Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches

2017 ◽  
pp. 165-176 ◽  
Author(s):  
Sunghee Oh ◽  
Seongho Song
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Differential Gene Expression ◽  
Bayesian Modeling ◽  
Dynamic Processes ◽  
Hierarchical Bayesian ◽  
Temporal Dynamic ◽  
Hierarchical Bayesian Modeling ◽  
Modeling Approaches ◽  
Differential Gene

scholarly journals Differential gene expression and alternative splicing in insect immune specificity

BMC Genomics ◽  
2014 ◽  
Vol 15 (1) ◽  
pp. 1031 ◽  
Author(s):  
Carolyn E Riddell ◽  
Juan D Lobaton Garces ◽  
Sally Adams ◽  
Seth M Barribeau ◽  
David Twell ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Differential Gene Expression ◽  
Differential Gene

scholarly journals Analysis of differential gene expression and alternative splicing is significantly influenced by choice of reference genome

RNA ◽  
2019 ◽  
Vol 25 (6) ◽  
pp. 669-684 ◽  
Author(s):  
Erin Slabaugh ◽  
Jigar S. Desai ◽  
Ryan C. Sartor ◽  
Lovely Mae F. Lawas ◽  
S.V. Krishna Jagadish ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Differential Gene Expression ◽  
Reference Genome ◽  
Differential Gene

scholarly journals Revealing Neurocomputational Mechanisms of Reinforcement Learning and Decision-Making With the hBayesDM Package

2017 ◽  
Vol 1 ◽  
pp. 24-57 ◽  
Author(s):  
Woo-Young Ahn ◽  
Nathaniel Haines ◽  
Lei Zhang
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Computational Modeling ◽  
Bayesian Modeling ◽  
Hierarchical Bayesian ◽  
Hierarchical Bayesian Modeling ◽  
Model Based ◽  
Modeling Approaches ◽  
Wide Range ◽  
User Friendly

Reinforcement learning and decision-making (RLDM) provide a quantitative framework and computational theories with which we can disentangle psychiatric conditions into the basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assessing and potentially diagnosing psychiatric patients, and there is growing enthusiasm for both RLDM and computational psychiatry among clinical researchers. Such a framework can also provide insights into the brain substrates of particular RLDM processes, as exemplified by model-based analysis of data from functional magnetic resonance imaging (fMRI) or electroencephalography (EEG). However, researchers often find the approach too technical and have difficulty adopting it for their research. Thus, a critical need remains to develop a user-friendly tool for the wide dissemination of computational psychiatric methods. We introduce an R package called hBayesDM (hierarchical Bayesian modeling of Decision-Making tasks), which offers computational modeling of an array of RLDM tasks and social exchange games. The hBayesDM package offers state-of-the-art hierarchical Bayesian modeling, in which both individual and group parameters (i.e., posterior distributions) are estimated simultaneously in a mutually constraining fashion. At the same time, the package is extremely user-friendly: users can perform computational modeling, output visualization, and Bayesian model comparisons, each with a single line of coding. Users can also extract the trial-by-trial latent variables (e.g., prediction errors) required for model-based fMRI/EEG. With the hBayesDM package, we anticipate that anyone with minimal knowledge of programming can take advantage of cutting-edge computational-modeling approaches to investigate the underlying processes of and interactions between multiple decision-making (e.g., goal-directed, habitual, and Pavlovian) systems. In this way, we expect that the hBayesDM package will contribute to the dissemination of advanced modeling approaches and enable a wide range of researchers to easily perform computational psychiatric research within different populations.

scholarly journals Alternative splicing and differential gene expression in colon cancer detected by a whole genome exon array

BMC Genomics ◽  
2006 ◽  
Vol 7 (1) ◽  
Author(s):  
Paul J Gardina ◽  
Tyson A Clark ◽  
Brian Shimada ◽  
Michelle K Staples ◽  
Qing Yang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colon Cancer ◽  
Alternative Splicing ◽  
Differential Gene Expression ◽  
Exon Array ◽  
Whole Genome ◽  
Differential Gene

scholarly journals Evaluating Methods for Differential Gene Expression And Alternative Splicing Using Internal Synthetic Controls

