Visualizing mutation occurrence using Big Data

Abstract

The volume of collected genetic data has been growing exponentially in the past few years and we need to improve the way we store, analyze and visualize it in order to be able to draw relevant conclusions that could improve the life quality of people. Extracting patterns and predicting future mutations and their impact will rely heavily on the efficient use of Big Data. Often a mutation on its own cannot provide enough information about a disorder or disease. Only if we combine the genetic information with the organism's environment we can draw some conclusions about penetrance and expressively of the mutation. Because many genes can cause a single disease and at the same time a single gene can cause multiple diseases, we need to analyze the whole context of a person.In this work, a distributed solution that provides demographics and metrics about diagnostics and mutations is pro posed. Seeing the occurrence of a mutation in a particular geographic region can help medical special ists narrow down the search for a patient's mutations without sequencing the whole genome.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.3325v1 | CC BY 4.0 Open Access | rec: 5

Dynamically generating T32 training documents using structured data

“…We are now able to add and remove mentors from a T32 grant submission, with changes reflected when the Word file is regenerated. Because the publication data came from a centrally maintained source system, the feedback we received during this process will be used to update other systems: VIVO, our public-facing researcher profile system; the Faculty Review Tool, a homegrown application for evaluating faculty contributions in a given year; and VIVO Dashboard, our publication reporting tool (Figure 3) [9]. Further, this work gave us an impetus to resolve dozens of cases in which an individual was missing an institutional identifier or had been assigned multiple identifiers.…”