Assembly of 500,000 inter-specific catfish expressed sequence tags and large scale gene-associated marker development for whole genome association studies
- Shaolin Wang1,
- Eric Peatman1,
- Jason Abernathy1,
- Geoff Waldbieser2,
- Erika Lindquist3,
- Paul Richardson3,
- Susan Lucas3,
- Mei Wang3,
- Ping Li1,
- Jyothi Thimmapuram4,
- Lei Liu4,
- Deepika Vullaganti4,
- Huseyin Kucuktas1,
- Christopher Murdock2,
- Brian C Small2,
- Melanie Wilson5,
- Hong Liu1,
- Yanliang Jiang1,
- Yoona Lee1,
- Fei Chen1,
- Jianguo Lu1,
- Wenqi Wang1,
- Peng Xu1,
- Benjaporn Somridhivej1,
- Puttharat Baoprasertkul1,
- Jonas Quilang1,
- Zhenxia Sha1,
- Baolong Bao1,
- Yaping Wang1,
- Qun Wang1,
- Tomokazu Takano1,
- Samiran Nandi1,
- Shikai Liu1,
- Lilian Wong1,
- Ludmilla Kaltenboeck1,
- Sylvie Quiniou2,
- Eva Bengten5,
- Norman Miller5,
- John Trant6,
- Daniel Rokhsar3, 7,
- Zhanjiang Liu1Email author and
- the Catfish Genome Consortium
© Wang et al.; licensee BioMed Central Ltd. 2010
Received: 3 November 2009
Accepted: 22 January 2010
Published: 22 January 2010
Through the Community Sequencing Program, a catfish EST sequencing project was carried out through a collaboration between the catfish research community and the Department of Energy's Joint Genome Institute. Prior to this project, only a limited EST resource from catfish was available for the purpose of SNP identification.
A total of 438,321 quality ESTs were generated from 8 channel catfish (Ictalurus punctatus) and 4 blue catfish (Ictalurus furcatus) libraries, bringing the number of catfish ESTs to nearly 500,000. Assembly of all catfish ESTs resulted in 45,306 contigs and 66,272 singletons. Over 35% of the unique sequences had significant similarities to known genes, allowing the identification of 14,776 unique genes in catfish. Over 300,000 putative SNPs have been identified, of which approximately 48,000 are high-quality SNPs identified from contigs with at least four sequences and the minor allele presence of at least two sequences in the contig. The EST resource should be valuable for identification of microsatellites, genome annotation, large-scale expression analysis, and comparative genome analysis.
This project generated a large EST resource for catfish that captured the majority of the catfish transcriptome. The parallel analysis of ESTs from two closely related Ictalurid catfishes should also provide powerful means for the evaluation of ancient and recent gene duplications, and for the development of high-density microarrays in catfish. The inter- and intra-specific SNPs identified from all catfish EST dataset assembly will greatly benefit the catfish introgression breeding program and whole genome association studies.
Catfish is the major aquaculture species in the United States, accounting for over 60% of all US aquaculture production. While channel catfish (Ictalurus punctatus) accounts for the majority of commercial aquaculture production, the closely related blue catfish (Ictalurus furcatus) possesses several economically important traits that led to the production of an inter-specific hybrid (channel catfish female × blue catfish male) available for commercial use . This specific hybrid shows strong heterosis and superior performance traits in disease resistance, growth rate, feed conversion efficiency, processing yields, and seinability. Channel catfish is also an important model species for the study of comparative immunology, reproductive physiology, and toxicology. The channel catfish immune system is among the best characterized of any fish species, with decades of research leading to identification and characterization of catfish immune genes [2, 3], establishment of clonal functionally distinct lymphocyte cell lines , characterization of much of the machinery of catfish innate [5, 6] and adaptive immunity and production of panels of specific monoclonal antibodies for detection of catfish immunocytes [7–9].
Genome research requires the development of a number of resources that facilitate both structural and functional analysis of the genome. Many of the required resources have been developed in catfish, including a large number of polymorphic markers [10, 11], linkage maps [12–14], bacterial artificial chromosome (BAC) libraries [15, 16], physical maps [17, 18], and BAC end sequences (BES) [19, 20]. However, expressed sequence tag (EST) resources were low from catfish [21–26], hindering both functional and comparative genome analysis in catfish. Large numbers of ESTs have been produced for most model species as well as a number of agriculturally important species [27–32], including cattle (1.5 million), swine (1.4 million), chicken (600,000), Atlantic salmon (471,000), and rainbow trout (281,000). The availability of such EST resources has allowed efficient gene discovery and gene identification in these species, and rapid progress has been made through comparative genome analysis in understanding structural, organizational, and functional properties of the genomes of these species.
