Big Data: Astronomical or Genomical?

Abstract

Genomics is a Big Data science and is going to get much bigger, very soon, but it is not known whether the needs of genomics will exceed other Big Data domains. Projecting to the year 2025, we compared genomics with three other major generators of Big Data: astronomy, YouTube, and Twitter. Our estimates show that genomics is a “four-headed beast”—it is either on par with or the most demanding of the domains analyzed here in terms of data acquisition, storage, distribution, and analysis. We discuss aspects of new technologies that will need to be developed to rise up and meet the computational challenges that genomics poses for the near future. Now is the time for concerted, community-wide planning for the “genomical” challenges of the next decade.

Fulfilling the promise of the materials genome initiative with high-throughput experimental methodologies

“…194 Appropriate application of machine learning 195 techniques will be critical to capitalize on the scientific value of such large data volumes. Analysis can be performed by the vast array of machine learning algorithms that have successfully extracted actionable knowledge in a range of fields including social networks, 196 genetics, 197 and finance. 198 Similarly, it can be used to mine materials data, for example, composition-structure relationships, from large amounts of computational and experimental materials data, as is illustrated in Fig.…”

Genomic Research Data Generation, Analysis and Sharing – Challenges in the African Setting

“…There is a need to systematically organize this data and to disseminate the resulting information through technically sound means to provide opportunities to academics and researchers globally. This information dissemination can facilitate advances in biomedical research for the improvement of health (Stephens et al 2015). Much of the data generated by NGS applications and other omics (including genomics, transcriptomics, proteomics and other high-throughput methods) technologies are housed in public databases (Rung and Brazma 2013).…”

Big Data – kihívások és kockázatok

“…A 2000-es évektől kezdve leginkább három nagyobb, egymással napi frissítésben álló adattár-ÖSSZEFOGLALÓ KÖZLEMÉNY házról (az amerikai NCBI/GenBank, az európai EMBLBank [28] és az ázsiai DDBJ), valamint több szétszórt kutatói/intézeti/vállalati adattárházról beszélhettünk, amelyek között referencia-ID-kkel lehetett eligazodni. A közel 15 év alatt ezen adattárházak tovább, egyre csak tovább híztak, nemcsak méretben, hanem komplexitá-sukban is [29].…”