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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.

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Updated: Feb 28, 2026

An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

haploR: an R package for querying web-based annotation tools.

Ilya Y Zhbannikov¹, Konstantin Arbeev^1,2, Svetlana Ukraintseva^1,2

¹Biodemography of Aging Research Unit (BARU) at Social Science Research Institute, Duke University, Durham, NC, USA.

|June 20, 2017

Summary

We created haploR, an R package that simplifies accessing genome annotation data from HaploReg and RegulomeDB. This tool aids in the rapid discovery and interpretation of genetic associations after genome-wide association studies.

Keywords:

R data mining databases genetic variants genome annotation genomics

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases.
Interpreting the functional significance of these variants often requires integrating data from multiple genome annotation tools.
Current methods for data retrieval and integration can be time-consuming and complex.

Purpose of the Study:

To develop an R package, haploR, for streamlined querying of web-based genome annotation tools.
To facilitate the integration of annotation data into downstream bioinformatics analyses.
To accelerate the discovery and interpretation of genetic associations identified through GWAS.

Main Methods:

Developed haploR, an R package utilizing R programming language.
Implemented functions to query HaploReg and RegulomeDB web services.
Designed the package to consolidate queried information into a data frame format.

Main Results:

haploR successfully retrieves and organizes data from HaploReg and RegulomeDB.
The package provides a unified data frame suitable for further bioinformatic analysis.
This facilitates efficient post-GWAS data exploration.

Conclusions:

haploR enhances the efficiency of analyzing genome annotation data.
The package supports rapid discovery and interpretation of genetic associations.
haploR is a valuable tool for researchers conducting post-GWAS analyses.