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Search Results: 1 - 10 of 3354 matches for " Gordon Smyth "
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featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features
Yang Liao,Gordon K Smyth,Wei Shi
Quantitative Biology , 2013, DOI: 10.1093/bioinformatics/btt656
Abstract: 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.
Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes
Alicia Oshlack, Dianne Emslie, Lynn M Corcoran, Gordon K Smyth
Genome Biology , 2007, DOI: 10.1186/gb-2007-8-1-r2
Abstract: Normalization of microarray data is the process of removing systematic bias and variation caused by technical artifacts while maintaining the important biological variation of interest. After appropriate normalization, variation in microarray data should be unbiased with respect to the samples being compared. As normalization is performed to adjust relative intensities between samples, microarray studies are most effective when looking at expression differences between samples rather than expression differences between genes. The extent of normalization required for an experiment depends on the quality and consistency of the arrays and samples being compared. Different microarray platforms require different strategies but the most widely used methods are intensity dependent. It has been shown that alternative normalization procedures can have substantial effects on results for a variety of platforms [1-3]. For two-color microarrays, intensity-dependent lowess normalization has emerged as a general purpose method and is the most commonly used procedure for normalization.Lowess normalization attempts to correct the expression log-ratios for inequalities between the labeling dyes. The relative preponderance of one dye over the other often changes with the intensity of the measurements. Therefore the fit to the expression log-ratios of the two cannels (M) is performed against the average log-intensity of the two cannels (A) i.e. on an MA-plot [4]. Effective lowess normalization relies on the assumption that either: the majority of genes are not differentially expressed; or there is symmetry in the expression levels of the up and down regulated genes [5]. Furthermore, as the procedure is intensity dependent it requires a sufficient number of genes with these properties at the full range of intensities. These assumptions are typically very reasonable for large-scale genome arrays because differences between RNA samples will typically relate to molecular pathways involving
Gene Regulation in Primates Evolves under Tissue-Specific Selection Pressures
Ran Blekhman equal contributor,Alicia Oshlack equal contributor,Adrien E. Chabot,Gordon K. Smyth,Yoav Gilad
PLOS Genetics , 2008, DOI: 10.1371/journal.pgen.1000271
Abstract: Regulatory changes have long been hypothesized to play an important role in primate evolution. To identify adaptive regulatory changes in humans, we performed a genome-wide survey for genes in which regulation has likely evolved under natural selection. To do so, we used a multi-species microarray to measure gene expression levels in livers, kidneys, and hearts from six humans, chimpanzees, and rhesus macaques. This comparative gene expression data allowed us to identify a large number of genes, as well as specific pathways, whose inter-species expression profiles are consistent with the action of stabilizing or directional selection on gene regulation. Among the latter set, we found an enrichment of genes involved in metabolic pathways, consistent with the hypothesis that shifts in diet underlie many regulatory adaptations in humans. In addition, we found evidence for tissue-specific selection pressures, as well as lower rates of protein evolution for genes in which regulation evolves under natural selection. These observations are consistent with the notion that adaptive circumscribed changes in gene regulation have fewer deleterious pleiotropic effects compared with changes at the protein sequence level.
