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Author

Matthew E. Ritchie

Other affiliations: University of Cambridge, University of Melbourne
Bio: Matthew E. Ritchie is an academic researcher from Walter and Eliza Hall Institute of Medical Research. The author has contributed to research in topic(s): Bioconductor & Progenitor cell. The author has an hindex of 42, co-authored 135 publication(s) receiving 20980 citation(s). Previous affiliations of Matthew E. Ritchie include University of Cambridge & University of Melbourne.
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Journal ArticleDOI
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Abstract: 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.

13,819 citations


Journal ArticleDOI
20 Sep 2007-Bioinformatics
TL;DR: The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates, and methods which stabilize the variances of the log-ratios along the intensity range perform the best.
Abstract: Motivation: Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments. Results: Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data. Availability: The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org. Contact: smyth@wehi.edu.au Supplementary information: Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.

915 citations


Journal ArticleDOI
07 Feb 2020-Genome Biology
TL;DR: The current landscape of available tools is reviewed, the principles of error correction, base modification detection, and long-read transcriptomics analysis are focused on, and the challenges that remain are highlighted.
Abstract: Long-read technologies are overcoming early limitations in accuracy and throughput, broadening their application domains in genomics. Dedicated analysis tools that take into account the characteristics of long-read data are thus required, but the fast pace of development of such tools can be overwhelming. To assist in the design and analysis of long-read sequencing projects, we review the current landscape of available tools and present an online interactive database, long-read-tools.org, to facilitate their browsing. We further focus on the principles of error correction, base modification detection, and long-read transcriptomics analysis and highlight the challenges that remain.

476 citations


Journal ArticleDOI
15 Aug 2007-Bioinformatics
TL;DR: The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored in convenient R classes and users are free to choose between various methods of image processing, background correction and normalization in their analysis rather than using the defaults in Illumina's; proprietary software.
Abstract: Summary The R/Bioconductor package beadarray allows raw data from Illumina experiments to be read and stored in convenient R classes. Users are free to choose between various methods of image processing, background correction and normalisation in their analysis rather than using the defaults in Illumina’s proprietary software. The package als oallow squalit yassessmen tt ob ecarrie dou to nth era wdata .The data can then be summarised and stored in a format which can be used by other R/Bioconductor packages to perform downstream analyses. Summarised data processed by Illumina’s BeadStudio software can also be read and analysed in the same manner. Availability: The beadarray package is available from the Bioconducto rwe bpag ea twww.bioconductor.org . A user’ s guid e an d e xample dat aset sar eprovide dwit hth epackage.

461 citations


Journal ArticleDOI
18 Dec 2014-Cell
TL;DR: The apoptotic caspase cascade functions to render mitochondrial apoptosis immunologically silent, and suppresses type I interferon (IFN) production by cells undergoing Bak/Bax-mediated apoptosis.
Abstract: Activated caspases are a hallmark of apoptosis induced by the intrinsic pathway, but they are dispensable for cell death and the apoptotic clearance of cells in vivo. This has led to the suggestion that caspases are activated not just to kill but to prevent dying cells from triggering a host immune response. Here, we show that the caspase cascade suppresses type I interferon (IFN) production by cells undergoing Bak/Bax-mediated apoptosis. Bak and Bax trigger the release of mitochondrial DNA. This is recognized by the cGAS/STING-dependent DNA sensing pathway, which initiates IFN production. Activated caspases attenuate this response. Pharmacological caspase inhibition or genetic deletion of caspase-9, Apaf-1, or caspase-3/7 causes dying cells to secrete IFN-β. In vivo, this precipitates an elevation in IFN-β levels and consequent hematopoietic stem cell dysfunction, which is corrected by loss of Bak and Bax. Thus, the apoptotic caspase cascade functions to render mitochondrial apoptosis immunologically silent.

455 citations


Cited by
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Journal ArticleDOI
TL;DR: The philosophy and design of the limma package is reviewed, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
Abstract: 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.

