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Related Concept Videos

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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GeneMarkeR: A Database and User Interface for scRNA-seq Marker Genes.

Brianna M Paisley1,2, Yunlong Liu1,3

  • 1Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, United States.

Frontiers in Genetics
|November 12, 2021
PubMed
Summary
This summary is machine-generated.

GeneMarkeR is a new web tool that helps researchers find specific marker genes for cell types using single-cell RNA sequencing data. It standardizes gene data across species and studies for better cell classification.

Keywords:
cell type4database5marker gene3scRNA-seq2single-cell RNA-seq1web-interface6

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cell Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) allows the study of cellular heterogeneity.
  • Accurate cell type identification is vital for robust scRNA-seq analysis.
  • Existing marker gene databases lack standardization, hierarchical consideration, and sample diversity.

Purpose of the Study:

  • To develop a standardized, updatable web tool for retrieving cell type-specific marker genes.
  • To address limitations in current marker gene databases.

Main Methods:

  • Developed GeneMarkeR, an R Shiny web tool utilizing a novel algorithm.
  • Integrated a MySQL database for updatability and online submission.
  • Employed reactive programming to access standardized public data.

Main Results:

  • GeneMarkeR provides standardized marker gene results across species, methodologies, and sample types.
  • The tool hosts over 261,000 standardized marker gene entries.
  • Data covers 25 studies, 21,012 genomic entities, and 99 cell types mapped to hierarchical ontologies.

Conclusions:

  • GeneMarkeR offers a flexible and standardized solution for marker gene discovery in scRNA-seq research.
  • The tool enhances cell type classification by providing reliable, cross-species, and cross-study marker gene data.