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      Alternative Splicing Outcomes Across an RNA-Binding Protein Concentration Gradient

      , , , ,
      Journal of Molecular Biology
      Elsevier BV

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          Abstract

          <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d1191347e119">Alternative splicing (AS) is a dynamic RNA processing step that produces multiple RNA isoforms from a single pre-mRNA transcript and contributes to the complexity of the cellular transcriptome and proteome. This process is regulated through a network of cis-regulatory sequence elements and trans-acting factors, most-notably RNA binding proteins (RBPs). The muscleblind-like (MBNL) and RNA binding fox-1 homolog (RBFOX) are two well characterized families of RBPs that regulate fetal to adult AS transitions critical for proper muscle, heart, and central nervous system development. To better understand how the concentration of these RBPs influences AS transcriptome wide, we engineered a MBNL1 and RBFOX1 inducible HEK-293 cell line. Modest induction of exogenous RBFOX1 in this cell line modulated MBNL1-dependent AS outcomes in 3 skipped exon events, despite significant levels of endogenous RBFOX1 and RBFOX2. Due to background RBFOX levels, we conducted a focused analysis of dose-dependent MBNL1 skipped exon AS outcomes and generated transcriptome wide dose-response curves. Analysis of this data demonstrates that MBNL1-regulated exclusion events may require higher concentrations of MBNL1 protein to properly regulate AS outcomes compared to inclusion events and that multiple arrangements of YGCY motifs can produce similar splicing outcomes. These results suggest that rather than a simple relationship between the organization of RBP binding sites and a specific splicing outcome, that complex interaction networks govern both AS inclusion and exclusion events across a RBP gradient. </p>

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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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            SciPy 1.0: fundamental algorithms for scientific computing in Python

            SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.
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              Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.

              R. Edgar (2002)
              The Gene Expression Omnibus (GEO) project was initiated in response to the growing demand for a public repository for high-throughput gene expression data. GEO provides a flexible and open design that facilitates submission, storage and retrieval of heterogeneous data sets from high-throughput gene expression and genomic hybridization experiments. GEO is not intended to replace in house gene expression databases that benefit from coherent data sets, and which are constructed to facilitate a particular analytic method, but rather complement these by acting as a tertiary, central data distribution hub. The three central data entities of GEO are platforms, samples and series, and were designed with gene expression and genomic hybridization experiments in mind. A platform is, essentially, a list of probes that define what set of molecules may be detected. A sample describes the set of molecules that are being probed and references a single platform used to generate its molecular abundance data. A series organizes samples into the meaningful data sets which make up an experiment. The GEO repository is publicly accessible through the World Wide Web at http://www.ncbi.nlm.nih.gov/geo.
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                Author and article information

                Contributors
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                Journal
                Journal of Molecular Biology
                Journal of Molecular Biology
                Elsevier BV
                00222836
                August 2023
                August 2023
                : 435
                : 15
                : 168156
                Article
                10.1016/j.jmb.2023.168156
                37230319
                ebef577a-474f-45ff-ac4c-b59f38abb70e
                © 2023

                https://www.elsevier.com/tdm/userlicense/1.0/

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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