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Chad A. Shaw

Bio: Chad A. Shaw is an academic researcher from Baylor College of Medicine. The author has contributed to research in topics: Comparative genomic hybridization & Copy-number variation. The author has an hindex of 78, co-authored 247 publications receiving 21067 citations. Previous affiliations of Chad A. Shaw include Baylor University & Rice University.


Papers
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Journal ArticleDOI
30 May 2008-Science
TL;DR: It is shown that MeCP2 associates with the transcriptional activator CREB1 at the promoter of an activated target but not a repressed target, and that it can function as both an activator and a repressor of transcription.
Abstract: Mutations in the gene encoding the transcriptional repressor methyl-CpG binding protein 2 (MeCP2) cause the neurodevelopmental disorder Rett syndrome Loss of function as well as increased dosage of the MECP2 gene cause a host of neuropsychiatric disorders To explore the molecular mechanism(s) underlying these disorders, we examined gene expression patterns in the hypothalamus of mice that either lack or overexpress MeCP2 In both models, MeCP2 dysfunction induced changes in the expression levels of thousands of genes, but unexpectedly the majority of genes (∼85%) appeared to be activated by MeCP2 We selected six genes and confirmed that MeCP2 binds to their promoters Furthermore, we showed that MeCP2 associates with the transcriptional activator CREB1 at the promoter of an activated target but not a repressed target These studies suggest that MeCP2 regulates the expression of a wide range of genes in the hypothalamus and that it can function as both an activator and a repressor of transcription

1,672 citations

Journal ArticleDOI
09 Jun 2011-Neuron
TL;DR: A genome-wide analysis of rare copy-number variation in 1124 autism spectrum disorder families, each comprised of a single proband, unaffected parents, and, in most kindreds, an unaffected sibling, finds significant association of ASD with de novo duplications of 7q11.23, where the reciprocal deletion causes Williams-Beuren syndrome.

1,198 citations

Journal ArticleDOI
19 May 2006-Cell
TL;DR: An interaction network for 54 proteins involved in 23 inherited ataxias is developed and expanded by incorporating literature-curated and evolutionarily conserved interactions and provides a tool for understanding pathogenic mechanisms common for this class of neurodegenerative disorders.

773 citations

Journal ArticleDOI
TL;DR: These studies show that hematopoietic stem cells are not protected from aging, and loss of epigenetic regulation at the chromatin level may drive both functional attenuation of cells, as well as other manifestations of aging, including the increased propensity for neoplastic transformation.
Abstract: Age-related defects in stem cells can limit proper tissue maintenance and hence contribute to a shortened lifespan. Using highly purified hematopoietic stem cells from mice aged 2 to 21 mo, we demonstrate a deficit in function yet an increase in stem cell number with advancing age. Expression analysis of more than 14,000 genes identified 1,500 that were age-induced and 1,600 that were age-repressed. Genes associated with the stress response, inflammation, and protein aggregation dominated the up-regulated expression profile, while the down-regulated profile was marked by genes involved in the preservation of genomic integrity and chromatin remodeling. Many chromosomal regions showed coordinate loss of transcriptional regulation; an overall increase in transcriptional activity with age and inappropriate expression of genes normally regulated by epigenetic mechanisms was also observed. Hematopoietic stem cells from early-aging mice expressing a mutant p53 allele reveal that aging of stem cells can be uncoupled from aging at an organismal level. These studies show that hematopoietic stem cells are not protected from aging. Instead, loss of epigenetic regulation at the chromatin level may drive both functional attenuation of cells, as well as other manifestations of aging, including the increased propensity for neoplastic transformation.