A whole genome sequence is not available for most aquaculture species. In the absence of the whole genome sequence of catfish, we initiated this large-scale EST project to provide transcriptomic resources in channel catfish and blue catfish. These ESTs will serve as resources for gene discovery and gene identification, supply the framework for high-density microarray platforms, provide a foundation for the analysis of full-length cDNAs, and assist in the identification of genetic markers such as microsatellites and single nucleotide polymorphisms (SNPs). In this study, we have taken a unique inter-specific approach. The inter-specific approach will help develop markers that are inter-specific and species specific. These resources will also be of great use for comparative genome analysis. The inter-specific EST approach to produce parallel EST resources from two closely related Ictalurid species will allow the resolution of some of the most difficult issues in teleost genome research, such as paralog confusions involving duplicated genomes [33–35]. Here we report the generation and analysis of nearly 500,000 ESTs from catfish, including 354,377 ESTs from channel catfish and 139,475 ESTs from blue catfish.
cDNA libraries and EST sequencing
cDNA library information and sequencing summary
Nature of library
Organ, tissue, or cell line
Stomach, muscle, olfactory tissue and trunk kidney
Stomach, muscle, olfactory tissue and trunk kidney
Head kidney, gill, intestine, spleen, skin and liver
Head kidney, gill, intestine, spleen, skin and liver
Mixed leukocytes of parallel blood leukocytes
Catfish whole fry library
Kidney, gill, intestine, spleen, skin and liver
Kidney, gill, intestine, spleen, skin and liver
Stomach, muscle, olfactory tissue and trunk kidney
Stomach, muscle, olfactory tissue and trunk kidney
Liver, pituitary, ovary and testis
Peripheral blood leukocytes stimulated with LPS
EST assembly statistics
Short and simple sequences removed
Sequences for assembly
Average number of sequences per contig
Total unique sequences
Gene identification and annotation
There was a positive correlation between the length of ORF and BLASTX match. Of the identified ORFs, 91% had a length of more than 100 bp. Within these ORFs, 53% had significant (1E-10) BLASTX matches (Figures 5b, c). However, only 6% of the ORFs with less than 100 bp had significant BLASTX matches (Figure 5d).
Summary of BLASTX search analysis of catfish ESTs
% of total unique proteins
54% of 27,996
56% of 24,461
57% of 23,118
33% of 38,342
35% of 35,236
50% of 22,194
Cumulative unique (E-10)†
Assessment of the sequenced catfish transcriptome
In order to assess the level to which the catfish transcriptome has been captured, the unique catfish sequences (111,578) were also searched against the NCBI Refseq and Ensembl databases. A number of significant hits were identified within zebrafish, medaka, Tetraodon, human, mouse, and chicken reference protein databases (Table 3). After removal of the redundant protein hits, 14,988 - 11,059 unique reference proteins were identified within zebrafish, medaka, Tetraodon, human, mouse, and chicken databases respectively (Table 3). The unique catfish sequences had hits to 54% to 57% of the unique proteins of zebrafish, medaka, and green-spotted pufferfish. To allow comparison of catfish unique protein coverage with that expected between species with complete genome sequences, all Tetraodon Ensembl proteins were searched against the medaka Ensembl protein database. A total of 22,150 Tetraodon proteins have significant hits to 15,054 (61% of total unique) medaka Ensembl proteins with a cutoff E-value of 1E-10. Similarly, zebrafish Refseq proteins were searched against the human Refseq protein database. In this case, 24,971 zebrafish proteins have significant hits to 13,789 (36% of total unique) human proteins with a cutoff E-value of 1E-10. Taken together, these numbers provide strong evidence that this project has captured a large majority of the catfish transcriptome.
Prediction of full-length cDNAs
The catfish EST sequences provide a platform for the identification and characterization of full-length cDNA clones without having to use expensive and labor-intensive primer walking sequencing. In the context of this work, full-length cDNA inserts were defined as a cDNA from a single clone with the start codon and poly (A) tail contained within the clone. A total of 10,037 channel catfish and 7,382 blue catfish putative full-length cDNAs were identified from the assembly with a cutoff E-value of 1E-5. A well characterized full-length cDNA set from catfish will be crucial in ongoing studies of teleost gene duplication and gene family structure, as well as aiding in annotation of the catfish whole genome sequence. Current efforts are focused, therefore, on characterization and re-sequencing of these full-length cDNAs.
