- Data Note
- Open Access
- Open Peer Review
High-quality genome assembly of channel catfish, Ictalurus punctatus
- Xiaohui Chen†1, 2,
- Liqiang Zhong†1, 2,
- Chao Bian†3,
- Pao Xu†4,
- Ying Qiu†3,
- Xinxin You†3,
- Shiyong Zhang1, 2,
- Yu Huang3,
- Jia Li3,
- Minghua Wang1, 2,
- Qin Qin1, 2,
- Xiaohua Zhu1, 2,
- Chao Peng3,
- Alex Wong5,
- Zhifei Zhu6, 7,
- Min Wang3, 6, 7,
- Ruobo Gu4, 6,
- Junmin Xu3, 6, 7Email author,
- Qiong Shi3, 6, 7, 8, 9Email author and
- Wenji Bian1, 2Email author
© The Author(s). 2016
- Received: 20 May 2016
- Accepted: 3 August 2016
- Published: 22 August 2016
The channel catfish (Ictalurus punctatus), a species native to North America, is one of the most important commercial freshwater fish in the world, especially in the United States’ aquaculture industry. Since its introduction into China in 1984, both cultivation area and yield of this species have been dramatically increased such that China is now the leading producer of channel catfish. To aid genomic research in this species, data sets such as genetic linkage groups, long-insert libraries, physical maps, bacterial artificial clones (BAC) end sequences (BES), transcriptome assemblies, and reference genome sequences have been generated. Here, using diverse assembly methods, we provide a comparable high-quality genome assembly for a channel catfish from a breeding stock inbred in China for more than three generations, which was originally imported to China from North America.
Approximately 201.6 gigabases (Gb) of genome reads were sequenced by the Illumina HiSeq 2000 platform. Subsequently, we generated high quality, cost-effective and easily assembled sequences of the channel catfish genome with a scaffold N50 of 7.2 Mb and 95.6 % completeness. We also predicted that the channel catfish genome contains 21,556 protein-coding genes and 275.3 Mb (megabase pairs) of repetitive sequences.
We report a high-quality genome assembly of the channel catfish, which is comparable to a recent report of the “Coco” channel catfish. These generated genome data could be used as an initial platform for molecular breeding to obtain novel catfish varieties using genomic approaches.
- Channel catfish
- Whole genome sequencing
- Gene prediction
- Repetitive sequence
Library construction, read sequencing and filtering
To generate genome sequence data, genomic DNA from mixed tissues (including muscle and skin) of channel catfish was extracted from a chosen individual cultured at a local base of the Freshwater Fisheries Research Institute (Jiangsu Province, Nanjing, China) using Qiagen GenomicTip100 (Qiagen, Hilden, DE) as per standard protocols. Isolated genomic DNA was subsequently used to construct short-insert libraries (250, 500 and 800 bp) and long-insert libraries (2, 5, 10 and 20 kb) with the standard protocol provided by Illumina (San Diego, USA). Paired-end sequencing was performed using the Illumina HiSeq 2000 platform to generate 125-bp reads using a whole genome shotgun sequencing (WGS) strategy .
To improve the quality of sequenced reads, we trimmed 4 bases with edges from the reads of short-insert libraries and long-insert libraries, discarded duplicated reads from the long-insert libraries, and removed reads containing 10 or more Ns and low-quality bases. Finally, a total of 201.6-Gb clean reads were generated for further genome assembly.
Genome assembly and quality assessments
At first, we estimated the channel catfish genome size using k-mer analysis  with the formula: G = N*(L − 17 + 1)/K_depth, where N is the total number of reads, and K_depth indicates the frequency of reads occurring more frequently than the others. The calculated genome size is 0.839 Gb, which is shorter than that (1 Gb) from a 2016 report of an American-native channel catfish .
