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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...
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
This technique helps gather information regarding the protein from which the peptide was obtained and to study the peptides’ amino acid sequence. Identifying peptides from a complex mixture is an important component of the growing field of...
Next-generation Sequencing03:00

Next-generation Sequencing

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
Although all next-generation methods use different technologies, they all share a set of standard features.

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Updated: May 15, 2026

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
09:49

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Combining multiple ChIP-seq peak detection systems using combinatorial fusion.

Christina Schweikert1, Stuart Brown, Zuojian Tang

  • 1Laboratory for Informatics and Data Mining, Department of Computer and Information Science, Fordham University, New York, NY 10023, USA. cschweikert@cis.fordham.edu

BMC Genomics
|January 4, 2013
PubMed
Summary
This summary is machine-generated.

Combining peak detection systems improves Chromatin Immunoprecipitation sequencing (ChIP-seq) analysis. Fusion methods enhance accuracy by increasing true positives and reducing false positives for better genome-wide protein-DNA interaction identification.

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Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
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Last Updated: May 15, 2026

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing
09:49

Oncogenic Gene Fusion Detection Using Anchored Multiplex Polymerase Chain Reaction Followed by Next Generation Sequencing

Published on: July 5, 2019

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations
11:52

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Chromatin Immunoprecipitation sequencing (ChIP-seq) generates vast data on genome-wide protein-DNA interactions.
  • Advancements in next-generation sequencing fuel the need for robust analysis techniques.
  • Accurate identification of protein binding sites is crucial for understanding gene regulation.

Purpose of the Study:

  • To evaluate and enhance computational and statistical methods for protein binding site detection in ChIP-seq data.
  • To compare the performance of six leading peak detection systems: CisGenome, MACS, PeakSeq, QuEST, SISSRs, and TRLocator.
  • To develop fusion methods for improving the accuracy of ChIP-seq peak identification.

Main Methods:

  • Developed two novel methods for merging and re-scoring peak regions from different detection systems.
  • Analyzed system performance using metrics such as average precision and transcription start site coverage.
  • Implemented score and rank combination strategies for peak detection fusion.

Main Results:

  • Fusion of peak detection systems significantly improves ChIP-seq analysis.
  • Score or rank combination enhances peak detection accuracy.
  • The proposed fusion methods increase true positive rates and decrease false positive rates.

Conclusions:

  • Combination and fusion analysis provide a framework for assessing ChIP-seq technologies.
  • Researchers can utilize these methods to select optimal systems or fusion strategies for their data.
  • The approach offers a valuable alternative for refining ChIP-seq peak identification.