Woods : A fast and accurate functional annotator and classifier of genomic and metagenomic sequences

• Functional annotation of the gigantic metagenomic data is one of the major time-consuming and computationally demanding tasks, which is currently a bottleneck for the efficient analysis. The commonly used homology-based methods to functionally annotate and classify proteins are extremely slow. Therefore, to achieve faster and accurate functional annotation, we have developed an orthology-based functional classifier 'Woods' by using a combination of machine learning and similarity-based approaches. Woods displayed a precision of 98.79% on independent genomic dataset, 96.66% on simulated metagenomic dataset and >97% on two real metagenomic datasets. In addition, it performed >87 times faster than BLAST on the two real metagenomic datasets. Woods can be used as a highly efficient and accurate classifier with high-throughput capability which facilitates its usability on large metagenomic datasets.

Subject headings

NATURVETENSKAP  -- Biologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences (hsv//eng)

Keyword

Functional annotation

Machine learning

Metagenome

Random Forest

Publication and Content Type

ref (subject category)

art (subject category)