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Genome Annotation and Assembly

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.

Genome-wide Association Studies-GWAS

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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Related Experiment Video

Updated: Sep 12, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Genome-Wide Uncertainty-Moderated Extraction of Signal Annotations from Multi-Sample Functional Genomics Data.

Nolan H Hamilton^1,2,3, Yu-Chen E Huang^1,2,3, Benjamin D McMichael^1,2

¹Department of Genetics, University of North Carolina at Chapel Hill.

Biorxiv : the Preprint Server for Biology

|August 6, 2025

Summary

This summary is machine-generated.

Consenrich improves genome-wide signal estimation from noisy sequencing data by modeling spatial dependencies and sample-specific noise. This technique enhances differential analyses and identifies functionally enriched genomic regions.

Keywords:

chromatin profiling differential analysis functional genomics genomic signal processing multi-sample data integration regulatory element discovery signal quantification uncertainty quantification

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Last Updated: Sep 12, 2025

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Published on: August 21, 2016

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Sequencing-based functional genomics datasets are often corrupted by noise.
Accurate signal estimation is crucial for downstream analyses like differential analysis and identifying functional genomic regions.

Purpose of the Study:

To present Consenrich, a novel technique for genome-wide signal estimation in noisy multi-sample sequencing data.
To improve the accuracy of signal extraction from functional genomics datasets.

Main Methods:

Consenrich employs a sequential prediction-correction framework.
It models spatial dependencies between genomic loci and regional, sample-specific noise processes.

Main Results:

Consenrich demonstrates significant improvements over existing benchmarks in challenging estimation problems.
The technique effectively reconciles noisy functional genomics data samples.

Conclusions:

Consenrich offers a principled and effective method for extracting reliable signals from noisy sequencing data.
The refined signal extraction has practical applications in differential analyses and identifying functionally enriched genomic regions.