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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.

Next-generation Sequencing

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.
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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...

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Updated: Jun 19, 2025

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

Published on: February 24, 2015

Whole-genome bisulfite sequencing data analysis learning module on Google Cloud Platform.

Yujia Qin¹, Angela Maggio², Dale Hawkins³

¹Department of Quantitative Health Sciences, John A. Burns School of Medicine, University of Hawaii at Manoa, 651 Ilalo Street, Honolulu, HI 96813, United States.

Briefings in Bioinformatics

|July 23, 2024

Summary

This summary is machine-generated.

This study introduces an interactive learning module for whole-genome bisulfite sequencing (WGBS) data analysis using cloud computing. It enhances accessibility to epigenomic research tools and accelerates advancements in the field.

Keywords:

DNA methylation Google Cloud platform WGBS bioinformatics education cloud computing epigenetics

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Last Updated: Jun 19, 2025

Enhanced Reduced Representation Bisulfite Sequencing for Assessment of DNA Methylation at Base Pair Resolution

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Published on: February 24, 2015

Targeted DNA Methylation Analysis by Next-generation Sequencing

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Published on: February 24, 2015

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

Area of Science:

Epigenetics and Genomics
Bioinformatics and Computational Biology
Educational Technology

Background:

Whole-genome bisulfite sequencing (WGBS) is crucial for understanding DNA methylation patterns and epigenetic regulation.
Analyzing large-scale WGBS datasets requires significant computational resources and expertise.
Cloud computing offers scalable solutions for complex genomic data analysis.

Purpose of the Study:

To develop an interactive learning module for whole-genome bisulfite sequencing (WGBS) data analysis.
To facilitate the use of cloud-based tools, specifically on Google Cloud Platform, for WGBS data.
To enhance the accessibility and adoption of cloud computing in epigenomic research.

Main Methods:

Development of a resource module integrated into the NIGMS Sandbox for Cloud-based Learning platform.
Utilizing cloud-based tools such as Google Cloud Storage, Vertex AI notebooks, and Google Batch.
Providing step-by-step tutorials for WGBS data preprocessing and differential methylation analysis.

Main Results:

The module offers interactive learning for WGBS data analysis, covering preprocessing and differential methylation identification.
A streamlined workflow for handling large datasets using cloud infrastructure is demonstrated.
The module integrates WGBS analysis with cloud resource utilization, deepening user understanding.

Conclusions:

The developed module enhances the accessibility of cloud computing for epigenomic research.
It aims to accelerate advancements in epigenetics by simplifying WGBS data analysis.
The learning module supports both bulk and single-cell ATAC-seq data analysis, expanding its utility.