Technical Variability Is Greater than Biological Variability in a Microarray Experiment but Both Are Outweighed by Changes Induced by Stimulation
Penelope A. Bryant, Gordon K. Smyth, Roy Robins-Browne, Nigel Curtis
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0019556
Abstract: Introduction A central issue in the design of microarray-based analysis of global gene expression is that variability resulting from experimental processes may obscure changes resulting from the effect being investigated. This study quantified the variability in gene expression at each level of a typical in vitro stimulation experiment using human peripheral blood mononuclear cells (PBMC). The primary objective was to determine the magnitude of biological and technical variability relative to the effect being investigated, namely gene expression changes resulting from stimulation with lipopolysaccharide (LPS). Methods and Results Human PBMC were stimulated in vitro with LPS, with replication at 5 levels: 5 subjects each on 2 separate days with technical replication of LPS stimulation, amplification and hybridisation. RNA from samples stimulated with LPS and unstimulated samples were hybridised against common reference RNA on oligonucleotide microarrays. There was a closer correlation in gene expression between replicate hybridisations (0.86–0.93) than between different subjects (0.66–0.78). Deconstruction of the variability at each level of the experimental process showed that technical variability (standard deviation (SD) 0.16) was greater than biological variability (SD 0.06), although both were low (SD<0.1 for all individual components). There was variability in gene expression both at baseline and after stimulation with LPS and proportion of cell subsets in PBMC was likely partly responsible for this. However, gene expression changes after stimulation with LPS were much greater than the variability from any source, either individually or combined. Conclusions Variability in gene expression was very low and likely to improve further as technical advances are made. The finding that stimulation with LPS has a markedly greater effect on gene expression than the degree of variability provides confidence that microarray-based studies can be used to detect changes in gene expression of biological interest in infectious diseases.
Detection of Gene Expression in an Individual Cell Type within a Cell Mixture Using Microarray Analysis
Penelope A. Bryant, Gordon K. Smyth, Roy Robins-Browne, Nigel Curtis
PLOS ONE , 2009, DOI: 10.1371/journal.pone.0004427
Abstract: Background A central issue in the design of microarray-based analysis of global gene expression is the choice between using cells of single type and a mixture of cells. This study quantified the proportion of lipopolysaccharide (LPS) induced differentially expressed monocyte genes that could be measured in peripheral blood mononuclear cells (PBMC), and determined the extent to which gene expression in the non-monocyte cell fraction diluted or obscured fold changes that could be detected in the cell mixture. Methodology/Principal Findings Human PBMC were stimulated with LPS, and monocytes were then isolated by positive (Mono+) or negative (Mono?) selection. The non-monocyte cell fraction (MonoD) remaining after positive selection of monocytes was used to determine the effect of non-monocyte cells on overall expression. RNA from LPS-stimulated PBMC, Mono+, Mono? and MonoD samples was co-hybridised with unstimulated RNA for each cell type on oligonucleotide microarrays. There was a positive correlation in gene expression between PBMC and both Mono+ (0.77) and Mono? (0.61–0.67) samples. Analysis of individual genes that were differentially expressed in Mono+ and Mono? samples showed that the ability to detect expression of some genes was similar when analysing PBMC, but for others, differential expression was either not detected or changed in the opposite direction. As a result of the dilutional or obscuring effect of gene expression in non-monocyte cells, overall about half of the statistically significant LPS-induced changes in gene expression in monocytes were not detected in PBMC. However, 97% of genes with a four fold or greater change in expression in monocytes after LPS stimulation, and almost all (96–100%) of the top 100 most differentially expressed monocyte genes were detected in PBMC. Conclusions/Significance The effect of non-responding cells in a mixture dilutes or obscures the detection of subtle changes in gene expression in an individual cell type. However, for studies in which only the most highly differentially expressed genes are of interest, separating and analysing individual cell types may be unnecessary.
