scholarly journals Differential Gene Expression Responding to Low Phosphate Stress in Leaves and Roots of Maize by cDNA-SRAP

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Lei Yan ◽  
Liang Su ◽  
Rui Li ◽  
Hao Li ◽  
Jianrong Bai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Differentially Expressed ◽  
Complex Adaptive ◽  
Differential Gene ◽  
Low Pi Stress ◽  
Leaves And Roots ◽  
Diverse Groups ◽  
Pi Stress

Phosphate (Pi) deficiency in soil can have severe impacts on the growth, development, and production of maize worldwide. In this study, a cDNA-sequence-related amplified polymorphism (cDNA-SRAP) transcript profiling technique was used to evaluate the gene expression in leaves and roots of maize under Pi stress for seven days. A total of 2494 differentially expressed fragments (DEFs) were identified in response to Pi starvation with 1202 and 1292 DEFs in leaves and roots, respectively, using a total of 60 primer pairs in the cDNA-SRAP analysis. These DEFs were categorized into 13 differential gene expression patterns. Results of sequencing and functional analysis showed that 63 DEFs (33 in leaves and 30 in roots) were annotated to a total of 54 genes involved in diverse groups of biological pathways, including metabolism, photosynthesis, signal transduction, transcription, transport, cellular processes, genetic information, and organismal system. This study demonstrated that (1) the cDNA-SRAP transcriptomic profiling technique is a powerful method to analyze differential gene expression in maize showing advantageous features among several transcriptomic methods; (2) maize undergoes a complex adaptive process in response to low Pi stress; and (3) a total of seven differentially expressed genes were identified in response to low Pi stress in leaves or roots of maize and could be used in the genetic modification of maize.

scholarly journals RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

scholarly journals Carbohydrate-induced Differential Gene Expression Patterns in the Hyperthermophilic BacteriumThermotoga maritima

2002 ◽  
Vol 278 (9) ◽  
pp. 7540-7552 ◽  
Author(s):  
Swapnil R. Chhabra ◽  
Keith R. Shockley ◽  
Shannon B. Conners ◽  
Kevin L. Scott ◽  
Russell D. Wolfinger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Differential Gene

Differential gene expression patterns and colocalization of ATP-gated P2X6/P2X4 ion channels during rat small intestine ontogeny

2016 ◽  
Vol 21 (2) ◽  
pp. 81-88 ◽  
Author(s):  
Karla Padilla ◽  
David Gonzalez-Mendoza ◽  
Laura C. Berumen ◽  
Jesica E. Escobar ◽  
Ricardo Miledi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Small Intestine ◽  
Ion Channels ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Rat Small Intestine ◽  
Differential Gene

scholarly journals Differential Gene Expression Patterns of EBV Infected EBNA-3A Positive and Negative Human B Lymphocytes

2009 ◽  
Vol 5 (7) ◽  
pp. e1000506 ◽  
Author(s):  
Marie L. Hertle ◽  
Claudia Popp ◽  
Sabine Petermann ◽  
Sabine Maier ◽  
Elisabeth Kremmer ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Lymphocytes ◽  
Differential Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Differential Gene

PADGE: analysis of heterogeneous patterns of differential gene expression

2007 ◽  
Vol 32 (1) ◽  
pp. 154-159 ◽  
Author(s):  
Li Li ◽  
Amitabha Chaudhuri ◽  
John Chant ◽  
Zhijun Tang
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Statistical Power ◽  
Profile Analysis ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Cancer Genes ◽  
Data Set ◽  
Differential Gene

We have devised a novel analysis approach, percentile analysis for differential gene expression (PADGE), for identifying genes differentially expressed between two groups of heterogeneous samples. PADGE was designed to compare expression profiles of sample subgroups at a series of percentile cutoffs and to examine the trend of relative expression between sample groups as expression level increases. Simulation studies showed that PADGE has more statistical power than t-statistics, cancer outlier profile analysis (COPA) (Tomlins SA, Rhodes DR, Perner S, Dhanasekaran SM, Mehra R, Sun XW, Varambally S, Cao X, Tchinda J, Kuefer R, Lee C, Montie JE, Shah RB, Pienta KJ, Rubin MA, Chinnaiyan AM. Science 310: 644–648, 2005), and kurtosis (Teschendorff AE, Naderi A, Barbosa-Morais NL, Caldas C. Bioinformatics 22: 2269–2275, 2006). Application of PADGE to microarray data sets in tumor tissues demonstrated its utility in prioritizing cancer genes encoding potential therapeutic targets or diagnostic markers. A web application was developed for researchers to analyze a large gene expression data set from heterogeneous biological samples and identify differentially expressed genes between subsets of sample classes using PADGE and other available approaches. Availability: http://www.cgl.ucsf.edu/Research/genentech/padge/ .

scholarly journals Relationship of differential gene expression profiles in CD34+ myelodysplastic syndrome marrow cells to disease subtype and progression

Blood ◽  
2009 ◽  
Vol 114 (23) ◽  
pp. 4847-4858 ◽  
Author(s):  
Kunju Sridhar ◽  
Douglas T. Ross ◽  
Robert Tibshirani ◽  
Atul J. Butte ◽  
Peter L. Greenberg
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Myelodysplastic Syndrome ◽  
Differential Gene Expression ◽  
Myeloid Leukemia ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differentially Expressed ◽  
Differential Gene ◽  
Acute Myeloid

AbstractMicroarray analysis with 40 000 cDNA gene chip arrays determined differential gene expression profiles (GEPs) in CD34+ marrow cells from myelodysplastic syndrome (MDS) patients compared with healthy persons. Using focused bioinformatics analyses, we found 1175 genes significantly differentially expressed by MDS versus normal, requiring a minimum of 39 genes to separately classify these patients. Major GEP differences were demonstrated between healthy and MDS patients and between several MDS subgroups: (1) those whose disease remained stable and those who subsequently transformed (tMDS) to acute myeloid leukemia; (2) between del(5q) and other MDS patients. A 6-gene “poor risk” signature was defined, which was associated with acute myeloid leukemia transformation and provided additive prognostic information for International Prognostic Scoring System Intermediate-1 patients. Overexpression of genes generating ribosomal proteins and for other signaling pathways was demonstrated in the tMDS patients. Comparison of del(5q) with the remaining MDS patients showed 1924 differentially expressed genes, with underexpression of 1014 genes, 11 of which were within the 5q31-32 commonly deleted region. These data demonstrated (1) GEPs distinguishing MDS patients from healthy and between those with differing clinical outcomes (tMDS vs those whose disease remained stable) and cytogenetics [eg, del(5q)]; and (2) molecular criteria refining prognostic categorization and associated biologic processes in MDS.

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