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

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

Published on: December 10, 2012

An MCMC algorithm for haplotype assembly from whole-genome sequence data.

Vikas Bansal¹, Aaron L Halpern, Nelson Axelrod

¹Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA. vibansal@cs.ucsd.edu <vibansal@cs.ucsd.edu>

Genome Research

|August 5, 2008

Summary

This summary is machine-generated.

We developed HASH, a novel Markov chain Monte Carlo (MCMC) algorithm for accurate haplotype assembly from whole-genome sequencing data. HASH significantly outperforms existing methods, providing a valuable tool for human evolutionary studies and genetic research.

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Area of Science:

Genomics
Computational Biology
Human Evolutionary Genetics

Background:

Haplotypes are crucial for genetic studies, offering more insight than genotypes alone.
Current haplotype inference relies on computational methods using population genotype data.
Whole-genome sequencing data offers potential for constructing long, individual-specific haplotypes.

Purpose of the Study:

To introduce HASH, a Markov chain Monte Carlo (MCMC) algorithm for haplotype assembly from sequenced DNA fragments.
To enable accurate haplotype construction from whole-genome sequence data for individuals.
To provide a general framework applicable to various sequencing technologies.

Main Methods:

Developed a Markov chain Monte Carlo (MCMC) algorithm named HASH (haplotype assembly for single human).
Utilized min-cut computations on graphs derived from sequenced fragments to generate Markov chain transitions.
Applied the algorithm to whole-genome shotgun sequence data from a human individual.

Main Results:

HASH successfully assembled long haplotypes (N50 length ~ 350 kb) from high-coverage whole-genome sequence data.
HASH-inferred haplotypes demonstrated significantly higher accuracy compared to previous heuristic and MCMC methods.
A low switch error rate of approximately 1.1% was estimated using HapMap project data.

Conclusions:

HASH provides a highly accurate method for individual haplotype assembly from whole-genome sequence data.
The algorithm's general framework is adaptable to different sequencing technologies.
The developed code and phased haplotypes are publicly available for research use.