Office of Technology Transfer – University of Michigan

Chromosome Breakability Index

Technology #6835

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Categories
Researchers
Indika Rajapakse
Managed By
Drew Bennett
Associate Director - Software Licensing 734-615-4004
Publications
Functional Organization of the human 4D Nucleome.
Proc Natl Acad Sci U S A. 2015 Jun 30;112(26):8002-7. doi: 10.1073/pnas.1505822112. Epub 2015 Jun 15., 2015

This technology allows researchers to determine how likely a given portion of the genome is to translocate. Genomic translocations occur when one section of the genome is moved to another section of the genome, sometimes disrupting or removing important genetic information. These translocations can lead to diseases, including cancer. Being able to predict where translocations will occur can help doctors and researchers predict and potentially prevent or treat diseases. This technology is a step toward that predictive power. It allows researchers to determine where genomic locations are likely to occur, and it can be applied to any cell population, allowing patient- and sample-specific predictions rather than a broader approach that would ignore individual differences.

Predicting Translocations with Mathematical Biology

[Hi-C] (http://www.ncbi.nlm.nih.gov/pubmed/20461051) is a method which allows researchers to determine which parts of the genome are close to each other in 3-dimensional space. From this information, a mathematical method called a Laplace Transform can be used to create a special vector called a Fiedler Vector. This technology uses the Fiedler vector to assign an “Index of breakability” score to each portion of the genome. It was shown that this score can differentiate between the genome at large and regions that undergo translocation. This means that researchers may be able to predict regions that are likely to translocate. From this information, risk for a translocation disease developing can be determined. This technology is applicable to cancer and disease research, diagnosis, and treatment. It is also cutting edge, with similar methods such as Translocation Capture Sequencing not offering the same predictive power as this method.

Applications

  • Predicting where translocations leading to cancer might occur
  • Predict where translocations involved in other genetic diseases occur

Advantages

  • Simple, mathematically sound method
  • Allows prediction of cancer translocations based on only healthy cells