Biology subjects

Berardini, T. Z.

Publications and source records attributed to Berardini, T. Z..

3 recordsLinked to original sources

bioRxiv2025

Advancing FAIR Data Management through AI-Assisted Curation of Morphological Data Matrices

Curation of biological and paleontological datasets is a labor-intensive process that requires standardization and validation to ensure data integrity. In particular, manual curation of datasets is prone to human errors such as typographical errors, inconsistent formatting, and incomplete metadata, which hinder reproducibility and compliance with Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Artificial Intelligence (AI) offers a transformative solution for enhancing research efficiency by automating data validation, improving accuracy, and streamlining curation workflows. This study presents a proof-of-concept implementation of an AI-assisted curation tool developed for MorphoBank, an open access repository established to enhance standardization and usability of morphological character datasets. Specifically, this work presents an AI tool designed to extract, structure, and standardize morphological character data from published literature into the NEXUS file format, a widely used format for phylogenetic analyses. This tool leverages machine learning techniques, including Large Language Models (LLMs), to automate the extraction of character names and states from text in various formats, reducing manual data entry errors and improving data completeness. The system enables efficient conversion of matrix-only files into complete, machine- and human-readable datasets that include key character metadata. By assisting with these tasks, the tool reduces the manual effort required for curation while improving consistency and standardization. This approach increases the FAIRness of morphological character data and provides a framework for extending AI-assisted curation to other types of biological data. These results illustrate the potential of AI-assisted workflows to support scalable data curation and reuse in paleontology, systematics, and evolutionary biology.

bioinformatics↗

bioRxiv2023

The Arabidopsis Information Resource in 2024

Since 1999, The Arabidopsis Information Resource (www.arabidopsis.org) has been curating data about the Arabidopsis thaliana genome. Its primary focus is integrating experimental gene function information from the peer-reviewed literature and codifying it as controlled vocabulary annotations. Our goal is to produce a gold standard functional annotation set that reflects the current state of knowledge about the Arabidopsis genome. At the same time, the resource serves as a nexus for community-based collaborations aimed at improving data quality, access and reuse. For the past decade, our work has been made possible by subscriptions from our global user base. This update covers our ongoing biocuration work, some of our modernization efforts that contribute to the first major infrastructure overhaul since 2011, the introduction of JBrowse2, and the resources role in community activities such as organizing the structural reannotation of the genome. For gene function assessment, we used Gene Ontology annotations as a metric to evaluate: (1) what is currently known about Arabidopsis gene function, and (2) the set of unknown genes. Currently, 74% of the proteome has been annotated to at least one Gene Ontology term. Of those loci, half have experimental support for at least one of the following aspects: molecular function, biological process, or cellular component. Our work sheds light on the genes for which we have not yet identified any published experimental data and have no functional annotation. Drawing attention to these unknown genes highlights knowledge gaps and potential sources of novel discoveries. Article SummaryThe Arabidopsis Information Resource (TAIR, www.arabidopsis.org) is a comprehensive website about Arabidopsis thaliana, a small plant thats very easy to grow and analyze in the laboratory and is used to understand how many other plants function. We share our progress in data collection and organization, website and tool improvement, and our involvement in community projects.

plant biology↗

bioRxiv2021

Crowdsourcing biocuration: the Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about known gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a ten-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills. Significance StatementThe primary scientific literature catalogs the results from publicly funded scientific research about gene function in human-readable format. Information captured from those studies in a widely adopted, machine-readable standard format comes in the form of Gene Ontology annotations about gene functions from all domains of life. Manual annotations based on inferences directly from the scientific literature, including the evidence used to make such inferences, represents the best return on investment by improving data accessibility across the biological sciences. To supplement professional curation, our CACAO project enabled annotation of the scientific literature by community annotators, in this case undergraduates, which resulted in contribution of thousands of validated entries to public resources. These annotations are now being used by scientists worldwide.

bioinformatics↗