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      A Comprehensive Annotation of the Channel Catfish ( Ictalurus punctatus) T Cell Receptor Alpha/Delta, Beta, and Gamma Loci

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          Abstract

          The complete germline repertoires of the channel catfish, Ictalurus punctatus, T cell receptor (TR) loci, TRAD, TRB, and TRG were obtained by analyzing genomic data from PacBio sequencing. The catfish TRB locus spans 214 kb, and contains 112 TRBV genes, a single TRBD gene, 31 TRBJ genes and two TRBC genes. In contrast, the TRAD locus is very large, at 1,285 kb. It consists of four TRDD genes, one TRDJ gene followed by the exons for TRDC, 125 TRAJ genes and the exons encoding the TRAC. Downstream of the TRAC, are 140 TRADV genes, and all of them are in the opposite transcriptional orientation. The catfish TRGC locus spans 151 kb and consists of four diverse V-J-C cassettes. Altogether, this locus contains 15 TRGV genes and 10 TRGJ genes. To place our data into context, we also analyzed the zebrafish TR germline gene repertoires. Overall, our findings demonstrated that catfish possesses a more restricted repertoire compared to the zebrafish. For example, the 140 TRADV genes in catfish form eight subgroups based on members sharing 75% nucleotide identity. However, the 149 TRAD genes in zebrafish form 53 subgroups. This difference in subgroup numbers between catfish and zebrafish is best explained by expansions of catfish TRADV subgroups, which likely occurred through multiple, relatively recent gene duplications. Similarly, 112 catfish TRBV genes form 30 subgroups, while the 51 zebrafish TRBV genes are placed into 36 subgroups. Notably, several catfish and zebrafish TRB subgroups share ancestor nodes. In addition, the complete catfish TR gene annotation was used to compile a TR gene segment database, which was applied in clonotype analysis of an available gynogenetic channel catfish transcriptome. Combined, the TR annotation and clonotype analysis suggested that the expressed TRA, TRB, and TRD repertoires were generated by different mechanisms. The diversity of the TRB repertoire depends on the number of TRBV subgroups and TRBJ genes, while TRA diversity relies on the many different TRAJ genes, which appear to be only minimally trimmed. In contrast, TRD diversity relies on nucleotide additions and the utilization of up to four TRDD segments.

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          Most cited references67

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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            MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

            The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.
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              MUSCLE: multiple sequence alignment with high accuracy and high throughput.

              We describe MUSCLE, a new computer program for creating multiple alignments of protein sequences. Elements of the algorithm include fast distance estimation using kmer counting, progressive alignment using a new profile function we call the log-expectation score, and refinement using tree-dependent restricted partitioning. The speed and accuracy of MUSCLE are compared with T-Coffee, MAFFT and CLUSTALW on four test sets of reference alignments: BAliBASE, SABmark, SMART and a new benchmark, PREFAB. MUSCLE achieves the highest, or joint highest, rank in accuracy on each of these sets. Without refinement, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data are freely available at http://www.drive5. com/muscle.
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                25 November 2021
                2021
                : 12
                : 786402
                Affiliations
                [1] 1 Department of Microbiology and Immunology, University of Mississippi Medical Center , Jackson, MS, United States
                [2] 2 Warmwater Aquaculture Research Unit, United States Department of Agriculture - Agricultural Research Service (USDA-ARS) , Stoneville, MS, United States
                [3] 3 Department of Pharmacology and Toxicology, University of Mississippi Medical Center , Jackson, MS, United States
                [4] 4 Department of Data Science, John D. Bower School of Population Health, University of Mississippi Medical Center , Jackson, MS, United States
                Author notes

                Edited by: Pierre Boudinot, Institut National de la Recherche Agronomique (INRA), France

                Reviewed by: Harry Wilson Dickerson, University of Georgia, United States; Eva-Stina Isabella Edholm, Arctic University of Norway, Norway

                *Correspondence: Eva Bengtén, ebengten@ 123456umc.edu ; Melanie Wilson, mwilson@ 123456umc.edu

                †These authors have contributed equally to this work

                This article was submitted to Comparative Immunology, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2021.786402
                8656973
                34899754
                7c8fe83d-7417-4be2-bb40-454b2b335f5f
                Copyright © 2021 Crider, Quiniou, Felch, Showmaker, Bengtén and Wilson

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 September 2021
                : 05 November 2021
                Page count
                Figures: 9, Tables: 1, Equations: 0, References: 69, Pages: 16, Words: 7738
                Funding
                Funded by: National Science Foundation , doi 10.13039/100000001;
                Award ID: NSF-IOS 1656419
                Funded by: National Institute of Food and Agriculture , doi 10.13039/100005825;
                Award ID: 2020-67034-31895
                Categories
                Immunology
                Original Research

                Immunology
                t cell receptor repertoire; trb locus,trad locus,trg locus,teleost fish,catfish,imgt
                Immunology
                t cell receptor repertoire; trb locus, trad locus, trg locus, teleost fish, catfish, imgt

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