Large scale maximum average power multiple inference on time‐course count data with application to RNA‐seq analysis

Biometrics ◽  
2019 ◽  
Vol 76 (1) ◽  
pp. 9-22 ◽  
Author(s):  
Meng Cao ◽  
Wen Zhou ◽  
F. Jay Breidt ◽  
Graham Peers
Keyword(s):  
Count Data ◽  
Large Scale ◽  
Time Course ◽  
Average Power ◽  
Rna Seq

scholarly journals Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Genes ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 352
Author(s):  
Vera-Khlara S. Oh ◽  
Robert W. Li
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Time Series Data ◽  
Cell Lineage ◽  
Series Data ◽  
Therapeutic Effects ◽  
Rna Seq ◽  
Response Models ◽  
Dynamic Methods

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

scholarly journals CRNET: an efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data

2017 ◽  
Vol 34 (10) ◽  
pp. 1733-1740 ◽  
Author(s):  
Xi Chen ◽  
Jinghua Gu ◽  
Xiao Wang ◽  
Jin-Gyoung Jung ◽  
Tian-Li Wang ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Large Scale ◽  
Time Course ◽  
Rna Seq ◽  
Efficient Sampling ◽  
Sampling Approach

scholarly journals QuickRNASeq: Guide for Pipeline Implementation and for Interactive Results Visualization

2017 ◽  
Author(s):  
Wen He ◽  
Shanrong Zhao ◽  
Chi Zhang ◽  
Michael S. Vincent ◽  
Baohong Zhang
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Time Course ◽  
Interactive Visualization ◽  
Complex Data ◽  
Rna Seq ◽  
Short Reads ◽  
Rna Molecules ◽  
Before And After ◽  
Drug Treatments

i.Summary/AbstractSequencing of transcribed RNA molecules (RNA-seq) has been used wildly for studying cell transcriptomes in bulk or at the single-cell level (1, 2, 3) and is becoming the de facto technology for investigating gene expression level changes in various biological conditions, on the time course, and under drug treatments. Furthermore, RNA-Seq data helped identify fusion genes that are related to certain cancers (4). Differential gene expression before and after drug treatments provides insights to mechanism of action, pharmacodynamics of the drugs, and safety concerns (5). Because each RNA-seq run generates tens to hundreds of millions of short reads with size ranging from 50bp-200bp, a tool that deciphers these short reads to an integrated and digestible analysis report is in high demand. QuickRNASeq (6) is an application for large-scale RNA-seq data analysis and real-time interactive visualization of complex data sets. This application automates the use of several of the best open-source tools to efficiently generate user friendly, easy to share, and ready to publish report. Figure 1 illustrates some of the interactive plots produced by QuickRNASeq. The visualization features of the application have been further improved since its first publication in early 2016. The original QuickRNASeq publication (6) provided details of background, software selection, and implementation. Here, we outline the steps required to implement QuickRNASeq in user’s own environment, as well as demonstrate some basic yet powerful utilities of the advanced interactive visualization modules in the report.

scholarly journals Transcriptional and morphological profiling of parvalbumin interneuron subpopulations in the mouse hippocampus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lin Que ◽  
David Lukacsovich ◽  
Wenshu Luo ◽  
Csaba Földy
Keyword(s):  
Large Scale ◽  
Cell Types ◽  
Rna Seq ◽  
Neuronal Identity ◽  
Parvalbumin Interneurons ◽  
Different Types ◽  
Parvalbumin Interneuron ◽  
Cam Profile ◽  
Developmental Domains

AbstractThe diversity reflected by >100 different neural cell types fundamentally contributes to brain function and a central idea is that neuronal identity can be inferred from genetic information. Recent large-scale transcriptomic assays seem to confirm this hypothesis, but a lack of morphological information has limited the identification of several known cell types. In this study, we used single-cell RNA-seq in morphologically identified parvalbumin interneurons (PV-INs), and studied their transcriptomic states in the morphological, physiological, and developmental domains. Overall, we find high transcriptomic similarity among PV-INs, with few genes showing divergent expression between morphologically different types. Furthermore, PV-INs show a uniform synaptic cell adhesion molecule (CAM) profile, suggesting that CAM expression in mature PV cells does not reflect wiring specificity after development. Together, our results suggest that while PV-INs differ in anatomy and in vivo activity, their continuous transcriptomic and homogenous biophysical landscapes are not predictive of these distinct identities.