Microsatellite and SNP marker identification
Summary of microsatellite marker identification from catfish ESTs
Total number of unique sequences
Number of unique sequences containing microsatellites
Number of unique sequences containing microsatellites with sufficient flanking sequences for PCR primer design
Summary of SNP identification from the catfish ESTs
Number of SNPs
SNP rate (kb)
SNP rate* (kb)
Quality assessment of the filtered putative SNPs identified from the catfish ESTs based on the number of sequences per contig and the sequence frequencies of the minor alleles
Number of sequences in the contig
Number of contigs with SNPs
Number of SNPs
SNP rate (per kb)
Estimation of proportions of inter-specific and intra-specific SNPs from the set of filtered SNPs identified from the interspecific all catfish EST assembly
From 1,000 random contigs
Estimated from the all catfish assembly
Estimated % of total filtered SNP
Intra-specific SNP, blue catfish2
Intra-specific SNP, channel catfish3
Intra-specific SNP, blue catfish and channel catfish4
SNP from only blue catfish ESTs6
SNP from only channel catfish ESTs6
This project represents one of the major milestones in catfish research, and brings the catfish EST resources to almost a half million sequences in GenBank [21–26]. This EST resource will prove useful for gene discovery, molecular marker development, and genetic linkage and comparative mapping, and it will help facilitate whole genome sequencing and annotation. Parallel EST sequencing in two closely related species, I. punctatus and I. furcatus, may also provide the material basis for the analysis of genome duplication and genome evolution, providing the basis for establishment of orthologies through phylogenetics analysis.
The most important outcome of EST sequencing is gene discovery. This project allowed identification of 70,717 unique sequences in channel catfish and 54,815 unique sequences in blue catfish. We also conducted EST assembly using ESTs from both channel catfish and blue catfish. Assembly of all the catfish ESTs resulted in 111,578 unique sequences. Comparison of channel catfish and blue catfish coding regions in this study indicated that the two species share, on average, 95% sequence identity. Therefore, combining genes identified from both species should provide a more complete picture as to what fraction of the catfish transcriptome was captured to date. Such an approach was taken also because of practical considerations. Hybrid catfish produced by inter-specific hybridization of channel catfish × blue catfish is one of the best production lines of catfish used in aquaculture, and many believe that industry-wide application of this hybrid may have a revolutionary impact on the catfish industry. One of the major catfish breeding programs is based on introgression of beneficial genes from blue catfish into channel catfish breeds. Genetic linkage mapping has been conducted in both the intra-specific resource families involving only channel catfish  and the inter-specific resource families made from backcrosses of the channel catfish × blue catfish hybrids [12, 13].
Given the close phylogenetic relationship of blue catfish and channel catfish, we expected that many of the contigs from the blue catfish and channel catfish EST assembly would merge together in an all catfish EST assembly. However, the all catfish EST assembly generated 45,306 contigs, a much greater number than the contigs generated in either the blue catfish (22,009) or channel catfish (28,941) EST assembly. There could be several reasons for this major increase in contig numbers with the all catfish EST assembly. First, some ESTs belonging to the contigs were only sequenced in blue catfish but not in channel catfish, and vice versa; second, singletons in either blue catfish or channel catfish were brought together to form new contigs; third, sequence variations or splicing differences between the two species may have led to the formation of a larger number of contigs under our assembly parameters; fourth, ESTs derived primarily from transcript untranslated regions of the two species may differ sufficiently to prevent placement in the same contig. Thorough analysis of the dataset has revealed that all four of these factors contributed to the high number of contigs in the all catfish assembly.
Analysis of the all catfish unique sequences showed that a large proportion of the catfish transcriptome has been captured. BLASTX searches identified 37% of total transcripts with significant hits, similar to levels reported in the salmon EST project . The 111,578 unique catfish sequences had hits to 54% to 57% of the unique proteins of zebrafish, medaka, and green-spotted pufferfish using a cutoff value of E-10. This percentage appeared at first glance to be lower than our expectations. We therefore carried out best-hit searches using identical parameters as those used with catfish but comparing protein coverage of species with complete genome (transcriptome) sequences to serve as reference points. We found that the Tetraodon protein set had significant hits to only 61% of medaka proteins. By comparison, our catfish data set had significant hits to 56% and 57% of medaka and Tetraodon proteins, respectively. Similarly, zebrafish Refseq proteins were searched against the human Refseq protein database. In this case, zebrafish proteins had significant hits to 36% of total unique human proteins, compared to 33% in catfish-human alignments (Table 3). These reference numbers indicate both the high coverage of the catfish transcriptome obtained in this project, and the limitations of simple homology searches given the rapid divergence of many genes following speciation and the complexity of genome-wide and local gene duplication events within teleost species. Given that the identity of only 37% of the unique catfish sequences could be characterized by homology searches, the utility of the dataset should increase significantly with whole-genome sequencing of Ictalurid catfish and additional sequencing in closely related species within the order of Siluriformes. Interestingly, over 40,000 unique catfish sequences containing an ORF did not have a significant hit by homology searches. Further work will be needed to characterize whether low homology rates in these sequences is due to short read length, the rapid evolution of the encoded gene, or 'catfish-specific' gene duplication and divergence.
Large-scale EST sequences provide an enormous resource for molecular marker development. This project allowed identification of over 20,000 microsatellites within ESTs, of which 13,375 were located within unique ESTs and had sufficient flanking sequences for microsatellite primer design for genotyping (Table 6). Therefore, these microsatellites will be a major resource for genetic linkage and comparative mapping . In addition, over 300,000 putative SNP sites were identified, of which over 48,000 were identified from contigs with at least four ESTs and the minor sequence was represented at least twice (Table 7). The 48,000 filtered SNPs should be highly useful for the development of a SNP panel for whole genome association studies .
