CAZymes Analysis Toolkit (cat): Web service for searching and analyzing carbohydrate-active enzymes in a newly sequenced organism using CAZy database

  • Byung H. Park
  • , Tatiana V. Karpinets
  • , Mustafa H. Syed
  • , Michael R. Leuze
  • , Edward C. Uberbacher

Research output: Contribution to journalArticlepeer-review

262 Scopus citations

Abstract

The Carbohydrate-Active Enzyme (CAZy) database provides a rich set of manually annotated enzymes that degrade, modify, or create glycosidic bonds. Despite rich and invaluable information stored in the database, software tools utilizing this information for annotation of newly sequenced genomes by CAZy families are limited. We have employed two annotation approaches to fill the gap between manually curated high-quality protein sequences collected in the CAZy database and the growing number of other protein sequences produced by genome or metagenome sequencing projects. The first approach is based on a similarity search against the entire nonredundant sequences of the CAZy database. The second approach performs annotation using links or correspondences between the CAZy families and protein family domains. The links were discovered using the association rule learning algorithm applied to sequences from the CAZy database. The approaches complement each other and in combination achieved high specificity and sensitivity when cross-evaluated with the manually curated genomes of Clostridium thermocellum ATCC 27405 and Saccharophagus degradans 2-40. The capability of the proposed framework to predict the function of unknown protein domains and of hypothetical proteins in the genome of Neurospora crassa is demonstrated. The framework is implemented as a Web service, the CAZymes Analysis Toolkit, and is available at http://cricket.ornl.gov/cgi-bin/ cat.cgi.

Original languageEnglish
Pages (from-to)1574-1584
Number of pages11
JournalGlycobiology
Volume20
Issue number12
DOIs
StatePublished - Dec 2010

Funding

This research was supported by the BioEnergy Science Center and by the Genomic Science Program of the Office of Biological and Environmental Research, U.S. Department of Energy.

Keywords

  • biofuel
  • carbohydrate-active enzymes
  • computational annotation
  • protein families

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