scholarly journals PairGP: Gaussian process modeling of longitudinal data from paired multi-condition studies

2020 ◽  
Author(s):  
Michele Vantini ◽  
Henrik Mannerström ◽  
Sini Rautio ◽  
Helena Ahlfors ◽  
Brigitta Stockinger ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gaussian Process ◽  
Process Modeling ◽  
Simulated Data ◽  
Rna Seq ◽  
Gene Expression Dynamics ◽  
Study Designs ◽  
Compare Gene Expression ◽  
Process Method

AbstractWe propose PairGP, a non-stationary Gaussian process method to compare gene expression timeseries across several conditions that can account for paired longitudinal study designs and can identify groups of conditions that have different gene expression dynamics. We demonstrate the method on both simulated data and previously unpublished RNA-seq time-series with five conditions. The results show the advantage of modeling the pairing effect to better identify groups of conditions with different dynamics. The implementations is available at https://github.com/michelevantini/PairGP

scholarly journals Dual RNA-seq of Parasite and Host Reveals Gene Expression Dynamics during Filarial Worm–Mosquito Interactions

2014 ◽  
Vol 8 (5) ◽  
pp. e2905 ◽  
Author(s):  
Young-Jun Choi ◽  
Matthew T. Aliota ◽  
George F. Mayhew ◽  
Sara M. Erickson ◽  
Bruce M. Christensen
Keyword(s):  
Gene Expression ◽  
Rna Seq ◽  
Filarial Worm ◽  
Gene Expression Dynamics

scholarly journals Transcriptome dynamics during metamorphosis of imaginal discs into wings and thoracic dorsum in Apis mellifera castes

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Michelle Prioli Miranda Soares ◽  
Daniel Guariz Pinheiro ◽  
Flávia Cristina de Paula Freitas ◽  
Zilá Luz Paulino Simões ◽  
Márcia Maria Gentile Bitondi
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Imaginal Discs ◽  
Rna Seq ◽  
Transition Analysis ◽  
Holometabolous Insect ◽  
Complex Anatomy ◽  
Gene Expression Dynamics ◽  
Wing Discs ◽  
Developmental Phases

Abstract Background Much of the complex anatomy of a holometabolous insect is built from disc-shaped epithelial structures found inside the larva, i.e., the imaginal discs, which undergo a rapid differentiation during metamorphosis. Imaginal discs-derived structures, like wings, are built through the action of genes under precise regulation. Results We analyzed 30 honeybee transcriptomes in the search for the gene expression needed for wings and thoracic dorsum construction from the larval wing discs primordia. Analyses were carried out before, during, and after the metamorphic molt and using worker and queen castes. Our RNA-seq libraries revealed 13,202 genes, representing 86.2% of the honeybee annotated genes. Gene Ontology analysis revealed functional terms that were caste-specific or shared by workers and queens. Genes expressed in wing discs and descendant structures showed differential expression profiles dynamics in premetamorphic, metamorphic and postmetamorphic developmental phases, and also between castes. At the metamorphic molt, when ecdysteroids peak, the wing buds of workers showed maximal gene upregulation comparatively to queens, thus underscoring differences in gene expression between castes at the height of the larval-pupal transition. Analysis of small RNA libraries of wing buds allowed us to build miRNA-mRNA interaction networks to predict the regulation of genes expressed during wing discs development. Conclusion Together, these data reveal gene expression dynamics leading to wings and thoracic dorsum formation from the wing discs, besides highlighting caste-specific differences during wing discs metamorphosis.

scholarly journals Gene Expression Changes Occurring at Bolting Time are Associated with Leaf Senescence in Arabidopsis

2020 ◽  
Author(s):  
Will E Hinckley ◽  
Judy A. Brusslan
Keyword(s):  
Gene Expression ◽  
Phase Transition ◽  
Time Series ◽  
Leaf Senescence ◽  
Molecular Mechanisms ◽  
Rna Seq ◽  
Age Dependent ◽  
Temporal Coupling ◽  
Rosette Leaf ◽  
Bolting Time