13,819 citations


Journal ArticleDOI
Adam Auton1, Gonçalo R. Abecasis2, David Altshuler3, Richard Durbin4, David R. Bentley5, Aravinda Chakravarti6, Andrew G. Clark7, Peter Donnelly8, Evan E. Eichler9, Paul Flicek, Stacey Gabriel10, Richard A. Gibbs11, Eric D. Green12, Matthew E. Hurles4, Bartha Maria Knoppers13, Jan O. Korbel, Eric S. Lander10, Charles Lee14, Hans Lehrach15, Elaine R. Mardis16, Gabor T. Marth17, Gil McVean8, Deborah A. Nickerson9, Jeanette Schmidt18, Stephen T. Sherry12, Jun Wang, Richard K. Wilson16, Eric Boerwinkle11, Harsha Doddapaneni11, Yi Han11, Viktoriya Korchina11, Christie Kovar11, Sandra L. Lee11, Donna M. Muzny11, Jeffrey G. Reid11, Yiming Zhu11, Yuqi Chang19, Qiang Feng19, Qiang Feng20, Xiaodong Fang20, Xiaodong Fang19, Xiaosen Guo20, Xiaosen Guo19, Min Jian20, Min Jian19, Hui Jiang19, Hui Jiang20, Xin Jin19, Tianming Lan19, Guoqing Li19, Jingxiang Li19, Yingrui Li19, Shengmao Liu19, Xiao Liu20, Xiao Liu19, Yao Lu19, Xuedi Ma19, Meifang Tang19, Bo Wang19, Guangbiao Wang19, Honglong Wu19, Renhua Wu19, Xun Xu19, Ye Yin19, Dandan Zhang19, Wenwei Zhang19, Jiao Zhao19, Meiru Zhao19, Xiaole Zheng19, Namrata Gupta10, Neda Gharani21, Lorraine Toji21, Norman P. Gerry21, Alissa M. Resch21, Jonathan Barker, Laura Clarke, Laurent Gil, Sarah E. Hunt, Gavin Kelman, Eugene Kulesha, Rasko Leinonen, William M. McLaren, Rajesh Radhakrishnan, Asier Roa, Dmitriy Smirnov, Richard Smith, Ian Streeter, Anja Thormann, Iliana Toneva, Brendan Vaughan, Xiangqun Zheng-Bradley, Russell J. Grocock5, Sean Humphray5, Terena James5, Zoya Kingsbury5, Ralf Sudbrak22, M. Albrecht, Vyacheslav Amstislavskiy15, Tatiana A. Borodina, Matthias Lienhard15, Florian Mertes15, Marc Sultan15, Bernd Timmermann15, Marie-Laure Yaspo15, Lucinda Fulton16, Victor Ananiev12, Zinaida Belaia12, Dimitriy Beloslyudtsev12, Nathan Bouk12, Chao Chen12, Deanna M. Church, Robert M. Cohen12, Charles Cook12, John Garner12, Timothy Hefferon12, Mikhail Kimelman12, Chunlei Liu12, John Lopez12, Peter Meric12, Chris O’Sullivan12, Yuri Ostapchuk12, Lon Phan12, Sergiy Ponomarov12, Valerie A. Schneider12, Eugene Shekhtman12, Karl Sirotkin12, Douglas J. Slotta12, Hua Zhang12, Senduran Balasubramaniam4, John Burton4, Petr Danecek4, Thomas M. Keane4, Anja Kolb-Kokocinski4, Shane A. McCarthy4, James Stalker4, Michael A. Quail4, Christopher Davies18, Jeremy Gollub18, Teresa Webster18, Brant Wong18, Yiping Zhan18, Christopher L. Campbell1, Yu Kong1, Anthony Marcketta1, Fuli Yu11, Lilian Antunes11, Matthew N. Bainbridge11, Aniko Sabo11, Zhuoyi Huang11, Lachlan J. M. Coin19, Lin Fang19, Lin Fang20, Qibin Li19, Zhenyu Li19, Haoxiang Lin19, Binghang Liu19, Ruibang Luo19, Haojing Shao19, Haojing Shao23, Yinlong Xie19, Chen Ye19, Chang Yu19, Fan Zhang19, Hancheng Zheng19, Zhu Hongmei19, Can Alkan24, Elif Dal24, Fatma Kahveci24, Erik Garrison4, Deniz Kural, Wan-Ping Lee, Wen Fung Leong25, Michael Strömberg5, Alistair Ward17, Jiantao Wu5, Mengyao Zhang26, Mark J. Daly10, Mark A. DePristo, Robert E. Handsaker10, Robert E. Handsaker26, Eric Banks10, Gaurav Bhatia10, Guillermo del Angel10, Giulio Genovese10, Heng Li10, Seva Kashin10, Seva Kashin26, Steven A. McCarroll26, Steven A. McCarroll10, James Nemesh10, Ryan Poplin10, Seungtai Yoon27, Jayon Lihm27, Vladimir Makarov28, Srikanth Gottipati7, Alon Keinan7, Juan L. Rodriguez-Flores7, Tobias Rausch, Markus Hsi-Yang Fritz, Adrian M. Stütz, Kathryn Beal, Avik Datta, Javier Herrero29, Graham R. S. Ritchie, Daniel R. Zerbino, Pardis C. Sabeti10, Pardis C. Sabeti26, Ilya