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      Opportunities and Challenges for Genomic Data Analyses in Biobanks: A Call for Papers

      The GSA Journals are calling for submissions of papers on biobank-scale genomic data analyses. The closing date for submissions is May 31 2024.

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      Systematic profiling of subtelomeric silencing factors in budding yeast

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          Abstract

          Subtelomeric gene silencing is the negative transcriptional regulation of genes located close to telomeres. This phenomenon occurs in a variety of eukaryotes with salient physiological implications, such as cell adherence, virulence, immune-system escape, and ageing. The process has been widely studied in the budding yeast Saccharomyces cerevisiae, where genes involved in this process have been identified mostly on a gene-by-gene basis. Here, we introduce a quantitative approach to study gene silencing, that couples the classical URA3 reporter with GFP monitoring, amenable to high-throughput flow cytometry analysis. This dual silencing reporter was integrated into several subtelomeric loci in the genome, where it showed a gradual range of silencing effects. By crossing strains with this dual reporter at the COS12 and YFR057W subtelomeric query loci with gene-deletion mutants, we carried out a large-scale forward screen for potential silencing factors. The approach was replicable and allowed accurate detection of expression changes. Results of our comprehensive screen suggest that the main players influencing subtelomeric silencing were previously known, but additional potential factors underlying chromatin conformation are involved. We validate and report the novel silencing factor LGE1 , a protein with unknown molecular function required for histone H2B ubiquitination. Our strategy can be readily combined with other reporters and gene perturbation collections, making it a versatile tool to study gene silencing at a genome-wide scale.

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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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            Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists

            Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
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              Analyzing real-time PCR data by the comparative C(T) method.

              Two different methods of presenting quantitative gene expression exist: absolute and relative quantification. Absolute quantification calculates the copy number of the gene usually by relating the PCR signal to a standard curve. Relative gene expression presents the data of the gene of interest relative to some calibrator or internal control gene. A widely used method to present relative gene expression is the comparative C(T) method also referred to as the 2 (-DeltaDeltaC(T)) method. This protocol provides an overview of the comparative C(T) method for quantitative gene expression studies. Also presented here are various examples to present quantitative gene expression data using this method.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                G3 (Bethesda)
                Genetics
                g3journal
                G3: Genes|Genomes|Genetics
                Oxford University Press (US )
                2160-1836
                October 2023
                11 July 2023
                11 July 2023
                : 13
                : 10
                : jkad153
                Affiliations
                Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados , 36824 Irapuato, Guanajuato, Mexico
                Departamento de Ingeniería Genética, Unidad Irapuato, Centro de Investigación y de Estudios Avanzados , 36824 Irapuato, Guanajuato, Mexico
                Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados , 36824 Irapuato, Guanajuato, Mexico
                Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados , 36824 Irapuato, Guanajuato, Mexico
                Nouryon Chemicals Research Centre, Expert Capability Center Deventer, 7418AJ Deventer, Netherlands
                Department of Systems Biology, Harvard Medical School , Boston, MA 02115, USA
                Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados , 36824 Irapuato, Guanajuato, Mexico
                Instituto de Fisiología Celular, Universidad Nacional Autónoma de México , 04510 Ciudad de México, Mexico
                Departamento de Biología Celular, Facultad de Ciencias, Universidad Nacional Autónoma de México , 04510 Ciudad de México, Mexico
                Instituto de Fisiología Celular, Universidad Nacional Autónoma de México , 04510 Ciudad de México, Mexico
                Department of Systems Biology, Harvard Medical School , Boston, MA 02115, USA
                Departamento de Ingeniería Genética, Unidad Irapuato, Centro de Investigación y de Estudios Avanzados , 36824 Irapuato, Guanajuato, Mexico
                Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados , 36824 Irapuato, Guanajuato, Mexico
                Author notes
                Correspondence to: Unidad de Genómica Avanzada (Langebio), Centro de Investigación y de Estudios Avanzados, 36824 Irapuato, Guanajuato, Mexico. Email: alexander.deluna@ 123456cinvestav.mx

                Conflicts of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. This manuscript has been released as a pre-print at BioRxiv.

                Author information
                https://orcid.org/0000-0002-9732-3345
                https://orcid.org/0000-0003-4216-3556
                https://orcid.org/0000-0003-0146-8732
                https://orcid.org/0000-0002-9236-2804
                Article
                jkad153
                10.1093/g3journal/jkad153
                10542202
                37431950
                4d56ca1f-c48e-4b02-b9e3-fd990ea43862
                © The Author(s) 2023. Published by Oxford University Press on behalf of The Genetics Society of America.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 02 June 2023
                : 29 June 2023
                : 21 August 2023
                Page count
                Pages: 16
                Funding
                Funded by: Consejo Nacional de Ciencia y Tecnología de México, DOI 10.13039/501100003141;
                Award ID: CB-2015/164889, A1-S-31413/2017-2018
                Award ID: FORDECYT-PRONACES/103000/2020
                Funded by: Conacyt postdoctoral fellowship;
                Award ID: 167877
                Funded by: Welcome Trust Seed Award in Science;
                Categories
                Mutant Screen Report
                AcademicSubjects/SCI01180
                AcademicSubjects/SCI01140

                Genetics
                subtelomeric silencing,telomere position effect,genome-wide screening,transcriptional regulation,epigenetic silencing,saccharomyces cerevisiae

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