Predicting and clustering plant CLE genes with a new method developed specifically for short amino acid sequences.

ABSTRACT:

Background

The CLV3/ESR-RELATED (CLE) gene family encodes small secreted peptides (SSPs) and plays vital roles in plant growth and development by promoting cell-to-cell communication. The prediction and classification of CLE genes is challenging because of their low sequence similarity.

Results

We developed a machine learning-aided method for predicting CLE genes by using a CLE motif-specific residual score matrix and a novel clustering method based on the Euclidean distance of 12 amino acid residues from the CLE motif in a site-weight dependent manner. In total, 2156 CLE candidates-including 627 novel candidates-were predicted from 69 plant species. The results from our CLE motif-based clustering are consistent with previous reports using the entire pre-propeptide. Characte

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC7552357 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature