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      Specialized germline P-bodies are required to specify germ cell fate in Caenorhabditis elegans embryos

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          ABSTRACT

          In animals with germ plasm, specification of the germline involves ‘germ granules’, cytoplasmic condensates that enrich maternal transcripts in the germline founder cells. In Caenorhabditis elegans embryos, P granules enrich maternal transcripts, but surprisingly P granules are not essential for germ cell fate specification. Here, we describe a second condensate in the C. elegans germ plasm. Like canonical P-bodies found in somatic cells, ‘germline P-bodies’ contain regulators of mRNA decapping and deadenylation and, in addition, the intrinsically-disordered proteins MEG-1 and MEG-2 and the TIS11-family RNA-binding protein POS-1. Embryos lacking meg-1 and meg-2 do not stabilize P-body components, misregulate POS-1 targets, mis-specify the germline founder cell and do not develop a germline. Our findings suggest that specification of the germ line involves at least two distinct condensates that independently enrich and regulate maternal mRNAs in the germline founder cells.

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          [Related article:] Highlighted Article: This paper describes a new condensate in the C. elegans germ plasm, ‘germline P-bodies’, which contain regulators of RNA translation and decay and are essential to specify germ cell fate.

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          Most cited references78

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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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            HISAT: a fast spliced aligner with low memory requirements.

            HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                Journal
                Development
                Development
                DEV
                Development (Cambridge, England)
                The Company of Biologists Ltd
                0950-1991
                1477-9129
                1 November 2022
                8 November 2022
                8 November 2022
                : 149
                : 21
                : dev200920
                Affiliations
                Howard Hughes Medical Institute and Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine , Baltimore, MD 21205, USA
                Author notes
                [* ]Author for correspondence ( gseydoux@ 123456jhmi.edu )

                Handling Editor: Swathi Arur

                Competing interests

                G.S. serves on the Scientific Advisory Board of Dewpoint Therapeutics. The remaining authors declare no competing interests.

                Author information
                http://orcid.org/0000-0001-9377-1864
                http://orcid.org/0000-0001-8257-0493
                Article
                DEV200920
                10.1242/dev.200920
                9686995
                36196602
                7c787510-8d43-4bd1-ae31-0c0b83e4d97c
                © 2022. Published by The Company of Biologists Ltd

                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 use, distribution and reproduction in any medium provided that the original work is properly attributed.

                History
                : 8 May 2022
                : 6 September 2022
                Funding
                Funded by: National Institutes of Health, http://dx.doi.org/10.13039/100000002;
                Award ID: R37HD037047
                Award ID: T32 GM007445
                Funded by: National Science Foundation, http://dx.doi.org/10.13039/100000001;
                Award ID: DGE-1746891
                Funded by: Howard Hughes Medical Institute, http://dx.doi.org/10.13039/100000011;
                Funded by: Johns Hopkins University School of Medicine;
                Categories
                Research Article

                Developmental biology
                germ plasm,p-bodies,germline,primordial germ cells,rnp granules,c. elegans
                Developmental biology
                germ plasm, p-bodies, germline, primordial germ cells, rnp granules, c. elegans

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