Gene ontology analysis for RNA-seq: accounting for selection bias
Matthew D Young, Matthew J Wakefield, Gordon K Smyth, Alicia Oshlack
Genome Biology , 2010, DOI: 10.1186/gb-2010-11-2-r14
Abstract: Next generation sequencing of RNA (RNA-seq) gives unprecedented detail about the transcriptional landscape of an organism. Not only is it possible to accurately measure expression levels of transcripts in a sample [1], but this new technology promises to deliver a range of additional benefits, such as the investigation of alternative splicing [2], allele specific expression [3] and RNA editing [4]. However, in order to accurately make use of the data, it is vital that analysis techniques are developed to take into account the technical features of RNA-seq output. As many of the specific technical properties of RNA-seq data are not present in previous technologies such as microarrays, naive application of the same analysis methodologies, developed for these older technologies, may lead to bias in the results.In RNA-seq experiments the expression level of a transcript is estimated from the number of reads that map to that transcript. In many applications, the expected read count for a transcript is proportional to the gene's expression level multiplied by its transcript length. Therefore, even when two transcripts are expressed at the same level, differences in length will yield differing numbers of total reads. One consequence of this is that longer transcripts give more statistical power for detecting differential expression between samples [5]. Similarly, more highly expressed transcripts have a greater number of reads and greater power to detect differential expression. Hence, long or highly expressed transcripts are more likely to be detected as differentially expressed compared with their short and/or lowly expressed counterparts. The fact that statistical power increases with the number of reads is an unavoidable property of count data, which cannot be removed by normalization or re-scaling. Consequently, it is unsurprising that this selection bias has been shown to exist in a range of different experiments performed using different analysis methods, experiment
Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis
Andrew J Holloway, Alicia Oshlack, Dileepa S Diyagama, David DL Bowtell, Gordon K Smyth
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-511
Abstract: A methodology is described for evaluating the precision and sensitivity of whole-genome gene expression technologies such as microarrays. The method consists of an easy-to-construct titration series of RNA samples and an associated statistical analysis using non-linear regression. The method evaluates the precision and responsiveness of each microarray platform on a whole-array basis, i.e., using all the probes, without the need to match probes across platforms. An experiment is conducted to assess and compare four widely used microarray platforms. All four platforms are shown to have satisfactory precision but the commercial platforms are superior for resolving differential expression for genes at lower expression levels. The effective precision of the two-color platforms is improved by allowing for probe-specific dye-effects in the statistical model. The methodology is used to compare three data extraction algorithms for the Affymetrix platforms, demonstrating poor performance for the commonly used proprietary algorithm relative to the other algorithms. For probes which can be matched across platforms, the cross-platform variability is decomposed into within-platform and between-platform components, showing that platform disagreement is almost entirely systematic rather than due to measurement variability.The results demonstrate good precision and sensitivity for all the platforms, but highlight the need for improved probe annotation. They quantify the extent to which cross-platform measures can be expected to be less accurate than within-platform comparisons for predicting disease progression or outcome.In recent years there has been a rapidly growing understanding of how gene expression reflects and determines biological states. This has come about through the widespread use of microarray expression profiling [1]. Yet there have been concerns about the accuracy and reproducibility of the technology. Some early studies reported poor reproducibility and dramatic d
Illumina WG-6 BeadChip strips should be normalized separately
Wei Shi, Ashish Banerjee, Matthew E Ritchie, Steve Gerondakis, Gordon K Smyth
BMC Bioinformatics , 2009, DOI: 10.1186/1471-2105-10-372
