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      Nurse cell­–derived small RNAs define paternal epigenetic inheritance in Arabidopsis

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          Abstract

          The plant male germline undergoes DNA methylation reprogramming, which methylates genes de novo and thereby alters gene expression and regulates meiosis. Here, we reveal the molecular mechanism underlying this reprogramming. We demonstrate that genic methylation in the male germline, from meiocytes to sperm, is established by 24-nucleotide small interfering RNAs (siRNAs) transcribed from transposons with imperfect sequence homology. These siRNAs are synthesized by meiocyte nurse cells (tapetum) through activity of CLSY3, a chromatin remodeler absent in other anther cells. Tapetal siRNAs govern germline methylation throughout the genome, including the inherited methylation patterns in sperm. Tapetum-derived siRNAs also silence germline transposons, safeguarding genome integrity. Our results reveal that tapetal siRNAs are sufficient to reconstitute germline methylation patterns and drive functional methylation reprogramming throughout the male germline.

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          Most cited references 56

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          NIH Image to ImageJ: 25 years of image analysis

          For the past twenty five years the NIH family of imaging software, NIH Image and ImageJ have been pioneers as open tools for scientific image analysis. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
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            Cutadapt removes adapter sequences from high-throughput sequencing reads

             Marcel Martin (2011)
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              Near-optimal probabilistic RNA-seq quantification.

              We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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                Author and article information

                Contributors
                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                July 01 2021
                July 02 2021
                July 01 2021
                July 02 2021
                : 373
                : 6550
                : eabh0556
                Affiliations
                [1 ]Department of Cell and Developmental Biology, John Innes Centre, Norwich NR4 7UH, UK.
                Article
                10.1126/science.abh0556
                © 2021

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