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How robust are phylogenetic methods of orthology prediction to these data?
We have focused on specific issues in automated methods of orthology identification for high-throughput application, e.g. for functional annotation of whole genomes.
In future work, we are planning to implement strategies that will allow us to explore the effects of different methods of orthology detection.
Phylogenetic methods of orthology prediction analyze gene trees (or, more precisely, multi-gene trees containing groups of paralogs and orthologs) to localize duplication events on the tree and separate orthologs from paralogs; phylogenetic methods also enable biologists to perform more fine-tuned analyses, e.g. to discriminate between orthologs and super-orthologs [ 30].
This is illustrated in Figure 2. Promiscuous domains present a significant challenge to orthology prediction: all methods of orthology prediction that are based either implicitly (as in graph-based methods) or explicitly (as in domain-based phylogenetic methods) on local alignment can incorrectly cluster proteins with different domain architectures into orthology groups.
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The method is analogous to the existing method of orthology detection in expressed sequence tags (EST) called HaMStR (Hidden Markov Model based Search for Orthologs using Reciprocity) [ 15].
We propose a refined method of orthology assignment based on two paradigms for interpreting its definition: by genomic context or by sequence content.
InParanoid has been evaluated to be one of the best performing methods for orthology detection [ 29].
Herein we present the first systematic analysis of the functional consequences of 2R-WGD using state-of-the-art methods for inference of orthology and duplications.
We therefore additionally employed this method of putative orthology assignment as the D. melanogaster proteome is well annotated, and is the best annotated arthropod proteome derived from a complete genome sequence.
As more refined methods for automatic detection of orthology are developed (for example, [20], [21]) we plan to incorporate them into the P-POD tool, taking advantage of our modular design scheme.
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