A Need for Improved Cellulase Identification from Metagenomic Sequence Data.
Rebecca CoBarbara A KatzenbackPublished in: Applied and environmental microbiology (2020)
Improved sequencing technologies and the maturation of metagenomic approaches allow the identification of gene variants with potential industrial applications, including cellulases. Cellulase identification from metagenomic environmental surveys is complicated by inconsistent nomenclature and multiple categorization systems. Here, we summarize the current classification and nomenclature systems, with recommendations for improvements to these systems. Addressing the issues described will strengthen the annotation of cellulose-active enzymes from environmental sequence data sets-a rapidly growing resource in environmental and applied microbiology.
Keyphrases
- human health
- antibiotic resistance genes
- copy number
- bioinformatics analysis
- electronic health record
- big data
- life cycle
- wastewater treatment
- machine learning
- risk assessment
- heavy metals
- single cell
- genome wide
- deep learning
- cross sectional
- ionic liquid
- clinical practice
- data analysis
- climate change
- rna seq
- gene expression