Computing on Phenotypic Descriptions for Candidate Gene Discovery and Crop Improvement

Ian R Braun; Colleen F Yanarella; Carolyn J Lawrence-Dill

doi:10.34133/2020/1963251

Computing on Phenotypic Descriptions for Candidate Gene Discovery and Crop Improvement

Plant Phenomics. 2020 May 20:2020:1963251. doi: 10.34133/2020/1963251. eCollection 2020.

Authors

Ian R Braun^#^{1

2}, Colleen F Yanarella^#^{1

2}, Carolyn J Lawrence-Dill^{1

2

3}

Affiliations

¹ Interdepartmental Bioinformatics and Computational Biology, Iowa State University, Ames, IA 50011, USA.
² Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA.
³ Department of Agronomy, Iowa State University, Ames, IA 50011, USA.

^# Contributed equally.

Abstract

Many newly observed phenotypes are first described, then experimentally manipulated. These language-based descriptions appear in both the literature and in community datastores. To standardize phenotypic descriptions and enable simple data aggregation and analysis, controlled vocabularies and specific data architectures have been developed. Such simplified descriptions have several advantages over natural language: they can be rigorously defined for a particular context or problem, they can be assigned and interpreted programmatically, and they can be organized in a way that allows for semantic reasoning (inference of implicit facts). Because researchers generally report phenotypes in the literature using natural language, curators have been translating phenotypic descriptions into controlled vocabularies for decades to make the information computable. Unfortunately, this methodology is highly dependent on human curation, which does not scale to the scope of all publications available across all of plant biology. Simultaneously, researchers in other domains have been working to enable computation on natural language. This has resulted in new, automated methods for computing on language that are now available, with early analyses showing great promise. Natural language processing (NLP) coupled with machine learning (ML) allows for the use of unstructured language for direct analysis of phenotypic descriptions. Indeed, we have found that these automated methods can be used to create data structures that perform as well or better than those generated by human curators on tasks such as predicting gene function and biochemical pathway membership. Here, we describe current and ongoing efforts to provide tools for the plant phenomics community to explore novel predictions that can be generated using these techniques. We also describe how these methods could be used along with mobile speech-to-text tools to collect and analyze in-field spoken phenotypic descriptions for association genetics and breeding applications.