The parameters of quality SNP assessment may not be applied to the very large contigs. The utilization of a minor allele frequency of six for all the contigs containing 30 sequences or more resulted in higher SNP frequency from these contigs, such as 13.4 SNPs per kilobase in the contigs with 100 to 500 sequences, and 15 SNPs per kilobase in the contigs with 500 sequences or more. Information regarding contigs over 500 sequences can be found in Additional data file 1. High SNP frequency within these large contigs may be caused by the accumulation of sequencing errors or alignment of transcripts from multi-copy loci, so SNPs from large contigs will be avoided in future SNP genotyping.
In this project, generation and assembly of channel catfish and blue catfish ESTs allowed the identification of 45,306 contigs and 66,272 singletons, and a large majority of the catfish transcriptome was captured. Whole genome sequencing of channel catfish and blue catfish is currently underway, and the comparison between genome and transcriptome sequences will enable better understanding of the gene structure and organization. The analysis of the inter-specific ESTs resulted in the identification of 20,757 gene-associated microsatellites and over 300,000 putative SNPs, of which over 48,000 were filtered SNPs with the presence of the minor allele at least twice. These SNPs have been utilized to design the first generation high-density SNP chips using Illumina iSelect HD SNP genotyping panels for genome association studies. The inter- and intra-specific SNPs identified from the all catfish EST dataset assembly will greatly benefit catfish introgression breeding selection and whole genome association studies.
Materials and methods
cDNA library construction, EST sequencing and processing
The cDNA libraries were constructed by consortium investigators using various tissues, organs, and cell lines, including stomach, muscle, olfactory tissue, trunk kidney, head kidney, gill, intestine, spleen, skin, liver, pituitary, ovary and testis (Table 1). Total RNA was isolated from experimental tissues, reverse transcribed using an oligo-dT primer, directionally cloned into either the pSPORT-1 (Invitrogen Corp., Carlsbad, CA, USA) or pDNR-Lib (Clontech Laboratories Inc., Mountain View, CA, USA) plasmid vectors, and electroporated into competent Escherichia coli. One library (CBPN) underwent subtraction for highly expressed clones, ten libraries were normalized, and one library (CBCZ) was processed without normalization. Clone selection, arraying, and sequencing of all 12 libraries were performed at the US Dept. of Energy - Joint Genome Institute. Both ends of the insert were sequenced using Big Dye Terminator (V3.1) chemistry (Applied Biosystems, Foster City, CA, USA), and low quality sequences were trimmed. Contaminant sequences (E. coli, mitochondrial, cloning vector, rRNA, tRNA) were filtered.
Three separate assemblies were performed: blue catfish ESTs, channel catfish ESTs, and all catfish ESTs. The new EST sequences and existing EST sequences from channel catfish and blue catfish were clustered and assembled using the Paracel Transcript Assembler, based on the CAP3 assembler . Repeat sequences and poly (A) tails were masked and annotated. Prior to assembly, all EST sequences were compared to 'seed' sequences, which were existing catfish full-length or partial cDNA sequences in GenBank. New sequences sharing 80% similarity to seed sequences were clustered and assembled at 95% identity with at least a 50-bp overlap to generate seed-cluster contigs. The seed cluster assembly reduced the number of sequences for final assembly in order to minimize computational requirements. The remaining EST sequences were then clustered based on local similarity scores of pairwise comparisons with a minimum 88% similarity of at least 100 bp. Clusters containing only one sequence were denoted as singletons. The EST clusters were assembled into contiguous sequences (contigs) by multiple-sequence alignment with 95% identity of at least a 50-bp overlap, and a consensus sequence was generated for each cluster. Multiple contigs could be generated from each cluster, since EST clusters may not share enough similarity over their entire length to be assembled as a single contig. Multiple contigs could also be generated when ESTs in the cluster represented splice variant forms or paralogs. Single ESTs remaining in a cluster after the formation of contigs were designated as cluster singletons. The unique sequences for each assembly included the seed-cluster contigs, cluster contigs, cluster singletons, and singletons. All the sequence assemblies are available upon request to the corresponding author.
ORF searching, gene identification and gene ontology annotation
All unique sequences obtained after the assembly were analyzed by ESTScan  to search for ORFs, which could be used to distinguish coding and non-coding sequences [39, 40]. The putative protein sequences were also generated at the same time by ESTscan, which could be used to analyze splice variation, determine paralogs, and assess gene families. All unique sequences were compared against the nr and Uniprot databases using BLASTX (cutoff E-value of 1E-10) to obtain the putative identity. The NCBI Refseq protein and Ensemble databases (zebrafish, medaka, Tetraodon, human, mouse, and chicken) were also used to annotate the unique catfish genes.
