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      Pseudouridine guides germline small RNA transport and epigenetic inheritance

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          Summary:

          Epigenetic modifications that arise during plant and animal development, such as DNA and histone modification, are mostly reset during gamete formation, but some are inherited from the germline including those marking imprinted genes 1 . Small RNAs guide these epigenetic modifications, and some are also inherited by the next generation 2, 3 . In C. elegans, these inherited small RNAs have poly (UG) tails 4 , but how inherited small RNAs are distinguished in other animals and plants is unknown. Pseudouridine (Ψ) is the most abundant RNA modification but has not been explored in small RNAs. Here, we develop novel assays to detect Ψ in short RNA sequences, demonstrating its presence in mouse and Arabidopsis microRNAs and their precursors. We also detect substantial enrichment in germline small RNAs, namely epigenetically activated siRNAs (easiRNAs) in Arabidopsis pollen, and piwi-interacting piRNAs in mouse testis. In pollen, pseudouridylated easiRNAs are localized to sperm cells, and we found that PAUSED/HEN5 ( PSD), the plant homolog of Exportin-t, interacts genetically with Ψ and is required for transport of easiRNAs into sperm cells from the vegetative nucleus. We further show that Exportin-t is required for the triploid block: chromosome dosage-dependent seed lethality that is epigenetically inherited from pollen. Thus, Ψ has a conserved role in marking inherited small RNAs in the germline.

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          Pseudouridine marks germline small RNAs in plants and mammals, impacting epigenetic inheritance via nuclear transport.

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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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            The Sequence Alignment/Map format and SAMtools

            Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Journal
                bioRxiv
                BIORXIV
                bioRxiv
                Cold Spring Harbor Laboratory
                03 November 2023
                : 2023.05.27.542553
                Affiliations
                [1 ]Howard Hughes Medical Institute, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA
                [2 ]The Gurdon Institute, University of Cambridge, Tennis Court Road, Cambridge CB2 1QN, UK
                [3 ]Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
                [4 ]Department of Gene Expression, Institute of Molecular Biology and Biotechnology, Adam Mickiewicz University, Poznan, Poland
                [5 ]Center for Genomic Science of IIT@SEMM, Instituto Italiano di Tecnologia (IIT), 20139 Milan, Italy
                [6 ]Storm Therapeutics, Ltd., Moneta Building (B280), Babraham Research Campus, Cambridge CB22 3AT, UK
                Author notes

                Author contributions: R.P.H., J.D., C.S.A., V.M., T.K. and R.A.M. conceived and designed experiments. R.P.H., V.M., J.D., C.S.A., A.L, F.V., and A.H. performed the experiments. R.P.H., J.D., T.L. and A.J.S. performed bioinformatic analysis. F.B. provided code and advice for transposon analysis. R.A.M. and R.P.H. wrote the manuscript with input from V.M., J.D., C.S.A., A.J.S., T.L. and T.K.

                [‡]

                Present address: Department of Biochemistry, University of Otago, Dunedin, NZ

                [*]

                These authors contributed equally

                Author information
                http://orcid.org/0000-0002-4264-9292
                http://orcid.org/0000-0001-6141-8863
                http://orcid.org/0000-0003-4978-2245
                http://orcid.org/0000-0002-7388-2118
                http://orcid.org/0000-0001-7859-7415
                http://orcid.org/0000-0001-7043-2827
                http://orcid.org/0000-0002-4449-1863
                http://orcid.org/0000-0002-8604-0462
                http://orcid.org/0000-0002-8918-4162
                http://orcid.org/0000-0003-1285-9608
                Article
                10.1101/2023.05.27.542553
                10312437
                37398006
                2d47eec9-fb7f-4b1a-8abe-8012d4521da7

                This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.

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                Funding
                This work was supported by the Howard Hughes Medical Institute, and by grants from the National Institutes of Health (GM067014 and GM76396), the National Science Foundation Plant Genome Research Program, and the Robertson Research Foundation (to R.A.M.). Work in the Kouzarides laboratory is supported by a grant from Cancer Research UK (grant reference RG96894), in addition to benefiting from core support from the Welcome Trust (WT203144) and Cancer Research UK (grant reference C6946/A24843). V.M. was funded by a Kay Kendall Leukemia Fund project grant (grant reference RG88664) and Cancer Research UK (grant reference RG96894). J.D was funded by a grant from the Polish National Science Centre (2020/39/D/NZ1/01918). The authors acknowledge assistance from the Cold Spring Harbor Laboratory Shared Resources, which are funded in part by the Cancer Center (Support Grant 5PP30CA045508).
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