Multiscale Topology of Chromatin Folding

Benjamin Schweinhart; Kevin Emmett; Raul Rabadan

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Multiscale Topology of Chromatin Folding

Kevin Emmett, Benjamin Schweinhart, Raul Rabadan

Genomics · Submitted 4 Nov 2015

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We identify substantial multiscale topology in these datasets.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,398 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.06 · human participants 0.14 · controlled comparison 0.05 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.10 · mentions code or data availability 0.03.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

The three dimensional structure of DNA in the nucleus (chromatin) plays an important role in many cellular processes. Recent experimental advances have led to high-throughput methods of capturing information about chromatin conformation on genome-wide scales. New models are needed to quantitatively interpret this data at a global scale. Here we introduce the use of tools from topological data analysis to study chromatin conformation. We use persistent homology to identify and characterize conserved loops and voids in contact map data and identify scales of interaction. We demonstrate the utility of the approach on simulated data and then look data from both a bacterial genome and a human cell line. We identify substantial multiscale topology in these datasets.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	The three dimensional structure of DNA in the nucleus (chromatin) plays an important role in many cellular processes.	background 1.00	0.04	0.02	asserted 1.00
s1	Recent experimental advances have led to high-throughput methods of capturing information about chromatin conformation on genome-wide scales.	background 1.00	0.05	0.02	asserted 1.00
s2	New models are needed to quantitatively interpret this data at a global scale.	gap 0.97	0.05	0.03	asserted 0.94
s3	Here we introduce the use of tools from topological data analysis to study chromatin conformation.	contribution 0.90	0.86	0.04	asserted 0.94
s4	We use persistent homology to identify and characterize conserved loops and voids in contact map data and identify scales of interaction.	method 0.98	0.95	0.12	asserted 1.00
s5	We demonstrate the utility of the approach on simulated data and then look data from both a bacterial genome and a human cell line.	method 0.64	0.94	0.05	asserted 0.95
s6	We identify substantial multiscale topology in these datasets.	result 1.00	0.96	0.77	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from analysis of existing data 0.93.

What was examined, and what was not

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arXiv listing abstract v1

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full text
figures and tables
references and citing sentences
relations to other papers

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P abstract

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S sections
T body sentences
C citations
R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper

Original abstract

The three dimensional structure of DNA in the nucleus (chromatin) plays an important role in many cellular processes. Recent experimental advances have led to high-throughput methods of capturing information about chromatin conformation on genome-wide scales. New models are needed to quantitatively interpret this data at a global scale. Here we introduce the use of tools from topological data analysis to study chromatin conformation. We use persistent homology to identify and characterize conserved loops and voids in contact map data and identify scales of interaction. We demonstrate the utility of the approach on simulated data and then look data from both a bacterial genome and a human cell line. We identify substantial multiscale topology in these datasets.

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References

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

From Persistence to Survival: Hypothesis Testing, Effect Sizes and Vectorisation for Topological Features
stat.ML 2026-06 unverdicted novelty 7.0 of 10

STRAND treats persistence diagrams as survival data to derive a calibrated two-sample test, interpretable effect sizes, and a 1-Wasserstein-stable feature vector from one representation.
Topological Clustering via Sliced Wasserstien Kernels
math.ST 2026-08 conditional novelty 5.0 of 10

A convex combination of sliced Wasserstein kernels, one per homology group, yields a positive definite kernel for k-means on persistence diagrams, with interpretable per-homology weights.

How the literature uses it

5 arXiv papers cite this in 13 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 8 sentences without a confident label

Uses its method or tool 1 paper

To overcome the potential complications arising from the non-uniqueness of generators we develop two complementary approaches: matroid generators, which are efficiently computed using matroid theory [ref] , and minimal generators [ref] , which we tailor to sequential datasets.

Hypergraphs for multiscale cycles in structured data · Introduction · 0.89

Cites it as background 1 paper

One could adapt hyperTDA to other choices of generators [ref] depending on the data and the end goal; clearly the fledgling area of research into, and construction of, topology generators has opened up new mathematical avenues for mathematical exploration; but already they are proving to be immensely useful in topological data analysis.

Hypergraphs for multiscale cycles in structured data · Discussion · 0.81

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