We need to keep a reproducible trace of facts, predictions, and hypotheses from gene to function in the era of big data

• Published in: PLoS biology Vol. 18; no. 11; p. e3000999
• Main Authors: Kasif, Simon, Roberts, Richard J
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.11.2020; Public Library of Science (PLoS)
• Subjects: Algorithms; Annotations; Artificial Intelligence; Big Data; Biology; Biology and Life Sciences; Computational Biology; Computer and Information Sciences; Deoxyribonucleic acid; DNA; Experiments; Gene Regulatory Networks; Genes; Genetic Linkage; Genomics - statistics & numerical data; Genotype & phenotype; Glyceraldehyde; Glyceraldehyde 3-phosphate; Glyceraldehyde-3-phosphate dehydrogenase; Health risks; High-Throughput Nucleotide Sequencing; Humans; Hypotheses; Knowledge; Laboratories; Machine learning; Microorganisms; Models, Genetic; Organisms; Phenotypes; Physical Sciences; Predictions; Propagation; Proteins; Proteomics - statistics & numerical data; Research and Analysis Methods; Synthetic Biology; Systems Biology
• ISSN: 1545-7885; 1544-9173; 1545-7885
• DOI: 10.1371/journal.pbio.3000999

How do we scale biological science to the demand of next generation biology and medicine to keep track of the facts, predictions, and hypotheses? These days, enormous amounts of DNA sequence and other omics data are generated. Since these data contain the blueprint for life, it is imperative that we interpret it accurately. The abundance of DNA is only one part of the challenge. Artificial Intelligence (AI) and network methods routinely build on large screens, single cell technologies, proteomics, and other modalities to infer or predict biological functions and phenotypes associated with proteins, pathways, and organisms. As a first step, how do we systematically trace the provenance of knowledge from experimental ground truth to gene function predictions and annotations? Here, we review the main challenges in tracking the evolution of biological knowledge and propose several specific solutions to provenance and computational tracing of evidence in functional linkage networks.