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      VELCRO-IP RNA-seq reveals ribosome expansion segment function in translation genome-wide

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          SUMMARY

          Roles for ribosomal RNA (rRNA) in gene regulation remain largely unexplored. With hundreds of rDNA units positioned across multiple loci, it is not possible to genetically modify rRNA in mammalian cells, hindering understanding of ribosome function. It remains elusive whether expansion segments (ESs), tentacle-like rRNA extensions that vary in sequence and size across eukaryotic evolution, may have functional roles in translation control. Here, we develop variable expansion segment-ligand chimeric ribosome immunoprecipitation RNA sequencing (VELCRO-IP RNA-seq), a versatile methodology to generate species-adapted ESs and to map specific mRNA regions across the transcriptome that preferentially associate with ESs. Application of VELCRO-IP RNA-seq to a mammalian ES, ES9S, identified a large array of transcripts that are selectively recruited to ribosomes via an ES. We further characterize a set of 5′ UTRs that facilitate cap-independent translation through ES9S-mediated ribosome binding. Thus, we present a technology for studying the enigmatic ESs of the ribosome, revealing their function in gene-specific translation.

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          In Brief

          Leppek et al. develop a pulldown technology employing chimeric yeast ribosomes, VELCRO-IP RNA-seq, to map interactions between ribosomal RNA (rRNA) and mRNAs genome-wide with positional precision. They find that expansion segments (ESs), the extended rRNA tentacles of the ribosome, specifically bind 5′ UTR elements to enable cap-independent translation of select mRNAs.

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          Most cited references 58

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              limma powers differential expression analyses for RNA-sequencing and microarray studies

              limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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                Author and article information

                Journal
                101573691
                39703
                Cell Rep
                Cell Rep
                Cell reports
                2211-1247
                22 January 2021
                19 January 2021
                09 July 2021
                : 34
                : 3
                : 108629
                Affiliations
                [1 ]Department of Developmental Biology, Stanford University, Stanford, CA 94305, USA
                [2 ]Department of Genetics, Stanford University, Stanford, CA 94305, USA
                [3 ]These authors contributed equally
                [4 ]Lead contact
                Author notes
                [* ]Correspondence: mbarna@ 123456stanford.edu

                AUTHOR CONTRIBUTIONS

                M.B., K.L., and G.W.B. conceived the project, and M.B. supervised the project. K.L., G.W.B., and M.B. designed the experiments, and K.L. performed experiments. G.W.B. performed high-throughput data analysis and statistics. K.F. established the strategy for rRNA engineering and generated RPS2-FLAG yeast strains. K.L. performed the rest of the experiments and analyses. M.B., K.L., and G.W.B. wrote the manuscript, with input from all authors.

                Article
                NIHMS1664954
                10.1016/j.celrep.2020.108629
                8270675
                33472078

                This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Cell biology

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