Full length cDNA identification
Putative full-length cDNAs were identified by comparison to full-length genes and start signals in Uniprot databases using TargetIdentifier [34, 41] with a cutoff E-value of 1E-5. Once the start codon (ATG) and poly (A) tail were identified, the cDNA sequence was considered a full-length cDNA.
Microsatellite and SNP marker identification
All the unique sequences were used to search for microsatellite makers using Msatfinder  with a repeat threshold of eight di-nucleotide repeats or five tri-, tetra- penta-, or hexa-nucleotide repeats. Clones containing 50-bp sequence on both sides of the microsatellite repeat were considered sufficient for primer design .
All three assemblies were used for SNP identification using autoSNP . The parameters for minimum minor allele frequency for SNP detection varied with the number of sequences in the contig. A sequence variation was declared a SNP when: a mismatch was identified in contigs with four or fewer sequences; the minor allele sequence existed at least twice within contigs containing 5 to 6 sequences; the minor allele sequence existed at least three times within contigs containing 7 to 8 sequences; the minor allele sequence existed at least four times within contigs containing 9 to 12 sequences, or the minor allele sequence existed at least five times within contigs with 13 or more sequences. One thousand contigs containing filtered SNPs were randomly selected to inspect the inter- and intra-specific SNP calls.
bacterial artificial chromosome
expressed sequence tag
open reading frame
single nucleotide polymorphism.
This project was supported by the Community Sequencing Program of the Joint Genome Institute of the Department of Energy, and partially by grants from USDA NRI Animal Genome Basic Genome Reagents and Tools Program (USDA/NRICGP award # 2006-35616-16685 and USDA/NRICGP award # 2009-35205-05101) and by USDA ARS (CRIS 6402-31000-008-00). The sequencing work was performed under the auspices of the US Department of Energy's Office of Science, Biological and Environmental Research Program, and by the University of California, Lawrence Berkeley National Laboratory under contract No. DE-AC02-05CH11231, Lawrence Livermore National Laboratory under contract No. DE-AC52-07NA27344, and Los Alamos National Laboratory under contract No. DE-AC02-06NA25396. Thanks are given to Alabama Supercomputer Center for providing the computer capacity for the bioinformatics analysis of the ESTs. We are grateful to The Catfish Genome Consortium that supported this Community Sequencing Project, and the consortium is composed of those in the authorship as well as the following in an alphabetical order: Jerald Ainsworth, Ihan Altinok, Cova R Arias, Joel A Bader, Anita L Bilodeau, Curtis Bird, Jan Bogerd, Brian G Bosworth, Richard C Bruch, Karen Burnett, John T Caprio, Jesse Chappell, Nagaraj Chatakondi, Gregory Chinchar, Walton W Dickhoff, Richard T DiGiulio, Cunming Duan, Mary V Duke, Rex A Dunham, Steve Gabel, Troy A Giambernardi, WL Gray, Eric D Green, Larry A Hanson, Michael Hardman, Chongbo He, Jun-ichi Hikima, Alison Hutson, Liliana Jaso-Friedmann, Zhenlin Ju, Attila Karsi, Kevin Kelley, David Kingsley, Conrad Kleinholz, Philip H Klesius, Arif Kocabas, Won Kyo Lee, Mara Lennard, Wayne Litaker, Gary W Litman, Craig J Lobb, George Luker, Brad G Magor, Thomas J McConnell, William Muir, Edward Noga, Kenneth Nusbaum, Donald D Ourth, Victor Panangala, Reynaldo Patino, Brian C Peterson, Ronald Phelps, Karen P Plant, John H Postlethwait, Herbert E Quintero, Daniel Rodriguez, Holly L Saunders, Brian Scheffler, Tom Schwedler, Richard A Shelby, William Simco, Craig A Shoemaker, Letong Tang, Jeff Terhune, Ronald L Thune, Terrence R Tiersch, Gregory W Warr, Thomas Welker, Monte Westerfield, Kristie L Willett, Kenneth Williams, Richard Winn, Changgong Wu, Dehai Xu, Roger Yant, Hung-Y Yeh, Yonathan Zohar, and Jun Zou