719 citations

Journal ArticleDOI
TL;DR: The utility of SNP-CGH is demonstrated with two Infinium whole-genome genotyping BeadChips, assaying 109,000 and 317,000 SNP loci, and the statistical ability to detect common aberrations was modeled by analysis of an X chromosome titration model system, and sensitivity was modeling by titration of gDNA from a tumor cell with that of its paired normal cell line.
Abstract: Array-CGH is a powerful tool for the detection of chromosomal aberrations. The introduction of high-density SNP genotyping technology to genomic profiling, termed SNP-CGH, represents a further advance, since simultaneous measurement of both signal intensity variations and changes in allelic composition makes it possible to detect both copy number changes and copy-neutral loss-of-heterozygosity (LOH) events. We demonstrate the utility of SNP-CGH with two Infinium whole-genome genotyping BeadChips, assaying 109,000 and 317,000 SNP loci, to detect chromosomal aberrations in samples bearing constitutional aberrations as well tumor samples at sub-100 kb effective resolution. Detected aberrations include homozygous deletions, hemizygous deletions, copy-neutral LOH, duplications, and amplifications. The statistical ability to detect common aberrations was modeled by analysis of an X chromosome titration model system, and sensitivity was modeled by titration of gDNA from a tumor cell with that of its paired normal cell line. Analysis was facilitated by using a genome browser that plots log ratios of normalized intensities and allelic ratios along the chromosomes. We developed two modes of SNP-CGH analysis, a single sample and a paired sample mode. The single sample mode computes log intensity ratios and allelic ratios by referencing to canonical genotype clusters generated from ∼120 reference samples, whereas the paired sample mode uses a paired normal reference sample from the same individual. Finally, the two analysis modes are compared and contrasted for their utility in analyzing different types of input gDNA: low input amounts, fragmented gDNA, and Phi29 whole-genome pre-amplified DNA.

547 citations


Cited by
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Journal ArticleDOI
TL;DR: The survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
Abstract: Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

13,102 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
14 Jun 2007-Nature
TL;DR: Functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project are reported, providing convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts.
Abstract: We report the generation and analysis of functional data from multiple, diverse experiments performed on a targeted 1% of the human genome as part of the pilot phase of the ENCODE Project. These data have been further integrated and augmented by a number of evolutionary and computational analyses. Together, our results advance the collective knowledge about human genome function in several major areas. First, our studies provide convincing evidence that the genome is pervasively transcribed, such that the majority of its bases can be found in primary transcripts, including non-protein-coding transcripts, and those that extensively overlap one another. Second, systematic examination of transcriptional regulation has yielded new understanding about transcription start sites, including their relationship to specific regulatory sequences and features of chromatin accessibility and histone modification. Third, a more sophisticated view of chromatin structure has emerged, including its inter-relationship with DNA replication and transcriptional regulation. Finally, integration of these new sources of information, in particular with respect to mammalian evolution based on inter- and intra-species sequence comparisons, has yielded new mechanistic and evolutionary insights concerning the functional landscape of the human genome. Together, these studies are defining a path for pursuit of a more comprehensive characterization of human genome function.

5,091 citations

01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

4,409 citations

Journal ArticleDOI
TL;DR: This protocol provides a guide to interpreting the output of structure prediction servers in general and one such tool in particular, the protein homology/analogy recognition engine (Phyre), which can reliably detect up to twice as many remote homologies as standard sequence-profile searching.
Abstract: Determining the structure and function of a novel protein is a cornerstone of many aspects of modern biology. Over the past decades, a number of computational tools for structure prediction have been developed. It is critical that the biological community is aware of such tools and is able to interpret their results in an informed way. This protocol provides a guide to interpreting the output of structure prediction servers in general and one such tool in particular, the protein homology/analogy recognition engine (Phyre). New profile–profile matching algorithms have improved structure prediction considerably in recent years. Although the performance of Phyre is typical of many structure prediction systems using such algorithms, all these systems can reliably detect up to twice as many remote homologies as standard sequence-profile searching. Phyre is widely used by the biological community, with >150 submissions per day, and provides a simple interface to results. Phyre takes 30 min to predict the structure of a 250-residue protein.

4,403 citations