scholarly journals EchinoDB: An update to the web-based application for genomic and transcriptomic data on Echinoderms.

2022 ◽  
Author(s):  
Varnika Mittal ◽  
Robert W. Reid ◽  
Denis Jacob Machado ◽  
Vladimir Mashanov ◽  
Dan A Janies
Keyword(s):  
Regulatory Networks ◽  
Sequence Similarity ◽  
Lytechinus Variegatus ◽  
Rna Seq ◽  
Web Based ◽  
Transcriptomic Data ◽  
Green Sea Urchin ◽  
R Shiny ◽  
Gene Regulatory ◽  
Keyword Searches

Here we release a new version of EchinoDB (https://echinodb.uncc.edu). EchinoDB is a database of genomic and transcriptomic data on echinoderms. The initial database consisted of groups of 749,397 orthologous and paralogous transcripts arranged in orthoclusters by sequence similarity. The new version of EchinoDB includes RNA-seq data of the brittle star Ophioderma brevispinum and high-quality genomic assembly data of the green sea urchin Lytechinus variegatus. In addition, we enabled keyword searches for annotated data and installed an updated version of Sequenceserver to allow BLAST searches. The data are downloadable in FASTA format. The first version of EchinoDB appeared in 2016 and was implemented in GO on a local server. The new version has been updated using R Shiny to include new features and improvements in the application. Furthermore, EchinoDB now runs entirely in the cloud for increased reliability and scaling. EchinoDB enjoys a user base drawn from the fields of phylogenetics, developmental biology, genomics, physiology, neurobiology, and regeneration. As use cases, we illustrate how EchinoDB is used in discovering pathways and gene regulatory networks involved in the tissue regeneration process.

scholarly journals TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data

2020 ◽  
Author(s):  
Ashley Mae Conard ◽  
Nathaniel Goodman ◽  
Yanhui Hu ◽  
Norbert Perrimon ◽  
Ritambhara Singh ◽  
...  
Keyword(s):  
Time Series ◽  
Regulatory Networks ◽  
Omics Data ◽  
Rna Seq ◽  
Web Based ◽  
Protein Levels ◽  
Binding Data ◽  
Adaptive Time ◽  
Gene Regulatory ◽  
The Relationship

SummaryUncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.

scholarly journals Inferring gene regulatory networks from single-cell RNA-seq temporal snapshot data requires higher-order moments

Patterns ◽  
2021 ◽  
Vol 2 (9) ◽  
pp. 100332
Author(s):  
N. Alexia Raharinirina ◽  
Felix Peppert ◽  
Max von Kleist ◽  
Christof Schütte ◽  
Vikram Sunkara
Keyword(s):  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Higher Order ◽  
Rna Seq ◽  
Higher Order Moments ◽  
Gene Regulatory

scholarly journals Characterization of rare spindle and root cell transcriptional profiles in the stria vascularis of the adult mouse cochlea

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Shoujun Gu ◽  
Rafal Olszewski ◽  
Ian Taukulis ◽  
Zheng Wei ◽  
Daniel Martin ◽  
...  
Keyword(s):  
Hearing Loss ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Adult Mouse ◽  
Stria Vascularis ◽  
Cell Types ◽  
Rna Seq ◽  
Root Cells ◽  
Transcriptional Profiles ◽  
Gene Regulatory

Abstract The stria vascularis (SV) in the cochlea generates and maintains the endocochlear potential, thereby playing a pivotal role in normal hearing. Knowing transcriptional profiles and gene regulatory networks of SV cell types establishes a basis for studying the mechanism underlying SV-related hearing loss. While we have previously characterized the expression profiles of major SV cell types in the adult mouse, transcriptional profiles of rare SV cell types remained elusive due to the limitation of cell capture in single-cell RNA-Seq. The role of these rare cell types in the homeostatic function of the adult SV remain largely undefined. In this study, we performed single-nucleus RNA-Seq on the adult mouse SV in conjunction with sample preservation treatments during the isolation steps. We distinguish rare SV cell types, including spindle cells and root cells, from other cell types, and characterize their transcriptional profiles. Furthermore, we also identify and validate novel specific markers for these rare SV cell types. Finally, we identify homeostatic gene regulatory networks within spindle and root cells, establishing a basis for understanding the functional roles of these cells in hearing. These novel findings will provide new insights for future work in SV-related hearing loss and hearing fluctuation.

Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks

2014 ◽  
Vol 20 (12) ◽  
pp. 1903-1912 ◽  
Author(s):  
Bowen Yu ◽  
Harish Doraiswamy ◽  
Xi Chen ◽  
Emily Miraldi ◽  
Mario Luis Arrieta-Ortiz ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Visual Exploration ◽  
Web Based ◽  
Gene Regulatory

scholarly journals ComiRNet: a web-based system for the analysis of miRNA-gene regulatory networks

2015 ◽  
Vol 16 (Suppl 9) ◽  
pp. S7 ◽  
Author(s):  
Gianvito Pio ◽  
Michelangelo Ceci ◽  
Donato Malerba ◽  
Domenica D'Elia
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Mirna Gene ◽  
Web Based ◽  
Gene Regulatory ◽  
Web Based System

scholarly journals DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-Seq Data

2021 ◽  
Author(s):  
Jiaxing Chen ◽  
Chinwang Cheong ◽  
Liang Lan ◽  
Xin Zhou ◽  
Jiming Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Regulatory Networks ◽  
Deep Neural Network ◽  
Neighborhood Context ◽  
Cellular Heterogeneity ◽  
Specific Gene ◽  
Rna Seq ◽  
Cell Type Specific ◽  
Gene Regulatory

