Author-sourced capture of pathway knowledge in computable form using Biofactoid
- University of Toronto, Canada
- Oregon Health and Science University, United States
- University of Massachusetts Boston, United States
- Technische Universität München, Germany
- Dana-Farber Cancer Institute, United States
- Harvard University, United States
Abstract
Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a web-based platform that allows scientists to specify networks of interactions between genes, their products, and chemical compounds, and then translates this information into a representation suitable for computational analysis, search and discovery. We also report the results of a pilot study to encourage the wide adoption of Biofactoid by the scientific community.
Data availability
All Biofactoid data are available under the Creative Commons CC0 public domain license. To download the data and code, please refer to the documentation on the Biofactoid GitHub repository (github.com/PathwayCommons/factoid). More information on software availability is available in Materials and methods.
Article and author information
Author details
Funding
National Human Genome Research Institute (U41 HG006623)
- Jeffrey V Wong
- Max Franz
- Metin Can Siper
- Dylan Fong
- Funda Durupinar
- Christian Dallago
- Augustin Luna
- John M Giorgi
- Igor Rodchenkov
- Özgün Babur
- Emek Demir
- Gary D Bader
- Chris Sander
National Human Genome Research Institute (U41 HG003751)
- Jeffrey V Wong
- Max Franz
- Metin Can Siper
- Dylan Fong
- Funda Durupinar
- Christian Dallago
- Augustin Luna
- John M Giorgi
- Igor Rodchenkov
- Özgün Babur
- Emek Demir
- Gary D Bader
- Chris Sander
National Human Genome Research Institute (R01 HG009979)
- Max Franz
- Gary D Bader
National Institute of General Medical Sciences (P41 GM103504)
- Jeffrey V Wong
- Max Franz
- Metin Can Siper
- Dylan Fong
- Funda Durupinar
- Christian Dallago
- Augustin Luna
- John M Giorgi
- Igor Rodchenkov
- Özgün Babur
- Emek Demir
- Gary D Bader
- Chris Sander
Defense Advanced Research Projects Agency (Big Mechanism,ARO W911NF-14-C-0119)
- Metin Can Siper
- Funda Durupinar
- Özgün Babur
- John A Bachman
- Benjamin Gyori
- Emek Demir
Defense Advanced Research Projects Agency (Communicating with Computers,ARO W911NF-15-1-054)
- Metin Can Siper
- Funda Durupinar
- Özgün Babur
- John A Bachman
- Benjamin Gyori
- Emek Demir
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Participants of user testing provided written consent to volunteer, have their testing sessions recorded and have quotes obtained in the session published.
Reviewing Editor
- Helena Pérez Valle, eLife, United Kingdom
Publication history
- Preprint posted: March 11, 2021 (view preprint)
- Received: March 11, 2021
- Accepted: December 2, 2021
- Accepted Manuscript published: December 3, 2021 (version 1)
- Version of Record published: December 17, 2021 (version 2)
Copyright
© 2021, Wong et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Author-sourced capture of pathway knowledge in computable form using Biofactoid
eLife 10:e68292.
https://doi.org/10.7554/eLife.68292
Further reading
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Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.
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- Computational and Systems Biology
Predicting the function of a protein from its amino acid sequence is a long-standing challenge in bioinformatics. Traditional approaches use sequence alignment to compare a query sequence either to thousands of models of protein families or to large databases of individual protein sequences. Here we introduce ProteInfer, which instead employs deep convolutional neural networks to directly predict a variety of protein functions – Enzyme Commission (EC) numbers and Gene Ontology (GO) terms – directly from an unaligned amino acid sequence. This approach provides precise predictions which complement alignment-based methods, and the computational efficiency of a single neural network permits novel and lightweight software interfaces, which we demonstrate with an in-browser graphical interface for protein function prediction in which all computation is performed on the user’s personal computer with no data uploaded to remote servers. Moreover, these models place full-length amino acid sequences into a generalised functional space, facilitating downstream analysis and interpretation. To read the interactive version of this paper, please visit https://google-research.github.io/proteinfer/.