Shlyakhter10, Ilya Shlyakhter26, Stephen F. Schaffner26, Stephen F. Schaffner10, Joseph J. Vitti10, Joseph J. Vitti26, David Neil Cooper30, Edward V. Ball30, Peter D. Stenson30, Bret Barnes5, Markus J. Bauer5, R. Keira Cheetham5, Anthony J. Cox5, Michael A. Eberle5, Scott Kahn5, Lisa Murray5, John F. Peden5, Richard Shaw5, Eimear E. Kenny28, Mark A. Batzer31, Miriam K. Konkel31, Jerilyn A. Walker31, Daniel G. MacArthur26, Monkol Lek26, Ralf Herwig15, Li Ding16, Daniel C. Koboldt16, David E. Larson16, Kai Ye16, Simon Gravel13, Anand Swaroop12, Emily Y. Chew12, Tuuli Lappalainen32, Yaniv Erlich32, Melissa Gymrek26, Melissa Gymrek10, Thomas Willems33, Jared T. Simpson34, Mark D. Shriver35, Jeffrey A. Rosenfeld36, Carlos Bustamante37, Stephen B. Montgomery37, Francisco M. De La Vega37, Jake K. Byrnes, Andrew Carroll, Marianne K. DeGorter37, Phil Lacroute37, Brian K. Maples37, Alicia R. Martin37, Andrés Moreno-Estrada38, Andrés Moreno-Estrada37, Suyash Shringarpure37, Fouad Zakharia37, Eran Halperin39, Eran Halperin40, Yael Baran39, Eliza Cerveira, Jaeho Hwang, Ankit Malhotra, Dariusz Plewczynski, Kamen Radew, Mallory Romanovitch, Chengsheng Zhang, Fiona Hyland18, David Craig41, Alexis Christoforides41, Nils Homer42, Tyler Izatt41, Ahmet Kurdoglu41, Shripad Sinari41, Kevin Squire43, Chunlin Xiao12, Jonathan Sebat44, Danny Antaki44, Madhusudan Gujral44, Amina Noor44, Kenny Ye1, Esteban G. Burchard45, Ryan D. Hernandez45, Christopher R. Gignoux45, David Haussler46, David Haussler47, Sol Katzman46, W. James Kent46, Bryan Howie48, Andres Ruiz-Linares29, Emmanouil T. Dermitzakis49, Emmanouil T. Dermitzakis50, Scott E. Devine51, Hyun Min Kang2, Jeffrey M. Kidd2, Thomas W. Blackwell2, Sean Caron2, Wei Chen52, S. Emery2, Lars G. Fritsche2, Christian Fuchsberger2, Goo Jun53, Goo Jun2, Bingshan Li54, Robert H. Lyons2, Chris Scheller2, Carlo Sidore55, Carlo Sidore2, Carlo Sidore56, Shiya Song2, Elzbieta Sliwerska2, Daniel Taliun2, Adrian Tan2, Ryan P. Welch2, Mary Kate Wing2, Xiaowei Zhan57, Philip Awadalla58, Philip Awadalla34, Alan Hodgkinson58, Yun Li59, Xinghua Shi60, Andrew Quitadamo60, Gerton Lunter8, Jonathan Marchini8, Simon Myers8, Claire Churchhouse8, Olivier Delaneau50, Olivier Delaneau8, Anjali Gupta-Hinch8, Warren W. Kretzschmar8, Zamin Iqbal8, Iain Mathieson8, Androniki Menelaou61, Androniki Menelaou8, Andy Rimmer50, Dionysia Kiara Xifara8, Taras K. Oleksyk62, Yunxin Fu53, Xiaoming Liu53, Momiao Xiong53, Lynn B. Jorde17, David J. Witherspoon17, Jinchuan Xing36, Brian L. Browning9, Sharon R. Browning9, Fereydoun Hormozdiari9, Peter H. Sudmant9, Ekta Khurana7, Chris Tyler-Smith4, Cornelis A. Albers63, Qasim Ayub4, Yuan Chen4, Vincenza Colonna55, Vincenza Colonna4, Luke Jostins8, Klaudia Walter4, Yali Xue4, Mark Gerstein64, Alexej Abyzov65, Suganthi Balasubramanian64, Jieming Chen64, Declan Clarke64, Yao Fu64, Arif Harmanci64, Mike Jin64, Dong-Hoon Lee64, Jeremy Liu64, Xinmeng Jasmine Mu10, Xinmeng Jasmine Mu64, Jing Zhang64, Yan Zhang64, Christopher Hartl10, Khalid Shakir10, Jeremiah D. Degenhardt7, Sascha Meiers, Benjamin Raeder, Francesco Paolo Casale, Oliver Stegle, Eric-Wubbo Lameijer66, Ira M. Hall16, Vineet Bafna44, Jacob J. Michaelson44, Eugene J. Gardner51, Ryan E. Mills2, Gargi Dayama2, Ken Chen67, Xian Fan67, Zechen Chong67, Tenghui Chen67, Mark Chaisson9, John Huddleston9, Maika Malig9, Bradley J. Nelson9, Nicholas F. Parrish54, Ben Blackburne4, Sarah J. Lindsay4, Zemin Ning4, Yujun Zhang4, Hugo Y. K. Lam, Cristina Sisu64, Danny Challis11, Uday