The dChip survival analysis module for microarray data

Amin, Samir B.; Shah, Parantu K.; Yan, Aimin; Adamia, Sophia; Minvielle, Stéphane; Avet-Loiseau, Hervé; Munshi, Nikhil C.; Li, Cheng

doi:10.1186/1471-2105-12-72

Cited by 9 publications

(10 citation statements)

References 19 publications

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“…9, IV, sampled almost entirely from individuals with African ancestry, are noticeably narrower than the rest, indicating a relatively ancient haplotype that has undergone multiple noncoding mutations. This is consistent with the widely held observation that most of human genetic diversity exists in 1 The online version of this article contains supplemental material. Fig.…”

supporting

confidence: 75%

“…We used dChip (1,21) to analyze neck cell expression vis-a-vis other cell types within the gastric unit ( Fig. 1A) (36).…”

Section: Resultsmentioning

confidence: 99%

“…The SNPs that make up the coding haplotypes of the three GKN genes are detailed in Supplemental Table S1. 1 To determine whether the amino acid substitutions from the nonsynonymous SNPs would be deleterious, we used the amino acid substitution predictor SIFT (28). SIFT is a homology-based program that predicts where an amino acid substitution at a specific site would be deleterious to the protein (and therefore not tolerated) based on the alignment of similar protein sequences and those predicted to have similar functions.…”

Section: Resultsmentioning

confidence: 99%

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Evolution of the human gastrokine locus and confounding factors regarding the pseudogenicity ofGKN3

Geahlen

Lapid

Thorell

et al. 2013

Physiological Genomics

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Templeton AR, Mills JC. Evolution of the human gastrokine locus and confounding factors regarding the pseudogenicity of GKN3. Physiol Genomics 45: 667-683, 2013. First published May 28, 2013 doi:10.1152/physiolgenomics.00169.2012.-In a screen for genes expressed specifically in gastric mucous neck cells, we identified GKN3, the recently discovered third member of the gastrokine family. We present confirmatory mouse data and novel porcine data showing that mouse GKN3 expression is confined to mucous cells of the corpus neck and antrum base and is prominently expressed in metaplastic lesions. GKN3 was proposed originally to be expressed in some human populations and a pseudogene in others. To investigate that hypothesis, we studied human GKN3 evolution in the context of its paralogous genomic neighbors, GKN1 and GKN2. Haplotype analysis revealed that GKN3 mimics GKN2 in patterns of exonic SNP allocation, whereas GKN1 appeared to be more stringently selected. GKN3 showed signatures of both directional selection and population based selective sweeps in humans. One such selective sweep includes SNP rs10187256, originally identified as an ancestral tryptophan to premature STOP codon mutation. The derived (nonancestral) allele went to fixation in Asia. We show that another SNP, rs75578132, identified 5 bp downstream of rs10187256, exhibits a second selective sweep in almost all Europeans, some Latinos, and some Africans, possibly resulting from a reintroduction of European genes during African colonization. Finally, we identify a mutation that would destroy the splice donor site in the putative exon3-intron3 boundary, which occurs in all human genomes examined to date. Our results highlight a stomach-specific human genetic locus, which has undergone various selective sweeps across European, Asian, and African populations and thus reflects geographic and ethnic patterns in genome evolution.

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supporting

confidence: 75%

“…We used dChip (1,21) to analyze neck cell expression vis-a-vis other cell types within the gastric unit ( Fig. 1A) (36).…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Evolution of the human gastrokine locus and confounding factors regarding the pseudogenicity ofGKN3

Geahlen

Lapid

Thorell

et al. 2013

Physiological Genomics

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“…OneChannelGUI (11), dChip (23), AltAnalyze (12), GenePattern (7), and GEPAS (4) offer a full set of tools for microarray data analysis comparable to those of AMDA. While cross-platform normalization pre-processing is provided by OneChannelGUI, AltAnalyze, and ArrayMining, these applications do not offer different experimental designs or a comprehensive output report that documents data in a workflow.…”

