Institution
Walter and Eliza Hall Institute of Medical Research
Nonprofit•Melbourne, Victoria, Australia•
About: Walter and Eliza Hall Institute of Medical Research is a nonprofit organization based out in Melbourne, Victoria, Australia. It is known for research contribution in the topics: Antigen & Immune system. The organization has 5012 authors who have published 10620 publications receiving 873561 citations.
Topics: Antigen, Immune system, Population, T cell, Plasmodium falciparum
Papers published on a yearly basis
Papers
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TL;DR: The reliability and sensitivity of the test have been increased to the point where it can in many cases replace the [3H]thymidine uptake assay to measure cell proliferation or survival in growth factor or cytotoxicity assays.
4,631 citations
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TL;DR: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments, and the voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline.
Abstract: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.
4,475 citations
Harvard University1, University of North Carolina at Chapel Hill2, University of California, Berkeley3, Washington University in St. Louis4, Lawrence Berkeley National Laboratory5, University of California, San Francisco6, Mayo Clinic7, Memorial Sloan Kettering Cancer Center8, SRA International9, Walter and Eliza Hall Institute of Medical Research10
TL;DR: The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM) and proposed a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes as discussed by the authors.
Abstract: The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme (GBM). We describe a robust gene expression-based molecular classification of GBM into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and DNA copy number. Aberrations and gene expression of EGFR, NF1, and PDGFRA/IDH1 each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for GBM molecular stratification with important implications for future studies.
4,464 citations
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TL;DR: New insights into interactions among BCL-2 family proteins reveal how these proteins are regulated, but a unifying hypothesis for the mechanisms they use to activate caspases remains elusive.
Abstract: BCL-2 family proteins, which have either pro- or anti-apoptotic activities, have been studied intensively for the past decade owing to their importance in the regulation of apoptosis, tumorigenesis and cellular responses to anti-cancer therapy. They control the point of no return for clonogenic cell survival and thereby affect tumorigenesis and host-pathogen interactions and regulate animal development. Recent structural, phylogenetic and biological analyses, however, suggest the need for some reconsideration of the accepted organizational principles of the family and how the family members interact with one another during programmed cell death. Although these insights into interactions among BCL-2 family proteins reveal how these proteins are regulated, a unifying hypothesis for the mechanisms they use to activate caspases remains elusive.
4,246 citations
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TL;DR: A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies, and parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical.
Abstract: A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.
4,127 citations
Authors
Showing all 5041 results
Name | H-index | Papers | Citations |
---|---|---|---|
Martin White | 196 | 2038 | 232387 |
Stuart H. Orkin | 186 | 715 | 112182 |
Tien Yin Wong | 160 | 1880 | 131830 |
Mark J. Smyth | 153 | 713 | 88783 |
Anne B. Newman | 150 | 902 | 99255 |
James P. Allison | 137 | 483 | 83336 |
Scott W. Lowe | 134 | 396 | 89376 |
Rajkumar Buyya | 133 | 1066 | 95164 |
Peter Hall | 132 | 1640 | 85019 |
Ralph L. Brinster | 131 | 382 | 56455 |
Nico van Rooijen | 130 | 513 | 62623 |
David A. Hafler | 128 | 558 | 64314 |
Andreas Strasser | 128 | 509 | 66903 |
Marc Feldmann | 125 | 663 | 64916 |
Herman Waldmann | 118 | 586 | 49942 |