Estimating relative noise to signal in DNA microarray data

Hong, Huixiao; Hong, Qilong; Liu, Jie; Tong, Weida; Shi, Leming

doi:10.1504/ijbra.2013.056085

Cited by 10 publications

(7 citation statements)

References 24 publications

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“…Recent expression profiling studies of AD mouse models have reported up-regulation of genes in classical complement cascade and changes of synapse-related genes in early AD disease progression (D’Onofrio et al, 2011; Hong et al, 2013, 2016). Studies of AD mouse models in later phases of progression (Matarin et al, 2015; Saura et al, 2015) often report expression changes of genes related to inflammation and the immune system.…”

Section: Introductionmentioning

confidence: 99%

Mega-Analysis of Gene Expression in Mouse Models of Alzheimer’s Disease

Zhuang

Mancarci

Toker

et al. 2019

eNeuro

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While multiple studies have been conducted of gene expression in mouse models of Alzheimer’s disease (AD), their findings have not reached a clear consensus and have not accounted for the potentially confounding effects of changes in cellular composition. To help address this gap, we conducted a re-analysis based meta-analysis (mega-analysis) of ten independent studies of hippocampal gene expression in mouse models of AD.

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Section: Introductionmentioning

confidence: 99%

Mega-Analysis of Gene Expression in Mouse Models of Alzheimer’s Disease

Zhuang

Mancarci

Toker

et al. 2019

eNeuro

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“…Gene expression profile studies can be used to provide understanding of the molecular mechanisms underlying the development and maintenance of neuropathic pain [ 10 – 12 ]. Some studies using microarray and RNA sequencing (RNA-seq) analysis have been conducted to investigate the mechanism underlying the generation of neuropathic pain in spared nerve injury (SNI) model [ 13 , 14 ]. Although they identified several crucial differentially expressed genes (DEGs) and different immune actions in SNI models, the alteration of gene expression and mechanisms on neuropathic pain are still unclear.…”

Section: Introductionmentioning

confidence: 99%

Analyses of gene expression profiles in the rat dorsal horn of the spinal cord using RNA sequencing in chronic constriction injury rats

Shi

Wang

et al. 2018

J Neuroinflammation

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BackgroundNeuropathic pain is caused by damage to the nervous system, resulting in aberrant pain, which is associated with gene expression changes in the sensory pathway. However, the molecular mechanisms are not fully understood.MethodsWistar rats were employed for the establishment of the chronic constriction injury (CCI) models. Using the Illumina HiSeq 4000 platform, we examined differentially expressed genes (DEGs) in the rat dorsal horn by RNA sequencing (RNA-seq) between CCI and control groups. Then, enrichment analyses were performed for these DEGs using Gene Ontology (GO) function, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Hierarchical Cluster, and protein-protein interaction (PPI) network.ResultsA total of 63 DEGs were found significantly changed with 56 upregulated (e.g., Cxcl13, C1qc, Fcgr3a) and 7 downregulated (e.g., Dusp1) at 14 days after CCI. Quantitative reverse-transcribed PCR (qRT-PCR) verified changes in 13 randomly selected DEGs. GO and KEGG biological pathway analyses showed that the upregulated DEGs were mostly enriched in immune response-related biological processes, as well as 14 immune- and inflammation-related pathways. The downregulated DEGs were enriched in inactivation of mitogen-activated protein kinase (MAPK) activity. PPI network analysis showed that Cd68, C1qc, C1qa, Laptm5, and Fcgr3a were crucial nodes with high connectivity degrees. Most of these genes which have previously been linked to immune and inflammation-related pathways have not been reported in neuropathic pain (e.g., Laptm5, Fcgr3a).ConclusionsOur results revealed that immune and defense pathways may contribute to the generation of neuropathic pain after CCI. These mRNAs may represent new therapeutic targets for the treatment of neuropathic pain.Electronic supplementary materialThe online version of this article (10.1186/s12974-018-1316-0) contains supplementary material, which is available to authorized users.

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“…Data quality is the heart of science, and many efforts have been made to ensure and improve data quality in other fields such as genetics [ 64 , 65 ], genomics [ 66 , 67 , 68 ], and food science [ 69 ]. According to the recently established FAIR (finable, accessible, interoperable, and reusable) guiding principles, the reuse of nanosafety data involves data quality issues such as different levels of processed data, poorly described (meta)data, and limited harmonized reporting formats and tools for data integration and interpretation [ 70 ].…”

Section: Perspectivesmentioning

confidence: 99%

Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials

Guo

Sakkiah

et al. 2021

Nanomaterials

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Nanomaterials have drawn increasing attention due to their tunable and enhanced physicochemical and biological performance compared to their conventional bulk materials. Owing to the rapid expansion of the nano-industry, large amounts of data regarding the synthesis, physicochemical properties, and bioactivities of nanomaterials have been generated. These data are a great asset to the scientific community. However, the data are on diverse aspects of nanomaterials and in different sources and formats. To help utilize these data, various databases on specific information of nanomaterials such as physicochemical characterization, biomedicine, and nano-safety have been developed and made available online. Understanding the structure, function, and available data in these databases is needed for scientists to select appropriate databases and retrieve specific information for research on nanomaterials. However, to our knowledge, there is no study to systematically compare these databases to facilitate their utilization in the field of nanomaterials. Therefore, we reviewed and compared eight widely used databases of nanomaterials, aiming to provide the nanoscience community with valuable information about the specific content and function of these databases. We also discuss the pros and cons of these databases, thus enabling more efficient and convenient utilization.

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Estimating relative noise to signal in DNA microarray data

Cited by 10 publications

References 24 publications

Mega-Analysis of Gene Expression in Mouse Models of Alzheimer’s Disease

Mega-Analysis of Gene Expression in Mouse Models of Alzheimer’s Disease

Analyses of gene expression profiles in the rat dorsal horn of the spinal cord using RNA sequencing in chronic constriction injury rats

Nanomaterial Databases: Data Sources for Promoting Design and Risk Assessment of Nanomaterials

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