Catfish genome assembly and annotation statistics
Contig N50 size (kb)
Contig number (>100 bp)
Scaffold N50 size (Mb)
Scaffold number (>100 bp)
Total length (Mb)
Genome coverage (X)
Longest scaffold (bp)
Protein-coding gene number
Mean transcript length (kb)
Mean exons per gene
Mean exon length (bp)
Mean intron length (bp)
Comparison of genome assembly in sequenced fishes
Sequencing platform (Mb)
Assembled genome size (Mb)
scaffold N50 (kb)
contig N50 (kb)
catfish (Liu’s study )
Two typical methods were then used to assess the quality and completeness of the generated assembly. First, transcriptome evaluation was used to assess the completeness of gene regions in the genome assembly. We carried out de novo assembly of the RNA sequences of skin and muscle tissues using Trinity software . The assembled fragments were then aligned to the genome assembly with BLAT  (E-value = 10e-6, identity = 90 % and coverage >90 %). Our results indicate that the catfish genome assembly covered more than 90 % of gene-coding regions. Subsequently, Core Eukaryotic Genes Mapping Approach (CEGMA) software (version 2.3)  was employed with 248 conserved core eukaryotic genes (CEGs) to assess the gene space completeness within the generated genome assembly. These results demonstrate that the genome assembly covered more than 95 % of the CEG sequences, suggesting a high level of completeness.
Total RNA was extracted from muscle and skin tissues of a channel catfish (the same individual used for the above-mentioned genome sequencing) using TRIzol reagent (Invitrogen, USA). After purification using RNeasy Animal Mini Kit (Qiagen, USA), equal amounts of total RNA from each tissue were subjected to transcriptome sequencing (RNA-seq) on the HiSeq 2000 platform.
Firstly, RepeatModeller (version 1.04) and LTR_FINDER  were used to build a de novo repeat library with default parameters. Subsequently, RepeatMasker  (version 3.2.9) was utilized to map our sequences against the Repbase  transposable element (TE) library (version 14.04) and the de novo repeat library, so as to search for known and novel TEs. Next, we annotated tandem repeats using Tandem Repeat Finder  (version 4.04) with core parameters set as “Match = 2, Mismatch = 7, Delta = 7, PM = 80, PI = 10, Minscore = 50, and MaxPerid = 2000”. Furthermore, TE-relevant proteins were identified in our assembly using RepeatProteinMask software  (version 3.2.2). These identified repeat sequences accounted for 32.56 % of the channel catfish genome, of which the single largest class of TEs (representing 9.35 % of the whole genome) was the Tc1-mariner family.
Annotations of gene structure and function
The channel catfish genome assembly was annotated using three independent approaches: homology, de novo and RNA-seq annotations. For homology annotation, the protein sequences from zebrafish, Japanese fugu, spotted green pufferfish, Japanese medaka (Ensembl release 75), blue spotted mudskipper  and golden arowana  were mapped on the channel catfish genome using TblastN with e-value ≤ 1E-5. Genewise 2.2.0 software  was then employed to predict the potential gene structures of all alignments. Short genes (with fewer than 150 bp) and prematurely terminated or frame-shifted genes were discarded. Next, de novo annotation was used to annotate the gene structure from the genome assembly. We randomly selected 1000 complete genes from the homology annotation set to train the parameters for AUGUSTUS 2.5 . Simultaneously, all repetitive regions were replaced in the channel catfish genome with ‘N’ to decline the ratio of pseudogene annotations. Subsequently, we utilized AUGUSTUS 2.5 and GENSCAN 1.0  for de novo prediction of repeat-masked genome sequences. The filtered processes performed on the de novo annotation were the same as those used for homology prediction. Simultaneously, the RNA-seq annotation pipeline was also used to detect gene regions. We employed Tophat 1.2 software  to map the RNA reads extracted from the skin and muscle transcriptomes onto the channel catfish genome sequences. We then sorted and integrated Tophat alignments, and used Cufflink software  to analyze potential gene structures. Results from all three of the above-mentioned annotation pipelines were merged to produce a comprehensive and non-redundant gene set using GLEAN . This gene set contained 21,556 genes with an average of 8.7 exons per gene (Table 1). Because different annotation pipelines were applied, the total gene number predicted here is lower than the 26,661 reported in the American-native channel catfish genome . The Cuffdiff package  of Cufflink software (version 2.0.2.Linux_×86_64) with core parameters (−FDR 0.05 –geometric-norm TRUE –compatible-hits-norm TRUE) was utilized to calculate expression levels according to the GLEAN gene set and Tophat alignments. About 93.4 % of genes were predicted from at least two types of evidence, and approximate 78 % of the genes showed expression activity (fragments per kilobase of exon model per million mapped reads >0) in the skin and muscle tissues.
