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Genome Annotation and Assembly

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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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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
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Updated: Jun 16, 2026

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Informatic Analysis of Sequence Data from Batch Yeast 2-Hybrid Screens

Published on: June 28, 2018

Graph-based sequence annotation using a data integration approach.

Robert Pesch¹, Artem Lysenko, Matthew Hindle

¹Department of Computer Science, Bonn-Rhein-Sieg University of Applied Sciences, Germany.

Journal of Integrative Bioinformatics

|February 6, 2010

Summary

This summary is machine-generated.

This study introduces a novel bioinformatics system for automated gene function annotation by integrating diverse reference databases. The ONDEX system leverages information networks to improve prediction accuracy for genomics experiments.

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Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Automated annotation of high-throughput sequencing data is challenging.
Current methods use sequential pipelines and ignore links between reference datasets.
These links are crucial for assessing prediction confidence.

Purpose of the Study:

To develop a novel annotation system using information networks.
To enrich the annotation process for expert curators and unannotated genes.
To present the initial development stages and data integration steps.

Main Methods:

Established a core database integrating UniProt, PFAM, PDB, Gene Ontology (GO), and Ara-Cyc.
Utilized the ONDEX data integration system.
Compared different methods for integrating GO terms into the annotation pipeline.

Main Results:

Successfully integrated multiple reference databases into the ONDEX system.
Demonstrated the value of information links for assessing functional assignments.
Identified consequences of GO term integration for improving annotation accuracy.

Conclusions:

The ONDEX system provides a foundation for a novel annotation approach.
Integrating diverse data sources and their links enhances gene function prediction.
The developed methods and algorithms are available within the ONDEX system.