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      Extracellular vesicles derived from endometrial human mesenchymal stem cells enhance embryo yield and quality in an aged murine model†

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Gene Ontology: tool for the unification of biology

            Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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              Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

              DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.
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                Author and article information

                Contributors
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                Journal
                Biology of Reproduction
                Oxford University Press (OUP)
                0006-3363
                1529-7268
                May 2019
                May 01 2019
                December 28 2018
                May 2019
                May 01 2019
                December 28 2018
                : 100
                : 5
                : 1180-1192
                Affiliations
                [1 ]Stem Cell Therapy Unit, Jesús Usón Minimally Invasive Surgery Centre (JUMISC), Cáceres, Spain
                [2 ]Assisted Reproduction Unit, Jesús Usón Minimally Invasive Surgery Centre, Cáceres, Spain
                [3 ]CIBER de Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
                [4 ]Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain
                [5 ]Research Group of Intracellular Signaling and Technology of Reproduction (SINTREP), Institute of Biotechnology in Agriculture and Livestock (INBIO G+C), University of Extremadura, Cáceres, Spain
                [6 ]INIA, Department of Animal Reproduction, Madrid, Spain
                Article
                10.1093/biolre/ioy263
                30596891
                fc2cf648-cf05-4332-85cd-27e997e66cdd
                © 2018

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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