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      RNA-seq Transcriptional Profiling of Peripheral Blood Leukocytes from Cattle Infected with Mycobacterium bovis

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          Abstract

          Bovine tuberculosis, caused by infection with Mycobacterium bovis, is a major endemic disease affecting cattle populations worldwide, despite the implementation of stringent surveillance and control programs in many countries. The development of high-throughput functional genomics technologies, including gene expression microarrays and RNA-sequencing (RNA-seq), has enabled detailed analysis of the host transcriptome to M. bovis infection, particularly at the macrophage and peripheral blood level. In the present study, we have analyzed the peripheral blood leukocyte (PBL) transcriptome of eight natural M. bovis-infected and eight age- and sex-matched non-infected control Holstein-Friesian animals using RNA-seq. In addition, we compared gene expression profiles generated using RNA-seq with those previously generated using the high-density Affymetrix ® GeneChip ® Bovine Genome Array platform from the same PBL-extracted RNA. A total of 3,250 differentially expressed (DE) annotated genes were detected in the M. bovis-infected samples relative to the controls (adjusted P-value ≤0.05), with the number of genes displaying decreased relative expression (1,671) exceeding those with increased relative expression (1,579). Ingenuity ® Systems Pathway Analysis (IPA) of all DE genes revealed enrichment for genes with immune function. Notably, transcriptional suppression was observed among several of the top-ranking canonical pathways including Leukocyte Extravasation Signaling. Comparative platform analysis demonstrated that RNA-seq detected a larger number of annotated DE genes (3,250) relative to the microarray (1,398), of which 917 genes were common to both technologies and displayed the same direction of expression. Finally, we show that RNA-seq had an increased dynamic range compared to the microarray for estimating differential gene expression.

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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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            RNA-seq differential expression studies: more sequence or more replication?

            RNA-seq is replacing microarrays as the primary tool for gene expression studies. Many RNA-seq studies have used insufficient biological replicates, resulting in low statistical power and inefficient use of sequencing resources. We show the explicit trade-off between more biological replicates and deeper sequencing in increasing power to detect differentially expressed (DE) genes. In the human cell line MCF7, adding more sequencing depth after 10 M reads gives diminishing returns on power to detect DE genes, whereas adding biological replicates improves power significantly regardless of sequencing depth. We also propose a cost-effectiveness metric for guiding the design of large-scale RNA-seq DE studies. Our analysis showed that sequencing less reads and performing more biological replication is an effective strategy to increase power and accuracy in large-scale differential expression RNA-seq studies, and provided new insights into efficient experiment design of RNA-seq studies. The code used in this paper is provided on: http://home.uchicago.edu/∼jiezhou/replication/. The expression data is deposited in the Gene Expression Omnibus under the accession ID GSE51403.
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              Statistical design and analysis of RNA sequencing data.

              Next-generation sequencing technologies are quickly becoming the preferred approach for characterizing and quantifying entire genomes. Even though data produced from these technologies are proving to be the most informative of any thus far, very little attention has been paid to fundamental design aspects of data collection and analysis, namely sampling, randomization, replication, and blocking. We discuss these concepts in an RNA sequencing framework. Using simulations we demonstrate the benefits of collecting replicated RNA sequencing data according to well known statistical designs that partition the sources of biological and technical variation. Examples of these designs and their corresponding models are presented with the goal of testing differential expression.
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                Author and article information

                Contributors
                URI : http://frontiersin.org/people/u/139463
                URI : http://frontiersin.org/people/u/132796
                URI : http://frontiersin.org/people/u/179428
                URI : http://frontiersin.org/people/u/25243
                URI : http://frontiersin.org/people/u/153201
                URI : http://frontiersin.org/people/u/174205
                URI : http://frontiersin.org/people/u/171228
                URI : http://frontiersin.org/people/u/36539
                URI : http://frontiersin.org/people/u/179464
                URI : http://frontiersin.org/people/u/179427
                URI : http://frontiersin.org/people/u/23873
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                26 August 2014
                2014
                : 5
                : 396
                Affiliations
                [1] 1Animal Genomics Laboratory, UCD School of Agriculture and Food Science, University College Dublin , Dublin, Ireland
                [2] 2Smurfit Institute of Genetics, Trinity College Dublin , Dublin, Ireland
                [3] 3Animal and Bioscience Research Department, Animal and Grassland Research and Innovation Centre , Dunsany, Ireland
                [4] 4Comparative Immunology Group, School of Biochemistry and Immunology, Trinity Biosciences Institute, Trinity College Dublin , Dublin, Ireland
                [5] 5Tuberculosis Diagnostics and Immunology Research Centre, UCD School of Veterinary Medicine, University College Dublin , Dublin, Ireland
                [6] 6UCD School of Veterinary Medicine, University College Dublin , Dublin, Ireland
                [7] 7UCD Conway Institute of Biomolecular and Biomedical Research, University College Dublin , Dublin, Ireland
                Author notes

                Edited by: Uday Kishore, Brunel University, UK

                Reviewed by: Divyendu Singh, Indiana University Bloomington, USA; Jaya Talreja, Wayne State University, USA

                *Correspondence: David E. MacHugh, Animal Genomics Laboratory, Veterinary Sciences Centre, University College Dublin, Belfield, Dublin 4, Ireland e-mail: david.machugh@ 123456ucd.ie
                Present address: Stephen D. E. Park, IdentiGEN Ltd., Dublin, Ireland

                This article was submitted to Molecular Innate Immunity, a section of the journal Frontiers in Immunology.

                Article
                10.3389/fimmu.2014.00396
                4143615
                fb814816-1715-40ab-98e9-754fb1e5a021
                Copyright © 2014 McLoughlin, Nalpas, Rue-Albrecht, Browne, Magee, Killick, Park, Hokamp, Meade, O’Farrelly, Gormley, Gordon and MacHugh.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 July 2014
                : 04 August 2014
                Page count
                Figures: 4, Tables: 0, Equations: 0, References: 72, Pages: 12, Words: 10030
                Categories
                Immunology
                Original Research

                Immunology
                mycobacterium bovis,tuberculosis,rna-seq,biomarker,cattle,microarray,peripheral blood
                Immunology
                mycobacterium bovis, tuberculosis, rna-seq, biomarker, cattle, microarray, peripheral blood

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