- Chatakondi NG, Yant DR, Dunham RA: Commercial production and performance evaluation of channel catfish, Ictalurus punctatus female × blue catfish, Ictalurus furcatus male F-1 hybrids. Aquaculture. 2005, 247: 8-Google Scholar
- Bao B, Peatman E, Peng X, Baoprasertkul P, Wang G, Liu Z: Characterization of 23 CC chemokine genes and analysis of their expression in channel catfish (Ictalurus punctatus). Dev Comp Immunol. 2006, 30: 783-796. 10.1016/j.dci.2005.10.007.PubMedView ArticleGoogle Scholar
- Peatman E, Liu Z: Evolution of CC chemokines in teleost fish: a case study in gene duplication and implications for immune diversity. Immunogenetics. 2007, 59: 613-623. 10.1007/s00251-007-0228-4.PubMedView ArticleGoogle Scholar
- Miller N, Wilson M, Bengten E, Stuge T, Warr G, Clem W: Functional and molecular characterization of teleost leukocytes. Immunol Rev. 1998, 166: 187-197. 10.1111/j.1600-065X.1998.tb01263.x.PubMedView ArticleGoogle Scholar
- Peatman E, Baoprasertkul P, Terhune J, Xu P, Nandi S, Kucuktas H, Li P, Wang S, Somridhivej B, Dunham R, Liu Z: Expression analysis of the acute phase response in channel catfish (Ictalurus punctatus) after infection with a Gram-negative bacterium. Dev Comp Immunol. 2007, 31: 1183-1196. 10.1016/j.dci.2007.03.003.PubMedView ArticleGoogle Scholar
- Peatman E, Terhune J, Baoprasertkul P, Xu P, Nandi S, Wang S, Somridhivej B, Kucuktas H, Li P, Dunham R, Liu Z: Microarray analysis of gene expression in the blue catfish liver reveals early activation of the MHC class I pathway after infection with Edwardsiella ictaluri. Mol Immunol. 2008, 45: 553-566. 10.1016/j.molimm.2007.05.012.PubMedView ArticleGoogle Scholar
- Bengten E, Clem LW, Miller NW, Warr GW, Wilson M: Channel catfish immunoglobulins: repertoire and expression. Dev Comp Immunol. 2006, 30: 77-92. 10.1016/j.dci.2005.06.016.PubMedView ArticleGoogle Scholar
- Evenhuis J, Bengten E, Snell C, Quiniou SM, Miller NW, Wilson M: Characterization of additional novel immune type receptors in channel catfish, Ictalurus punctatus. Immunogenetics. 2007, 59: 661-671. 10.1007/s00251-007-0230-x.PubMedView ArticleGoogle Scholar
- Sahoo M, Edholm ES, Stafford JL, Bengten E, Miller NW, Wilson M: B cell receptor accessory molecules in the channel catfish, Ictalurus punctatus. Dev Comp Immunol. 2008, 32: 1385-1397. 10.1016/j.dci.2008.05.008.PubMedPubMed CentralView ArticleGoogle Scholar
- Serapion J, Kucuktas H, Feng J, Liu Z: Bioinformatic mining of type I microsatellites from expressed sequence tags of channel catfish (Ictalurus punctatus). Mar Biotechnol. 2004, 6: 364-377. 10.1007/s10126-003-0039-z.PubMedView ArticleGoogle Scholar
- Somridhivej B, Wang S, Sha Z, Liu H, Quilang J, Xu P, Li P, Hu Z, Liu Z: Characterization, polymorphism assessment, and database construction for microsatellites from BAC end sequences of channel catfish (Ictalurus punctatus): A resource for integration of linkage and physical maps. Aquaculture. 2008, 275: 76-80. 10.1016/j.aquaculture.2008.01.013.View ArticleGoogle Scholar
- Kucuktas H, Wang S, Li P, He C, Xu P, Sha Z, Liu H, Jiang Y, Baoprasertkul P, Somridhivej B, Wang Y, Abernathy J, Guo X, Liu L, Muir W, Liu Z: Construction of genetic linkage maps and comparative genome analysis of catfish using gene-associated markers. Genetics. 2009, 181: 1649-1660. 10.1534/genetics.108.098855.PubMedPubMed CentralView ArticleGoogle Scholar
- Liu Z, Karsi A, Li P, Cao D, Dunham R: An AFLP-based genetic linkage map of channel catfish (Ictalurus punctatus) constructed by using an interspecific hybrid resource family. Genetics. 2003, 165: 687-694.PubMedPubMed CentralGoogle Scholar
- Waldbieser GC, Bosworth BG, Nonneman DJ, Wolters WR: A microsatellite-based genetic linkage map for channel catfish, Ictalurus punctatus. Genetics. 2001, 158: 727-734.PubMedPubMed CentralGoogle Scholar
