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      EccDNAs are apoptotic products with high innate immunostimulatory activity

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          Abstract

          Extrachromosomal circular DNA elements (EccDNAs) have been described in the literature for several decades, and are known for their broad existence across different species 1, 2 . However, their biogenesis, and functions are largely unknown. By developing a new circular DNA enrichment method, here we purified, and sequenced full-length eccDNAs with Nanopore sequencing. We found that eccDNAs are mapped across the entire genome in a close to random fashion, suggesting a biogenesis mechanism of random ligation of genomic DNA fragments. Consistently, we found that apoptosis inducers can increase eccDNA generation, which is dependent on apoptotic DNA fragmentation followed by ligation by the DNA ligase 3. Importantly, we demonstrated that eccDNAs can function as potent innate immunostimulants in a sequence-independent, but circularity, and cytosolic DNA sensing Sting-dependent fashion. Collectively, our study not only revealed the origin, biogenesis, and immunostimulant function of eccDNAs, but also uncovered their sensing pathway and potential clinical implications in immune response.

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          Most cited references56

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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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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              STAR: ultrafast universal RNA-seq aligner.

              Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                12 July 2022
                November 2021
                20 October 2021
                19 July 2022
                : 599
                : 7884
                : 308-314
                Affiliations
                [1 ]Howard Hughes Medical Institute, 200 Longwood Av., Boston, MA 02115.
                [2 ]Program in Cellular and Molecular Medicine, Boston Children’s Hospital, 200 Longwood Av., Boston, MA 02115.
                [3 ]Department of Genetics, 200 Longwood Av., Boston, MA 02115.
                [4 ]Wyss Institute for Biologically Inspired Engineering, Harvard University, 200 Longwood Av., Boston, MA 02115.
                [5 ]Department of Systems Biology, Harvard Medical School, 200 Longwood Av., Boston, MA 02115.
                [6 ]Harvard Stem Cell Institute WAB-149G, 200 Longwood Av., Boston, MA 02115.
                Author notes

                Author Contributions

                Y.Z. conceived the project; Y.W. and Y.Z. designed the experiments; Y.W. performed the majority of the experiments; H.C. generated the ligase KO cell lines; M.W. developed the method to call eccDNA from Nanopore data and performed all the bioinformatics analysis; M.N.D performed pilot analysis of eccDNA sequencing data; D.L. helped with the SAFM imaging; F.A. supervised the work by H.C.; Y.W. and Y.Z. wrote the manuscript.

                [# ]To whom correspondence should be addressed yzhang@ 123456genetics.med.harvard.edu
                Article
                HHMIMS1815429
                10.1038/s41586-021-04009-w
                9295135
                34671165
                8ec02377-d4a5-4774-ad34-4ec954700f43

                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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