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PubMed Journals Articles About "Multicohort Analysis Whole Blood Gene Expression Data Does" RSS

21:26 EST 14th November 2018 | BioPortfolio

Multicohort Analysis Whole Blood Gene Expression Data Does PubMed articles on BioPortfolio. Our PubMed references draw on over 21 million records from the medical literature. Here you can see the latest Multicohort Analysis Whole Blood Gene Expression Data Does articles that have been published worldwide.

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Showing "Multicohort Analysis Whole Blood Gene Expression Data Does" PubMed Articles 1–25 of 67,000+

Fatty liver is associated with blood pathways of inflammatory response, immune system activation and prothrombotic state in Young Finns Study.

Fatty liver (FL) disease is the most common type of chronic liver disease. We hypothesized that liver's response to the process where large droplets of triglyceride fat accumulate in liver cells is reflected also in gene pathway expression in blood. Peripheral blood genome wide gene expression analysis and ultrasonic imaging of liver were performed for 1,650 participants (316 individuals with FL and 1,334 controls) of the Young Finns Study. Gene set enrichment analysis (GSEA) was performed for the expressio...


A comparative study of RNA-Seq and microarray data analysis on the two examples of rectal-cancer patients and Burkitt Lymphoma cells.

Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand. Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances.

Haemopedia RNA-seq: a database of gene expression during haematopoiesis in mice and humans.

During haematopoiesis, haematopoietic stem cells differentiate into restricted potential progenitors before maturing into the many lineages required for oxygen transport, wound healing and immune response. We have updated Haemopedia, a database of gene-expression profiles from a broad spectrum of haematopoietic cells, to include RNA-seq gene-expression data from both mice and humans. The Haemopedia RNA-seq data set covers a wide range of lineages and progenitors, with 57 mouse blood cell types (flow sorted ...


Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure.

Gene-set analysis provides insight into which functional and biological properties of genes are aetiologically relevant for a particular phenotype. But genes have multiple properties, and these properties are often correlated across genes. This can cause confounding in a gene-set analysis, because one property may be statistically associated even if biologically irrelevant to the phenotype, by being correlated with gene properties that are relevant. To address this issue we present a novel conditional and i...

BrainEXP: a database featuring with spatiotemporal expression variations and co-expression organizations in human brains.

Gene expression changes over the lifespan and varies among different tissues or cell types. Gene co-expression also changes by sex, age, different tissues or cell types. However, gene expression under the normal state and gene co-expression in the human brain has not been fully defined and quantified. Here we present a database named Brain EXPression Database (BrainEXP) which provides spatiotemporal expression of individual genes and co-expression in normal human brains. BrainEXP consists of 4,567 samples f...

MiPanda: A Resource for Analyzing and Visualizing Next-Generation Sequencing Transcriptomics Data.

The Michigan Portal for the Analysis of NGS data portal (http://mipanda.org) is an open-access online resource that provides the scientific community with access to the results of a large-scale computational analysis of thousands of high-throughput RNA sequencing (RNA-seq) samples. The portal provides access to gene expression profiles, enabling users to interrogate expression of genes across myriad normal and cancer tissues and cell lines. From these data, tissue- and cancer-specific expression patterns ca...

WEADE: A workflow for enrichment analysis and data exploration.

Data analysis based on enrichment of Gene Ontology terms has become an important step in exploring large gene or protein expression datasets and several stand-alone or web tools exist for that purpose. However, a comprehensive and consistent analysis downstream of the enrichment calculation is missing so far. With WEADE we present a free web application that offers an integrated workflow for the exploration of genomic data combining enrichment analysis with a versatile set of tools to directly compare and i...

Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data.

Identifying co-expressed gene clusters can provide evidence for genetic or physical interactions. Thus, co-expression clustering is a routine step in large-scale analyses of gene expression data. We show that commonly used clustering methods produce results that substantially disagree and that do not match the biological expectations of co-expressed gene clusters. We present clust, a method that solves these problems by extracting clusters matching the biological expectations of co-expressed genes and outpe...