Simultaneously, all protein sequences from GLEAN results were mapped to SwissProt and TrEMBL  (UniProt release 2011.06) databases using BlastP  with an E-value ≤ 1e-5 to find the best hit for each protein. We also used InterProScan 4.7 software  to align the protein sequences against public databases, including Pfam , PRINTS , ProDom  and SMART , to examine the known motifs and domains in our sequences. Over 94.5 % of these predicted genes possessed at least one related functional assignment from other public databases (SwissProt , Interpro , TrEMBL and KEGG ). In addition, the gene structures (including exon length, intron regions and mRNAs) and exon number distributions (Table 1) were predicted to be similar to other representative teleost species such as zebrafish and medaka.
We generated a channel catfish genome assembly with high quality and comparable structures to other published fish genomes, especially the Coco catfish genome . This new assembly is a valuable resource and reference for further construction of high-density genetic linkage maps and identification of quantitative trait loci for molecular breeding of catfishes.
Supporting data are available in the GigaDB database . Raw whole genome sequencing and transcriptome data are deposited in the SRA under bioproject number PRJNA319455.
BAC, bacterial artificial clone; BES, BAC end sequences/sequencing; CEG, core eukaryotic genes; CEGMA, core eukaryotic genes mapping approach; Gb, gigabases; kb, kilobases; Mb, megabases; TE, transposable element; WGS, whole genome shotgun
This study was supported by the National Key Technology R&D Program of China (No. 2012BAD26B03), Fund for Independent Innovation of Agricultural Science and Technology of Jiangsu Province (No. CX(15)1013), Human Resources and Social Security of Jiangsu Province (No. 2014-NY-008), Three-Side Innovation Projects for Aquaculture in Jiangsu Province (No. Y2014-25 &Y2015-12), Shenzhen Special Program for Future Industrial Development (No. JSGG20141020113728803), and Zhenjiang Leading Talent Program for Innovation and Entrepreneurship.
XC, QS, CB, JX and WB conceived the project. LZ, YH, SZ, MHW, QQ, XY, CP, AW, ZZ, MW and RG collected the samples and extracted the genomic DNA. CB, YQ, JL and YH performed the genome assembly and data analysis. CB, QS, XC, LZ, XP, XZ and WB wrote the paper and all authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- You X, Bian C, Zan Q, Xu X, Liu X, Chen J, et al. Mudskipper genomes provide insights into the terrestrial adaptation of amphibious fishes. Nat Commun. 2014;5:5594.View ArticlePubMedPubMed CentralGoogle Scholar
- Li R, Fan W, Tian G, Zhu H, He L, Cai J, et al. The sequence and de novo assembly of the giant panda genome. Nature. 2010;463(7279):311–7.View ArticlePubMedGoogle Scholar
- Liu Z, Liu S, Yao J, Bao L, Zhang J, Li Y, et al. The channel catfish genome sequence provides insights into the evolution of scale formation in teleosts. Nat Commun. 2016;7:11757.View ArticlePubMedPubMed CentralGoogle Scholar
- Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience. 2012;1:12.View ArticleGoogle Scholar
- Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, et al. Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data. Nat Biotechnol. 2011;29(7):644–52.View ArticlePubMedPubMed CentralGoogle Scholar
- Kent WJ. BLAT–the BLAST-like alignment tool. Genome Res. 2002;12(4):656–64.View ArticlePubMedPubMed CentralGoogle Scholar