AbstractIn plants, the vegetative to reproductive phase transition (termed bolting in Arabidopsis) generally precedes age-dependent leaf senescence (LS). Many studies describe a temporal link between bolting time and LS, as plants that bolt early, senesce early, and plants that bolt late, senesce late. However, the molecular mechanisms underlying this relationship are unknown and are potentially agriculturally important, as they may allow for the development of crops that can overcome early LS caused by stress-related early phase transition. We hypothesized that gene expression changes associated with bolting time were regulating LS. We used a mutant that displays both early bolting and early LS as a model to test this hypothesis. An RNA-seq time series experiment was completed to compare the early bolting mutant to vegetative WT plants of the same age. This allowed us to identify bolting time-associated genes (BAGs) expressed in an older rosette leaf at the time of inflorescence emergence. The BAG list contains many well characterized LS regulators (ORE1, WRKY45, NAP, WRKY28), and GO analysis revealed enrichment for LS and LS-related processes. These bolting associated LS regulators likely contribute to the temporal coupling of bolting time to LS.

scholarly journals Integrative Analysis of Axolotl Gene Expression Data From Regenerative and Wound Healing Limb Tissues

2019 ◽  
Author(s):  
Mustafa Sibai ◽  
Cüneyd Parlayan ◽  
Pelin Tuğlu ◽  
Gürkan Öztürk ◽  
Turan Demircan
Keyword(s):  
Gene Expression ◽  
Wound Healing ◽  
Differential Expression Analysis ◽  
Limb Regeneration ◽  
Ambystoma Mexicanum ◽  
Integrative Analysis ◽  
Control Group ◽  
Rna Seq ◽  
Amputation Injury ◽  
Study Designs

ABSTRACTAxolotl (Ambystoma mexicanum) is a urodele amphibian endowed with remarkable regenerative capacities manifested in scarless wound healing and full restoration of amputated limbs. Several regenerative cues of the axolotl limb were successfully unraveled due to the advent of high-throughput technologies and their employment in tackling research questions on several OMICS levels. The field of regenerative biology and medicine has therefore utilized the axolotl as a major and powerful experimental model. Studies which have previously unraveled differentially expressed (DE) genes en masse in different phases of the axolotl limb regeneration have primarily used microarrays and RNA-Seq technologies. However, as different labs are conducting such experiments, sufficient consistency may be lacking due to statistical limitations arising from limited number of sample replicates as well as possible differences in study designs. This study, therefore, aims to bridge such gaps by performing an integrative analysis of publicly available microarray and RNA-Seq data from axolotl limb samples having comparable study designs. Three biological groups were conceived for the analysis; homeostatic tissues (control group), from amputation/injury timepoint up to around 50 hours post amputation (wound healing group), and from 50 hours to 28 days post amputation/injury (regenerative group). Integrative analysis was separately carried out on the selected microarray and RNA-Seq data from axolotl limb samples using the “merging” method. Differential expression analysis was separately implemented on the processed data from both technologies using the R/Bioconductor “limma” package. A total of 1254 genes (adjusted P < 0.01) were found DE in regenerative samples compared to the control, out of which 351 showed magnitudes of Log Fold Changes (LogFC) > 1 and were identified as the top DE genes from data of both technologies. Downstream analyses illustrated consistent correlations of the logFCs of DE genes distributed among the biological comparisons, within and between both technologies. Gene ontology annotations demonstrated concordance with the literature on the biological process involved in the axolotl limb regeneration. qPCR analysis validated the observed gene expression level differences between regenerative and control samples for a set of five genes. Future studies may benefit from the utilized concept and approach for enhanced statistical power and robust discovery of biomarkers of regeneration.

Modelling Gene Expression Dynamics with Gaussian Process Inference

2019 ◽  
pp. 879-20
Author(s):  
Magnus Rattray ◽  
Jing Yang ◽  
Sumon Ahmed ◽  
Alexis Boukouvalas
Keyword(s):  
Gene Expression ◽  
Gaussian Process ◽  
Gene Expression Dynamics

Simultaneous Profiling of DNA Accessibility and Gene Expression Dynamics with ATAC-Seq and RNA-Seq

2018 ◽  
pp. 317-333 ◽  
Author(s):  
David G. Hendrickson ◽  
Ilya Soifer ◽  
Bernd J. Wranik ◽  
David Botstein ◽  
R. Scott McIsaac
Keyword(s):  
Gene Expression ◽  
Rna Seq ◽  
Dna Accessibility ◽  
Gene Expression Dynamics

scholarly journals Transcriptomic profiling of human orbital fat and differentiating orbital fibroblasts

