. V., Palatov, D. M., . . . Bolotov, I. N. (2018). DNA
analysis of a non-native lineage of \emph{Sinanodonta woodiana} species
complex (Bivalvia: Unionidae) from Middle Asia supports the Chinese
origin of the European invaders. \emph{Zootaxa,} \emph{4462} (4),
511-522. doi:10.11646/zootaxa.4462.4.4

Koren, S., Walenz, B. P., Berlin, K., Miller, J. R., Bergman, N. H., \&
Phillippy, A. M. (2017). Canu: scalable and accurate long-read assembly
via adaptive k-mer weighting and repeat separation. \emph{Genome
Research} , \emph{27} (5), 722-736. doi:10.1101/gr.215087.116

Korf, I. (2004). Gene finding in novel genomes. \emph{BMC
bioinformatics} , \emph{5} (1), 59. doi:10.1186/1471-2105-5-59

Korniushin, A. V. (2004). A revision of some Asian and African
freshwater clams assigned to \emph{Corbicula fluminalis} (Müller, 1774)
(Mollusca: Bivalvia: Corbiculidae), with a review of anatomical
characters and reproductive features based on museum
collections.\emph{Hydrobiologia} . \emph{529} (3), 251-270.
doi:10.1007/s10750-004-9322-x

Kumar, S., Stecher, G., Suleski, M., \& Hedges, S. B. (2017). TimeTree:
a resource for timelines, timetrees, and divergence
times.\emph{Molecular Biology \& Evolution} , \emph{34} (7), 1812-1819.
doi: 10.1093/molbev/msx116

Li, H., \& Durbin, R. (2009). Fast and accurate short read alignment
with Burrows-Wheeler transform. \emph{Bioinformatics} , \emph{25} (14),
1754--1760. doi:10.1093/bioinformatics/btp324

Li, L., Stoeckert, C. J., \& Roos, D. S. (2003). OrthoMCL:
identification of ortholog groups for eukaryotic genomes. \emph{Genome
Research} , \emph{13} (9), 2178-2189. doi:10.1101/gr.1224503

Lowe, T. M., \& Eddy, S. R. (1997). tRNAscan-SE: a program for improved
detection of transfer RNA genes in genomic sequence. \emph{Nucleic acids
research} , \emph{25} (5), 955-964. doi:10.1093/nar/25.5.955

Majoros, W. H., Pertea, M., \& Salzberg, S. L. (2004). TigrScan and
GlimmerHMM: two open source ab initio eukaryotic
gene-finders.\emph{Bioinformatics} , \emph{20} (16), 2878-2879.
doi:10.1093/bioinformatics/bth315

Marçais, G., \& Kingsford, C. (2011). A fast, lock-free approach for
efficient parallel counting of occurrences of
k-mers.\emph{Bioinformatics} , \emph{27} (6), 764-770.
doi:10.1093/bioinformatics/btr011.

Marchler, B., Lu, S. N., Anderson, J. B., Chitsaz, F., Derbyshire, M.
K., . . . Bryant, S. H. (2011). CDD: a Conserved Domain Database for the
functional annotation of proteins. \emph{Nucleic Acids Research}
,\emph{39} (1), 225-229. doi:10.1093/nar/gkq1189

Moore. \& Raymond, C. (1969). Treatise on invertebrate
paleontology.\emph{Geological Society of America} , \emph{18} (23),
167-172.

Nawrocki, E. P., \& Eddy, S. R. (2013). Infernal 1.1: 100-fold faster
RNA homology searches. \emph{Bioinformatics} , \emph{29} (22),
2933-2935. doi:10.1093/bioinformatics/btt509

Parra, G., Bradnam, K., \& Korf, I. (2007). CEGMA: a pipeline to
accurately annotate core genes in eukaryotic
genomes.\emph{Bioinformatics} , \emph{23} (9), 1061-1067.
doi:10.1093/bioinformatics/btm071

Peng, T. C., Subeq, Y. M., Lee, C. J., Lee, C. C., Tsai, C. J., \&
Chang, F. M. (2008). Freshwater clam extract ameliorates acute liver
injury induced by hemorrhage in rats. \emph{American Journal of Chinese
Medicine} , \emph{36} (6), 1121-1133. doi:10.1142/S0192415X08006466

Pertea, M., Pertea, G. M., Antonescu, C. M., Chang, T. C., Mendell, J.
T., \& Salzberg, S. L. (2015). StringTie enables improved reconstruction
of a transcriptome from RNA-seq reads. \emph{Nature Biotechnology}
,\emph{33} (3), 290-295. doi:10.1038/nbt.3122

Peterson, K. J., Cotton, J. A., Gehling, J. G., \& Pisani, D. (2008).
The Ediacaran emergence of bilaterians: congruence between the genetic
and the geological fossil records. \emph{Philos Trans R Soc Lond B Biol
Sci} , \emph{363} (1496), 1435-1443. doi:10.1098/rstb.2007.2233.