- Parra G, Bradnam K, Korf I. CEGMA: a pipeline to accurately annotate core genes in eukaryotic genomes. Bioinformatics. 2007;23(9):1061–7.View ArticlePubMedGoogle Scholar
- Xu Z, Wang H. LTR_FINDER: an efficient tool for the prediction of full-length LTR retrotransposons. Nucleic Acids Res. 2007;35(Web Server issue):W265–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Tarailo-Graovac M, Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2009;Chapter 4:Unit 4. 10.PubMedGoogle Scholar
- Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005;110(1–4):462–7.View ArticlePubMedGoogle Scholar
- Benson G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 1999;27(2):573–80.View ArticlePubMedPubMed CentralGoogle Scholar
- Bian C, Hu Y, Ravi V, Kuznetsova IS, Shen X, Mu X, et al. The Asian arowana (Scleropages formosus) genome provides new insights into the evolution of an early lineage of teleosts. Sci Rep. 2016;6:24501.View ArticlePubMedPubMed CentralGoogle Scholar
- Birney E, Clamp M, Durbin R. GeneWise and Genomewise. Genome Res. 2004;14(5):988–95.View ArticlePubMedPubMed CentralGoogle Scholar
- Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34(Web Server issue):W435–9.View ArticlePubMedPubMed CentralGoogle Scholar
- Burge C, Karlin S. Prediction of complete gene structures in human genomic DNA. J Mol Biol. 1997;268(1):78–94.View ArticlePubMedGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11.View ArticlePubMedPubMed CentralGoogle Scholar
- Trapnell C, Hendrickson DG, Sauvageau M, Goff L, Rinn JL, Pachter L. Differential analysis of gene regulation at transcript resolution with RNA-seq. Nat Biotechnol. 2013;31(1):46–53.View ArticlePubMedGoogle Scholar
- Elsik CG, Mackey AJ, Reese JT, Milshina NV, Roos DS, Weinstock GM. Creating a honey bee consensus gene set. Genome Biol. 2007;8(1):R13.View ArticlePubMedPubMed CentralGoogle Scholar
- Bairoch A, Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 2000;28(1):45–8.View ArticlePubMedPubMed CentralGoogle Scholar
- Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 1997;25(17):3389–402.View ArticlePubMedPubMed CentralGoogle Scholar
- Zdobnov EM, Apweiler R. InterProScan–an integration platform for the signature-recognition methods in InterPro. Bioinformatics. 2001;17(9):847–8.View ArticlePubMedGoogle Scholar
- Bateman A, Birney E, Durbin R, Eddy SR, Howe KL, Sconnhammer EL. The Pfam protein families database. Nucleic Acids Res. 2000;28(1):263–6.View ArticlePubMedPubMed CentralGoogle Scholar
- Attwood TK, Cronig MD, Flower DR, Lewis AP, Mabey JE, Scordis P, et al. PRINTS-S: the database formerly known as PRINTS. Nucleic Acids Res. 2000;28(1):225–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Corpet F, Gouzy J, Kahn D. Recent improvements of the ProDom database of protein domain families. Nucleic Acids Res. 1999;27(1):263–7.View ArticlePubMedPubMed CentralGoogle Scholar
- Schult J, Copley RR, Doerks T, Ponting CP, Bork P. SMART: a web-based tool for the study of genetically mobile domains. Nucleic Acids Res. 2000;28(1):231–4.View ArticleGoogle Scholar
- Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999;27(1):29–34.View ArticlePubMedPubMed CentralGoogle Scholar
- Chen X, Zhong L, Bian C, Xu P, Qiu Y, You X, Zhang S, Yu H, Li J, Wang M, Qin Q, Zhu X, Peng C, Wong A, Zhu Z, Wang M, Ruobo G, Xu J, Shi Q, Bian W. Supporting data for “High-quality genome assembly of channel catfish, Ictalurus punctatus”. 2016. GigaScience Database, http://dx.doi.org/10.5524/100212.Google Scholar