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Related Concept Videos

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.
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Efficiently identifying genome-wide changes with next-generation sequencing data.

Weichun Huang1, David M Umbach, Nicole Vincent Jordan

  • 1Biostatistics Branch, National Institute of Environmental Health Sciences, RTP, NC 27709, USA. weichun.huang@nih.gov

Nucleic Acids Research
|August 2, 2011
PubMed
Summary
This summary is machine-generated.

We introduce EpiCenter, a statistical framework and software for analyzing genome-wide epigenetic and gene expression data. It accurately identifies differential changes using robust normalization and statistical testing, controlling errors effectively.

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Last Updated: May 30, 2026

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)
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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
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Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Analyzing high-throughput sequencing data like ChIP-seq and mRNA-seq is crucial for understanding epigenetic modifications and gene expression.
  • Existing methods may lack robust normalization strategies or comprehensive statistical testing for differential analysis.
  • Accurate identification of genomic changes requires methods that account for various sources of biological and technical variation.

Purpose of the Study:

  • To develop a novel statistical framework and associated software (EpiCenter) for identifying genome-wide differential epigenetic marks and gene expression.
  • To provide flexible normalization options and rigorous statistical tests to enhance the accuracy of differential analyses.
  • To offer a comprehensive tool for analyzing ChIP-seq and mRNA-seq data, including transcription factor binding site identification.

Main Methods:

  • A statistical framework incorporating multiple normalization methods tailored to different experimental scenarios.
  • A sequential three-test approach to effectively eliminate background noise and model various sources of variation.
  • Multiple testing adjustment strategies to control the false discovery rate (FDR) or family-wise type I error.

Main Results:

  • Simulations demonstrate the framework's ability to maintain a low FDR across diverse read coverages and biological variations.
  • Real-data examples showcase the framework's effectiveness in identifying differential epigenetic changes and gene expression.
  • The proposed 'parsimony' normalization method proved superior to the standard 'tagRatio' method in performance.

Conclusions:

  • The EpiCenter framework provides an effective and robust solution for genome-wide differential analyses using sequencing data.
  • EpiCenter offers a user-friendly platform for identifying epigenetic changes, gene expression differences, and transcription factor binding sites.
  • The developed statistical methods and software tool are publicly available, facilitating broader research applications.