Yanhui Hu, Sudhir Gopal Tattikota, Yifang Liu, Aram Comjean, Yue Gao, Corey Forman, Grace Kim, Jonathan Rodiger, Irene Papatheodorou, Gilberto Dos Santos, Stephanie E Mohr, and Norbert Perrimon. 2021. “
DRscDB: A single-cell RNA-seq resource for data mining and data comparison across species.” Comput Struct Biotechnol J, 19, Pp. 2018-2026.
AbstractWith the advent of single-cell RNA sequencing (scRNA-seq) technologies, there has been a spike in studies involving scRNA-seq of several tissues across diverse species including
Drosophila. Although a few databases exist for users to query genes of interest within the scRNA-seq studies, search tools that enable users to find orthologous genes and their cell type-specific expression patterns across species are limited. Here, we built a new search database, DRscDB (
https://www.flyrnai.org/tools/single_cell/web/), to address this need. DRscDB serves as a comprehensive repository for published scRNA-seq datasets for
Drosophila and relevant datasets from human and other model organisms. DRscDB is based on manual curation of
Drosophila scRNA-seq studies of various tissue types and their corresponding analogous tissues in vertebrates including zebrafish, mouse, and human. Of note, our search database provides most of the literature-derived marker genes, thus preserving the original analysis of the published scRNA-seq datasets. Finally, DRscDB serves as a web-based user interface that allows users to mine gene expression data from scRNA-seq studies and perform cell cluster enrichment analyses pertaining to various scRNA-seq studies, both within and across species.
DRscDB.pdf Ashley Mae Conard, Nathaniel Goodman, Yanhui Hu, Norbert Perrimon, Ritambhara Singh, Charles Lawrence, and Erica Larschan. 2021. “
TIMEOR: a web-based tool to uncover temporal regulatory mechanisms from multi-omics data.” Nucleic Acids Res, 49, W1, Pp. W641-W653.
AbstractUncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. 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 integrating ordered RNA-seq data with protein-DNA binding data to distinguish direct from indirect interactions are urgently needed. 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 determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein-DNA binding data, and protein-protein interaction networks. TIMEOR's user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at
https://github.com/ashleymaeconard/TIMEOR.git and
http://timeor.brown.edu.