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Related Concept Videos

RNA-seq03:21

RNA-seq

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. 
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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Related Experiment Video

Updated: Jun 9, 2026

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2
10:10

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

Published on: September 18, 2021

CoGAPS: an R/C++ package to identify patterns and biological process activity in transcriptomic data.

Elana J Fertig1, Jie Ding, Alexander V Favorov

  • 1Department of Oncology and Division of Oncology, Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA. ejfertig@jhmi.edu

Bioinformatics (Oxford, England)
|September 3, 2010
PubMed
Summary
This summary is machine-generated.

Coordinated Gene Activity in Pattern Sets (CoGAPS) enhances biological process inference from transcriptomic data. This open-source software integrates a novel algorithm for improved gene set activity analysis.

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Last Updated: Jun 9, 2026

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Area of Science:

  • Transcriptomics
  • Systems Biology
  • Bioinformatics

Background:

  • Gene expression analysis is crucial for understanding biological processes.
  • Existing methods for inferring biological processes from transcriptomic data have limitations.
  • The need for integrated and robust tools for gene set enrichment analysis is apparent.

Purpose of the Study:

  • To introduce Coordinated Gene Activity in Pattern Sets (CoGAPS) as an integrated package for biological process inference.
  • To enhance the accuracy and interpretability of gene expression data analysis.
  • To provide an open-source software solution for researchers.

Main Methods:

  • Utilizes a Markov chain Monte Carlo (MCMC) matrix factorization algorithm (GAPS).
  • Incorporates a threshold-independent statistic for inferring gene set activity.
  • Developed as open-source C++ code with an R interface, leveraging JAGS software.

Main Results:

  • CoGAPS effectively isolates gene expression driven by specific biological processes.
  • The software improves upon existing enrichment measurement methods.
  • Provides enhanced inference of biological processes from transcriptomic data.

Conclusions:

  • CoGAPS offers an integrated approach to gene expression analysis.
  • The tool enhances the understanding of biological processes through transcriptomic data.
  • CoGAPS represents a significant advancement in bioinformatics software for biological discovery.