• MapReduce;
  • data-enabled life sciences;
  • sequence similarity;
  • computational bioinfor-matics;
  • protein annotation;
  • protein sequence universe;
  • PSU, COG;
  • UniProt;
  • UniRef;
  • DELSA;
  • multidimensional scaling;
  • data visualization;
  • BLAST;
  • Azure;
  • Sammon;
  • Twister;
  • Hadoop;
  • Needleman–Wunsch;
  • Hive;
  • MPI;
  • EM


Modern biology is experiencing a rapid increase in data volumes that challenges our analytical skills and existing cyberinfrastructure. Exponential expansion of the protein sequence universe (PSU), the protein sequence space, together with the costs and complexities of manual curation creates a major bottleneck in life sciences research. Existing resources lack scalable visualization tools that are instrumental for functional annotation. Here, we describe a new visualization tool using multidimensional scaling to create a 3D embedding of the protein space. The advantages of the proposed PSU method include the ability to scale to large numbers of sequences, integrate different similarity measures with other functional and experimental data, and facilitate protein annotation. We applied the method to visualize the prokaryotic PSU using sequence alignment scores. As an annotation example, we used the interpolation approach to map the set of annotated archaeal proteins into the prokaryotic PSU. Transdisciplinary approaches akin to the one described in this paper are urgently needed to quickly and efficiently translate the influx of new data into tangible innovations and groundbreaking discoveries. Copyright © 2013 John Wiley & Sons, Ltd.