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      Large-scale transcriptome-wide profiling of microRNAs in human placenta and maternal plasma at early to mid gestation

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          ABSTRACT

          MicroRNAs (miRNAs) are increasingly seen as important regulators of placental development and opportunistic biomarker targets. Given the difficulty in obtaining samples from early gestation and subsequent paucity of the same, investigation of the role of miRNAs in early gestation human placenta has been limited. To address this, we generated miRNA profiles using 96 placentas from presumed normal pregnancies, across early gestation, in combination with matched profiles from maternal plasma. Placenta samples range from 6 to 23 weeks’ gestation, a time period that includes placenta from the early, relatively low but physiological (6–10 weeks’ gestation) oxygen environment, and later, physiologically normal oxygen environment (11–23 weeks’ gestation).

          We identified 637 miRNAs with expression in 86 samples (after removing poor quality samples), showing a clear gestational age gradient from 6 to 23 weeks’ gestation. We identified 374 differentially expressed (DE) miRNAs between placentas from 6–10 weeks’ versus 11–23 weeks’ gestation. We see a clear gestational age group bias in miRNA clusters C19MC, C14MC, miR-17 ~ 92 and paralogs, regions that also include many DE miRNAs. Proportional change in expression of placenta-specific miRNA clusters was reflected in maternal plasma.

          The presumed introduction of oxygenated maternal blood into the placenta (between ~10 and 12 weeks’ gestation) changes the miRNA profile of the chorionic villus, particularly in placenta-specific miRNA clusters. Data presented here comprise a clinically important reference set for studying early placenta development and may underpin the generation of minimally invasive methods for monitoring placental health.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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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
                RNA Biol
                RNA Biol
                RNA Biology
                Taylor & Francis
                1547-6286
                1555-8584
                19 August 2021
                2021
                19 August 2021
                : 18
                : Suppl 1
                : 507-520
                Affiliations
                [a ]Robinson Research Institute, University of Adelaide; , Adelaide, SA, Australia
                [b ]Adelaide Medical School, University of Adelaide; , Adelaide, SA, Australia
                [c ]Flinders Health and Medical Research Institute, Flinders University; , Bedford Park, SA, Australia
                [d ]Centre for Cancer Biology, University of South Australia/SA Pathology; , Adelaide, SA, Australia
                [e ]School of Agriculture Food and Wine, Waite Research Institute, University of Adelaide; , Adelaide, SA, Australia
                [f ]South Australian Genomics Centre, South Australian Health & Medical Research Institute; , Adelaide, SA, Australia
                Author notes
                CONTACT Melanie D. Smith melanie.smith@ 123456adelaide.edu.au ;Robinson Research Institute, University of Adelaide; , Adelaide, SA 5005, Australia; Adelaide Medical School, University of Adelaide, Adelaide SA 5005, Australia; Flinders Health and Medical Research Institute, Flinders University, Bedford Park SA 5042, Australia
                Author information
                https://orcid.org/0000-0001-7016-8245
                https://orcid.org/0000-0002-5869-889X
                https://orcid.org/0000-0003-4293-359X
                https://orcid.org/0000-0003-4207-4940
                https://orcid.org/0000-0002-5776-2052
                https://orcid.org/0000-0002-8431-5338
                https://orcid.org/0000-0001-6184-0925
                https://orcid.org/0000-0002-9250-2192
                Article
                1963105
                10.1080/15476286.2021.1963105
                8677031
                34412547
                760199f0-4b86-47e5-8769-a3d45c32d1f6
                © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

                History
                Page count
                Figures: 6, Tables: 3, References: 84, Pages: 14
                Categories
                Research Article
                Research Paper

                Molecular biology
                mirna,placenta,pregnancy,c19mc,c14mc,dlk1-di03,mir-17~92
                Molecular biology
                mirna, placenta, pregnancy, c19mc, c14mc, dlk1-di03, mir-17~92

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