scholarly journals SPLICE-q: a Python tool for genome-wide quantification of splicing efficiency

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Verônica R. de Melo Costa ◽  
Julianus Pfeuffer ◽  
Annita Louloupi ◽  
Ulf A. V. Ørom ◽  
Rosario M. Piro
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Time Course ◽  
Progressive Increase ◽  
Rna Seq ◽  
Transcript Processing ◽  
Aberrant Transcript ◽  
Genome Wide ◽  
Splicing Efficiency ◽  
Nascent Rna

Abstract Background Introns are generally removed from primary transcripts to form mature RNA molecules in a post-transcriptional process called splicing. An efficient splicing of primary transcripts is an essential step in gene expression and its misregulation is related to numerous human diseases. Thus, to better understand the dynamics of this process and the perturbations that might be caused by aberrant transcript processing it is important to quantify splicing efficiency. Results Here, we introduce SPLICE-q, a fast and user-friendly Python tool for genome-wide SPLICing Efficiency quantification. It supports studies focusing on the implications of splicing efficiency in transcript processing dynamics. SPLICE-q uses aligned reads from strand-specific RNA-seq to quantify splicing efficiency for each intron individually and allows the user to select different levels of restrictiveness concerning the introns’ overlap with other genomic elements such as exons of other genes. We applied SPLICE-q to globally assess the dynamics of intron excision in yeast and human nascent RNA-seq. We also show its application using total RNA-seq from a patient-matched prostate cancer sample. Conclusions Our analyses illustrate that SPLICE-q is suitable to detect a progressive increase of splicing efficiency throughout a time course of nascent RNA-seq and it might be useful when it comes to understanding cancer progression beyond mere gene expression levels. SPLICE-q is available at: https://github.com/vrmelo/SPLICE-q

scholarly journals Multiple Alu exonization in 3’UTR of a primate specific isoform of CYP20A1 creates a potential miRNA sponge

2020 ◽  
Author(s):  
Aniket Bhattacharya ◽  
Vineet Jha ◽  
Khushboo Singhal ◽  
Mahar Fatima ◽  
Dayanidhi Singh ◽  
...  
Keyword(s):  
Heat Shock ◽  
Cortical Neurons ◽  
Regulatory Networks ◽  
Large Scale ◽  
Neuronal Development ◽  
Random Sets ◽  
Rna Seq ◽  
Orphan Gene ◽  
Mirna Sponge ◽  
Human Neurons

Abstract Alu repeats contribute to phylogenetic novelties in conserved regulatory networks in primates. Our study highlights how exonized Alus could nucleate large-scale mRNA-miRNA interactions. Using a functional genomics approach, we characterize a transcript isoform of an orphan gene, CYP20A1 (CYP20A1_Alu-LT) that has exonization of 23 Alus in its 3’UTR. CYP20A1_Alu-LT, confirmed by 3’RACE, is an outlier in length (9 kb 3’UTR) and widely expressed. Using publically available datasets, we demonstrate its expression in higher primates and presence in single nucleus RNA-seq of 15928 human cortical neurons. miRanda predicts ∼4700 miRNA recognition elements (MREs) for ∼1000 miRNAs, primarily originated within these 3’UTR-Alus. CYP20A1_Alu-LT could be a potential multi-miRNA sponge as it harbors ≥10 MREs for 140 miRNAs and has cytosolic localization. We further tested whether expression of CYP20A1_Alu-LT correlates with mRNAs harboring similar MRE targets. RNA-seq with conjoint miRNA-seq analysis was done in primary human neurons where we observed CYP20A1_Alu-LT to be downregulated during heat shock response and upregulated in HIV1-Tat treatment. 380 genes were positively correlated with its expression (significantly downregulated in heat shock and upregulated in Tat) and they harbored MREs for nine expressed miRNAs which were also enriched in CYP20A1_Alu-LT. MREs were significantly enriched in these 380 genes compared to random sets of differentially expressed genes (p = 8.134e-12). Gene ontology suggested involvement of these genes in neuronal development and hemostasis pathways thus proposing a novel component of Alu-miRNA mediated transcriptional modulation that could govern specific physiological outcomes in higher primates.

