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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...
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.
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...

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Updated: May 25, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Published on: June 23, 2012

A strand specific high resolution normalization method for chip-sequencing data employing multiple experimental

Stefan Enroth1,2, Claes R Andersson3, Robin Andersson1,4

  • 1The Linnaeus Centre for Bioinformatics, Department of Cell and Molecular Biology, Science for Life Laboratory, Biomedical Center, Uppsala University, Box 598, SE-75124 Uppsala, Sweden.

Algorithms for Molecular Biology : AMB
|January 18, 2012
PubMed
Summary
This summary is machine-generated.

A new normalization method effectively removes background noise in high-throughput sequencing data. This approach improves signal detection for protein-DNA interactions and epigenetic modifications, enhancing downstream analysis accuracy.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • High-throughput sequencing is crucial for studying protein-DNA interactions and epigenetics.
  • Sequencing data often contains noise from repetitive regions and non-specific binding, complicating analysis like peak-calling.
  • Existing methods use background data mainly for peak-calling adjustment, not as a pre-processing step to isolate signal.

Purpose of the Study:

  • To develop a universal normalization method for extracting biological signal from noisy genomic data.
  • To create a pre-processing step that effectively discerns signal from background noise in sequencing data.

Main Methods:

  • A novel normalization method based on linear regression was formulated.
  • The method was implemented in R and C++.
  • Tested on simulated and public ChIP-seq data (MAX, FOXA1, Input, IgG).

Main Results:

  • The normalization method was compared against raw data using different background correction strategies.
  • Peak-calling on normalized data yielded the highest fraction of regions with transcription factor binding motifs.
  • Evaluation using qPCR data indicated improved sensitivity and specificity for normalized data.

Conclusions:

  • The proposed method successfully removes background noise from sequencing data.
  • It can simultaneously correct for multiple bias sources by handling several control samples.
  • The approach enhances the accuracy of genomic pattern analysis.