2020 ◽  
Author(s):  
Sudeep Mehrotra ◽  
Revital Bronstein ◽  
Daniel Navarro-Gomez ◽  
Ayellet V. Segrè ◽  
Eric A. Pierce
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Differential Gene Expression ◽  
Internal Control ◽  
Accurate Determination ◽  
Transcriptome Profiling ◽  
Rna Seq ◽  
Specificity And Sensitivity ◽  
Differential Gene ◽  
Synthetic Controls

AbstractHigh-throughput transcriptome sequencing has become a powerful tool in the study of human diseases. Identification of causal mechanisms may entail analysis of differential gene expression (DGE), differential transcript/isoform expression (DTE) and identification, classification and quantification of alternative splicing (AS) and/or detection of novel AS events. For such a global transcriptome profiling execution of multi-level data analysis methodologies is required. Each level presents its own unique challenges and the questions about their performance remains. In this work we present results from systematic and consistent assessing and comparing a number of widely used methods for detecting DGE, DTE and AS using internal control “spike-in” sequences (Sequins) in RNA-seq data. We demonstrated that inclusion of internal controls in RNA-seq experiments allows accurate determination of lower bounds detection levels, and better assessment of DGE, DTE and AS accuracy and sensitivity. Tools for RNA-seq read alignment and detection of DGE performed reasonably. More efforts are needed to improve specificity and sensitivity of DTE and AS detection. Low expression of isoforms accompanied with sequencing depth does impact sensitivity and specificity of DTE and AS tools.

scholarly journals Differential gene expression and alternative splicing between diploid and tetraploid watermelon

2014 ◽  
Vol 66 (5) ◽  
pp. 1369-1385 ◽  
Author(s):  
Thangasamy Saminathan ◽  
Padma Nimmakayala ◽  
Sumanth Manohar ◽  
Sridhar Malkaram ◽  
Aldo Almeida ◽  
...  
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Differential Gene Expression ◽  
Differential Gene

scholarly journals Alternative splicing and gene expression play contrasting roles in the parallel phenotypic evolution of a salmonid fish

2020 ◽  
Author(s):  
Arne Jacobs ◽  
Kathryn R. Elmer
Keyword(s):  
Gene Expression ◽  
Alternative Splicing ◽  
Differential Gene Expression ◽  
Differentially Expressed Genes ◽  
Adaptive Evolution ◽  
Salmonid Fish ◽  
Differentially Expressed ◽  
Molecular Processes ◽  
Regulatory Regions ◽  
Differential Gene

AbstractUnderstanding the contribution of different molecular processes to the evolution and development of divergent phenotypes is crucial for identifying the molecular routes of rapid adaptation. Here, we used RNA-seq data to compare patterns of alternative splicing and differential gene expression in a case of parallel adaptive evolution, the replicated postglacial divergence of the salmonid fish Arctic charr (Salvelinus alpinus) into benthic and pelagic ecotypes across multiple independent lakes.We found that genes that were differentially spliced and differentially expressed between the benthic and pelagic ecotypes were mostly independent (<6% overlap) and were involved in different processes. Differentially spliced genes were primarily enriched for muscle development and functioning, while differentially expressed genes were mostly involved in energy metabolism, immunity and growth. Together, these likely explain different axes of divergence between ecotypes in swimming performance and activity. Furthermore, we found that alternative splicing and gene expression are mostly controlled by independent cis-regulatory quantitative trait loci (<3.4% overlap). Cis-regulatory regions were associated with the parallel divergence in splicing (16.5% of intron clusters) and expression (6.7 - 10.1% of differentially expressed genes), indicating shared regulatory variation across ecotype pairs. Contrary to theoretical expectation, we found that differentially spliced genes tended to be highly central in regulatory networks (‘hub genes’) and were annotated to significantly more gene ontology terms compared to non-differentially spliced genes, consistent with a higher level of connectivity and pleiotropy.Together, our results suggest that the concerted regulation of alternative splicing and differential gene expression through different regulatory regions leads to the divergence of complementary phenotypes important for local adaptation. This study provides novel insights into the importance of contrasting but putatively complementary molecular processes for rapid and parallel adaptive evolution.

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