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Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells.

ABSTRACT: The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of 1s and 0s based on a set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic cells and classify cell types, potentially leading to a better understanding of leukemia pathogenesis.

SUBMITTER: Tanaka A

PROVIDER: S-EPMC7728210 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells.

Tanaka Azusa A Ishitsuka Yasuhiro Y Ohta Hiroki H Fujimoto Akihiro A Yasunaga Jun-Ichirou JI Matsuoka Masao M

PLoS computational biology 20201130 11

The huge amount of data acquired by high-throughput sequencing requires data reduction for effective analysis. Here we give a clustering algorithm for genome-wide open chromatin data using a new data reduction method. This method regards the genome as a string of 1s and 0s based on a set of peaks and calculates the Hamming distances between the strings. This algorithm with the systematically optimized set of peaks enables us to quantitatively evaluate differences between samples of hematopoietic ...[more]

PMID: 33253153

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