A golden age for working with public proteomics data

Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature 'omics' disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets

Make Research Data Public? -- Not Always so Simple: A Dialogue for Statisticians and Science Editors

Putting data into the public domain is not the same thing as making those data accessible for intelligent analysis. A distinguished group of editors and experts who were already engaged in one way or another with the issues inherent in making research data public came together with statisticians to initiate a dialogue about policies and practicalities of requiring published research to be accompanied by publication of the research data. This dialogue carried beyond the broad issues of the advisability, the intellectual integrity, the scientific exigencies to the relevance of these issues to statistics as a discipline and the relevance of statistics, from inference to modeling to data exploration, to science and social science policies on these issues.Comment: Published in at http://dx.doi.org/10.1214/10-STS320 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu

Big Data in Genomics: Ethical Challenges and Risks

Genomic information is a class of Big Data in expanding use thanks to technological developments. Here, we review three categories of ethical risks and challenges associated with genomic information: privacy issues, the management of incidental findings, and challenges in data storage and sharing. First, we need to implement strong mechanisms to protect privacy, but genomic data faces specific risks and we need to acknowledge the possibility of re-identification. Proper usage of genomic information has to be regulated, including recommendations on incidental finding management. Also, clear policies for data sharing and explicit efforts to promote central repositories of genomic data should be established. However, technology and new applications of genetic information will develop fast and we should anticipate potential new risks

Tackling the translational challenges of multi-omics research in the realm of European personalised medicine : A workshop report

Personalised medicine (PM) presents a great opportunity to improve the future of individualised healthcare. Recent advances in -omics technologies have led to unprecedented efforts characterising the biology and molecular mechanisms that underlie the development and progression of a wide array of complex human diseases, supporting further development of PM. This article reflects the outcome of the 2021 EATRIS-Plus Multi-omics Stakeholder Group workshop organised to 1) outline a global overview of common promises and challenges that key European stakeholders are facing in the field of multi-omics research, 2) assess the potential of new technologies, such as artificial intelligence (AI), and 3) establish an initial dialogue between key initiatives in this space. Our focus is on the alignment of agendas of European initiatives in multi-omics research and the centrality of patients in designing solutions that have the potential to advance PM in long-term healthcare strategies.Peer reviewe

What are the Prospects for -Omics- Based Molecular Technologies in Cancer Diagnostics and Treatment

Cancer remains one of the leading causes of death in the United States, following the heart disease. New technologies are needed to fight and eventually to eradicate cancer. Omic technologies is a new emerging field of cancer research that may offer cancer patients long awaited opportunities to get faster, more precise personalized medical care, while letting doctors do their job more effectively. The rapid development of omic technologies and large datasets promise a new type of health care system when the patients can be treated according to their own individualized molecular characteristics

'Big Data' en genómica: retos y riesgos éticos

La informació genòmica és un tipus de 'Big Data' d'ús creixent a causa de millores tecnològiques. En aquest treball, revisem tres grups de reptes i riscos ètics associats amb aquesta informació: riscos de privadesa, gestió de les troballes incidentals i reptes en l'emmagatzematge i compartició de dades. En primer lloc, hem d'establir mecanismes sòlids per protegir la privadesa, però les dades genòmiques presenten riscos específics i hem d'admetre la possibilitat de reidentificació. Cal regular l'ús adequat de la informació genòmica incloent-hi recomanacions per a la gestió de les troballes incidentals. També cal establir polítiques clares per compartir dades i fomentar l'ús de repositoris de dades genòmiques. No obstant això, hem d'esperar desenvolupaments ràpids a la tecnologia i noves aplicacions de la informació genètica, i hem d'anticipar-nos als riscos potencials futurs.Genomic information is a class of Big Data in expanding use thanks to technological developments. Here, we review three categories of ethical risks and challenges associated with genomic information: privacy issues, the management of incidental findings, and challenges in data storage and sharing. First, we need to implement strong mechanisms to protect privacy, but genomic data faces specific risks and we need to acknowledge the possibility of re-identification. Proper usage of genomic information has to be regulated, including recommendations on incidental finding management. Also, clear policies for data sharing and explicit efforts to promote central repositories of genomic data should be established. However, technology and new applications of genetic information will develop fast and we should anticipate potential new risks.La información genómica es un tipo de 'Big Data' de uso creciente debido a mejoras tecnológicas. En este trabajo, revisamos tres grupos de retos y riesgos éticos asociados con esta información: riesgos de privacidad, gestión de los hallazgos incidentales y retos en el almacenamiento y compartición de datos. En primer lugar, debemos establecer mecanismos sólidos para proteger la privacidad, pero los datos genómicos presentan riesgos específicos y debemos admitir la posibilidad de reidentificación. Hay que regular el uso adecuado de la información genómica incluyendo recomendaciones para la gestión de los hallazgos incidentales. También hay que establecer políticas claras para compartir datos y fomentar el uso de repositorios de datos genómicos. No obstante, debemos esperar desarrollos rápidos en la tecnología y nuevas aplicaciones de la información genética, y debemos anticiparnos a los futuros riesgos potenciales