scholarly journals Charting Shifts in Saccharomyces cerevisiae Gene Expression across Asynchronous Time Trajectories with Diffusion Maps

mBio ◽  
2021 ◽  
Author(s):  
Taylor Reiter ◽  
Rachel Montpetit ◽  
Ron Runnebaum ◽  
C. Titus Brown ◽  
Ben Montpetit
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Gene Expression Data ◽  
Pinot Noir ◽  
Expression Data ◽  
Analysis Method ◽  
Diffusion Maps ◽  
Content Type

In this work, Saccharomyces cerevisiae gene expression was used as a biosensor to capture differences across and between fermentations of Pinot noir grapes from 15 unique sites representing eight American Viticultural Areas. This required development of a novel analysis method, DMap-DE, for investigation of asynchronous gene expression data.

scholarly journals Mapping global shifts in Saccharomyces cerevisiae gene expression across asynchronous time trajectories with diffusion maps

2021 ◽  
Author(s):  
Taylor Reiter ◽  
Rachel Montpetit ◽  
Ron Runnebaum ◽  
C. Titus Brown ◽  
Ben Montpetit
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Expression Patterns ◽  
Pinot Noir ◽  
Wine Fermentation ◽  
Gene Expression Patterns ◽  
Expression Data ◽  
Site Specific ◽  
Time Series Gene Expression ◽  
Specific Factors

AbstractGrapes grown in a particular geographic region often produce wines with consistent characteristics, suggesting there are site-specific factors driving recurrent fermentation outcomes. However, our understanding of the relationship between site-specific factors, microbial metabolism, and wine fermentation outcomes are not well understood. Here, we used differences in Saccharomyces cerevisiae gene expression as a biosensor for differences among Pinot noir fermentations from 15 vineyard sites. We profiled time series gene expression patterns of primary fermentations, but fermentations proceeded at different rates, making analyzes of these data with conventional differential expression tools difficult. This led us to develop a novel approach that combines diffusion mapping with continuous differential expression analysis. Using this method, we identified vineyard specific deviations in gene expression, including changes in gene expression correlated with the activity of the non-Saccharomyces yeast Hanseniaspora uvarum, as well as with initial nitrogen concentrations in grape musts. These results highlight novel relationships between site-specific variables and Saccharomyces cerevisiae gene expression that are linked to repeated wine fermentation outcomes. In addition, we demonstrate that our analysis approach can extract biologically relevant gene expression patterns in other contexts (e.g., hypoxic response of Saccharomyces cerevisiae), indicating that this approach offers a general method for investigating asynchronous time series gene expression data.ImportanceWhile it is generally accepted that foods, in particular wine, possess sensory characteristics associated with or derived from their place of origin, we lack knowledge of the biotic and abiotic factors central to this phenomenon. We have used Saccharomyces cerevisiae gene expression as a biosensor to capture differences in fermentations of Pinot noir grapes from 15 vineyards across two vintages. We find that gene expression by non-Saccharomyces yeasts and initial nitrogen content in the grape must correlates with differences in gene expression among fermentations from these vintages. These findings highlight important relationships between site-specific variables and gene expression that can be used to understand, or possibly modify, wine fermentation outcomes. Our work also provides a novel analysis method for investigating asynchronous gene expression data sets that is able to reveal both global shifts and subtle differences in gene expression due to varied cell – environment interactions.

Insights into Responses to Extreme Environmental Conditions in Humans from Studies on Saccharomyces cerevisiae

2015 ◽  
Vol 65 (6) ◽  
pp. 444
Author(s):  
Ramesh C. Meena ◽  
Amitabha Chakrabarti
Keyword(s):  
Gene Expression ◽  
Saccharomyces Cerevisiae ◽  
Stress Response ◽  
Gene Expression Data ◽  
Molecular Mechanisms ◽  
Multiple Stressors ◽  
Expression Data ◽  
Environmental Stress Response ◽  
Yeast Saccharomyces Cerevisiae ◽  
Pathways Analysis

<p>The versatility of the yeast experimental model has aided in innumerable ways in the understanding of fundamental cellular functions and has also contributed towards the elucidation of molecular mechanisms underlying several pathological conditions in humans. Genome-wide expression, functional, localization and interaction studies on the yeast Saccharomyces cerevisiae exposed to various stressors have made profound contributions towards the understanding of stress response pathways. Analysis of gene expression data from S. cerevisiae cells indicate that the expression of a common set of genes is altered upon exposure to all the stress conditions examined. This common response to multiple stressors is known as the Environmental stress response. Knowledge gained from studies on the yeast model has now become helpful in understanding stress response pathways and associated disease conditions in humans. Cross-species microarray experiments and analysis of data with ever improving computational methods has led to a better comparison of gene expression data between diverse organisms that include yeast and humans.</p>