Regularization and grouping -omics data by GCA method: A transcriptomic case.

The paper presents the application of Grade Correspondence Analysis (GCA) and Grade Correspondence Cluster Analysis (GCCA) for ordering and grouping -omics datasets, using transcriptomic data as an example. Based on gene expression data describing 256 patients with Multiple Myeloma it was shown that the GCA method could be used to find regularities in the analyzed collections and to create characteristic gene expression profiles for individual groups of patients. GCA iteratively permutes rows and columns to...

Distinct Gene Expression Patterns between Nasal Mucosal Cells and Blood Collected from Allergic Rhinitis Sufferers.

Investigations of gene expression in allergic rhinitis (AR) typically rely on invasive nasal biopsies (site of inflammation) or blood samples (systemic immunity) to obtain sufficient genetic material for analysis. New methodologies to circumvent the need for invasive sample collection offer promise to further the understanding of local immune mechanisms relevant in AR.

Parent of origin gene expression in a founder population identifies two new candidate imprinted genes at known imprinted regions.

Genomic imprinting is the phenomena that leads to silencing of one copy of a gene inherited from a specific parent. Mutations in imprinted regions have been involved in diseases showing parent of origin effects. Identifying genes with evidence of parent of origin expression patterns in family studies allows the detection of more subtle imprinting. Here, we use allele specific expression in lymphoblastoid cell lines from 306 Hutterites related in a single pedigree to provide formal evidence for parent of ori...

Co-expression network analysis of peripheral blood transcriptome identifies dysregulated protein processing in endoplasmic reticulum and immune response in recurrent MDD in older adults.

The molecular factors involved in the pathophysiology of major depressive disorder (MDD) remain poorly understood. One approach to examine the molecular basis of MDD is co-expression network analysis, which facilitates the examination of complex interactions between expression levels of individual genes and how they influence biological pathways affected in MDD. Here, we applied an unsupervised gene-network based approach to a prospective experimental design using microarray genome-wide gene expression from...

Gene expression profile of peripheral blood mononuclear cells may contribute to the identification and immunological classification of breast cancer patients.

It has been reported that the gene expression profile of peripheral blood mononuclear cells (PBMCs) exhibits a unique gene expression signature in several types of cancer. In this study, we aimed to explore the breast cancer patient-specific gene expression profile of PBMCs and discuss immunological insight on host antitumor immune responses.

GECO: gene expression correlation analysis after genetic algorithm-driven deconvolution.

Large-scale gene expression analysis is a valuable asset for data-driven hypothesis generation. However, the convoluted nature of large expression datasets often hinders extraction of meaningful biological information. Results: To this end, we developed GECO, a gene expression correlation analysis software that uses a genetic algorithm-driven approach to deconvolute complex expression datasets into two subpopulations that display positive and negative correlations between a pair of queried genes. GECO's mut...

Comparison of gene expression and flow cytometry for immune profiling in chronic lymphocytic leukaemia.

Understanding how cancer and cancer therapies affect the immune system is integral to the rational application of immunotherapies. Flow cytometry is the gold standard method of peripheral blood immune cell profiling. However, the requirement for viable cells can limit its applicability, especially in studies of retrospective clinical cohorts. We aimed to determine if gene expression, analysed using the NanoString platform, could be used to quantify the immune populations present in cryopreserved peripheral ...

A statistical method for measuring activation of gene regulatory networks.

Gene expression data analysis is of great importance for modern molecular biology, given our ability to measure the expression profiles of thousands of genes and enabling studies rooted in systems biology. In this work, we propose a simple statistical model for the activation measuring of gene regulatory networks, instead of the traditional gene co-expression networks.

Differential Expression Profile of MicroRNAs During Prolonged Storage of Platelet Concentrates As a Quality Measurement Tool in Blood Banks.

Platelet concentrate (PC) is a key blood component, which even in good storage conditions, susceptible to cellular damage over time. Hence, blood banks discard unused PC bags after 5 days of storage. Biomarkers of PC quality are therefore highly sought after in blood bank governance. We used the data (Gene Expression Omnibus: GSE61856) generated with next-generation sequencing to examine the expression profiles of microRNAs (miRNAs) from PCs that were stored for 6 days in a blood bank, that is, 1 day longer...

