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      Correlation of circular RNA abundance with proliferation – exemplified with colorectal and ovarian cancer, idiopathic lung fibrosis, and normal human tissues

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          Abstract

          Circular RNAs are a recently (re-)discovered abundant RNA species with presumed function as miRNA sponges, thus part of the competing endogenous RNA network. We analysed the expression of circular and linear RNAs and proliferation in matched normal colon mucosa and tumour tissues. We predicted >1,800 circular RNAs and proved the existence of five randomly chosen examples using RT-qPCR. Interestingly, the ratio of circular to linear RNA isoforms was always lower in tumour compared to normal colon samples and even lower in colorectal cancer cell lines. Furthermore, this ratio correlated negatively with the proliferation index. The correlation of global circular RNA abundance (the circRNA index) and proliferation was validated in a non-cancerous proliferative disease, idiopathic pulmonary fibrosis, ovarian cancer cells compared to cultured normal ovarian epithelial cells, and 13 normal human tissues. We are the first to report a global reduction of circular RNA abundance in colorectal cancer cell lines and cancer compared to normal tissues and discovered a negative correlation of global circular RNA abundance and proliferation. This negative correlation seems to be a general principle in human tissues as validated with three different settings. Finally, we present a simple model how circular RNAs could accumulate in non-proliferating cells.

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          featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

          , , (2013)
          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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            ImmunoRatio: a publicly available web application for quantitative image analysis of estrogen receptor (ER), progesterone receptor (PR), and Ki-67

            Introduction Accurate assessment of estrogen receptor (ER), progesterone receptor (PR), and Ki-67 is essential in the histopathologic diagnostics of breast cancer. Commercially available image analysis systems are usually bundled with dedicated analysis hardware and, to our knowledge, no easily installable, free software for immunostained slide scoring has been described. In this study, we describe a free, Internet-based web application for quantitative image analysis of ER, PR, and Ki-67 immunohistochemistry in breast cancer tissue sections. Methods The application, named ImmunoRatio, calculates the percentage of positively stained nuclear area (labeling index) by using a color deconvolution algorithm for separating the staining components (diaminobenzidine and hematoxylin) and adaptive thresholding for nuclear area segmentation. ImmunoRatio was calibrated using cell counts defined visually as the gold standard (training set, n = 50). Validation was done using a separate set of 50 ER, PR, and Ki-67 stained slides (test set, n = 50). In addition, Ki-67 labeling indexes determined by ImmunoRatio were studied for their prognostic value in a retrospective cohort of 123 breast cancer patients. Results The labeling indexes by calibrated ImmunoRatio analyses correlated well with those defined visually in the test set (correlation coefficient r = 0.98). Using the median Ki-67 labeling index (20%) as a cutoff, a hazard ratio of 2.2 was obtained in the survival analysis (n = 123, P = 0.01). ImmunoRatio was shown to adapt to various staining protocols, microscope setups, digital camera models, and image acquisition settings. The application can be used directly with web browsers running on modern operating systems (e.g., Microsoft Windows, Linux distributions, and Mac OS). No software downloads or installations are required. ImmunoRatio is open source software, and the web application is publicly accessible on our website. Conclusions We anticipate that free web applications, such as ImmunoRatio, will make the quantitative image analysis of ER, PR, and Ki-67 easy and straightforward in the diagnostic assessment of breast cancer specimens.
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              Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM).

              A critical task in high-throughput sequencing is aligning millions of short reads to a reference genome. Alignment is especially complicated for RNA sequencing (RNA-Seq) because of RNA splicing. A number of RNA-Seq algorithms are available, and claim to align reads with high accuracy and efficiency while detecting splice junctions. RNA-Seq data are discrete in nature; therefore, with reasonable gene models and comparative metrics RNA-Seq data can be simulated to sufficient accuracy to enable meaningful benchmarking of alignment algorithms. The exercise to rigorously compare all viable published RNA-Seq algorithms has not been performed previously. We developed an RNA-Seq simulator that models the main impediments to RNA alignment, including alternative splicing, insertions, deletions, substitutions, sequencing errors and intron signal. We used this simulator to measure the accuracy and robustness of available algorithms at the base and junction levels. Additionally, we used reverse transcription-polymerase chain reaction (RT-PCR) and Sanger sequencing to validate the ability of the algorithms to detect novel transcript features such as novel exons and alternative splicing in RNA-Seq data from mouse retina. A pipeline based on BLAT was developed to explore the performance of established tools for this problem, and to compare it to the recently developed methods. This pipeline, the RNA-Seq Unified Mapper (RUM), performs comparably to the best current aligners and provides an advantageous combination of accuracy, speed and usability. The RUM pipeline is distributed via the Amazon Cloud and for computing clusters using the Sun Grid Engine (http://cbil.upenn.edu/RUM). ggrant@pcbi.upenn.edu; epierce@mail.med.upenn.edu The RNA-Seq sequence reads described in the article are deposited at GEO, accession GSE26248.
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                Author and article information

                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group
                2045-2322
                27 January 2015
                2015
                : 5
                : 8057
                Affiliations
                [1 ]Department of Obstetrics and Gynaecology, Molecular Oncology Group, Comprehensive Cancer Centre, Medical University of Vienna & Ludwig Boltzmann Cluster Translational Oncology , Vienna, Austria
                [2 ]Department of Surgery, Medical University of Vienna , Vienna, Austria
                [3 ]Department of Pathology, Medical University of Vienna , Vienna, Austria
                [4 ]Department of Medicine I, Comprehensive Cancer Centre, Medical University of Vienna & Ludwig Boltzmann Cluster Oncology , Vienna, Austria
                Author notes
                [*]

                These authors contributed equally to this work.

                Article
                srep08057
                10.1038/srep08057
                4306919
                25624062
                747bdc32-4a3a-4549-8803-43c57c95307c
                Copyright © 2015, Macmillan Publishers Limited. All rights reserved

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder in order to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

                History
                : 15 September 2014
                : 12 December 2014
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