Section: Resultsmentioning

confidence: 99%

AMDA 2.13: A Major Update for Automated Cross-Platform Microarray Data Analysis

et al. 2012

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Microarray platforms require analytical pipelines with modules for data pre-processing including data normalization, statistical analysis for identification of differentially expressed genes, cluster analysis, and functional annotation. We previously developed the Automated Microarray Data Analysis (AMDA, version 2.3.5) pipeline to process Affymetrix 3 IVT GeneChips. The availability of newer technologies that demand open-source tools for microarray data analysis has impelled us to develop an updated multi-platform version, AMDA 2.13. It includes additional quality control metrics, annotation-driven (annotation grade of Affymetrix NetAffx) and signal-driven (Inter-Quartile Range) gene filtering, and approaches to experimental design. To enhance understanding of biological data, differentially expressed genes have been mapped into KEGG pathways. Finally, a more stable and user-friendly interface was designed to integrate the requirements for different platforms. AMDA 2.13 allows the analysis of Affymetrix (cartridges and plates) and whole transcript probe design (Gene 1.0/1.1 ST and Exon 1.0 ST GeneChips), Illumina Bead Arrays, and one-channel Agilent 444 arrays. Relative to early versions, it supports various experimental designs and delivers more insightful biological understanding and up-to-date annotations.

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“…Samples were processed at Microarray Core of Dana Farber Cancer Institute using GeneChip Mouse Gene 1.0 ST Array. Results were analyzed using dChip software (2). Pathway analysis was performed with Genecodis 2.0 (15,58).…”

Section: N-glycosidase F Treatmentmentioning

confidence: 99%

Cardiomyocyte ATP release through pannexin 1 aids in early fibroblast activation

Dolmatova

Spagnol

Boassa

et al. 2012

American Journal of Physiology-Heart and Circulatory Physiology

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HS. Cardiomyocyte ATP release through pannexin 1 aids in early fibroblast activation. Am J Physiol Heart Circ Physiol 303: H1208 -H1218, 2012. First published September 14, 2012; doi:10.1152/ajpheart.00251.2012.-Fibrosis following myocardial infarction is associated with increases in arrhythmias and sudden cardiac death. Initial steps in the development of fibrosis are not clear; however, it is likely that cardiac fibroblasts play an important role. In immune cells, ATP release from pannexin 1 (Panx1) channels acts as a paracrine signal initiating activation of innate immunity. ATP has been shown in noncardiac systems to initiate fibroblast activation. Therefore, we propose that ATP release through Panx1 channels and subsequent fibroblast activation in the heart drives the development of fibrosis in the heart following myocardial infarction. We identified for the first time that Panx1 is localized within sarcolemmal membranes of canine cardiac myocytes where it directly interacts with the postsynaptic density 95/Drosophila disk large/zonula occludens-1-containing scaffolding protein synapseassociated protein 97 via its carboxyl terminal domain (amino acids 300 -357). Induced ischemia rapidly increased glycosylation of Panx1, resulting in increased trafficking to the plasma membrane as well as increased interaction with synapse-associated protein 97. Cellular stress enhanced ATP release from myocyte Panx1 channels, which, in turn, causes fibroblast transformation to the activated myofibroblast phenotype via activation of the MAPK and p53 pathways, both of which are involved in the development of cardiac fibrosis. ATP release through Panx1 channels in cardiac myocytes during ischemia may be an early paracrine event leading to profibrotic responses to ischemic cardiac injury. fibrosis; paracrine signal; sudden cardiac death; ischemia IN CARDIAC INJURY, fibroblasts are activated, causing them to transdifferentiate into myofibroblasts, leading to profibrotic responses such as production of extracellular matrix proteins, collagen, and cytokines (8). With time, expansion of the extracellular matrix leads to separation of surviving myocyte bundles, forming a heterogeneous substrate that leads to slowed conduction which promotes life-threatening arrhythmias. In regions distant to the site of injury, fibroblasts activate and migrate to sites of injury and are highly responsive to cytokines and chemokines (64). While activated fibroblasts are known to produce and secrete many of the signals for myofibroblast formation and migration, thus causing a positive feedback loop of activation, little is known about the initial stimulus within the injured region that begins the activation process. It has been assumed that fibroblasts sense the environment and respond accordingly, but how and what they are sensing during very early stages of cardiac injury are unknown. Our studies suggest that ATP released from cardiac myocyte pannexin 1 (Panx1) channels initiates signaling in fibroblasts to begin the fibrotic process.Panx1 is ubiqui...

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The dChip survival analysis module for microarray data

Cited by 9 publications

References 19 publications

Evolution of the human gastrokine locus and confounding factors regarding the pseudogenicity ofGKN3

Evolution of the human gastrokine locus and confounding factors regarding the pseudogenicity ofGKN3

AMDA 2.13: A Major Update for Automated Cross-Platform Microarray Data Analysis

Cardiomyocyte ATP release through pannexin 1 aids in early fibroblast activation

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