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Related Concept Videos

Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Regulation of Expression Occurs at Multiple Steps02:24

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a...
Ribosome Profiling02:24

Ribosome Profiling

Ribosome profiling or ribo-sequencing is a deep sequencing technique that produces a snapshot of active translation in a cell. It selectively sequences the mRNAs protected by ribosomes to get an insight into a cell’s translation landscape at any given point in time.
Applications of ribosome profiling
Ribosome profiling has many applications, including in vivo monitoring of translation inside a particular organ or tissue type and quantifying new protein synthesis levels.
The technique helps...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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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High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions
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Extracting regulatory modules from gene expression data by sequential pattern mining.

Mingoo Kim1, Hyunjung Shin, Tae Su Chung

  • 1Seoul National University Biomedical Informatics, Seoul National University College of Medicine, Seoul 110799, Korea.

BMC Genomics
|February 29, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for identifying gene regulatory modules (RMs) from microarray data. The new method is scalable and noise-robust, enabling discovery of biologically significant gene expression patterns.

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Area of Science:

  • Bioinformatics
  • Functional Genomics
  • Computational Biology

Background:

  • Identifying regulatory modules (RMs) is crucial in functional genomics.
  • Biclustering, particularly order-preserving methods using sequential pattern mining, is common for RM extraction.
  • Existing methods face challenges with computational scalability and noise sensitivity in large microarray datasets.

Purpose of the Study:

  • To develop a novel sequential pattern mining algorithm for identifying regulatory modules.
  • To create a scalable and noise-robust method for analyzing microarray gene-expression data.
  • To infer regulatory modules and their inter-relations from biological data.

Main Methods:

  • Proposed a novel sequential pattern mining algorithm.
  • Applied the algorithm to yeast microarray data.
  • Developed methods for inferring regulatory modules and inter-module relations.

Main Results:

  • The algorithm successfully identified long, order-preserving patterns in yeast data.
  • These patterns were biologically significant and robust to data shuffling.
  • Identified patterns were enriched with known annotations and consistent with biological knowledge.

Conclusions:

  • The developed approach for identifying RMs is valuable for genome-wide gene regulation studies.
  • This method offers a systematic way to reveal gene regulatory mechanisms.
  • The findings contribute to a deeper understanding of biological systems at a molecular level.