15 papers · ranked by Valyu relevance
Samuel Bharti, Nikita Krishnan, Arian Veyssi, Maryam Momeni + 1 more
Microarray data enables biologists to extract differentially expressed genes (DEGs) across multiple phenotypes. While several pipelines and tools exist to perform microarray data analysis, they are targeted to users with moderate to advanced computational understanding and lack an easy-to-use, interactive and dynamic…
Guy P Hunt, Rafael Henkin, Fabrizio Smeraldi, Michael R Barnes
Over the past three decades there have been numerous molecular biology developments that have led to an explosion in the number of gene expression studies being performed. Many of these gene expression studies publish their data to the public database GEO, making them freely available. By analysing gene expression…
S. Ashokraj, E. Edwin Raj, K.N. Chandrashekara, R. Govindaraj + 2 more
The blister blight (BB) and grey blight (GB) diseases are the major biotic stresses, which affecting the plant health, yield and quality of tea. The study aims to understand the gene response of tea plants against destructing foliar diseases in terms of differential gene expression and their pathways through microarray…
Ernur Saka, Benjamin J. Harrison, Kirk West, Jeffrey C. Petruska + 1 more
Since the introduction of microarrays in 1995, researchers world-wide have used both commercial and custom-designed microarrays for understanding differential expression of transcribed genes. Public databases such as ArrayExpress and the Gene Expression Omnibus (GEO) have made millions of samples readily available. One…
Lavida R.K. Brooks, George I. Mias
Alzheimer’s disease (AD) has been categorized by the Centers for Disease Control and Prevention (CDC) as the 6^th^ leading cause of death in the United States. AD is a significant health-care burden because of its increased occurrence (specifically in the elderly population) and the lack of effective treatments and…
Christopher A Mancuso, Jacob L Canfield, Deepak Singla, Arjun Krishnan
While there are >2 million publicly-available human microarray gene-expression profiles, these profiles were measured using a variety of platforms that each cover a pre-defined, limited set of genes. Therefore, key to reanalyzing and integrating this massive data collection are methods that can computationally…
Alina Frolova, Vladyslav Bondarenko, Maria Obolenska
According to major public repositories statistics an overwhelming majority of the existing and newly uploaded data originates from microarray experiments. Unfortunately, the potential of this data to bring new insights is limited by the effects of individual study-specific biases due to small number of biological…
Adam Giangreco
Lung squamous cell carcinoma (SqCC) accounts for 30% of lung cancers, with over 400,000 deaths per year worldwide. Although evidence suggests that chronic lung injury drives carcinogenesis, a comprehensive understanding of this process remains elusive. Here, I used a comparative microarray analysis to identify gene…
Roman Mezencev, Scott Auerbach
Whole-genome expression data generated by microarray studies have shown promise for quantitative human health risk assessment. While numerous approaches have been developed to determine benchmark doses (BMDs) from probeset-level dose responses, sensitivity of the results to methods used for normalization of the data…
Yin Liang, Mengxue Wang, Yun Liu, Chen Wang + 2 more
Gravity affects the function and maintenance of organs, such as bones, muscles, and the heart. Several studies have used DNA microarrays to identify genes with altered expressions in response to gravity. However, it is technically challenging to combine the results from various microarray datasets because of their…
Jaclyn N. Taroni, Casey S. Greene
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. The majority of available RNA assays are run on microarray, while RNA-seq is becoming the platform of choice for new experiments. The data structure and distributions between the platforms differ, making it…
Chloe J. Bennett, Rodolfo Aramayo
RNA sequencing (RNA-seq) is a commonly used method to identify changes in gene expression between two conditions. The analysis of RNA-seq output is complicated, with the possibility of getting different results from the same raw data. We developed and deployed four parallel pipelines to reanalyze an existing dataset of…
David G. Robinson, Jean Wang, John D. Storey
Understanding the differences between microarray and RNA-Seq technologies for measuring gene expression is necessary for informed design of experiments and choice of data analysis methods. Previous comparisons have come to sometimes contradictory conclusions, which we suggest result from a lack of attention to the…
Michael A. Bertagna, Lydia J. Bright, Fei Ye, Yu-Yang Jiang + 6 more
Although an established model organism, Tetrahymena thermophila remains comparatively inaccessible to high throughput screens, and alternative bioinformatic approaches still rely on unconnected datasets and outdated algorithms. Here, we report a new approach to consolidating RNA-seq and microarray data based on a…
Mengyi Sun, Jianzhi Zhang
Gene expression noise refers to the variation of the expression level of a gene among isogenic cells in the same environment, and has two sources: extrinsic noise arising from the disparity of the cell state and intrinsic noise arising from the stochastic process of gene expression in the same cell state. Due to the…