Challenges and prospects for biomarker research: A current perspective from the developing world

Majority of deaths due to communicable and non-communicable diseases occur in the low and middle-income nations (LMNs), mainly due to the lack of early diagnoses and timely treatments. In such a scenario, biomarkers serve as an indispensible resource that can be used as indicators of biological processes, specific disease conditions or response to therapeutic interventions. Evaluation, diagnosis and management of diseases in developing world by following/extrapolating the findings obtained on the basis of the research work involving only the populations from the developed countries, could often be highly misleading due to existence of diverse patterns of diseases in developing countries compared to the developed world. Biomarker candidates identified from high-throughput integrated omics technologies have promising potential; however, their actual clinical applications are found to be limited, primarily due to the challenges of disease heterogeneity and pre-analytical variability associated with the biomarker discovery pipeline. Additionally, in the developing world, economic crunches, lack of awareness and education, paucity of biorepositories, enormous diversities in socio-epidemiological background, ethnicity, lifestyle, diet, exposure to various environmental risk factors and infectious agents, and ethical and social issues also cumulatively hinder biomarker discovery ventures. Establishment of standard operating procedures, comprehensive data repositories and exchange of scientific findings are crucial for reducing the variability and fragmentation of data. This review highlights the challenges associated with the discovery, validation and translational phases of biomarker research in LMNs with some of their amenable solutions and future prospects. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

A posteriori quality control for the curation and reuse of public proteomics data

Proteomics is a rapidly expanding field encompassing a multitude of complex techniques and data types. To date much effort has been devoted to achieving the highest possible coverage of proteomes with the aim to inform future developments in basic biology as well as in clinical settings. As a result, growing amounts of data have been deposited in publicly available proteomics databases. These data are in turn increasingly reused for orthogonal downstream purposes such as data mining and machine learning. These downstream uses however, need ways to a posteriori validate whether a particular data set is suitable for the envisioned purpose. Furthermore, the (semi-)automatic curation of repository data is dependent on analyses that can highlight misannotation and edge conditions for data sets. Such curation is an important prerequisite for efficient proteomics data reuse in the life sciences in general. We therefore present here a selection of quality control metrics and approaches for the a posteriori detection of potential issues encountered in typical proteomics data sets. We illustrate our metrics by relying on publicly available data from the Proteomics Identifications Database (PRIDE), and simultaneously show the usefulness of the large body of PRIDE data as a means to derive empirical background distributions for relevant metrics.