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.

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 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 scImpute: Accurate And Robust Imputation For Single Cell RNA-Seq Data

2017 ◽  
Author(s):  
Wei Vivian Li ◽  
Jingyi Jessica Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Differential Expression Analysis ◽  
Embryonic Stem ◽  
Rna Seq ◽  
Human Blood Cells ◽  
Gene Expression Dynamics ◽  
Rare Cells ◽  
Downstream Analysis ◽  
Zero Counts

The emerging single cell RNA sequencing (scRNA-seq) technologies enable the investigation of transcriptomic landscapes at single-cell resolution. The analysis of scRNA-seq data is complicated by excess zero or near zero counts, the so-called dropouts due to the low amounts of mRNA sequenced within individual cells. Downstream analysis of scRNA-seq would be severely biased if the dropout events are not properly corrected. We introduce scImpute, a statistical method to accurately and robustly impute the dropout values in scRNA-seq data. ScImpute automatically identifies gene expression values affected by dropout events, and only perform imputation on these values without introducing new bias to the rest data. ScImpute also detects outlier or rare cells and excludes them from imputation. Evaluation based on both simulated and real scRNA-seq data on mouse embryos, mouse brain cells, human blood cells, and human embryonic stem cells suggests that scImpute is an effective tool to recover transcriptome dynamics masked by dropout events. scImpute is shown to correct false zero counts, enhance the clustering of cell populations and subpopulations, improve the accuracy of differential expression analysis, and aid the study of gene expression dynamics.

scholarly journals Alignment of time-course single-cell RNA-seq data with CAPITAL

2019 ◽  
Author(s):  
Reiichi Sugihara ◽  
Yuki Kato ◽  
Tomoya Mori ◽  
Yukio Kawahara
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Time Course ◽  
Rna Seq ◽  
Experimental Conditions ◽  
Tree Alignment ◽  
Public Data ◽  
Gene Expression Dynamics ◽  
Time Course Data ◽  
Cell Trajectory

AbstractRecent techniques on single-cell RNA sequencing have boosted transcriptome-wide observation of gene expression dynamics of time-course data at a single-cell scale. Typical examples of such analysis include inference of a pseudotime cell trajectory, and comparison of pseudotime trajectories between different experimental conditions will tell us how feature genes regulate a dynamic cellular process. Existing methods for comparing pseudotime trajectories, however, force users to select trajectories to be compared because they can deal only with simple linear trajectories, leading to the possibility of making a biased interpretation. Here we present CAPITAL, a method for comparing pseudotime trajectories with tree alignment whereby trajectories including branching can be compared without any knowledge of paths to be compared. Computational tests on time-series public data indicate that CAPITAL can align non-linear pseudotime trajectories and reveal gene expression dynamics.

scholarly journals Rewired Pathways and Disrupted Pathway Crosstalk in Schizophrenia Transcriptomes by Multiple Differential Coexpression Methods

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 665
Author(s):  
Hui Yu ◽  
Yan Guo ◽  
Jingchun Chen ◽  
Xiangning Chen ◽  
Peilin Jia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Focal Adhesion ◽  
Biological Pathways ◽  
Postmortem Brain ◽  
Rna Seq ◽  
Hub Genes ◽  
Differential Coexpression ◽  
Pathway Crosstalk ◽  
Microarray Datasets ◽  
Transcriptomic Studies

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found “Clostridium neurotoxicity” and “signaling events mediated by focal adhesion kinase” had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes.

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