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Topological Data Analysis Reveals Principles of Chromosome Structure in Cellular Differentiation

Authors Natalie Sauerwald, Yihang Shen, Carl Kingsford

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Author Details

Natalie Sauerwald
  • Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, 15213, USA
Yihang Shen
  • Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, 15213, USA
Carl Kingsford
  • Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, 15213, USA

Funding

This work has been supported in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grant GBMF4554 to C.K., and by the US National Institutes of Health (R01HG007104 and R01GM122935). Research reported in this publication was supported by the NIGMS of the NIH under award number P41GM103712 and by the Richard K. Mellon Presidential Fellowship in Life Sciences to N.S. This work was partially funded by The Shurl and Kay Curci Foundation.

Acknowledgements

The authors would like to thank Alessandro Bertero and William S. Noble for useful information about their data, and Guillaume Marçais for comments on the manuscript.

Cite AsGet BibTex

Natalie Sauerwald, Yihang Shen, and Carl Kingsford. Topological Data Analysis Reveals Principles of Chromosome Structure in Cellular Differentiation. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 23:1-23:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.WABI.2019.23

Abstract

Topological data analysis (TDA) is a mathematically well-founded set of methods to derive robust information about the structure and topology of data. It has been applied successfully in several biological contexts. Derived primarily from algebraic topology, TDA rigorously identifies persistent features in complex data, making it well-suited to better understand the key features of three-dimensional chromosome structure. Chromosome structure has a significant influence in many diverse genomic processes and has recently been shown to relate to cellular differentiation. While there exist many methods to study specific substructures of chromosomes, we are still missing a global view of all geometric features of chromosomes. By applying TDA to the study of chromosome structure through differentiation across three cell lines, we provide insight into principles of chromosome folding and looping. We identify persistent connected components and one-dimensional topological features of chromosomes and characterize them across cell types and stages of differentiation. Availability: Scripts to reproduce the results from this study can be found at https://github.com/Kingsford-Group/hictda

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
  • Applied computing
Keywords
  • topological data analysis
  • chromosome structure
  • Hi-C
  • topologically associating domains

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