2021 ◽  
Author(s):  
Dong Won Kim ◽  
Kamil Taneja ◽  
Thanh Hoang ◽  
Clayton Pio Santiago ◽  
Timothy James McCulley ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Time Course ◽  
Principal Component ◽  
Developmental Time ◽  
Rna Seq ◽  
Orbital Fat ◽  
Orbital Fibroblasts ◽  
Compare Gene Expression

Purpose: Orbital fat hyperplasia has a central role in the manifestations of thyroid-associated orbitopathy (TAO). To better understand the pathways involved in adipogenesis in TAO, we have used transcriptomic methods to analyze gene expression in control and TAO patients, as well as in differentiating orbital fibroblasts (OFs). Methods: We performed bulk RNA sequencing (RNA-Seq) on intraconal orbital fat to compare gene expression in control and TAO patients. We treated cultured OFs derived from TAO patients with media containing dexamethasone, insulin, rosiglitazone, and isobutylmethylxanthine (IBMX) to induce adipogenesis. We used single nuclear RNA-Seq (snRNA-Seq) profiling of treated OFs to compare gene expression over time in order to identify pathways that are involved in orbital adipogenesis in vitro and compared the dynamic patterns of gene expression identify differences in gene expression in control and TAO orbital fat. Results: Orbital fat from TAO and control patients segregate with principal component analysis (PCA). Numerous signaling pathways are enriched in orbital fat isolated from TAO patients. SnRNA-Seq of orbital fibroblasts undergoing adipogenesis reveals differential expression of adipocyte-specific genes over the developmental time course. Furthermore, genes that are enriched in TAO orbital fat are also upregulated in orbital adipocytes that differentiate in vitro, while genes that are enriched in control orbital fat are enriched in orbital fibroblasts prior to differentiation. Conclusions: Differentiating orbital fibroblasts serve as a model to study orbital fat hyperplasia seen in TAO. We demonstrate that the insulin-like growth factor-1 receptor (IGF-1R) and Wnt signaling pathways are differentially expressed early in orbital adipogenesis.

Experimental Design for Time-Series RNA-Seq Analysis of Gene Expression and Alternative Splicing

2021 ◽  
pp. 173-188
Author(s):  
Nikoleta A. Tzioutziou ◽  
Allan B. James ◽  
Wenbin Guo ◽  
Cristiane P. G. Calixto ◽  
Runxuan Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Alternative Splicing ◽  
Experimental Design ◽  
Rna Seq

contamDE-lm: linear model-based differential gene expression analysis using next-generation RNA-seq data from contaminated tumor samples

2020 ◽  
Vol 36 (8) ◽  
pp. 2492-2499
Author(s):  
Yifan Ji ◽  
Chang Yu ◽  
Hong Zhang
Keyword(s):  
Gene Expression ◽  
Linear Model ◽  
False Positive Rate ◽  
Simulated Data ◽  
True Positive Rate ◽  
Supplementary Information ◽  
Large Sample Size ◽  
Rna Seq ◽  
Next Generation ◽  
Positive Rate

Abstract Motivation Tumor and adjacent normal RNA samples are commonly used to screen differentially expressed genes between normal and tumor samples or among tumor subtypes. Such paired-sample design could avoid numerous confounders in differential expression (DE) analysis, but the cellular contamination of tumor samples can be an important noise and confounding factor, which can both inflate false-positive rate and deflate true-positive rate. The existing DE tools that use next-generation RNA-seq data either do not account for cellular contamination or are computationally extensive with increasingly large sample size. Results A novel linear model was proposed to avoid the problem that could arise from tumor–normal correlation for paired samples. A statistically robust and computationally very fast DE analysis procedure, contamDE-lm, was developed based on the novel model to account for cellular contamination, boosting DE analysis power through the reduction in individual residual variances using gene-wise information. The desired advantages of contamDE-lm over some state-of-the-art methods (limma and DESeq2) were evaluated through the applications to simulated data, TCGA database and Gene Expression Omnibus (GEO) database. Availability and implementation The proposed method contamDE-lm was implemented in an updated R package contamDE (version 2.0), which is freely available at https://github.com/zhanghfd/contamDE. Supplementary information Supplementary data are available at Bioinformatics online.

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