Identification of perturbed signaling pathways from gene expression data using information divergence

2017 ◽  
Vol 13 (9) ◽  
pp. 1797-1804
Author(s):  
Xinying Hu ◽  
Hang Wei ◽  
Haoran Zheng
Keyword(s):  
Gene Expression ◽  
Probability Distribution ◽  
Signaling Pathways ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Expression Data ◽  
Analysis Method ◽  
Information Divergence

We propose a pathway analysis method based on information divergence and the probability distribution of the regulation capacity.

scholarly journals Nonlinear-Model-Based Analysis Methods for Time-Course Gene Expression Data

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Li-Ping Tian ◽  
Li-Zhi Liu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Nonlinear Model ◽  
Clustering Analysis ◽  
Time Course ◽  
Disease Diagnosis ◽  
Expression Data ◽  
Analysis Method ◽  
Significance Analysis ◽  
Analysis Methods

Microarray technology has produced a huge body of time-course gene expression data and will continue to produce more. Such gene expression data has been proved useful in genomic disease diagnosis and drug design. The challenge is how to uncover useful information from such data by proper analysis methods such as significance analysis and clustering analysis. Many statistic-based significance analysis methods and distance/correlation-based clustering analysis methods have been applied to time-course expression data. However, these techniques are unable to account for the dynamics of such data. It is the dynamics that characterizes such data and that should be considered in analysis of such data. In this paper, we employ a nonlinear model to analyse time-course gene expression data. We firstly develop an efficient method for estimating the parameters in the nonlinear model. Then we utilize this model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles. The verification with two synthetic datasets shows that our developed significance analysis method and cluster analysis method outperform some existing methods. The application to one real-life biological dataset illustrates that the analysis results of our developed methods are in agreement with the existing results.

Cancer data classification by quantum-inspired immune clone optimization-based optimal feature selection using gene expression data: deep learning approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nageswara Rao Eluri ◽  
Gangadhara Rao Kancharla ◽  
Suresh Dara ◽  
Venkatesulu Dondeti
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Selection Model ◽  
Expression Data ◽  
Content Type ◽  
Cancer Data ◽  
Optimal Feature Selection ◽  
Optimal Feature

PurposeGene selection is considered as the fundamental process in the bioinformatics field. The existing methodologies pertain to cancer classification are mostly clinical basis, and its diagnosis capability is limited. Nowadays, the significant problems of cancer diagnosis are solved by the utilization of gene expression data. The researchers have been introducing many possibilities to diagnose cancer appropriately and effectively. This paper aims to develop the cancer data classification using gene expression data.Design/methodology/approachThe proposed classification model involves three main phases: “(1) Feature extraction, (2) Optimal Feature Selection and (3) Classification”. Initially, five benchmark gene expression datasets are collected. From the collected gene expression data, the feature extraction is performed. To diminish the length of the feature vectors, optimal feature selection is performed, for which a new meta-heuristic algorithm termed as quantum-inspired immune clone optimization algorithm (QICO) is used. Once the relevant features are selected, the classification is performed by a deep learning model called recurrent neural network (RNN). Finally, the experimental analysis reveals that the proposed QICO-based feature selection model outperforms the other heuristic-based feature selection and optimized RNN outperforms the other machine learning methods.FindingsThe proposed QICO-RNN is acquiring the best outcomes at any learning percentage. On considering the learning percentage 85, the accuracy of the proposed QICO-RNN was 3.2% excellent than RNN, 4.3% excellent than RF, 3.8% excellent than NB and 2.1% excellent than KNN for Dataset 1. For Dataset 2, at learning percentage 35, the accuracy of the proposed QICO-RNN was 13.3% exclusive than RNN, 8.9% exclusive than RF and 14.8% exclusive than NB and KNN. Hence, the developed QICO algorithm is performing well in classifying the cancer data using gene expression data accurately.Originality/valueThis paper introduces a new optimal feature selection model using QICO and QICO-based RNN for effective classification of cancer data using gene expression data. This is the first work that utilizes an optimal feature selection model using QICO and QICO-RNN for effective classification of cancer data using gene expression data.

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