Detection of pathogenic bacteria in the blood from sepsis patients using 16S rRNA gene amplicon sequencing analysis.

Prompt identification of causative pathogenic bacteria is imperative for the treatment of patients suffering from infectious diseases, including sepsis and pneumonia. However, current culture-based methodologies have several drawbacks including their limitation of use to culturable bacterial species. To circumvent these problems, we attempted to detect bacterial DNA in blood using next-generation DNA sequencing (NGS) technology. We conducted metagenomic and 16S ribosomal RNA (rRNA) gene amplicon sequencing ...

FTO mRNA expression in the lower quartile is associated with bad prognosis in clear cell renal cell carcinoma based on TCGA data mining.

Fat mass and obesity associated (FTO) is a protein-coding gene, also known as the obesity gene. It has been reported previously to be associated with a variety of malignant cancers, such as breast, thyroid and acute myeloid leukemia. The aim of the present study was to investigate the FTO mRNA expression in human clear cell renal cell carcinoma and its clinical value. FTO mRNA expression and its prognostic value were investigated by bioinformatic analysis of the data from The Cancer Genome Atlas (TCGA, http...

Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles.

The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair re...

SigEMD: A powerful method for differential gene expression analysis in single-cell RNA sequencing data.

Differential gene expression analysis is one of the significant efforts in single cell RNA sequencing (scRNAseq) analysis to discover the specific changes in expression levels of individual cell types. Since scRNAseq exhibits multimodality, large amounts of zero counts, and sparsity, it is different from the traditional bulk RNA sequencing (RNAseq) data. The new challenges of scRNAseq data promote the development of new methods for identifying differentially expressed (DE) genes. In this study, we proposed ...

Whole peripheral blood miR-146a and miR-155 expression levels in Systemic lupus erythematosus patients.

Objective: To evaluate the diagnostic value of peripheral blood microribonucleic acid (miRNA, miR)-146a and miR-155 expression in systemic lupus erythematosus (SLE). Methods: Expression levels of miR-155 and miR-146a in whole peripheral blood samples from 40 SLE patients and 32 healthy controls (HCs) were determined by quantitative reverse transcription-polymerase chain reaction qRT-PCR (SYBR Green technology) and 2-∆∆Ct method was used for analysis. SPSS v20 was used for receiver operating characterist...

Harnessing the biological complexity of Big Data from LINCS gene expression signatures.

Gene expression profiling using transcriptional drug perturbations are useful for many biomedical discovery studies including drug repurposing and elucidation of drug mechanisms (MoA) and many other pharmacogenomic applications. However, limited data availability across cell types has severely hindered our capacity to progress in these areas. To fill this gap, recently, the LINCS program generated almost 1.3 million profiles for over 40,000 drug and genetic perturbations for over 70 different human cell typ...

A group of lncRNAs identified by data mining can predict the prognosis of lung adenocarcinoma.

lncRNA is reported to be potential cancer biomarkers. This study aims to find new lncRNA biomarker relevant to lung adenocarcinoma. Gene expression profile and clinical data of lung adenocarcinoma and lung squamous cell carcinoma patients were downloaded from UCSC Xena database. These data were analyzed to identify potential lncRNA prognostic biomarkers and the candidate lncRNAs were analyzed and verified with association analysis, meta-analysis, survival analysis, gene ontology analysis, gene set enrichmen...

The effect of childhood trauma on blood transcriptome expression in major depressive disorder.

Childhood trauma (CT) increases the likelihood of developing severe mental illnesses, such as major depressive disorder (MDD), during adulthood. Several studies have suggested an inflammatory immune system dysregulation as a biological mediator; however, the molecular mechanisms underlying this relationship remain largely undetermined. Moreover, different types of CT, in particular, emotional abuse and neglect, confer a higher risk of developing MDD, and recent meta-analyses showed that each CT can be assoc...


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