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      RBFOX and PTBP1 proteins regulate the alternative splicing of micro-exons in human brain transcripts

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          Abstract

          Ninety-four percent of mammalian protein-coding exons exceed 51 nucleotides (nt) in length. The paucity of micro-exons (≤ 51 nt) suggests that their recognition and correct processing by the splicing machinery present greater challenges than for longer exons. Yet, because thousands of human genes harbor processed micro-exons, specialized mechanisms may be in place to promote their splicing. Here, we survey deep genomic data sets to define 13,085 micro-exons and to study their splicing mechanisms and molecular functions. More than 60% of annotated human micro-exons exhibit a high level of sequence conservation, an indicator of functionality. While most human micro-exons require splicing-enhancing genomic features to be processed, the splicing of hundreds of micro-exons is enhanced by the adjacent binding of splice factors in the introns of pre-messenger RNAs. Notably, splicing of a significant number of micro-exons was found to be facilitated by the binding of RBFOX proteins, which promote their inclusion in the brain, muscle, and heart. Our analyses suggest that accurate regulation of micro-exon inclusion by RBFOX proteins and PTBP1 plays an important role in the maintenance of tissue-specific protein–protein interactions.

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          Most cited references75

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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 and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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              TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

              TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at http://ccb.jhu.edu/software/tophat.
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                Author and article information

                Journal
                Genome Res
                Genome Res
                genome
                genome
                GENOME
                Genome Research
                Cold Spring Harbor Laboratory Press
                1088-9051
                1549-5469
                January 2015
                January 2015
                : 25
                : 1
                : 1-13
                Affiliations
                [1 ]MRC Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, United Kingdom;
                [2 ]Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, United Kingdom
                Author notes
                [3]

                These authors contributed equally to this work.

                [4]

                Present address: Department of Genetics, Stanford University, Stanford, CA 94305, USA

                Author information
                http://orcid.org/0000-0002-0736-251X
                Article
                9518021
                10.1101/gr.181990.114
                4317164
                25524026
                d188f1f5-be3b-4f8e-a872-7613daaa44df
                © 2015 Li et al.; Published by Cold Spring Harbor Laboratory Press

                This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0.

                History
                : 23 July 2014
                : 27 October 2014
                Page count
                Pages: 13
                Categories
                Research

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