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      The Blimp-1 transcription factor acts in non-neuronal cells to regulate terminal differentiation of the Drosophila eye

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          ABSTRACT

          The formation of a functional organ such as the eye requires specification of the correct cell types and their terminal differentiation into cells with the appropriate morphologies and functions. Here, we show that the zinc-finger transcription factor Blimp-1 acts in secondary and tertiary pigment cells in the Drosophila retina to promote the formation of a bi-convex corneal lens with normal refractive power, and in cone cells to enable complete extension of the photoreceptor rhabdomeres. Blimp-1 expression depends on the hormone ecdysone, and loss of ecdysone signaling causes similar differentiation defects. Timely termination of Blimp-1 expression is also important, as its overexpression in the eye has deleterious effects. Our transcriptomic analysis revealed that Blimp-1 regulates the expression of many structural and secreted proteins in the retina. Blimp-1 may function in part by repressing another transcription factor; Slow border cells is highly upregulated in the absence of Blimp-1, and its overexpression reproduces many of the effects of removing Blimp-1. This work provides insight into the transcriptional networks and cellular interactions that produce the structures necessary for visual function.

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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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            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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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
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                Journal
                Development
                The Company of Biologists
                0950-1991
                1477-9129
                April 01 2022
                April 01 2022
                March 31 2022
                : 149
                : 7
                Affiliations
                [1 ]Skirball Institute for Biomolecular Medicine and Department of Cell Biology, NYU Grossman School of Medicine, 540 First Avenue, New York, NY 10016, USA
                [2 ]Department of Molecular, Cell and Developmental Biology, University of California at Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095, USA
                Article
                10.1242/dev.200217
                58b35deb-41ab-4348-a7fc-5d7cdbd4f6e2
                © 2022

                http://www.biologists.com/user-licence-1-1/

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