Plazzi, F., \& Passamonti, M. (2010). Towards a molecular phylogeny of
Mollusks: bivalves' early evolution as revealed by mitochondrial
genes.\emph{Mol Phylogenet Evol, 57} (2), 641-657.
doi:10.1016/j.ympev.2010.08.032.

Price, A. L., Jones, N. C., \& Pevzner, P. A. (2005). De novo
identification of repeat families in large genomes.\emph{Bioinformatics}
, \emph{21} (suppl\_1), i351-i358. doi:10.1093/bioinformatics/bti1018

Qiu, A. D., Shi, A. J., \& Komaru, A. (2001). Yellow and brown shell
color morphs of \emph{corbicula fluminea} (bivalvia : corbiculidae) from
sichuan province, china, are triploids and tetraploids. \emph{Journal of
Shellfish Research} , \emph{20} (1), 323-328. (in Chinese)

Ramos, R. J., Pras-Raves, M. L., Gerrits, J., Willemsen, M., Prinsen,
H., . . . Nanda, M. (2017). Vitamin B6 is essential for serine de novo
biosynthesis. \emph{J Inherit Metab Dis} , \emph{40} (6), 883-891.
doi:10.1007/s10545-017-0061-3

Schmidt, M. H., Vogel, A., Denton, A. K. (2017). De Novo Assembly of a
New Solanum pennellii Accession Using Nanopore Sequencing. \emph{Plant
Cell} , \emph{29} (3), 2336-2348. doi:10.1105/tpc.17.00521

Servant, N., Varoquaux, N., Lajoie, B. R., Eric, V. \& Emmanuel, B.
(2015). HiC-Pro: an optimized and flexible pipeline for Hi-C data
processing. \emph{Genome Biol} , \emph{16} (1), 259-262.
doi:10.1186/s13059-015-0831-x

She, R., Chu, J. S., Uyar, B., Wang, J., Wang, K., \& Chen, N. (2011).
genBlastG: using BLAST searches to build homologous gene
models.\emph{Bioinformatics} , \emph{27} (15), 2141-2143.
doi:10.1093/bioinformatics/btr342

Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V., \&
Zdobnov, E. M. (2015). BUSCO: assessing genome assembly and annotation
completeness with single-copy orthologs. \emph{Bioinformatics}
,\emph{31} (19), 3210-3212. doi:10.1093/bioinformatics/btv351

Sirirat, S., Joong, K. P., \& Foighil, D. Ó. (2000). Two lineages of the
introduced asian freshwater clam \emph{corbicula} occur in north
america. \emph{Journal of Molluscan Studies, 17} (3), 275-286.
doi:10.1093/mollus/66.3.423

Stanke, M., \& Waack, S. (2003). Gene prediction with a hidden Markov
model and a new intron submodel. \emph{Bioinformatics} ,\emph{19}
(suppl\_2), ii215-ii225. doi:10.1093/bioinformatics/btg1080

Stanley, S. M. (1968). Post-Paleozoic adaptive radiation of infaunal
bivalve molluscs - a consequence of mantle fusion and siphon
formation.\emph{J. Paleontol} , \emph{3} (42), 214-229.

Stöger, I., Sigwart, J. D., Kano, Y., Knebelsberger, T., Marshall, B.
A., Schwabe, E., Schrödl, M. (2013). The continuing debate on deep
molluscan phylogeny: evidence for Serialia (Mollusca, Monoplacophora +
Polyplacophora). \emph{Biomed Res Int} , \emph{2013} (40), 70-72.
doi:10.1155/2013/407072.

Sun, H. (1995). Utilization and culture of \emph{Corbicula fluminea}
.\emph{Scientific fish culture} , \emph{34} (02), 30-31. (in Chinese)

Sun, J., Zhang, Y., . . . Xu, T. (2017). Adaptation to deep-sea
chemosynthetic environments as revealed by mussel genomes. \emph{Nat
Ecol Evol,} \emph{1} (5), 121-126. doi:10.1038/s41559-017-0121

Tang, S., Lomsadze, A., \& Borodovsky, M. (2015). Identification of
protein coding regions in RNA transcripts. \emph{Nucleic acids research}
, \emph{43} (12), e78. doi:10.1093/nar/gkv227

Tao, Z. Y., Deng, Y. H., \& Li, C. G. (2016). Embryonic and
postembryonic development of \emph{Corbicula fluminea} . \emph{Jiangsu
Agricultural Sciences} , \emph{44} (10), 305-307. (in Chinese)

Tarailo-Graovac, M., \& Chen, N. (2009). Using RepeatMasker to identify
repetitive elements in genomic sequences. \emph{Current protocols in
bioinformatics} , \emph{5} (1), 4.10.11-14.10.14.
doi:10.1002/0471250953.bi0410s25

Tatusov, R. L., Fedorova, N. D., Jackson, J. D., Jacobs, A. R.,
Kiryutin, B., Koonin, E. V., . . . Natale, D. A. (2003). The COG
database: an updated version includes eukaryotes. \emph{BMC
bioinformatics} , \emph{4} (1), 41. doi:10.1186/1471-2105-4-41

