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Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Complexity of automated gene annotation.

Zoran Nikoloski¹, Sergio Grimbs, Sebastian Klie

¹Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Strasse 24-25, Potsdam, Germany. nikoloski@mpimp-golm.mpg.de

|January 12, 2011

Summary

Graph theory methods help annotate unannotated genes by analyzing high-throughput data. Ontological relationships do not simplify complex gene function annotation, highlighting the need for heuristic algorithms.

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

High-throughput data analysis requires methods to determine gene functions.
Graph-theoretic approaches offer a promising avenue for automated gene function annotation.
Understanding gene function is crucial for unraveling biological mechanisms.

Purpose of the Study:

To review and categorize existing graph-theoretic methods for automated gene function annotation.
To analyze the computational complexity of these approaches.
To investigate the impact of ontological relationships on annotation complexity.

Main Methods:

Classification of graph-theoretic methods into two categories based on transductive learning on networks (dynamic vs. constant costs).
Analysis of computational complexity in relation to classical graph problems (bisection, multiway cut).
Evaluation of the role of functional term ontology in complexity.

Main Results:

Existing graph-theoretic methods for gene annotation were categorized.
The computational complexity of these methods was characterized.
Ontological structure of functional knowledge does not reduce the complexity of transductive learning with dynamic costs.
Automated gene annotation is NP-hard.

Conclusions:

Graph-theoretic methods are valuable for gene function annotation.
The complexity of automated gene annotation remains high, even with ontological information.
Development of heuristic or approximation algorithms is essential for future research in systems biology.