Standardized nomenclature and open science in Human Genomics

Two recent papers have highlighted the vital importance of using standardized nomenclature in reporting data, especially when this is of clinical relevance. Higgins et al. [1] have drawn attention to the crucial matter of the appropriate nomenclature of DNA variants in scientific publications. It is critical that DNA variants can be identified unambiguously. And Fujiyoshi et al. [2] have called for gene products to be referenced using the approved gene symbol for the encoding gene, along with an appropriate database ID (HGNC ID, with UniProt ID where required, see Table 1 for resources for vertebrate genes). Confusion can impede data sharing and scientific progress, as well as potentially result in patient harm.

Table 1 Resources for standardized identifiers

Human Genomics has always required the use of standardized gene symbols [3], and we now ask all authors, editors, reviewers, etc. to utilize the correct and verified nomenclature additionally for gene products and DNA variants in all submissions to this journal. Adherence to this policy will ensure full understanding by all readers and reproducibility of findings involving genes, gene products, and DNA variants. The usage of historic nomenclature in addition to this policy may be helpful in some fields to assist certain readers. We note that other journals have already taken a keen interest in these matters [4].

We also note here in the broader context that Human Genomics strongly encourages the sharing of data to facilitate open science (https://en.wikipedia.org/wiki/Open_science), reproducibility, and full understanding of scientific advances. Therefore, depositing all relevant omic information in general, such as genomic, epigenomic, transcriptomic, metabolomic, and proteomic data, would be of great value to the scientific community. For example, the open-access MetaboLights repository of raw experimental metabolomic data and associated metadata has been recently re-designed to facilitate the growing demand for reproducibility and integration with other “omics” [5]. The recently engineered auto-deconvolution MSHub/GNPS platform has further enabled the community to store, process, share, annotate, compare, quantify reproducibility, and perform molecular networking of mass spectrometry metabolomic data in the context of multi-omics studies [6]. Accordingly, we strongly encourage depositing all relevant omic data relating to publications in our journal to aid advancement and reproducibility in science (Table 2).

Table 2 Examples of relevant omic databases