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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
Reporter Genes02:11

Reporter Genes

Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
Commonly used reporter...
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...
What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
Real Time RT-PCR02:57

Real Time RT-PCR

Real-time reverse transcription-polymerase chain reaction, or Real-time RT-PCR, is an analytical tool used to determine the expression level of target genes. The method involves converting mRNA to complementary DNA with the help of an enzyme known as reverse transcriptase, followed by the PCR amplification of the cDNA. These two processes can be performed simultaneously in a single tube or separately as a two-step reaction.
The real-time quantification of the number of amplified products is...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Updated: Jul 4, 2026

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts
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Published on: January 2, 2018

Detecting periodic genes from irregularly sampled gene expressions: a comparison study.

Wentao Zhao1, Kwadwo Agyepong, Erchin Serpedin

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

EURASIP Journal on Bioinformatics & Systems Biology
|June 28, 2008
PubMed
Summary

This study compared spectral analysis methods for identifying cell cycle genes from irregular gene expression data. The Lomb-Scargle method proved most effective for discovering periodically expressed genes in yeast and fruit fly datasets.

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Last Updated: Jul 4, 2026

Determining Genome-wide Transcript Decay Rates in Proliferating and Quiescent Human Fibroblasts
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Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae
07:00

Visualization and Analysis of mRNA Molecules Using Fluorescence In Situ Hybridization in Saccharomyces cerevisiae

Published on: June 14, 2013

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Time series gene expression data from microarrays are crucial for understanding cellular processes like cell cycles.
  • Experimental limitations often result in irregularly sampled microarray data, posing challenges for analysis.
  • Accurate identification of periodically expressed genes is essential for deciphering cell cycle mechanisms.

Purpose of the Study:

  • To compare the efficacy and efficiency of three spectral analysis methods (Lomb-Scargle, Capon, and MAPES) for recovering periodically expressed genes from irregularly sampled microarray data.
  • To identify genes exhibiting periodic expression patterns in Saccharomyces cerevisiae and Drosophila melanogaster.

Main Methods:

  • In silico experiments were conducted using microarray data from Saccharomyces cerevisiae.
  • Three spectral analysis techniques were evaluated: Lomb-Scargle, Capon, and Missing-Data Amplitude and Phase Estimation (MAPES).
  • The performance was assessed based on the ability to recover known or simulated periodically expressed genes.

Main Results:

  • The Lomb-Scargle method demonstrated superior efficacy and efficiency in recovering periodically expressed genes compared to Capon and MAPES.
  • In silico experiments on Saccharomyces cerevisiae data confirmed Lomb-Scargle's effectiveness.
  • Analysis of Drosophila melanogaster microarray data identified 149 periodically expressed genes using the Lomb-Scargle method.

Conclusions:

  • The Lomb-Scargle spectral analysis scheme is the most effective method for identifying periodically expressed genes from irregularly sampled time series microarray data.
  • This approach facilitates the discovery of cell cycle-related genes in diverse organisms.
  • The findings provide a robust method for analyzing challenging biological datasets.