How Does the Scientific Community Contribute to Gene Ontology?

Student biocuration projects as a learning environment

Background: Bioinformatics is becoming an essential tool for the majority of biological and biomedical researchers. Although bioinformatics data is exploited by academic and industrial researchers, limited focus is on teaching this area to undergraduates, postgraduates and senior scientists. Many scientists are developing their own expertise without formal training and often without appreciating the source of the data they are reliant upon. Some universities do provide courses on a variety of bioinformatics resources and tools, a few also provide biocuration projects, during which students submit data to annotation resources. Methods: To assess the usefulness and enjoyability of annotation projects a survey was sent to University College London (UCL) students who have undertaken Gene Ontology biocuration projects. Results: Analysis of survey responses suggest that these projects provide students with an opportunity not only to learn about bioinformatics resources but also to improve their literature analysis, presentation and writing skills. Conclusion: Biocuration student projects provide valuable annotations as well as enabling students to develop a variety of skills relevant to their future careers. It is also hoped that, as future scientists, these students will critically assess their own manuscripts and ensure that these are written with the biocurators of the future in mind.

Accelerating biological insight for understudied genes

The rapid expansion of genome sequence data is increasing the discovery of protein-coding genes across all domains of life. Annotating these genes with reliable functional information is necessary to understand evolution, to define the full biochemical space accessed by nature, and to identify target genes for biotechnology improvements. The vast majority of proteins are annotated based on sequence conservation with no specific biological, biochemical, genetic, or cellular function identified. Recent technical advances throughout the biological sciences enable experimental research on these understudied protein-coding genes in a broader collection of species. However, scientists have incentives and biases to continue focusing on well documented genes within their preferred model organism. This perspective suggests a research model that seeks to break historic silos of research bias by enabling interdisciplinary teams to accelerate biological functional annotation. We propose an initiative to develop coordinated projects of collaborating evolutionary biologists, cell biologists, geneticists, and biochemists that will focus on subsets of target genes in multiple model organisms. Concurrent analysis in multiple organisms takes advantage of evolutionary divergence and selection, which causes individual species to be better suited as experimental models for specific genes. Most importantly, multisystem approaches would encourage transdisciplinary critical thinking and hypothesis testing that is inherently slow in current biological research.

The accessible chromatin landscape during conversion of human embryonic stem cells to trophoblast by bone morphogenetic protein 4

Human embryonic stem cells (hESCs) exposed to the growth factor bone morphogenetic protein 4 (BMP4) in the absence of FGF2 have been used as a model to study the development of placental development. However, little is known about the cis-regulatory mechanisms underlying this important process. In this study, we used the public available chromatin accessibility data of hESC H1 cells and BMP4-induced trophoblast (TB) cell lines to identify DNase I hypersensitive sites (DHSs) in the two cell lines, as well as the transcription factor (TF) binding sites within the DHSs. By comparing read profiles in H1 and TB, we identified 17 472 TB-specific DHSs. The TB-specific DHSs are enriched in terms of "blood vessel" and "trophectoderm," consisting of TF motifs family: Leucine Zipper, Helix-Loop-Helix, GATA, and ETS. To validate differential expression of the TFs binding to these motifs, we analyzed public available RNA-seq and microarray data in the same context. Finally, by integrating the protein-protein interaction data, we constructed a TF network for placenta development and identified top 20 key TFs through centrality analysis in the network. Our results indicate BMP4-induced TB system provided an invaluable model for the study of TB development and highlighted novel candidate genes in placenta development in human.

Get GO! Retrieving GO Data Using AmiGO, QuickGO, API, Files, and Tools

The Gene Ontology Consortium (GOC) produces a wealth of resources widely used throughout the scientific community. In this chapter, we discuss the different ways in which researchers can access the resources of the GOC. We here share details about the mechanics of obtaining GO annotations, both by manually browsing, querying, and downloading data from the GO website, as well as computationally accessing the resources from the command line, including the ability to restrict the data being retrieved to subsets with only certain attributes.

Primer on the Gene Ontology

The Gene Ontology (GO) project is the largest resource for cataloguing gene function. The combination of solid conceptual underpinnings and a practical set of features have made the GO a widely adopted resource in the research community and an essential resource for data analysis. In this chapter, we provide a concise primer for all users of the GO. We briefly introduce the structure of the ontology and explain how to interpret annotations associated with the GO.

The Vision and Challenges of the Gene Ontology

The overarching goal of the Gene Ontology (GO) Consortium is to provide researchers in biology and biomedicine with all current functional information concerning genes and the cellular context under which these occur. When the GO was started in the 1990s surprisingly little attention had been given to how functional information about genes was to be uniformly captured, structured in a computable form, and made accessible to biologists. Because knowledge of gene, protein, ncRNA, and molecular complex roles is continuously accumulating and changing, the GO needed to be a dynamic resource, accurately tracking ongoing research results over time. Here I describe the progress that has been made over the years towards this goal, and the work that still remains to be done, to make of the Gene Ontology (GO) Consortium realize its goal of offering the most comprehensive and up-to-date resource for information on gene function.