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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...
Sanger Sequencing01:57

Sanger Sequencing

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...
Maxam-Gilbert Sequencing01:05

Maxam-Gilbert Sequencing

In the same year as the discovery of the Sanger sequencing method, another group of scientists, Allan Maxam and Walter Gilbert, demonstrated their chemical-cleavage method for DNA sequencing. The Maxam-Gilbert method relies on using different chemicals that can cleave the DNA sequence at specific sites, the separation of resulting DNA fragments of variable size using electrophoresis, and deciphering the DNA sequence from the resulting gel bands.
Challenges of the Maxam-Gilbert Method
The...
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.
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...

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Related Experiment Video

Updated: Jun 6, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Rapid innovation in ChIP-seq peak-calling algorithms is outdistancing benchmarking efforts.

Adam M Szalkowski1, Christoph D Schmid

  • 1ETH Zurich, Universitätstrasse 6, 8092 Zürich, Switzerland. adam.szalkowski@inf.ethz.ch

Briefings in Bioinformatics
|November 10, 2010
PubMed
Summary

Peak-calling software identifies protein-DNA interactions from sequencing data but results vary significantly. This study reviews current benchmarking methods for peak-calling, highlighting their limitations for transcription regulation analysis.

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Related Experiment Videos

Last Updated: Jun 6, 2026

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation
12:54

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Advances in genomic sequencing outpace understanding of transcription regulation.
  • Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) is a key technique for studying protein-DNA interactions.
  • Accurate identification of these interactions is crucial for understanding gene expression control.

Purpose of the Study:

  • To address the lack of systematic quantitative benchmarking for ChIP-seq peak-calling software.
  • To summarize existing benchmarking efforts for peak-calling algorithms.
  • To explain the potential drawbacks associated with current benchmarking methodologies.

Main Methods:

  • Review and summarization of existing literature on ChIP-seq data analysis and peak-calling.
  • Analysis of reported variations in peak-calling results from different software.
  • Identification and explanation of limitations in current quantitative benchmarking approaches.

Main Results:

  • Significant variation exists among peak-calling software results for ChIP-seq data.
  • Current benchmarking methods for peak-calling lack systematic quantitative evaluation.
  • Existing approaches may have inherent drawbacks that affect the reliability of comparisons.

Conclusions:

  • Systematic, quantitative benchmarking of peak-calling software is essential for reliable transcription regulation studies.
  • Understanding the limitations of current benchmarking methods is critical for interpreting ChIP-seq results.
  • Further development of robust benchmarking strategies is needed to advance the field.