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      Inter-generational nuclear crosstalk links the control of gene expression to programmed genome rearrangement during the Paramecium sexual cycle

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          Abstract

          Multinucleate cells are found in many eukaryotes, but how multiple nuclei coordinate their functions is still poorly understood. In the cytoplasm of the ciliate Paramecium tetraurelia, two micronuclei (MIC) serving sexual reproduction coexist with a somatic macronucleus (MAC) dedicated to gene expression. During sexual processes, the MAC is progressively destroyed while still ensuring transcription, and new MACs develop from copies of the zygotic MIC. Several gene clusters are successively induced and switched off before vegetative growth resumes. Concomitantly, programmed genome rearrangement (PGR) removes transposons and their relics from the new MACs. Development of the new MACs is controlled by the old MAC, since the latter expresses genes involved in PGR, including the PGM gene encoding the essential PiggyMac endonuclease that cleaves the ends of eliminated sequences. Using RNA deep sequencing and transcriptome analysis, we show that impairing PGR upregulates key known PGR genes, together with ∼600 other genes possibly also involved in PGR. Among these genes, 42% are no longer induced when no new MACs are formed, including 180 genes that are co-expressed with PGM under all tested conditions. We propose that bi-directional crosstalk between the two coexisting generations of MACs links gene expression to the progression of MAC development.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            FIMO: scanning for occurrences of a given motif

            Summary: A motif is a short DNA or protein sequence that contributes to the biological function of the sequence in which it resides. Over the past several decades, many computational methods have been described for identifying, characterizing and searching with sequence motifs. Critical to nearly any motif-based sequence analysis pipeline is the ability to scan a sequence database for occurrences of a given motif described by a position-specific frequency matrix. Results: We describe Find Individual Motif Occurrences (FIMO), a software tool for scanning DNA or protein sequences with motifs described as position-specific scoring matrices. The program computes a log-likelihood ratio score for each position in a given sequence database, uses established dynamic programming methods to convert this score to a P-value and then applies false discovery rate analysis to estimate a q-value for each position in the given sequence. FIMO provides output in a variety of formats, including HTML, XML and several Santa Cruz Genome Browser formats. The program is efficient, allowing for the scanning of DNA sequences at a rate of 3.5 Mb/s on a single CPU. Availability and Implementation: FIMO is part of the MEME Suite software toolkit. A web server and source code are available at http://meme.sdsc.edu. Contact: t.bailey@imb.uq.edu.au; t.bailey@imb.uq.edu.au Supplementary information: Supplementary data are available at Bioinformatics online.
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              VennDiagram: a package for the generation of highly-customizable Venn and Euler diagrams in R

              Background Visualization of orthogonal (disjoint) or overlapping datasets is a common task in bioinformatics. Few tools exist to automate the generation of extensively-customizable, high-resolution Venn and Euler diagrams in the R statistical environment. To fill this gap we introduce VennDiagram, an R package that enables the automated generation of highly-customizable, high-resolution Venn diagrams with up to four sets and Euler diagrams with up to three sets. Results The VennDiagram package offers the user the ability to customize essentially all aspects of the generated diagrams, including font sizes, label styles and locations, and the overall rotation of the diagram. We have implemented scaled Venn and Euler diagrams, which increase graphical accuracy and visual appeal. Diagrams are generated as high-definition TIFF files, simplifying the process of creating publication-quality figures and easing integration with established analysis pipelines. Conclusions The VennDiagram package allows the creation of high quality Venn and Euler diagrams in the R statistical environment.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                11 December 2023
                11 November 2023
                11 November 2023
                : 51
                : 22
                : 12337-12351
                Affiliations
                Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) , 91198, Gif-sur-Yvette, France
                Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) , 91198, Gif-sur-Yvette, France
                Institut Pasteur, Université Paris Cité, Inserm U1223, Innate Immunity Unit , Paris, France
                Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) , 91198, Gif-sur-Yvette, France
                Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) , 91198, Gif-sur-Yvette, France
                Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) , 91198, Gif-sur-Yvette, France
                Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) , 91198, Gif-sur-Yvette, France
                Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC) , 91198, Gif-sur-Yvette, France
                Author notes
                To whom correspondence should be addressed. Tel: +33 1 69 82 43 75; Email: olivier.arnaiz@ 123456i2bc.paris-saclay.fr
                Correspondence may also be addressed to Mireille Bétermier. Email: mireille.betermier@ 123456i2bc.paris-saclay.fr
                Author information
                https://orcid.org/0000-0001-5670-1453
                https://orcid.org/0000-0002-1369-3649
                https://orcid.org/0000-0002-1645-3210
                https://orcid.org/0000-0002-2842-6172
                https://orcid.org/0000-0002-7772-4774
                https://orcid.org/0000-0002-9626-1015
                https://orcid.org/0000-0002-5407-6292
                Article
                gkad1006
                10.1093/nar/gkad1006
                10711438
                37953377
                58deadd4-6260-490e-8163-c5805c802613
                © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 23 October 2023
                : 09 October 2023
                : 17 April 2023
                Page count
                Pages: 15
                Funding
                Funded by: Centre National de la Recherche Scientifique, DOI 10.13039/501100004794;
                Funded by: Agence Nationale de la Recherche, DOI 10.13039/501100001665;
                Award ID: ANR-14-CE10-0005
                Award ID: ANR-18-CE12-0005-02
                Award ID: ANR-21-CE12-0019-01
                Award ID: ANR-19-CE12-0015
                Funded by: Fondation pour la Recherche Médicale, DOI 10.13039/501100002915;
                Award ID: FRM EQU202103012766
                Funded by: Paris-Saclay University;
                Award ID: 577 SDSV
                Funded by: Department of Genome Biology of I2BC;
                Funded by: Investissement d’Avenir;
                Award ID: ANR-10-INBS-09
                Funded by: Agence Nationale de la Recherche, DOI 10.13039/501100001665;
                Award ID: ANR-11-EQPX-0029/Morphoscope
                Award ID: ANR-10-INBS-04/FranceBioImaging
                Award ID: ANR‐11‐IDEX‐0003‐02/ Saclay Plant Sciences
                Categories
                AcademicSubjects/SCI00010
                Genomics

                Genetics
                Genetics

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