S. Evani11, James T. Lu11, Uma Nagaswamy11, Jin Yu11, Wangshen Li19, Lukas Habegger64, Haiyuan Yu7, Fiona Cunningham, Ian Dunham, Kasper Lage26, Kasper Lage10, Jakob Berg Jespersen10, Jakob Berg Jespersen68, Jakob Berg Jespersen26, Heiko Horn10, Heiko Horn26, Donghoon Kim64, Rob DeSalle69, Apurva Narechania69, Melissa A. Wilson Sayres70, Fernando L. Mendez37, G. David Poznik37, Peter A. Underhill37, David Mittelman71, Ruby Banerjee4, Maria Cerezo4, Thomas W. Fitzgerald4, Sandra Louzada4, Andrea Massaia4, Fengtang Yang4, Divya Kalra11, Walker Hale11, Xu Dan19, Kathleen C. Barnes6, Christine Beiswanger21, Hongyu Cai19, Hongzhi Cao20, Hongzhi Cao19, Brenna M. Henn72, Danielle Jones7, Jane Kaye8, Alastair Kent73, Angeliki Kerasidou8, Rasika A. Mathias6, Pilar N. Ossorio74, Michael Parker8, Charles N. Rotimi12, Charmaine D.M. Royal75, Karla Sandoval37, Yeyang Su19, Zhongming Tian19, Sarah A. Tishkoff76, Marc Via77, Yuhong Wang19, Huanming Yang19, Ling Yang19, Jiayong Zhu19, Walter F. Bodmer8, Gabriel Bedoya78, Zhiming Cai19, Yang Gao79, Jiayou Chu80, Leena Peltonen, Andrés C. García-Montero81, Alberto Orfao81, Julie Dutil82, Juan Carlos Martínez-Cruzado62, R. Mathias6, Anselm Hennis83, Harold Watson83, Colin A. McKenzie83, Firdausi Qadri84, Regina C. LaRocque84, Xiaoyan Deng, Danny Asogun, Onikepe A. Folarin, Christian T. Happi26, Omonwunmi Omoniwa26, Matt Stremlau10, Matt Stremlau26, Ridhi Tariyal26, Ridhi Tariyal10, M Jallow85, M Jallow8, Fatoumatta Sisay Joof85, Fatoumatta Sisay Joof8, Tumani Corrah8, Tumani Corrah85, Kirk A. Rockett8, Kirk A. Rockett85, Dominic P. Kwiatkowski8, Dominic P. Kwiatkowski85, Jaspal S. Kooner86, Tran Tinh Hien8, Sarah J. Dunstan8, Sarah J. Dunstan87, Nguyen ThuyHang8, Richard Fonnie, Robert F. Garry88, Lansana Kanneh, Lina M. Moses88, John S. Schieffelin88, Donald S. Grant88, Carla Gallo89, Giovanni Poletti89, Danish Saleheen76, Asif Rasheed, Lisa D. Brooks12, Adam Felsenfeld12, Jean E. McEwen12, Yekaterina Vaydylevich12, Audrey Duncanson90, Michael Dunn90, Jeffery A. Schloss12 
Yeshiva University1, University of Michigan2, Vertex Pharmaceuticals3, Wellcome Trust Sanger Institute4, Illumina5, Johns Hopkins University6, Cornell University7, University of Oxford8, University of Washington9, Broad Institute10, Baylor College of Medicine11, National Institutes of Health12, McGill University13, Ewha Womans University14, Max Planck Society15, Washington University in St. Louis16, University of Utah17, Thermo Fisher Scientific18, Beijing Institute of Genomics19, University of Copenhagen20, Coriell Institute For Medical Research21, Maastricht University22, University of Queensland23, Bilkent University24, Kansas State University25, Harvard University26, Cold Spring Harbor Laboratory27, Icahn School of Medicine at Mount Sinai28, University College London29, Cardiff University30, Louisiana State University31, Columbia University32, Massachusetts Institute of Technology33, Ontario Institute for Cancer Research34, Pennsylvania State University35, Rutgers University36, Stanford University37, CINVESTAV38, Tel Aviv University39, University of California, Berkeley40, Translational Genomics Research Institute41, Life Technologies42, University of California, Los Angeles43, University of California, San Diego44, University of California, San Francisco45, University of California, Santa Cruz46, Howard Hughes Medical Institute47, University of Chicago48, Swiss Institute of Bioinformatics49, University of Geneva50, University of Maryland, Baltimore51, University