[1]

Knap PW, Kause A. 2018. Phenotyping for genetic improvement of feed efficiency in fish: lessons from pig breeding. Frontiers in Genetics 9:184

doi: 10.3389/fgene.2018.00184
[2]

Esmaeili N, Carter CG, Wilson R, Walker SP, Miller MR, et al. 2021. Proteomic investigation of liver and white muscle in efficient and inefficient Chinook salmon (Oncorhynchus tshawytscha): fatty acid metabolism and protein turnover drive feed efficiency. Aquaculture 542:736855

doi: 10.1016/j.aquaculture.2021.736855
[3]

Verdal H, Komen H, Quillet E, Chatain B, Allal F, et al. 2018. Improving feed efficiency in fish using selective breeding: a review. Reviews in Aquaculture 10:833−851

doi: 10.1111/raq.12202
[4]

Iversen A, Asche F, Hermansen Ø, Nystøyl R. 2020. Production cost and competitiveness in major salmon farming countries 2003–2018. Aquaculture 522:735089

doi: 10.1016/j.aquaculture.2020.735089
[5]

Alexandre PA, Kogelman LJA, Santana MHA, Passarelli D, Pulz LH, et al. 2015. Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle. BMC Genomics 16:1073

doi: 10.1186/s12864-015-2292-8
[6]

Gerber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, et al. 2013. Tackling climate change through livestock: a global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome, Italy. https://www.cabidigitallibrary.org/doi/full/10.5555/20133417883
[7]

Schumann M, Brinker A. 2020. Understanding and managing suspended solids in intensive salmonid aquaculture: a review. Reviews in Aquaculture 12:2109−2139

doi: 10.1111/raq.12425
[8]

Prakash A, Saxena VK, Singh MK. 2020. Genetic analysis of residual feed intake, feed conversion ratio and related growth parameters in broiler chicken: a review. World's Poultry Science Journal 76:304−317

doi: 10.1080/00439339.2020.1735978
[9]

Jorge-Smeding E, Bonnet M, Renand G, Taussat S, Graulet B, et al. 2021. Common and diet-specific metabolic pathways underlying residual feed intake in fattening Charolais yearling bulls. Scientific Reports 11:24346

doi: 10.1038/s41598-021-03678-x
[10]

Ramos PVB, de Oliveira Menezes GR, da Silva DA, Lourenco D, Santiago GG, et al. 2024. Genomic analysis of feed efficiency traits in beef cattle using random regression models. Journal of Animal Breeding and Genetics 141:291−303

doi: 10.1111/jbg.12840
[11]

Karisa B, Moore S, Plastow G. 2014. Analysis of biological networks and biological pathways associated with residual feed intake in beef cattle. Animal Science Journal 85:374−387

doi: 10.1111/asj.12159
[12]

de Verdal H, Mekkawy W, Lind CE, Vandeputte M, Chatain B, et al. 2017. Measuring individual feed efficiency and its correlations with performance traits in Nile tilapia, Oreochromis niloticus. Aquaculture 468:489−495

doi: 10.1016/j.aquaculture.2016.11.015
[13]

Grima L, Vandeputte M, Ruelle F, Vergnet A, Mambrini M, et al. 2010. In search for indirect criteria to improve residual feed intake in sea bass (Dicentrarchus labrax) Part I: Phenotypic relationship between residual feed intake and body weight variations during feed deprivation and re-feeding periods. Aquaculture 300:50−58

doi: 10.1016/j.aquaculture.2010.01.003
[14]

Jacobs A, Elmer KR. 2021. Alternative splicing and gene expression play contrasting roles in the parallel phenotypic evolution of a salmonid fish. Molecular Ecology 30:4955−4969

doi: 10.1111/mec.15817
[15]

Berget SM, Moore C, Sharp PA. 1977. Spliced segments at the 5′ terminus of adenovirus 2 late mRNA. Proceedings of the National Academy of Sciences of the United States of America 74:3171−3175

doi: 10.1073/pnas.74.8.3171
[16]

Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, et al. 2008. Alternative isoform regulation in human tissue transcriptomes. Nature 456:470−476

doi: 10.1038/nature07509
[17]

Stamm S, Ben-Ari S, Rafalska I, Tang Y, Zhang Z, et al. 2005. Function of alternative splicing. Gene 344:1−20

doi: 10.1016/j.gene.2004.10.022
[18]

Wright CJ, Smith CWJ, Jiggins CD. 2022. Alternative splicing as a source of phenotypic diversity. Nature Reviews Genetics 23:697−710

doi: 10.1038/s41576-022-00514-4
[19]

Thind AS, Monga I, Thakur PK, Kumari P, Dindhoria K, et al. 2021. Demystifying emerging bulk RNA-Seq applications: the application and utility of bioinformatic methodology. Briefings in Bioinformatics 22:bbab259

doi: 10.1093/bib/bbaa398
[20]