- Quiniou SM, Katagiri T, Miller NW, Wilson M, Wolters WR, Waldbieser GC: Construction and characterization of a BAC library from a gynogenetic channel catfish Ictalurus punctatus. Genet Sel Evol. 2003, 35: 673-683. 10.1186/1297-9686-35-7-673.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang S, Xu P, Thorsen J, Zhu B, de Jong PJ, Waldbieser G, Kucuktas H, Liu Z: Characterization of a BAC library from channel catfish Ictalurus punctatus: indications of high levels of chromosomal reshuffling among teleost genomes. Mar Biotechnol. 2007, 9: 701-711. 10.1007/s10126-007-9021-5.PubMedView ArticleGoogle Scholar
- Quiniou SM, Waldbieser GC, Duke MV: A first generation BAC-based physical map of the channel catfish genome. BMC genomics. 2007, 8: 40-10.1186/1471-2164-8-40.PubMedPubMed CentralView ArticleGoogle Scholar
- Xu P, Wang S, Liu L, Thorsen J, Kucuktas H, Liu Z: A BAC-based physical map of the channel catfish genome. Genomics. 2007, 90: 380-388. 10.1016/j.ygeno.2007.05.008.PubMedView ArticleGoogle Scholar
- Xu P, Wang S, Liu L, Peatman E, Somridhivej B, Thimmapuram J, Gong G, Liu Z: Channel catfish BAC-end sequences for marker development and assessment of syntenic conservation with other fish species. Anim Genet. 2006, 37: 321-326. 10.1111/j.1365-2052.2006.01453.x.PubMedView ArticleGoogle Scholar
- Liu H, Jiang YL, Wang S, Ninwichian P, Somridhivej B, Xu P, Abernathy J, Kucuktas H, Liu Z: Comparative analysis of catfish BAC end sequences with the zebrafish genome. BMC Genomics. 2009, 10: 592-10.1186/1471-2164-10-592.PubMedPubMed CentralView ArticleGoogle Scholar
- Cao D, Kocabas A, Ju Z, Karsi A, Li P, Patterson A, Liu Z: Transcriptome of channel catfish (Ictalurus punctatus): initial analysis of genes and expression profiles of the head kidney. Anim Genet. 2001, 32: 169-188. 10.1046/j.1365-2052.2001.00753.x.PubMedView ArticleGoogle Scholar
- Ju Z, Karsi A, Kocabas A, Patterson A, Li P, Cao D, Dunham R, Liu Z: Transcriptome analysis of channel catfish (Ictalurus punctatus): genes and expression profile from the brain. Gene. 2000, 261: 373-382. 10.1016/S0378-1119(00)00491-1.PubMedView ArticleGoogle Scholar
- Karsi A, Cao D, Li P, Patterson A, Kocabas A, Feng J, Ju Z, Mickett KD, Liu Z: Transcriptome analysis of channel catfish (Ictalurus punctatus): initial analysis of gene expression and microsatellite-containing cDNAs in the skin. Gene. 2002, 285: 157-168. 10.1016/S0378-1119(02)00414-6.PubMedView ArticleGoogle Scholar
- Kocabas AM, Kucuktas H, Dunham RA, Liu Z: Molecular characterization and differential expression of the myostatin gene in channel catfish (Ictalurus punctatus). Biochim Biophys Acta. 2002, 1575: 99-107.PubMedView ArticleGoogle Scholar
- Li P, Peatman E, Wang S, Feng J, He C, Baoprasertkul P, Xu P, Kucuktas H, Nandi S, Somridhivej B, Serapion J, Simmons M, Turan C, Liu L, Muir W, Dunham R, Brady Y, Grizzle J, Liu Z: Towards the ictalurid catfish transcriptome: generation and analysis of 31,215 catfish ESTs. BMC genomics. 2007, 8: 177-10.1186/1471-2164-8-177.PubMedPubMed CentralView ArticleGoogle Scholar
- Nonneman D, Waldbieser GC: Isolation and enrichment of abundant microsatellites from a channel catfish (Ictalurus punctatus) brain cDNA library. Anim Biotechnol. 2005, 16: 103-116. 10.1080/10495390500262908.PubMedView ArticleGoogle Scholar
- Clark MS, Edwards YJ, Peterson D, Clifton SW, Thompson AJ, Sasaki M, Suzuki Y, Kikuchi K, Watabe S, Kawakami K, Sugano S, Elgar G, Johnson SL: Fugu ESTs: new resources for transcription analysis and genome annotation. Genome Res. 2003, 13: 2747-2753. 10.1101/gr.1691503.PubMedPubMed CentralView ArticleGoogle Scholar
- Lo J, Lee S, Xu M, Liu F, Ruan H, Eun A, He Y, Ma W, Wang W, Wen Z, Peng J: 15000 unique zebrafish EST clusters and their future use in microarray for profiling gene expression patterns during embryogenesis. Genome Res. 2003, 13: 455-466. 10.1101/gr.885403.PubMedPubMed CentralView ArticleGoogle Scholar
- Poustka AJ, Groth D, Hennig S, Thamm S, Cameron A, Beck A, Reinhardt R, Herwig R, Panopoulou G, Lehrach H: Generation, annotation, evolutionary analysis, and database integration of 20,000 unique sea urchin EST clusters. Genome Res. 2003, 13: 2736-2746. 10.1101/gr.1674103.PubMedPubMed CentralView ArticleGoogle Scholar