of Pittsburgh52, University of Texas Health Science Center at Houston53, Vanderbilt University54, National Research Council55, University of Sassari56, University of Texas Southwestern Medical Center57, Université de Montréal58, University of North Carolina at Chapel Hill59, University of North Carolina at Charlotte60, Utrecht University61, University of Puerto Rico at Mayagüez62, Radboud University Nijmegen63, Yale University64, Mayo Clinic65, Leiden University66, University of Texas MD Anderson Cancer Center67, Technical University of Denmark68, American Museum of Natural History69, Arizona State University70, Virginia Tech71, Stony Brook University72, Genetic Alliance73, University of Wisconsin-Madison74, Duke University75, University of Pennsylvania76, University of Barcelona77, University of Antioquia78, Peking University79, Peking Union Medical College80, University of Salamanca81, Ponce Health Sciences University82, University of the West Indies83, International Centre for Diarrhoeal Disease Research, Bangladesh84, Medical Research Council85, Hammersmith Hospital86, University of Melbourne87, Tulane University88, Cayetano Heredia University89, Wellcome Trust90
01 Oct 2015-Nature
TL;DR: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations, and has reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-generation sequencing, deep exome sequencing, and dense microarray genotyping.
Abstract: The 1000 Genomes Project set out to provide a comprehensive description of common human genetic variation by applying whole-genome sequencing to a diverse set of individuals from multiple populations. Here we report completion of the project, having reconstructed the genomes of 2,504 individuals from 26 populations using a combination of low-coverage whole-genome sequencing, deep exome sequencing, and dense microarray genotyping. We characterized a broad spectrum of genetic variation, in total over 88 million variants (84.7 million single nucleotide polymorphisms (SNPs), 3.6 million short insertions/deletions (indels), and 60,000 structural variants), all phased onto high-quality haplotypes. This resource includes >99% of SNP variants with a frequency of >1% for a variety of ancestries. We describe the distribution of genetic variation across the global sample, and discuss the implications for common disease studies.

9,821 citations


Journal ArticleDOI
01 Apr 2014-Bioinformatics
Abstract: MOTIVATION: 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. RESULTS: 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. AVAILABILITY AND IMPLEMENTATION: 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.

8,495 citations


Book ChapterDOI
01 Jan 2005-
TL;DR: This chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments with technical as well as biological replication.
Abstract: A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data.

5,536 citations


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Author's H-index: 42

No. of papers from the Author in previous years
YearPapers
202122
202019
201910
201812
201711
201610

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Author's top 5 most impactful journals

bioRxiv

25 papers, 100 citations

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9 papers, 445 citations

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9 papers, 1.7K citations

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6 papers, 448 citations

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