Department of Computer Science,
Florida State University

"A Machine Learning Algorithm to Accurately and Efficiently Detect DNA Mutations"

Apr 13, 2022 Schedule:

Tea Time - Virtual ( Zoom)
03:00 to 03:30 PM Eastern Time (US and Canada)

Colloquium - F2F ( 499 DSL) / Virtual ( Zoom)
03:30 to 04:30 PM Eastern Time (US and Canada)

Meeting # 942 7359 5552


Human DNA is 3 billion bases long. Mutations on the DNA are those nucleotide changes that have a potential to lead to genetic diseases such as cancer. Mutations vary in the bases involved, from a single nucleotide, to small insertions and deletions (< 50 bases), to large structural variations (> 50 bases).

The advent of the massively parallel sequencing makes it possible to read human DNA by fragments, allowing some clinical applications such as detecting mutations on DNA. However, the data that we obtained from sequencing machines may contain errors which will lead to false positive and false negative detections. In addition, the amount of data is huge, leading to a “big data” problem. In this talk, I will summarize my past work in developing computational tools to detect mutations, followed by the introduction of a particular algorithm, called OMIndel, for detecting small insertions and deletions in human DNA.