Thorp \& James, H. (1991). Ecology and classification of north american
freshwater invertebrates. \emph{Quarterly Review of Biology} ,\emph{39}
(4), 209-212. doi:10.1021/ba-1995-0246.pr001

Ueland, P. M., McCann, A., Midttun, Ø., \& Ulvik, A. (2017).
Inflammation, vitamin B6 and related pathways. \emph{Mol Aspects Med,8}
(53), 10-27. doi:10.1016/j.mam.2016.08.001

Vaser, R., Sovi\selectlanguage{polish}ć, \selectlanguage{english}I., Nagaranjan, N., \& \selectlanguage{polish}Š\selectlanguage{english}iki\selectlanguage{polish}ć, \selectlanguage{english}M. (2017). Fast and
accurate de novo genome assembly from long uncorrected
reads.\emph{Genome Research} , \emph{27} (5), 737-746.
doi:10.1101/gr.214270.11632.

Walker, B. J., Abeel, T., Shea, T., Priest, M., Abouelliel, A.,
Sakthikumar, S., . . . Earl, A. M. (2014). Pilon: an integrated tool for
comprehensive microbial variant detection and genome assembly
improvement. \emph{Plos One} , \emph{9} (11), e112963.
doi:10.1371/journal.pone.0112963

Wang, Y., \& Liu, D. H. (2010). Research status and Prospect of
functional components of \emph{Corbicula fluminea} . \emph{Food and
fermentation industry} , \emph{36} (6), 122-124. (in Chinese)

Wang, Y., \& Liu, D. H. (2010). Research status and prospect of
functional components in Corbicula fluminea. Food and fermentation
industry, 1(06), 128-130. (in Chinese)

Wu, Y. T., \& Sun, H. L. (2007). Enzymatic utilization of marine
shellfish protein resources. \emph{Chinese Journal of bioengineering}
,\emph{27} (9), 120-125. (in Chinese)

Xiao, L. Z., Jiao, G., Hui, J., Xiao, Y. N., \& Kuan, Y. L. (2015).
Effects of \emph{corbicula fluminea} in lake taihu on improvement of
eutrophic water quality. \emph{Journal of Lake ences} , \emph{27} (3),
486-492. (in Chinese)

Xu, G. C., Xu, T. J., Zhu, R., Zhang, Y., \& Li, G. T. (2019).
LR\_Gapcloser: a tiling path-based gap closer that uses long reads to
complete genome assembly. \emph{Gigascience} , \emph{8} (1), 157-160.
doi:10.1093/gigascience/giy157

Xu, Z., \& Wang, H. (2007). LTR\_FINDER: an efficient tool for the
prediction of full-length LTR retrotransposons. \emph{Nucleic acids
research} , \emph{35} (Web Server issue), W265-W268.
doi:10.1093/nar/gkm286

Yan, X., Nie, H., Huo, Z., Yan, X., Nie, H., . . . Dong, D. L. (2019).
Clam Genome Sequence Clarifies the Molecular Basis of Its Benthic
Adaptation and Extraordinary Shell Color Diversity. \emph{IScience, 19}
(3), 1225-1237. doi:10.1016/j.isci.2019.08.049

Yang, Z. (2007). PAML 4: Phylogenetic Analysis by Maximum
Likelihood.\emph{Molecular Biology \& Evolution} , \emph{24} (8),
1586-1591. doi:10.1093/molbev/msm088

Zhang, P. (1996). Utilization and cultivation of \emph{Corbicula
fluminea} . \emph{Practical science and technology information in rural
areas} , \emph{7} (3), 19-21. (in Chinese)

Zhang, S., Wang, H., Yu, J., Jiang, F., Yue, X., \& Liu, B. (2018).
Identification of a gene encoding microphthalmia-associated
transcription factor and its association with shell color in the
clam\emph{Meretrix petechialis} . \emph{Comp Biochem Physiol, 34} (225),
75-83. doi:10.1016/j.cbpb.2018.04.007

Zhang, S., Yue, X., Jiang, F., Wang, H., \& Liu, B. (2017).
Identification of an MITF gene and its polymorphisms associated with the
Vibrio resistance trait in the clam Meretrix petechialis. \emph{Fish
Shellfish Immunol,} \emph{13} (68), 466-473.
doi:10.1016/j.fsi.2017.07.035

Zhao L., \& Liu, H. Q. (2010). Evaluation of protein nutritional value
in \emph{Corbicula fluminea} extraction. \emph{Anhui Agricultural
Science} , \emph{23} (8), 4105-4107. (in Chinese)

Zhao, G. Q., Zhao, Q., Zhou, X., \& Mattei, M. G. (1993). a basic
helix-loop-helix protein, forms heterodimers with TFE3 and inhibits
TFE3-dependent transcription activation. \emph{Mol Cell Biol, 13} (8),
4505-4512. doi:10.1128/mcb.13.8.4505

Zhuang, P., Song, C., \& Zhang, L. Z. (2009). Analysis and evaluation of
nutritional components of \emph{Corbicula fluminea} in the Yangtze River
Estuary. \emph{Acta nutrition Sinica} , \emph{31} (3), 304-306. (in
Chinese)\selectlanguage{english}
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