AbstractSingle-cell RNA sequencing is used to capture cell-specific gene expression, thus allowing reconstruction of gene regulatory networks. The existing algorithms struggle to deal with dropouts and cellular heterogeneity, and commonly require pseudotime-ordered cells. Here, we describe DeepDRIM a supervised deep neural network that represents gene pair joint expression as images and considers the neighborhood context to eliminate the transitive interactions. Deep-DRIM yields significantly better performance than the other nine algorithms used on the eight cell lines tested, and can be used to successfully discriminate key functional modules between patients with mild and severe symptoms of coronavirus disease 2019 (COVID-19).

scholarly journals ISMARA: Completely automated inference of gene regulatory networks from high-throughput data

2017 ◽  
Author(s):  
Mikhail Pachkov ◽  
Piotr J Balwierz ◽  
Phil Arnold ◽  
Andreas J Gruber ◽  
Mihaela Zavolan ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Regulatory Networks ◽  
Target Genes ◽  
Chromatin State ◽  
Response Analysis ◽  
Rna Seq ◽  
High Throughput Data ◽  
Micro Rnas ◽  
Gene Regulatory

As the costs of high-throughput measurement technologies continue to fall, experimental approaches in biomedicine are increasingly data intensive and the advent of big data is justifiably seen as holding the promise to transform medicine. However, as data volumes mount, researchers increasingly realize that extracting concrete, reliable, and actionable biological predictions from high-throughput data can be very challenging. Our laboratory has pioneered a number of methods for inferring key gene regulatory interactions from high-throughput data. For example, we developed motif activity response analysis (MARA)[, which models genome-wide gene expression (RNA-Seq, or microarray) and chromatin state (ChIP-Seq) data in terms of comprehensive predictions of regulatory sites for hundreds of mammalian regulators (TFs and micro-RNAs). Using these models, MARA identifies the key regulators driving gene expression and chromatin state changes, the activities of these regulators across the input samples, their target genes, and the sites on the genome through which these regulators act. We recently completely automated MARA in an integrated web-server (ismara.unibas.ch) that allows researchers to analyze their own data by simply uploading RNA-Seq or ChIP-Seq datasets, and provides results in an integrated web interface as well as in downloadable flat form.

scholarly journals Identifying progressive gene network perturbation from single-cell RNA-seq data

2018 ◽  
Author(s):  
Sumit Mukherjee ◽  
Alberto Carignano ◽  
Georg Seelig ◽  
Su-In Lee
Keyword(s):  
Single Cell ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Simulated Data ◽  
Alternative Methods ◽  
Rna Seq ◽  
Differential Network Analysis ◽  
Master Regulators ◽  
Differential Network ◽  
Gene Regulatory

AbstractIdentifying the gene regulatory networks that control development or disease is one of the most important problems in biology. Here, we introduce a computational approach, called PIPER (ProgressIve network PERturbation), to identify the perturbed genes that drive differences in the gene regulatory network across different points in a biological progression. PIPER employs algorithms tailor-made for single cell RNA sequencing (scRNA-seq) data to jointly identify gene networks for multiple progressive conditions. It then performs differential network analysis along the identified gene networks to identify master regulators. We demonstrate that PIPER outperforms state-of-the-art alternative methods on simulated data and is able to predict known key regulators of differentiation on real scRNA-Seq datasets.

scholarly journals A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets

2016 ◽  
Vol 12 (06) ◽  
pp. 340-341 ◽  
Author(s):  
Fereshteh Izadi ◽  
◽  
Hamid Najafi Zarrini ◽  
Nadali Babaeian Jelodar ◽  
◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Rna Seq ◽  
Gene Regulatory ◽  
Analytical Assay

scholarly journals Unraveling the Genetic Basis for the Rapid Diversification of Male Genitalia between Drosophila Species

2020 ◽  
Author(s):  
Joanna F D Hagen ◽  
Cláudia C Mendes ◽  
Shamma R Booth ◽  
Javier Figueras Jimenez ◽  
Kentaro M Tanaka ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Genetic Basis ◽  
Posterior Lobe ◽  
Rna Seq ◽  
New Genes ◽  
Genital Development ◽  
Rapid Diversification ◽  
Gene Regulatory ◽  
Phenotypic Diversification ◽  
Morphological Differences

Abstract In the last 240,000 years, males of the Drosophila simulans species clade have evolved striking differences in the morphology of their epandrial posterior lobes and claspers (surstyli). These appendages are used for grasping the female during mating and so their divergence is most likely driven by sexual selection. Mapping studies indicate a highly polygenic and generally additive genetic basis for these morphological differences. However, we have limited understanding of the gene regulatory networks that control the development of genital structures and how they evolved to result in this rapid phenotypic diversification. Here, we used new D. simulans/D. mauritiana introgression lines on chromosome arm 3L to generate higher resolution maps of posterior lobe and clasper differences between these species. We then carried out RNA-seq on the developing genitalia of both species to identify the expressed genes and those that are differentially expressed between the two species. This allowed us to test the function of expressed positional candidates during genital development in D. melanogaster. We identified several new genes involved in the development and possibly the evolution of these genital structures, including the transcription factors Hairy and Grunge. Furthermore, we discovered that during clasper development Hairy negatively regulates tartan (trn), a gene known to contribute to divergence in clasper morphology. Taken together, our results provide new insights into the regulation of genital development and how this has evolved between species.

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