Dataset Information

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. Characterization of CLE candidates provided systematic statistics on protein lengths, signal peptides, relative motif positions, amino acid compositions of different parts of the CLE precursor proteins, and decisive factors of CLE prediction. The approach taken here provides information on the evolution of the CLE gene family and provides evidence that the CLE and IDA/IDL genes share a common ancestor.

Conclusions

Our new approach is applicable to SSPs or other proteins with short conserved domains and hence, provides a useful tool for gene prediction, classification and evolutionary analysis.

SUBMITTER: Zhang Z

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Zhang Zhe Z Liu Lei L Kucukoglu Melis M Tian Dongdong D Larkin Robert M RM Shi Xueping X Zheng Bo B

BMC genomics 20201012 1

<h4>Background</h4>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.<h4>Results</h4>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 ...[more]

PMID: 33045986

Similar Datasets

Project description:Next-generation sequencing (NGS)-based 16S rRNA sequencing by jointly using the PCR amplification and NGS technology is a cost-effective technique, which has been successfully used to study the phylogeny and taxonomy of samples from complex microbiomes or environments. Clustering 16S rRNA sequences into operational taxonomic units (OTUs) is often the first step for many downstream analyses. Heuristic clustering is one of the most widely employed approaches for generating OTUs. However, most heuristic OTUs clustering methods just select one single seed sequence to represent each cluster, resulting in their outcomes suffer from either overestimation of OTUs number or sensitivity to sequencing errors. In this paper, we present a novel dynamic multi-seeds clustering method (namely DMSC) to pick OTUs. DMSC first heuristically generates clusters according to the distance threshold. When the size of a cluster reaches the pre-defined minimum size, then DMSC selects the multi-core sequences (MCS) as the seeds that are defined as the n-core sequences (n ≥ 3), in which the distance between any two sequences is less than the distance threshold. A new sequence is assigned to the corresponding cluster depending on the average distance to MCS and the distance standard deviation within the MCS. If a new sequence is added to the cluster, dynamically update the MCS until no sequence is merged into the cluster. The new method DMSC was tested on several simulated and real-life sequence datasets and also compared with the traditional heuristic methods such as CD-HIT, UCLUST, and DBH. Experimental results in terms of the inferred OTUs number, normalized mutual information (NMI) and Matthew correlation coefficient (MCC) metrics demonstrate that DMSC can produce higher quality clusters with low memory usage and reduce OTU overestimation. Additionally, DMSC is also robust to the sequencing errors. The DMSC software can be freely downloaded from https://github.com/NWPU-903PR/DMSC.

Dataset Information

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

Background

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets