Family classification without domain chaining

Motivation: Classification of gene and protein sequences into homologous families, i.e. sets of sequences that share common ancestry, is an essential step in comparative genomic analyses. This is typically achieved by construction of a sequence homology network, followed by clustering to identify de...

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Bibliographic Details
Published in:Bioinformatics 2009-06, Vol.25 (12), p.i45-i53
Main Authors: Joseph, Jacob M., Durand, Dannie
Format: Article
Language:English
Subjects:
Animals
Computational Biology - methods
Conserved Sequence
Databases, Protein
Genome
Humans
Mice
Proteins - chemistry
Proteins - classification
Proteins - genetics
Sequence Analysis, Protein - methods
Sequence Homology, Amino Acid
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Summary:Motivation: Classification of gene and protein sequences into homologous families, i.e. sets of sequences that share common ancestry, is an essential step in comparative genomic analyses. This is typically achieved by construction of a sequence homology network, followed by clustering to identify dense subgraphs corresponding to families. Accurate classification of single domain families is now within reach due to major algorithmic advances in remote homology detection and graph clustering. However, classification of multidomain families remains a significant challenge. The presence of the same domain in sequences that do not share common ancestry introduces false edges in the homology network that link unrelated families and stymy clustering algorithms. Results: Here, we investigate a network-rewiring strategy designed to eliminate edges due to promiscuous domains. We show that this strategy can reduce noise in and restore structure to artificial networks with simulated noise, as well as to the yeast genome homology network. We further evaluate this approach on a hand-curated set of multidomain sequences in mouse and human, and demonstrate that classification using the rewired network delivers dramatic improvement in Precision and Recall, compared with current methods. Families in our test set exhibit a broad range of domain architectures and sequence conservation, demonstrating that our method is flexible, robust and suitable for high-throughput, automated processing of heterogeneous, genome-scale data. contact: jacobmj@cmu.edu
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btp207