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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. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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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...
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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.
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DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
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Updated: May 17, 2026

A Semiautomated ChIP-Seq Procedure for Large-scale Epigenetic Studies
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Published on: August 13, 2020

A co-localization model of paired ChIP-seq data using a large ENCODE data set enables comparison of multiple samples.

Kazumitsu Maehara1, Jun Odawara, Akihito Harada

  • 1Department of Advanced Initiative Medicine, Faculty of Medicine, Kyushu University, JST-CREST, Fukuoka 812-8582, Japan.

Nucleic Acids Research
|November 6, 2012
PubMed
Summary

A new model effectively compares two chromatin immunoprecipitation by sequencing (ChIP-seq) datasets, distinguishing meaningful biological signals from background noise. This approach aids in identifying factors related to RNA polymerase II CTD serine 2 phosphorylation and understanding unknown functions.

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Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Deep sequencing methods like ChIP-seq identify genomic binding sites and modifications.
  • Comparing ChIP-seq data is crucial for predicting unknown factor functions.
  • A computational model is needed to effectively analyze and compare complex ChIP-seq datasets.

Purpose of the Study:

  • To develop a computational model for comparing two distinct ChIP-seq datasets.
  • To differentiate between meaningful overlapping signals and background noise in ChIP-seq data.
  • To identify factors associated with RNA polymerase II CTD serine 2 phosphorylation and explore RNA polymerase II CTD serine 7 phosphorylation functions.

Main Methods:

  • Development of a novel computational model to represent and compare co-localization of two ChIP-seq datasets.
  • Application of the model to analyze ChIP-seq data of RNA polymerase II CTD serine 2 phosphorylation against ENCODE project data.
  • Utilizing peak-called data from ChIP-seq and other deep sequencing experiments.

Main Results:

  • The model successfully separates meaningful overlapping signals from background noise.
  • Identified factors related to RNA polymerase II CTD serine 2 phosphorylation in HeLa cells.
  • Demonstrated similarity in localization for transcription factors and histone modifications within the ENCODE dataset.

Conclusions:

  • The proposed model is effective for comparing ChIP-seq data and identifying biologically relevant signals.
  • The model aids in understanding the functions of factors, particularly those with unknown roles.
  • This approach is valuable for analyzing complex genomic datasets and uncovering novel biological insights.