A training curriculum for retrieving, structuring, and aggregating information derived from the biomedical literature and large-scale data repositories.

Abstract

Background: Biomedical research over the past two decades has become data and information rich. This trend has been in large part driven by the development of systems-scale molecular profiling capabilities and by the increasingly large volume of publications contributed by the biomedical research community. It has therefore become important for early career researchers to learn to leverage this wealth of information in their own research. Methods: Here we describe in detail a training curriculum focusing on the development of foundational skills necessary to retrieve, structure, and aggregate information available from vast stores of publicly available information. It is provided along with supporting material and an illustrative use case. The stepwise workflow encompasses; 1) Selecting a candidate gene; 2) Retrieving background information about the gene; 3) Profiling its literature; 4) Identifying in the literature instances where its transcript abundance changes in blood of patients; 5) Retrieving transcriptional profiling data from public blood transcriptome and reference datasets; and 6) Drafting a manuscript, submitting it for peer-review, and publication. Results: This resource may be leveraged by instructors who wish to organize hands-on workshops. It can also be used by independent trainees as a self-study toolkit. The workflow presented as proof-of-concept was designed to establish a resource for assessing a candidate gene’s potential utility as a blood transcriptional biomarker. Trainees will learn to retrieve literature and public transcriptional profiling data associated with a specific gene of interest. They will also learn to extract, structure, and aggregate this information to support downstream interpretation efforts as well as the preparation of a manuscript. Conclusions: This resource should support early career researchers in their efforts to acquire skills that will permit them to leverage the vast amounts of publicly available large-scale profiling data.

Large Language Model-Driven Selection of Glutathione Peroxidase 4 as a Candidate Blood Transcriptional Biomarker for Circulating Erythroid Cells

“…As the top candidate gene for module M14.51, GPX4 will be subjected to further in-depth characterization using a separate work ow (49,50). However, it is worth discussing here the background and potential relevance GPX4 in the context of the six statements used for scoring the candidate genes.…”

Assessing the potential relevance of CEACAM6 as a blood transcriptional biomarker

“…The workflow implemented here to assess the potential of CEACAM6 as a blood transcriptional biomarker has been described in detail in a separate methods paper. 10 The approach was devised as part of a training module focused on the development of skills for the retrieval, structuring, aggregation, and interpretation of information derived from the literature and publicly available large-scale profiling datasets. Relevant resources that have been employed and generated in the context of this work are presented in Table 1.…”

“…The approach has been described in two published study guides: from a high-level perspective as part of the COD1 workflow 10 and in more detail in a separate study guide dedicated to literature profiling. 18 An overview of the steps implemented in the profiling of the literature associated with CEACAM6 is provided here:…”

“…Thus, we have developed an approach to identify, retrieve, structure, and aggregate such information in a manner that would support the rational selection of candidate genes for inclusion in targeted assays destined to be used in clinical or research settings. 10 Here we decided to focus on CEACAM6, a gene encoding a protein of the carcinoembryonic antigen (CEA) family whose members are glycosylphosphatidylinositol (GPI)-linked cell surface proteins. 11,12 We employed an approach devised as part of a "collective omics data" (COD) training curriculum, 13 relying in particular on the recently published COD1 training module workflow.…”

“…11,12 We employed an approach devised as part of a "collective omics data" (COD) training curriculum, 13 relying in particular on the recently published COD1 training module workflow. 10 Briefly, it consists of: 1) selecting a gene and retrieving background information and associated literature; 2) profiling its literature at a high level, reporting instances where it has been associated with diseases, and its relevance as a biomarker in such settings; 3) profiling the literature in more depth and reporting changes in abundance for transcripts encoded by this gene measured in whole blood; 4) profiling the abundance of the candidate gene across multiple relevant transcriptome datasets; 5) preparing a manuscript and submit it for peer-review and publication. Throughout these steps trainees parse the information being retrieved (e.g., from the title or text of articles) and record it in a structured format (i.e., a spreadsheet).…”