Patrick M. Loerch
Merck & Co.
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Publication
Featured researches published by Patrick M. Loerch.
Nature | 2001
Daniel D. Shoemaker; Eric E. Schadt; Christopher D. Armour; Yudong He; Philip W. Garrett-engele; P. D. McDonagh; Patrick M. Loerch; Amy Leonardson; Pek Yee Lum; Guy Cavet; Lani F. Wu; Steven J. Altschuler; Seve Edwards; J. King; John S. Tsang; G. Schimmack; J. M. Schelter; J. Koch; M. Ziman; Matthew J. Marton; B. Li; P. Cundiff; T. Ward; John Castle; M. Krolewski; Michael R. Meyer; Mao Mao; Julja Burchard; M. J. Kidd; Hongyue Dai
The most important product of the sequencing of a genome is a complete, accurate catalogue of genes and their products, primarily messenger RNA transcripts and their cognate proteins. Such a catalogue cannot be constructed by computational annotation alone; it requires experimental validation on a genome scale. Using ‘exon’ and ‘tiling’ arrays fabricated by ink-jet oligonucleotide synthesis, we devised an experimental approach to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons. These methods can provide more accurate gene numbers and allow the detection of mRNA splice variants and identification of the tissue- and disease-specific conditions under which genes are expressed. We apply our technique to chromosome 22q under 69 experimental condition pairs, and to the entire human genome under two experimental conditions. We discuss implications for more comprehensive, consistent and reliable genome annotation, more efficient, full-length complementary DNA cloning strategies and application to complex diseases.
Genome Biology | 2003
John Castle; Phil Garrett-Engele; Christopher D. Armour; Sven Duenwald; Patrick M. Loerch; Michael R. Meyer; Eric E. Schadt; Roland Stoughton; Mark L Parrish; Daniel D. Shoemaker; Jason M. Johnson
Microarrays offer a high-resolution means for monitoring pre-mRNA splicing on a genomic scale. We have developed a novel, unbiased amplification protocol that permits labeling of entire transcripts. Also, hybridization conditions, probe characteristics, and analysis algorithms were optimized for detection of exons, exon-intron edges, and exon junctions. These optimized protocols can be used to detect small variations and isoform mixtures, map the tissue specificity of known human alternative isoforms, and provide a robust, scalable platform for high-throughput discovery of alternative splicing.
PLOS ONE | 2015
Luiz M. Camargo; Xiaohua Douglas Zhang; Patrick M. Loerch; Ramon Miguel Caceres; Shane Marine; Paolo Uva; Marc Ferrer; Emanuele de Rinaldis; David J. Stone; John Majercak; William J. Ray; Chen Yi-An; Mark S. Shearman; Kenji Mizuguchi
The progressive aggregation of Amyloid-β (Aβ) in the brain is a major trait of Alzheimers Disease (AD). Aβ is produced as a result of proteolytic processing of the β-amyloid precursor protein (APP). Processing of APP is mediated by multiple enzymes, resulting in the production of distinct peptide products: the non-amyloidogenic peptide sAPPα and the amyloidogenic peptides sAPPβ, Aβ40, and Aβ42. Using a pathway-based approach, we analyzed a large-scale siRNA screen that measured the production of different APP proteolytic products. Our analysis identified many of the biological processes/pathways that are known to regulate APP processing and have been implicated in AD pathogenesis, as well as revealing novel regulatory mechanisms. Furthermore, we also demonstrate that some of these processes differentially regulate APP processing, with some mechanisms favouring production of certain peptide species over others. For example, synaptic transmission having a bias towards regulating Aβ40 production over Aβ42 as well as processes involved in insulin and pancreatic biology having a bias for sAPPβ production over sAPPα. In addition, some of the pathways identified as regulators of APP processing contain genes (CLU, BIN1, CR1, PICALM, TREM2, SORL1, MEF2C, DSG2, EPH1A) recently implicated with AD through genome wide association studies (GWAS) and associated meta-analysis. In addition, we provide supporting evidence and a deeper mechanistic understanding of the role of diabetes in AD. The identification of these processes/pathways, their differential impact on APP processing, and their relationships to each other, provide a comprehensive systems biology view of the “regulatory landscape” of APP.
Science | 2003
Jason M. Johnson; John Castle; Philip W. Garrett-engele; Zhengyan Kan; Patrick M. Loerch; Christopher D. Armour; Ralph Santos; Eric E. Schadt; Roland Stoughton; Daniel D. Shoemaker
Nature Methods | 2009
Christopher D. Armour; John Castle; Ronghua Chen; Tomas Babak; Patrick M. Loerch; Stuart Jackson; Jyoti Shah; John Dey; Carol A. Rohl; Jason M. Johnson; Christopher K. Raymond
Archive | 2003
Christopher D. Armour; Patrick M. Loerch; John C. Castle; Jason M. Johnson
publisher | None
author
Archive | 2011
John C. Castle; Phil Garrett-Engele; Christopher D. Armour; Sven Duenwald; Patrick M. Loerch; Michael R. Meyer; Eric E. Schadt; Roland Stoughton; Mark L Parrish; Daniel D. Shoemaker; Jason M. Johnson
Archive | 2006
Christopher D. Armour; John C. Castle; Philip W. Garrett-engele; Zhengyan Kan; Patrick M. Loerch; Nicholas F. Tsinoremas
Archive | 2005
Christopher D. Armour; John C. Castle; Philip W. Garrett-engele; Zhengyan Kan; Patrick M. Loerch; Nicholas F. Tsinoremas