title: Evaluation of a hybrid approach using UBLAST and BLASTX for metagenomic sequences annotation of specific functional genes.
abstract: The fast development of next generation sequencing (NGS) has dramatically increased the application of metagenomics in various aspects. Functional annotation is a major step in the metagenomics studies. Fast annotation of functional genes has been a challenge because of the deluge of NGS data and expanding databases. A hybrid annotation pipeline proposed previously for taxonomic assignments was evaluated in this study for metagenomic sequences annotation of specific functional genes, such as antibiotic resistance genes, arsenic resistance genes and key genes in nitrogen metabolism. The hybrid approach using UBLAST and BLASTX is 44-177 times faster than direct BLASTX in the annotation using the small protein database for the specific functional genes, with the cost of missing a small portion (<1.8%) of target sequences compared with direct BLASTX hits. Different from direct BLASTX, the time required for specific functional genes annotation using the hybrid annotation pipeline depends on the abundance for the target genes. Thus this hybrid annotation pipeline is more suitable in specific functional genes annotation than in comprehensive functional genes annotation.
authors.affiliation: Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Yang
authors.affiliation: Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China.
authors.forename: Xiao-Tao
authors.initials: XT
authors.lastname: Jiang
authors.affiliation: Environmental Biotechnology Laboratory, Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China.
authors.forename: Tong
authors.initials: T
authors.lastname: Zhang
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D066264:Datasets as Topic; D059014:High-Throughput Nucleotide Sequencing; D056186:Metagenomics / Q000379:methods*; D058977:Molecular Sequence Annotation; D015203:Reproducibility of Results; D012984:Software; D013997:Time Factors