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      Contributions of RNA-seq to improve in vitro embryo production (IVP)


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          In Vitro Embryo Production (IVP) is widely used to improve the reproductive efficiency of livestock animals, however increasing the embryo development rates and pregnancy outcomes is still a challenge for some species. Thus, the lack of biological knowledge hinders developing specie-specific IVP protocols. Therefore, the contributions of RNA-seq to generate relevant biological knowledge and improve the efficiency of IVP in livestock animals are reviewed herein.

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          Understanding the Warburg effect: the metabolic requirements of cell proliferation.

          In contrast to normal differentiated cells, which rely primarily on mitochondrial oxidative phosphorylation to generate the energy needed for cellular processes, most cancer cells instead rely on aerobic glycolysis, a phenomenon termed "the Warburg effect." Aerobic glycolysis is an inefficient way to generate adenosine 5'-triphosphate (ATP), however, and the advantage it confers to cancer cells has been unclear. Here we propose that the metabolism of cancer cells, and indeed all proliferating cells, is adapted to facilitate the uptake and incorporation of nutrients into the biomass (e.g., nucleotides, amino acids, and lipids) needed to produce a new cell. Supporting this idea are recent studies showing that (i) several signaling pathways implicated in cell proliferation also regulate metabolic pathways that incorporate nutrients into biomass; and that (ii) certain cancer-associated mutations enable cancer cells to acquire and metabolize nutrients in a manner conducive to proliferation rather than efficient ATP production. A better understanding of the mechanistic links between cellular metabolism and growth control may ultimately lead to better treatments for human cancer.
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            Next-generation transcriptome assembly.

            Transcriptomics studies often rely on partial reference transcriptomes that fail to capture the full catalogue of transcripts and their variations. Recent advances in sequencing technologies and assembly algorithms have facilitated the reconstruction of the entire transcriptome by deep RNA sequencing (RNA-seq), even without a reference genome. However, transcriptome assembly from billions of RNA-seq reads, which are often very short, poses a significant informatics challenge. This Review summarizes the recent developments in transcriptome assembly approaches - reference-based, de novo and combined strategies - along with some perspectives on transcriptome assembly in the near future.
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              RNA-Seq: a revolutionary tool for transcriptomics.

              RNA-Seq is a recently developed approach to transcriptome profiling that uses deep-sequencing technologies. Studies using this method have already altered our view of the extent and complexity of eukaryotic transcriptomes. RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods. This article describes the RNA-Seq approach, the challenges associated with its application, and the advances made so far in characterizing several eukaryote transcriptomes.

                Author and article information

                Anim Reprod
                Anim Reprod
                Animal Reproduction
                Colégio Brasileiro de Reprodução Animal - CBRA
                24 October 2019
                Apr-Jun 2019
                : 16
                : 2
                : 249-259
                [1 ] originalFederal Rural University of Amazon, Capitão-Poço, Pará, Brazil.
                [2 ] originalLaboratory of Genomics and Bioinformatics, Federal University of Pará, Belém, PA, Brazil.
                [3 ] originalLaboratory of In Vitro Fertilization, Institute of Biological Science, Federal University of Pará, Belém, PA, Brazil.
                Author notes
                [§ ]Corresponding author: ppbsantana@ 123456gmail.com
                Copyright © The Author(s). Published by CBRA.

                This is an Open Access article under the Creative Commons Attribution License (CC BY 4.0 license)

                Page count
                Figures: 1, Tables: 2, Equations: 0, References: 92, Pages: 11


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