Thu, 21 Aug 2008 11:01:41 BST CiteULike: stajichs Lynch http://www.citeulike.org/user/stajich/author/Lynch CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/stajich/article/2341756 <i>BMC Bioinformatics, Vol. 9, No. 1. (2008)</i><br /><br />BACKGROUND:Illumina bead-based arrays are becoming increasingly popular due to their high degree of replication and reported high data quality. However, little attention has been paid to the pre-processing of Illumina data. In this paper, we present our experience of analysing the raw data from an Illumina spike-in experiment and offer guidelines for those wishing to analyse expression data or develop new methodologies for this technology.RESULTS:We find that the local background estimated by Illumina is consistently low, and subtracting this background is beneficial for detecting differential expression (DE). Illumina's summary method performs well at removing outliers; producing estimates which are less biased and are less variable than other robust summary methods. However, quality assessment on summarised data may miss spatial artefacts present in the raw data. Also, we find that the background normalisation method used in Illumina's proprietary software (BeadStudio) can cause problems with a standard DE analysis. We demonstrate that variances calculated from the raw data can be used as inverse weights in the DE analysis to improve power. Finally, variability in both expression levels and DE statistics can be attributed to differences in probe composition. These differences are not accounted for by current analysis methods and require further investigation.CONCLUSIONS:Analysing Illumina expression data using BeadStudio is reasonable because of the conservative estimates of summary values produced by the software. Improvements can however be made by not using background normalisation. Access to the raw data allows for a more detailed quality assessment and flexible analyses. In the case of a gene expression study, data can be analysed on an appropriate scale using established tools. Similar improvements can be expected for other Illumina assays. Statistical issues in the analysis of Illumina data Mark Dunning Nuno Morais Andy Lynch Simon Tavare Matthew Ritchie doi:10.1186/1471-2105-9-85 BMC Bioinformatics, Vol. 9, No. 1. (2008) 2008-02-06T14:10:49-00:00 2008 BMC Bioinformatics 9 1 array illumina methods shortread statistics http://www.citeulike.org/user/stajich/article/1673084 <i>Nature Reviews Genetics, Vol. 8, No. 10., pp. 803-813.</i> The evolution of genetic networks by non-adaptive processes Michael Lynch doi:10.1038/nrg2192 Nature Reviews Genetics, Vol. 8, No. 10., pp. 803-813. 2007-09-19T03:51:54-00:00 Nature Reviews Genetics 1471-0056 8 10 803 813 Nature Publishing Group evolution network http://www.citeulike.org/user/stajich/article/1318687 <i>Proc Natl Acad Sci U S A (9 May 2007)</i><br /><br />The vast majority of biologists engaged in evolutionary studies interpret virtually every aspect of biodiversity in adaptive terms. This narrow view of evolution has become untenable in light of recent observations from genomic sequencing and population-genetic theory. Numerous aspects of genomic architecture, gene structure, and developmental pathways are difficult to explain without invoking the nonadaptive forces of genetic drift and mutation. In addition, emergent biological features such as complexity, modularity, and evolvability, all of which are current targets of considerable speculation, may be nothing more than indirect by-products of processes operating at lower levels of organization. These issues are examined in the context of the view that the origins of many aspects of biological diversity, from gene-structural embellishments to novelties at the phenotypic level, have roots in nonadaptive processes, with the population-genetic environment imposing strong directionality on the paths that are open to evolutionary exploitation. The frailty of adaptive hypotheses for the origins of organismal complexity. Michael Lynch doi:10.1073/pnas.0702207104 Proc Natl Acad Sci U S A (9 May 2007) 2007-05-22T05:37:17-00:00 2007 Proc Natl Acad Sci U S A 0027-8424 adaptive evolution http://www.citeulike.org/user/stajich/article/1318668 <i>(30 March 2007)</i><br /><br />With official genomic blueprints now available for hundreds of species, and thousands more expected in the near future, the field of biology has been forever transformed. Such readily accessible data have encouraged the proliferation of adaptive arguments for the evolution of gene and genomic features, often with little or no attention being given to simpler and more powerful alternative explanations. By integrating the central observations from molecular biology and population genetics relevant to comparative genomics, Lynch shows why the details matter. <P>Presented in a nontechnical fashion, at both the population-genetic and molecular-genetic levels, this book offers a unifying explanatory framework for how the peculiar architectural diversity of eukaryotic genomes and genes came to arise. Under Lynch's hypothesis, the genome-wide repatterning of eukaryotic gene structure, which resulted primarily from nonadaptive processes, provided an entirely novel resource from which natural selection could secondarily build new forms of organismal complexity. The Origins of Genome Architecture Michael Lynch (30 March 2007) 2007-05-22T05:23:10-00:00 2007 W.H. Freeman & Company lab_library