Combining gene prediction methods to improve metagenomic gene annotation.

ABSTRACT:

Background

Traditional gene annotation methods rely on characteristics that may not be available in short reads generated from next generation technology, resulting in suboptimal performance for metagenomic (environmental) samples. Therefore, in recent years, new programs have been developed that optimize performance on short reads. In this work, we benchmark three metagenomic gene prediction programs and combine their predictions to improve metagenomic read gene annotation.

Results

We not only analyze the programs' performance at different read-lengths like similar studies, but also separate different types of reads, including intra- and intergenic regions, for analysis. The main deficiencies are in the algorithms' ability to predict non-coding regions and gene edges, resul

SUBMITTER: Yok NG 

PROVIDER: S-EPMC3042383 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature