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      BET activity plays an essential role in control of stem cell attributes in Xenopus

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          ABSTRACT

          Neural crest cells are a stem cell population unique to vertebrate embryos that retains broad multi-germ layer developmental potential through neurulation. Much remains to be learned about the genetic and epigenetic mechanisms that control the potency of neural crest cells. Here, we examine the role that epigenetic readers of the BET (bromodomain and extra terminal) family play in controlling the potential of pluripotent blastula and neural crest cells. We find that inhibiting BET activity leads to loss of pluripotency at blastula stages and a loss of neural crest at neurula stages. We compare the effects of HDAC (an eraser of acetylation marks) and BET (a reader of acetylation) inhibition and find that they lead to similar cellular outcomes through distinct effects on the transcriptome. Interestingly, loss of BET activity in cells undergoing lineage restriction is coupled to increased expression of genes linked to pluripotency and prolongs the competence of initially pluripotent cells to transit to a neural progenitor state. Together these findings advance our understanding of the epigenetic control of pluripotency and the formation of the vertebrate neural crest.

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          [Related article:] Highlighted Article: BET-family epigenetic readers are essential regulators of the developmental potential of both pluripotent blastula and neural crest cells.

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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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              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 July 2024
                3 July 2024
                3 July 2024
                : 151
                : 13
                : dev202990
                Affiliations
                [ 1 ]Department of Molecular Biosciences, Northwestern University , Evanston, IL 60208, USA
                [ 2 ]National Institute for Theory and Mathematics in Biology, Northwestern University , Evanston, IL 60208, USA
                Author notes
                [* ]Author for correspondence ( clabonne@ 123456northwestern.edu )

                Handling Editor: Benoit Bruneau

                Competing interests

                The authors declare no competing or financial interests.

                Author information
                http://orcid.org/0000-0001-6001-7179
                Article
                DEV202990
                10.1242/dev.202990
                11266789
                38884356
                54304223-9269-4438-af7f-f07fc1794758
                © 2024. 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
                : 26 April 2024
                : 31 May 2024
                Funding
                Funded by: National Institutes of Health, http://dx.doi.org/10.13039/100000002;
                Award ID: R01GM116538
                Funded by: National Science Foundation, http://dx.doi.org/10.13039/100000001;
                Award ID: 1764421
                Funded by: Simons Foundation, http://dx.doi.org/10.13039/100000893;
                Award ID: SFARI 597491-RWC
                Categories
                Research Article

                Developmental biology
                bromodomain,brd4,histone acetylation,pluripotency,stem cell,xenopus,neural crest
                Developmental biology
                bromodomain, brd4, histone acetylation, pluripotency, stem cell, xenopus, neural crest

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