Sifting through genomes with iterative-sequence clustering produces a large, phylogenetically diverse protein-family resource
- Equal contributors
1 The J. David Gladstone Institutes, University of California San Francisco, San Francisco, CA, 94158, USA
2 UC Davis Genome Center, University of California, Davis, Davis, CA, 95616, USA
3 Department of Biochemistry & Molecular Biology, Dalhousie University, Halifax, Nova Scotia, Canada
4 Department of Epidemiology & Biostatistics, Institute for Human Genetics, University of California San Francisco, San Francisco, CA, 94158, USA
5 Deptartment of Evolution and Ecology, University of California, Davis, Davis, CA, 95616, USA
6 Deptartment of Medical Microbiology and Immunology, University of California, Davis, Davis, CA, 95616, USA
7 Department of Energy Joint Genome Institute, Walnut Creek, CA, 94598, USA
Citation and License
BMC Bioinformatics 2012, 13:264 doi:10.1186/1471-2105-13-264Published: 13 October 2012
New computational resources are needed to manage the increasing volume of biological data from genome sequencing projects. One fundamental challenge is the ability to maintain a complete and current catalog of protein diversity. We developed a new approach for the identification of protein families that focuses on the rapid discovery of homologous protein sequences.
We implemented fully automated and high-throughput procedures to de novo cluster proteins into families based upon global alignment similarity. Our approach employs an iterative clustering strategy in which homologs of known families are sifted out of the search for new families. The resulting reduction in computational complexity enables us to rapidly identify novel protein families found in new genomes and to perform efficient, automated updates that keep pace with genome sequencing. We refer to protein families identified through this approach as “Sifting Families,” or SFams. Our analysis of ~10.5 million protein sequences from 2,928 genomes identified 436,360 SFams, many of which are not represented in other protein family databases. We validated the quality of SFam clustering through statistical as well as network topology–based analyses.
We describe the rapid identification of SFams and demonstrate how they can be used to annotate genomes and metagenomes. The SFam database catalogs protein-family quality metrics, multiple sequence alignments, hidden Markov models, and phylogenetic trees. Our source code and database are publicly available and will be subject to frequent updates (http://edhar.genomecenter.ucdavis.edu/sifting_families/ webcite).