Machine learning: A powerful tool for gene function prediction in plants

Abstract

Recent advances in sequencing and informatic technologies have led to a deluge of publicly available genomic data. While it is now relatively easy to sequence, assemble, and identify genic regions in diploid plant genomes, functional annotation of these genes is still a challenge. Over the past decade, there has been a steady increase in studies utilizing machine learning algorithms for various aspects of functional prediction, because these algorithms are able to integrate large amounts of heterogeneous data and detect patterns inconspicuous through rule‐based approaches. The goal of this review is to introduce experimental plant biologists to machine learning, by describing how it is currently being used in gene function prediction to gain novel biological insights. In this review, we discuss specific applications of machine learning in identifying structural features in sequenced genomes, predicting interactions between different cellular components, and predicting gene function and organismal phenotypes. Finally, we also propose strategies for stimulating functional discovery using machine learning–based approaches in plants.

Synergizing Off-Target Predictions for In Silico Insights of CENH3 Knockout in Cannabis through CRISPR/Cas

“…Recently, the application of machine learning algorithms has been successfully investigated in different areas such as genome editing [ 18 , 19 , 20 , 42 ], prediction of transcription factor target genes [ 43 , 44 ], phenomics [ 45 , 46 , 47 ], and plant tissue culture [ 46 , 48 , 49 , 50 , 51 ]. Conventional statistical methods such as ANOVA and simple regression methods are typically recommended for small datasets with limited dimensions [ 48 ].…”

“…This enables the RF algorithm to generate stable and better prediction for new data lines not necessary existing in the training dataset [ 52 ]. The successful use of RF has been reported in different areas of plant science [ 43 , 48 , 52 ].…”

Multiple routes of functional diversification of the plant BAHD acyltransferase family revealed by comparative biochemical and genomic analyses

“…e.g. using machine learning (Mahood et al, 2020). At the same time, among the 42 multi-species OGs not represented among characterized BAHDs, many show broad conservation across angiosperms and vascular plants.…”

Machine learning in plant science and plant breeding

“…We do not review applications of ML to process raw measurement data (e.g. in the analysis of long-read sequencing data ( Amarasinghe et al., 2020 )) or established tools to analyze -omics data, such as predicting gene models or protein localization ( Mahood et al., 2020 ). The examples listed below indicate the increasingly prominent role of ML as a tool to interpret biochemical data in order to improve our understanding of plant biology.…”