Background: Neither the abundance of the exo/endoglucanase GH modules nor the taxonomy affiliation is informative enough in inferring whether a genome is of a potential cellulolytic microbe or not. By interpreting the complete genomes of 2642 microbe strains whose phenotypes have been well documented, we are trying to reveal a more reliable genotype and phenotype correlation on the specific function niche of cellulose hydrolysis.Results: By incorporating into the annotation approach an

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... recognition of the potential synergy machineries, a more reliable prediction on the corresponding microbes' cellulolytic competency could be achieved. The potential cellulose hydrolyzing microbes could be categorized into 5 groups according to the varying synergy machineries among the carbohydrate active modules/genes annotated. Results of the analysis on the 2642 genomes revealed that some cellulosome gene clusters were in lack of the surface layer homology module (SLH) and microbe strains annotated with such cellulosome gene clusters were not certainly cellulolytic. Hypothesized in this study was that cellulosome-independent genes harboring both the SLH module (mediate the attachment of the enzymes to the host microbe's cell surface) and the cellulose-binding carbohydrate binding module (mediate the attachment of the enzymes to the cellulose substrate) were likely an alternative gene apparatus initiating the formation of the cellulose-enzyme-microbe (CEM) complexes; and their role is important especially for the cellulolytic anaerobes without cellulosome gene clusters. Conclusions: In the genome-centric prediction on the corresponding microbes' cellulolytic activity, recognition of the synergy machineries that include but are not limited to the cellulosome gene clusters is equally important as the annotation of the individual carbohydrate active modules or genes. This is the first time that a pipeline was developed for an automatic recognition of the synergy among the carbohydrate active units annotated. With promising resolution and reliability, this pipeline should be a good add to the bioinformatic tools for the genome-centric interpretations on the specific function niche of cellulose hydrolysis.