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      Translatome and transcriptome co-profiling reveals a role of TPRXs in human zygotic genome activation

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          Abstract

          Translational regulation plays a critical role during the oocyte-to-embryo transition (OET) and zygotic genome activation (ZGA). Here, we integrated ultra-low-input Ribo-seq with mRNA-seq to co-profile the translatome and transcriptome in human oocytes and early embryos. Comparison with mouse counterparts identified widespread differentially translated genes functioning in epigenetic reprogramming, transposon defense, and small RNA biogenesis, in part driven by species-specific regulatory elements in 3′ untranslated regions. Moreover, PRD-like homeobox transcription factors, including TPRXL, TPRX1, and TPRX2, are highly translated around ZGA. TPRX1/2/L knockdown leads to defective ZGA and preimplantation development. Ectopically expressed TPRXs bind and activate key ZGA genes in human embryonic stem cells. These data reveal the conservation and divergence of translation landscapes during OET and identify critical regulators of human ZGA.

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          Is Open Access

          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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              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

                Contributors
                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                September 08 2022
                Affiliations
                [1 ]Center for Stem Cell Biology and Regenerative Medicine, MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing 100084, China.
                [2 ]Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing 100084, China.
                [3 ]Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
                [4 ]Center for Reproductive Medicine, Shandong University, Jinan, Shandong 250012, China.
                [5 ]Key Laboratory of Reproductive Endocrinology of Ministry of Education, Shandong University, Jinan, Shandong 250012, China.
                [6 ]Shandong Key Laboratory of Reproductive Medicine, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250012, China.
                [7 ]Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, Shandong 250012, China.
                [8 ]National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong University, Jinan, Shandong 250012, China.
                [9 ]Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program, School of Life Sciences, Tsinghua University, Beijing 100084, China.
                Article
                10.1126/science.abo7923
                36074823
                a5fff29b-ce79-41dd-88b6-be4abc1dfd62
                © 2022
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