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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Annotation inconsistencies beyond sequence similarity-based function prediction - phylogeny and genome structure.

Vasilis J Promponas¹, Ioannis Iliopoulos², Christos A Ouzounis³

¹Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, PO Box 20537, CY-1678 Nicosia, Cyprus.

Standards in Genomic Sciences

|November 24, 2015

Summary

Computational biology now focuses on system-wide pathways, not just protein functions. Genome-aware methods reveal broader errors in genome sequences, impacting phylogenetic and regional data.

Keywords:

Error propagation Genome evolution Genome structure Genome-aware methods Genome-wide annotation Mis-annotation modeling Next-generation sequencing Protein function prediction

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Traditional function annotation focused on individual protein roles.
Modern approaches analyze system-wide metabolic pathways and genomic structures.
Misannotation extends beyond protein function to phylogenetic and genomic regions.

Purpose of the Study:

To highlight the expansion of misannotation in genome sequences.
To categorize and illustrate error propagation in biological databases.
To raise awareness for improved solutions in genome annotation.

Main Methods:

Review of genome-aware methods in computational biology.
Analysis of error propagation across different levels of biological organization.
Presentation of examples demonstrating misannotation inconsistencies.

Main Results:

Identification of three main categories of error propagation in databases.
Demonstration of how misannotations affect phylogenetic and genomic structure analysis.
Examples range from traditional to emerging misannotation issues.

Conclusions:

Genome-aware methods reveal complex, system-wide annotation errors.
Error propagation in biological databases requires broader attention.
Awareness of these issues is crucial for developing future solutions.