NDLI: Clustering orthologs based on sequence and domain similarities

Content Provider	IEEE Xplore Digital Library
Author	Fa Zhang ShengZhong Feng Ozer, H. Bo Yuan
Copyright Year	2005
Description	Author affiliation: Inst. of Comput. Technol., Chinese Acad. of Sci., Beijing (Fa Zhang; ShengZhong Feng)
Abstract	In this paper, we present a fully automatic computational method to cluster orthologs and in-paralogs from multiple species. We use the program Blastp to generate a pairwise distance matrix, which is then normalized for each homologous group between and within the species included. We also used protein domains and their organization in protein sequences as an additional criterion for filtering false relationships. Ortholog clusters are first seeded with multiple reciprocal best pairwise matches, after which the Markov graph-flow algorithm is applied to include in-paralogs. Classification parameters such as the inflation index are optimized according to the functional consistency in each of the clusters. This was inferred by the comparison of ontological annotations available for each of the sequences belonging to the same cluster. We apply our program on six completely sequenced eukaryotic genomes, assigns confidence values for both orthologs and in-paralogs. We note significant improvement for the clustering of orthologs with recent paralogs, comparing our results with similar efforts at NCBI and TIGR. This provides an automatic and robust method to cluster orthologous genes of multiple genomes
Starting Page	7
Ending Page	651
File Size	384026
Page Count	645
File Format	PDF
ISBN	0769524869
DOI	10.1109/HPCASIA.2005.27
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bioinformatics Genomics Databases Proteins Phylogeny Systematics Computers Biomedical informatics Biomedical computing Filtering
Content Type	Text
Resource Type	Article

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