Recent developments and future directions in meta-analysis of differential gene expression in livestock RNA-Seq

被引:12
作者
Keel, Brittney N. N. [1 ]
Lindholm-Perry, Amanda K. K. [1 ]
机构
[1] USDA ARS, Roman L Hruska US Meat Anim Res Ctr, Clay Ctr, NE 68933 USA
关键词
RNA-seq; meta-analysis; livestock; p-value combination; gene expression; FEED-EFFICIENCY; TRANSCRIPTOME; MUSCLE;
D O I
10.3389/fgene.2022.983043
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Decreases in the costs of high-throughput sequencing technologies have led to continually increasing numbers of livestock RNA-Seq studies in the last decade. Although the number of studies has increased dramatically, most livestock RNA-Seq experiments are limited by cost to a small number of biological replicates. Meta-analysis procedures can be used to integrate and jointly analyze data from multiple independent studies. Meta-analyses increase the sample size, which in turn increase both statistical power and robustness of the results. In this work, we discuss cutting edge approaches to combining results from multiple independent RNA-Seq studies to improve livestock transcriptomics research. We review currently published RNA-Seq meta-analyses in livestock, describe many of the key issues specific to RNA-Seq meta-analysis in livestock species, and discuss future perspectives.
引用
收藏
页数:10
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共 73 条
  • [51] Promoting an open research culture
    Nosek, B. A.
    Alter, G.
    Banks, G. C.
    Borsboom, D.
    Bowman, S. D.
    Breckler, S. J.
    Buck, S.
    Chambers, C. D.
    Chin, G.
    Christensen, G.
    Contestabile, M.
    Dafoe, A.
    Eich, E.
    Freese, J.
    Glennerster, R.
    Goroff, D.
    Green, D. P.
    Hesse, B.
    Humphreys, M.
    Ishiyama, J.
    Karlan, D.
    Kraut, A.
    Lupia, A.
    Mabry, P.
    Madon, T. A.
    Malhotra, N.
    Mayo-Wilson, E.
    McNutt, M.
    Miguel, E.
    Paluck, E. Levy
    Simonsohn, U.
    Soderberg, C.
    Spellman, B. A.
    Turitto, J.
    VandenBos, G.
    Vazire, S.
    Wagenmakers, E. J.
    Wilson, R.
    Yarkoni, T.
    [J]. SCIENCE, 2015, 348 (6242) : 1422 - 1425
  • [52] KARL PEARSON'S META-ANALYSIS REVISITED
    Owen, Art B.
    [J]. ANNALS OF STATISTICS, 2009, 37 (6B) : 3867 - 3892
  • [53] Effect of overconditioning on the hepatic global gene expression pattern of dairy cows at the end of pregnancy
    Pascottini, O. Bogado
    De Koster, J.
    Van Nieuwerburgh, F.
    Van Poucke, M.
    Peelman, L.
    Fievez, V.
    Leroy, J. L. M. R.
    Opsomer, G.
    [J]. JOURNAL OF DAIRY SCIENCE, 2021, 104 (07) : 8152 - 8163
  • [54] Exposure to non-esterified fatty acids in vitro results in changes in the ovarian and follicular environment in cattle
    Pedroza, Gabriela H.
    Lanzon, Lawrence F.
    Rabaglino, Maria B.
    Walker, Wendy L.
    Vahmani, Payam
    Denicol, Anna C.
    [J]. ANIMAL REPRODUCTION SCIENCE, 2022, 238
  • [55] Peripheral Biomarkers in Schizophrenia: A Meta-Analysis of Microarray Gene Expression Datasets
    Piras, Ignazio S.
    Manchia, Mirko
    Huentelman, Matthew J.
    Pinna, Federica
    Zai, Clement C.
    Kennedy, James L.
    Carpiniello, Bernardo
    [J]. INTERNATIONAL JOURNAL OF NEUROPSYCHOPHARMACOLOGY, 2019, 22 (03) : 186 - 193
  • [56] Improving the completeness of public metadata accompanying omics studies
    Rajesh, Anushka
    Chang, Yutong
    Abedalthagafi, Malak S.
    Wong-Beringer, Annie
    Love, Michael I.
    Mangul, Serghei
    [J]. GENOME BIOLOGY, 2021, 22 (01)
  • [57] Differential meta-analysis of RNA-seq data from multiple studies
    Rau, Andrea
    Marot, Guillemette
    Jaffrezic, Florence
    [J]. BMC BIOINFORMATICS, 2014, 15
  • [58] Data-based filtering for replicated high-throughput transcriptome sequencing experiments
    Rau, Andrea
    Gallopin, Melina
    Celeux, Gilles
    Jaffrezic, Florence
    [J]. BIOINFORMATICS, 2013, 29 (17) : 2146 - 2152
  • [59] Genetic variance and covariance and breed differences for feed intake and average daily gain to improve feed efficiency in growing cattle
    Retallick, K. J.
    Bormann, J. M.
    Weaber, R. L.
    MacNeil, M. D.
    Bradford, H. L.
    Freetly, H. C.
    Hales, K. E.
    Moser, D. W.
    Snelling, W. M.
    Thallman, R. M.
    Kuehn, L. A.
    [J]. JOURNAL OF ANIMAL SCIENCE, 2017, 95 (04) : 1444 - 1450
  • [60] edgeR: a Bioconductor package for differential expression analysis of digital gene expression data
    Robinson, Mark D.
    McCarthy, Davis J.
    Smyth, Gordon K.
    [J]. BIOINFORMATICS, 2010, 26 (01) : 139 - 140