Li HJ, He CB, Yang Q, Shan ZG, Tan KF, et al. 2010. Characterization of single nucleotide polymorphisms from expressed sequence tags of Chinese mitten crab Eriocheir sinensis. Aquatic Biology 11:193−199

doi: 10.3354/ab00308
[21]

Feng Y, Zhang D, Lv J, Gao B, Li J, et al. 2019. Identification of SNP markers correlated with the tolerance of low-salinity challenge in swimming crab (Portunus trituberculatus). Acta Oceanologica Sinica 38:41−47

doi: 10.1007/s13131-019-1428-0
[22]

Jarosch A, Stolle E, Crewe RM, Moritz RFA. 2011. Alternative splicing of a single transcription factor drives selfish reproductive behavior in honeybee workers (Apis mellifera). Proceedings of the National Academy of Sciences of the United States of America 108:15282−15287

doi: 10.1073/pnas.1109343108
[23]

Chen X, Mei J, Wu J, Jing J, Ma W, et al. 2015. A comprehensive transcriptome provides candidate genes for sex determination/differentiation and SSR/SNP markers in yellow catfish. Marine Biotechnology 17:190−198

doi: 10.1007/s10126-014-9607-7
[24]

Xu P, Zhang X, Wang X, Li J, Liu G, et al. 2014. Genome sequence and genetic diversity of the common carp, Cyprinus carpio. Nature Genetics 46:1212−1219

doi: 10.1038/ng.3098
[25]

Sun X, Zhang J, Shi Y, Wang J, Gong Y. 1995. Studies on the genetic characteristic of Jian carp (Cyprinus carpio var. Jian) in China. Aquaculture 137:276−277

doi: 10.1016/0044-8486(96)83571-X
[26]

Wang W, Xu Y, Zhang Z, Jiang K, Li J, et al. 2025. Transcriptomic profiling reveals potential regulatory genes and molecular mechanisms of residual feed intake in Jian carp (Cyprinus carpio var. Jian). Aquaculture 595:741616

doi: 10.1016/j.aquaculture.2024.741616
[27]

Salmela L, Rivals E. 2014. LoRDEC: accurate and efficient long read error correction. Bioinformatics 30:3506−3514

doi: 10.1093/bioinformatics/btu538
[28]

Wu TD, Watanabe CK. 2005. GMAP: a genomic mapping and alignment program for mRNA and EST sequences. Bioinformatics 21:1859−1875

doi: 10.1093/bioinformatics/bti310
[29]

Abdel-Ghany SE, Hamilton M, Jacobi JL, Ngam P, Devitt N, et al. 2016. A survey of the sorghum transcriptome using single-molecule long reads. Nature Communications 7:11706

doi: 10.1038/ncomms11706
[30]

Mount DW. 2007. Using the basic local alignment search tool (BLAST). CSH Protocols 2007:pdb.top17

doi: 10.1101/pdb.top17
[31]

Buchfink B, Xie C, Huson DH. 2015. Fast and sensitive protein alignment using DIAMOND. Nature Methods 12:59−60

doi: 10.1038/nmeth.3176
[32]

Finn RD, Clements J, Eddy SR. 2011. HMMER web server: interactive sequence similarity searching. Nucleic Acids Research 39:W29−W37

doi: 10.1093/nar/gkr367
[33]

Xie C, Mao X, Huang J, Ding Y, Wu J, et al. 2011. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Research 39:W316−W322

doi: 10.1093/nar/gkr483
[34]

Chen C, Chen H, Zhang Y, Thomas HR, Frank MH, et al. 2020. TBtools: an integrative toolkit developed for interactive analyses of big biological data. Molecular Plant 13:1194−1202

doi: 10.1016/j.molp.2020.06.009
[35]

Xu Y, Yu F, Feng W, Wei J, Su S, et al. 2024. Genetic variation mining of the Chinese mitten crab (Eriocheir sinensis) based on transcriptome data from public databases. Briefings in Functional Genomics 23:816−827

doi: 10.1093/bfgp/elae030
[36]

Wang Y, Song L, Ning M, Hu J, Cai H, et al. 2023. Identification of alternative splicing events related to fatty liver formation in duck using full-length transcripts. BMC Genomics 24:92

doi: 10.1186/s12864-023-09160-4
[37]

Bhadra M, Howell P, Dutta S, Heintz C, Mair WB. 2020. Alternative splicing in aging and longevity. Human Genetics 139:357−369

doi: 10.1007/s00439-019-02094-6
[38]

Zhang Y, Qian J, Gu C, Yang Y. 2021. Alternative splicing and cancer: a systematic review. Signal Transduction and Targeted Therapy 6:78

doi: 10.1038/s41392-021-00486-7
[39]

