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      Systematic evaluation of retroviral LTRs as cis-regulatory elements in mouse embryos

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      1 , 2 , 1 , 2 , 1 , 2 , 3 , *
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          SUMMARY

          In mammals, many retrotransposons are de-repressed during zygotic genome activation (ZGA). However, their functions in early development remain elusive largely due to the challenge to simultaneously manipulate thousands of retrotransposon insertions in embryos. Here, we applied CRISPR interference (CRISPRi) to perturb the long terminal repeat (LTR) MT2_Mm, a well-known ZGA and totipotency marker that exists in ~2,667 insertions throughout the mouse genome. CRISPRi robustly perturbed 2,485 (~93%) MT2_Mm insertions and 1,090 (~55%) insertions of the closely related MT2C_Mm in 2-cell embryos. Remarkably, such perturbation caused downregulation of hundreds of ZGA genes and embryonic arrest mostly at the morula stage. Mechanistically, MT2 LTRs are globally enriched for open chromatin and H3K27ac and function as promoters/enhancers downstream of OBOX/DUX proteins. Thus, we not only provide direct evidence to support the functional importance of MT2 activation in development but also systematically define cis-regulatory function of MT2 in embryos by integrating functional perturbation and multi-omic analyses.

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          In brief

          MT2 retrotransposon is transiently activated in mouse 2-cell embryos and is a well-known ZGA and totipotency marker. Yang et al. utilize a robust epigenome editing approach to directly evaluate biological significance of MT2 activation and systematically define cis-regulatory activities of ~3,500 MT2 copies in mouse preimplantation embryos.

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          Most cited references59

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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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            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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              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                3 April 2024
                26 March 2024
                20 February 2024
                18 April 2024
                : 43
                : 3
                : 113775
                Affiliations
                [1 ]Reproductive Sciences Center, Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH 45229, USA
                [2 ]Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45267, USA
                [3 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Conceptualization, Z.C.; methodology, Z.C.; investigation, J.Y., L.C., and Z.C.; writing – original draft, Z.C.; writing – review & editing, J.Y., L.C., and Z.C.; funding acquisition, Z.C.; supervision, Z.C.; project administration, Z.C.

                [* ]Correspondence: zhiyuan.chen@ 123456cchmc.org
                Article
                NIHMS1980898
                10.1016/j.celrep.2024.113775
                11024894
                38381606
                9082991a-8c3d-4157-9922-3c063d24bb77

                This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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                Cell biology
                Cell biology

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