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      Isoform-resolved transcriptome of the human preimplantation embryo

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          Abstract

          Human preimplantation development involves extensive remodeling of RNA expression and splicing. However, its transcriptome has been compiled using short-read sequencing data, which fails to capture most full-length mRNAs. Here, we generate an isoform-resolved transcriptome of early human development by performing long- and short-read RNA sequencing on 73 embryos spanning the zygote to blastocyst stages. We identify 110,212 unannotated isoforms transcribed from known genes, including highly conserved protein-coding loci and key developmental regulators. We further identify 17,964 isoforms from 5,239 unannotated genes, which are largely non-coding, primate-specific, and highly associated with transposable elements. These isoforms are widely supported by the integration of published multi-omics datasets, including single-cell 8CLC and blastoid studies. Alternative splicing and gene co-expression network analyses further reveal that embryonic genome activation is associated with splicing disruption and transient upregulation of gene modules. Together, these findings show that the human embryo transcriptome is far more complex than currently known, and will act as a valuable resource to empower future studies exploring development.

          Abstract

          Human embryo development involves extensive transcriptional remodeling. In this study, the authors apply long- and short-read RNA-Seq to profile the transcriptomes of 73 human preimplantation embryos spanning zygotic to blastocyst stages, identifying tens of thousands of additional isoforms transcribed from both known and unannotated gene loci.

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          Most cited references159

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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

                Contributors
                dalitb@tlvmc.gov.il
                robert.sebra@mssm.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                30 October 2023
                30 October 2023
                2023
                : 14
                : 6902
                Affiliations
                [1 ]Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, ( https://ror.org/04a9tmd77) New York, NY 10029 USA
                [2 ]Pacific Biosciences, Inc., ( https://ror.org/00fcszb13) Menlo Park, CA 94025 USA
                [3 ]Fertility and IVF Institute, Tel-Aviv Sourasky Medical Center, Affiliated to Tel Aviv University, ( https://ror.org/04nd58p63) Tel Aviv, 64239 Israel
                [4 ]Center for Advanced Genomics Technology, Icahn School of Medicine at Mount Sinai, ( https://ror.org/04a9tmd77) New York, NY 10029 USA
                [5 ]Icahn Genomics Institute, Icahn School of Medicine at Mount Sinai, ( https://ror.org/04a9tmd77) New York, NY 10029 USA
                [6 ]Department of Cell and Developmental Biology, Sackler Faculty of Medicine, Sagol School of Neuroscience, Tel-Aviv University, ( https://ror.org/04mhzgx49) Tel-Aviv, 69978 Israel
                [7 ]CORAL – Center Of Regeneration and Longevity, Tel-Aviv Sourasky Medical Center, ( https://ror.org/04nd58p63) Tel Aviv, 64239 Israel
                [8 ]GRID grid.511736.7, Immunai Inc., ; New York, NY 10016 USA
                [9 ]Department of Biochemistry and Molecular Genetics, University of Louisville, ( https://ror.org/01ckdn478) Louisville, KY 40202 USA
                [10 ]Department of Molecular Cell Biology, Weizmann Institute of Science, ( https://ror.org/0316ej306) 7610001 Rehovot, Israel
                [11 ]GRID grid.266093.8, ISNI 0000 0001 0668 7243, Department of Biological Chemistry, Center for Epigenetics and Metabolism, , University of California, ; Irvine, CA 92697 USA
                [12 ]Center for OncoGenomics and Innovative Therapeutics (COGIT); Department of Oncological Sciences, Icahn School of Medicine at Mount Sinai, ( https://ror.org/04a9tmd77) New York, NY 10029 USA
                [13 ]Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, ( https://ror.org/04a9tmd77) New York, NY 10029 USA
                Author information
                http://orcid.org/0000-0003-4615-223X
                http://orcid.org/0000-0003-1798-3587
                http://orcid.org/0000-0001-7764-5307
                http://orcid.org/0000-0002-7892-8808
                http://orcid.org/0000-0001-9267-2426
                Article
                42558
                10.1038/s41467-023-42558-y
                10616205
                3061b9d0-f79f-4d3e-859c-7911669533b3
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 17 January 2023
                : 15 October 2023
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                © Springer Nature Limited 2023

                Uncategorized
                embryology,transcriptomics,data integration
                Uncategorized
                embryology, transcriptomics, data integration

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