Rogers TF, Palmer DH, Wright AE. 2021. Sex-specific selection drives the evolution of alternative splicing in birds. Molecular Biology and Evolution 38:519−530

doi: 10.1093/molbev/msaa242
[40]

Sun HZ, Zhu Z, Zhou M, Wang J, Dugan MER, et al. 2020. Gene co-expression and alternative splicing analysis of key metabolic tissues to unravel the regulatory signatures of fatty acid composition in cattle. RNA Biology 18:854−862

doi: 10.1080/15476286.2020.1824060
[41]

Neumann A, Meinke S, Goldammer G, Strauch M, Schubert D, et al. 2020. Alternative splicing coupled mRNA decay shapes the temperature‐dependent transcriptome. EMBO Reports 21:e51369

doi: 10.15252/embr.202051369
[42]

Shen S, Park JW, Lu ZX, Lin L, Henry MD, et al. 2014. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proceedings of the National Academy of Sciences of the United States of America 111:e5593−e5601

doi: 10.1073/pnas.1419161111
[43]

Taiwo G, Idowu MD, Wilson M, Pech-Cervantes A, Estrada-Reyes ZM, et al. 2022. Residual feed intake in beef cattle is associated with differences in hepatic mRNA expression of fatty acid, amino acid, and mitochondrial energy metabolism genes. Frontiers in Animal Science 3:828591

doi: 10.3389/fanim.2022.828591
[44]

Matthews DE. 2020. Review of lysine metabolism with a focus on humans. The Journal of Nutrition 150:2548S−2555S

doi: 10.1093/jn/nxaa224
[45]

Goldansaz SA, Markus S, Berjanskii M, Rout M, Guo AC, et al. 2020. Candidate serum metabolite biomarkers of residual feed intake and carcass merit in sheep. Journal of Animal Science 98:skaa298

doi: 10.1093/jas/skaa298
[46]

Gatrell SK, Silverstein JT, Barrows FT, Grimmett JG, Cleveland BM, et al. 2017. Effect of dietary lysine and genetics on growth and indices of lysine catabolism in rainbow trout (Oncorhynchus mykiss). Aquaculture Nutrition 23:917−925

doi: 10.1111/anu.12459
[47]

Veen WAG, Vahl HA. 1984. The influence of the degradability of concentrate protein in the rumen and of the lysine content of the concentrate on growth and feed efficiency in early-weaned calves. Netherlands Journal of Agricultural Science 32:107−118

doi: 10.18174/njas.v32i2.16910
[48]

Ma F, Zou Y, Ma L, Ma R, Chen X. 2022. Evolution, characterization, and immune response function of long-chain acyl-CoA synthetase genes in rainbow trout (Oncorhynchus mykiss) under hypoxic stress. Comparative Biochemistry and Physiology Part B: Biochemistry and Molecular Biology 260:110737

doi: 10.1016/j.cbpb.2022.110737
[49]

Miyares RL, Stein C, Renisch B, Anderson JL, Hammerschmidt M, et al. 2013. Long-chain acyl-CoA synthetase 4A regulates Smad activity and dorsoventral patterning in the zebrafish embryo. Developmental Cell 27:635−647

doi: 10.1016/j.devcel.2013.11.011
[50]

Dessein AF, Hebbar E, Vamecq J, Lebredonchel E, Devos A, et al. 2022. A novel HADHA variant associated with an atypical moderate and late-onset LCHAD deficiency. Molecular Genetics and Metabolism Reports 31:100860

doi: 10.1016/j.ymgmr.2022.100860
[51]

Maeyashiki C, Oshima S, Otsubo K, Kobayashi M, Nibe Y, et al. 2017. HADHA, the alpha subunit of the mitochondrial trifunctional protein, is involved in long-chain fatty acid-induced autophagy in intestinal epithelial cells. Biochemical and Biophysical Research Communications 484:636−641

doi: 10.1016/j.bbrc.2017.01.159
[52]

Cui J, Wang HD, Liu SK, Zhu LF, Qiu XM, et al. 2014. SNP Discovery from transcriptome of the swimbladder of Takifugu rubripes. PLoS One 9:e92502

doi: 10.1371/journal.pone.0092502
[53]

Sterne-Weiler T, Sanford JR. 2014. Exon identity crisis: disease-causing mutations that disrupt the splicing code. Genome Biology 15:201

doi: 10.1186/gb4150
[54]

Hull J, Campino S, Rowlands K, Chan MS, Copley RR, et al. 2007. Identification of common genetic variation that modulates alternative splicing. PLoS Genetics 3:e99

doi: 10.1371/journal.pgen.0030099
[55]

Ma J, Yang J, Zhou L, Ren J, Liu X, et al. 2014. A splice mutation in the PHKG1 gene causes high glycogen content and low meat quality in pig skeletal muscle. PLoS Genetics 10:e1004710

doi: 10.1371/journal.pgen.1004710