scholarly journals Time-Course Transcriptome Analysis Reveals Resistance Genes of Panax ginseng Induced by Cylindrocarpon destructans Infection Using RNA-Seq

PLoS ONE ◽  
2016 ◽  
Vol 11 (2) ◽  
pp. e0149408 ◽  
Author(s):  
Yuan Gao ◽  
Xiaoli He ◽  
Bin Wu ◽  
Qiliang Long ◽  
Tianwei Shao ◽  
...  
Keyword(s):  
Panax Ginseng ◽  
Resistance Genes ◽  
Transcriptome Analysis ◽  
Time Course ◽  
Rna Seq ◽  
Cylindrocarpon Destructans

Parameter Space Analysis Suggests Multi-Site Plasticity Contributes to Motor Pattern Initiation in Tritonia

2007 ◽  
Vol 98 (4) ◽  
pp. 2382-2398 ◽  
Author(s):  
Robert J. Calin-Jageman ◽  
Mark J. Tunstall ◽  
Brett D. Mensh ◽  
Paul S. Katz ◽  
William N. Frost
Keyword(s):  
Parameter Space ◽  
Large Scale ◽  
Time Course ◽  
Scale Parameter ◽  
Motor Pattern ◽  
Rhythmic Activity ◽  
Excitatory Input ◽  
Space Analysis ◽  
Gastropod Mollusk ◽  
Detailed Simulation

This research examines the mechanisms that initiate rhythmic activity in the episodic central pattern generator (CPG) underlying escape swimming in the gastropod mollusk Tritonia diomedea. Activation of the network is triggered by extrinsic excitatory input but also accompanied by intrinsic neuromodulation and the recruitment of additional excitation into the circuit. To examine how these factors influence circuit activation, a detailed simulation of the unmodulated CPG network was constructed from an extensive set of physiological measurements. In this model, extrinsic input alone is insufficient to initiate rhythmic activity, confirming that additional processes are involved in circuit activation. However, incorporating known neuromodulatory and polysynaptic effects into the model still failed to enable rhythmic activity, suggesting that additional circuit features are also required. To delineate the additional activation requirements, a large-scale parameter-space analysis was conducted (∼2 × 106 configurations). The results suggest that initiation of the swim motor pattern requires substantial reconfiguration at multiple sites within the network, especially to recruit ventral swim interneuron-B (VSI) activity and increase coupling between the dorsal swim interneurons (DSIs) and cerebral neuron 2 (C2) coupling. Within the parameter space examined, we observed a tendency for rhythmic activity to be spontaneous and self-sustaining. This suggests that initiation of episodic rhythmic activity may involve temporarily restructuring a nonrhythmic network into a persistent oscillator. In particular, the time course of neuromodulatory effects may control both activation and termination of rhythmic bursting.

scholarly journals Large-scale Dynamics of Perceptual Decision Information across Human Cortex

2020 ◽  
Author(s):  
Niklas Wilming ◽  
Peter R Murphy ◽  
Florent Meyniel ◽  
Tobias H Donner
Keyword(s):  
Large Scale ◽  
Time Course ◽  
Action Plan ◽  
Frequency Channel ◽  
Human Cortex ◽  
Cortical Hierarchy ◽  
Specific Frequency ◽  
Sensory Evidence ◽  
Visual Cortical ◽  
Endogenous Component

AbstractPerceptual decisions entail the accumulation of sensory evidence for a particular choice towards an action plan. An influential framework holds that sensory cortical areas encode the instantaneous sensory evidence and downstream, action-related regions accumulate this evidence. The large-scale distribution of this computation across the cerebral cortex has remained largely elusive. We developed a regionally-specific magnetoencephalography decoding approach to exhaustively map the dynamics of stimulus- and choice-specific signals across the human cortical surface during a visual decision. Comparison with the evidence accumulation dynamics inferred from behavior enabled us to disentangle stimulus-dependent and endogenous components of choice-predictive activity across the visual cortical hierarchy. The endogenous component was present in primary visual cortex, expressed in a low (< 20 Hz) frequency-band, and its time course tracked, with delay, the build-up of choice-predictive activity in (pre-)motor regions. Our results are consistent with choice-specific cortical feedback signaling in a specific frequency channel during decision formation.

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