- Rise ML, von Schalburg KR, Brown GD, Mawer MA, Devlin RH, Kuipers N, Busby M, Beetz-Sargent M, Alberto R, Gibbs AR, Hunt P, Shukin R, Zeznik JA, Nelson C, Jones SR, Smailus DE, Jones SJ, Schein JE, Marra MA, Butterfield YS, Stott JM, Ng SH, Davidson WS, Koop BF: Development and application of a salmonid EST database and cDNA microarray: data mining and interspecific hybridization characteristics. Genome Res. 2004, 14: 478-490. 10.1101/gr.1687304.PubMedPubMed CentralView ArticleGoogle Scholar
- Udall JA, Swanson JM, Haller K, Rapp RA, Sparks ME, Hatfield J, Yu Y, Wu Y, Dowd C, Arpat AB, Sickler BA, Wilkins TA, Guo JY, Chen XY, Scheffler J, Taliercio E, Turley R, McFadden H, Payton P, Klueva N, Allen R, Zhang D, Haigler C, Wilkerson C, Suo J, Schulze SR, Pierce ML, Essenberg M, Kim H, Llewellyn DJ, et al: A global assembly of cotton ESTs. Genome Res. 2006, 16: 441-450. 10.1101/gr.4602906.PubMedPubMed CentralView ArticleGoogle Scholar
- Gorodkin J, Cirera S, Hedegaard J, Gilchrist MJ, Panitz F, Jørgensen C, Scheibye-Knudsen K, Arvin T, Lumholdt S, Sawera M, Green T, Nielsen BJ, Havgaard JH, Rosenkilde C, Wang J, Li H, Li R, Liu B, Hu S, Dong W, Li W, Yu J, Wang J, Staefeldt HH, Wernersson R, Madsen LB, Thomsen B, Hornshøj H, Bujie Z, Wang X, Wang X, et al: Porcine transcriptome analysis based on 97 non-normalized cDNA libraries and assembly of 1,021,891 expressed sequence tags. Genome Biol. 2007, 8: R45-10.1186/gb-2007-8-4-r45.PubMedPubMed CentralView ArticleGoogle Scholar
- Gut IG, Lathrop GM: Duplicating SNPs. Nat Genet. 2004, 36: 789-790. 10.1038/ng0804-789.PubMedView ArticleGoogle Scholar
- Koop BF, von Schalburg KR, Leong J, Walker N, Lieph R, Cooper GA, Robb A, Beetz-Sargent M, Holt RA, Moore R, Brahmbhatt S, Rosner J, Rexroad CE, McGowan CR, Davidson WS: A salmonid EST genomic study: genes, duplications, phylogeny and microarrays. BMC genomics. 2008, 9: 545-10.1186/1471-2164-9-545.PubMedPubMed CentralView ArticleGoogle Scholar
- Taylor JS, Braasch I, Frickey T, Meyer A, Peer Van de Y: Genome duplication, a trait shared by 22,000 species of ray-finned fish. Genome Res. 2003, 13: 382-390. 10.1101/gr.640303.PubMedPubMed CentralView ArticleGoogle Scholar
- Catfish EST Assembly. [http://www.animalgenome.org/aquaculture/catfish/projects/auburn/suppl2010.0113.html]
- Wang S, Sha Z, Sonstegard TS, Liu H, Xu P, Somridhivej B, Peatman E, Kucuktas H, Liu Z: Quality assessment parameters for EST-derived SNPs from catfish. BMC Genomics. 2008, 9: 450-10.1186/1471-2164-9-450.PubMedPubMed CentralView ArticleGoogle Scholar
- Huang X, Madan A: CAP3: A DNA sequence assembly program. Genome Res. 1999, 9: 868-877. 10.1101/gr.9.9.868.PubMedPubMed CentralView ArticleGoogle Scholar
- Iseli C, Jongeneel CV, Bucher P: ESTScan: a program for detecting, evaluating, and reconstructing potential coding regions in EST sequences. Proc Int Conf Intell Syst Mol Biol. 1999, 138-148.Google Scholar
- Lottaz C, Iseli C, Jongeneel CV, Bucher P: Modeling sequencing errors by combining Hidden Markov models. Bioinformatics. 2003, 19 (Suppl 2): ii103-112.PubMedView ArticleGoogle Scholar
- Min XJ, Butler G, Storms R, Tsang A: TargetIdentifier: a webserver for identifying full-length cDNAs from EST sequences. Nucleic Acids Res. 2005, 33: W669-672. 10.1093/nar/gki436.PubMedPubMed CentralView ArticleGoogle Scholar
- Thurston MI, Field D: Msatfinder: detection and characterisation of microsatellites. [http://www.genomics.ceh.ac.uk/msatfinder/]
- Rozen S, Skaletsky H: Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol. 2000, 132: 365-386.PubMedGoogle Scholar
- Barker G, Batley J, H OS, Edwards KJ, Edwards D: Redundancy based detection of sequence polymorphisms in expressed sequence tag data using autoSNP. Bioinformatics. 2003, 19: 421-422. 10.1093/bioinformatics/btf881.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.