Abstract: None of the normalization strategies proposed so far for this microarray platform allow for the possibility of systematic variation between the two strips comprising each array. That this variation can be substantial is illustrated by a data example. We demonstrate that normalizing at the strip-level rather than at the array-level can effectively remove this between-strip variation, improve the precision of gene expression measurements and discover more differentially expressed genes. The gain is substantial, yielding a 20% increase in statistical information and doubling the number of genes detected at a 5% false discovery rate. Functional analysis reveals that the extra genes found tend to have interesting biological meanings, dramatically strengthening the biological conclusions from the experiment. Strip-level normalization still outperforms array-level normalization when non-expressed probes are filtered out.Plots are proposed which demonstrate how the need for strip-level normalization relates to inconsistent intensity range variation between the strips. Strip-level normalization is recommended for the preprocessing of Illumina Sentrix-6 BeadChips whenever the intensity range is seen to be inconsistent between the strips. R code is provided to implement the recommended plots and normalization algorithms.Illumina Whole-Genome Expression BeadChips have been widely adopted for high-throughput gene expression analysis in the past few years. Most popular and comprehensive of these are the HumanWG-6 and MouseWG-6 (Sentrix-6) BeadChips. Each Sentrix-6 BeadChip allows the interrogation of six RNA samples in parallel and produces data that can be treated as coming from six independent microarrays. Physically, each Sentrix-6 BeadChip consists of twelve equally-spaced strips of beads (Figure 1). Each pair of adjacent strips comprises a single microarray and is hybridized with a single RNA sample. In the first generation of Human-6 and Mouse-6 BeadChips, the first strip o
Proximal genomic localization of STAT1 binding and regulated transcriptional activity
Samuel Wormald, Douglas J Hilton, Gordon K Smyth, Terence P Speed
BMC Genomics , 2006, DOI: 10.1186/1471-2164-7-254
Abstract: In response to IFN-γ, STAT1 bound proximally to regions of the genome that exhibit regulated transcriptional activity. This finding was consistent between different tiling microarray platforms, and between different measures of transcriptional activity, including differential binding of RNA polymerase II, and differential mRNA transcription. Re-analysis of tiling microarray data from a recent study of IFN-γ-induced STAT1 ChIP-chip and mRNA expression revealed that STAT1 binding is tightly associated with localized mRNA transcription in response to IFN-γ. Close relationships were also apparent between STAT1 binding, STAT2 binding, and mRNA transcription in response to IFN-α. Furthermore, we found that sites of STAT1 binding within the Encyclopedia of DNA Elements (ENCODE) region are precisely correlated with sites of either enhanced or diminished binding by the RNA polymerase II complex.Together, our results indicate that STAT1 binds proximally to regions of the genome that exhibit regulated transcriptional activity. This finding establishes a generalized basis for the positioning of STAT1 binding sites within the genome, and supports a role for STAT1 in the direct recruitment of the RNA polymerase II complex to the promoters of IFN-γ-responsive genes.Interferon-gamma (IFN-γ) is a potent pro-inflammatory cytokine that regulates a spectrum of biological processes, and is produced primarily in response to infection [1]. IFN-γ signal transduction begins at the cell surface with the formation of a heteromeric protein complex that includes IFN-γ, IFN-γ receptor-1, and IFN-γ receptor-2 [1]. Associated with the IFN-γ receptors are members of the Janus kinase (JAK) family of tyrosine kinases, which become activated upon formation of the IFN-γ receptor complex, and in turn phosphorylate the signal transducer and activator of transcription-1 (STAT1) transcription factor [2-4]. Upon its phosphorylation, STAT1 homo-dimerizes, and is transported into the nucleus where it binds to
Metastatic renal cell carcinoma management
Heldwein, Flavio L.;Escudier, Bernard;Smyth, Gordon;Souto, Carlos A. V.;Vallancien, Guy;
International braz j urol , 2009, DOI: 10.1590/S1677-55382009000300002
Abstract: purpose: to assess the current treatment of metastatic renal cell carcinoma, focusing on medical treatment options. material and methods: the most important recent publications have been selected after a literature search employing pubmed using the search terms: advanced and metastatic renal cell carcinoma, anti-angiogenesis drugs and systemic therapy; also significant meeting abstracts were consulted. results: progress in understanding the molecular basis of renal cell carcinoma, especially related to genetics and angiogenesis, has been achieved mainly through of the study of von hippel-lindau disease. a great variety of active agents have been developed and tested in metastatic renal cell carcinoma (mrcc) patients. new specific molecular therapies in metastatic disease are discussed. sunitinib, sorafenib and bevacizumab increase the progression-free survival when compared to therapy with cytokines. temsirolimus increases overall survival in high-risk patients. growth factors and regulatory enzymes, such as carbonic anhydrase ix may be targets for future therapies. conclusions: a broader knowledge of clear cell carcinoma molecular biology has permitted the beginning of a new era in mrcc therapy. benefits of these novel agents in terms of progression-free and overall survival have been observed in patients with mrcc, and, in many cases, have become the standard of care. sunitinib is now considered the new reference first-line treatment for mrcc. despite all the progress in recent years, complete responses are still very rare. currently, many important issues regarding the use of these agents in the management of metastatic renal cancer still need to be properly addressed.
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