ArticlePLoS computational biology2020
Systematic clustering algorithm for chromatin accessibility data and its application to hematopoietic cells.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 7 citations in OpenAlex.
- Intra-pol proviral open region of HTLV-1 controls the transcription from both long terminal repeats.Retrovirology · 2025Article
- Integrative analysis of ATAC-seq and RNA-seq for cells infected by human T-cell leukemia virus type 1.PLoS computational biology · 2025Article
- HTLV-1 bZIP Factor-Induced Reprogramming of Lactate Metabolism and Epigenetic Status Promote Leukemic Cell Expansion.Blood cancer discovery · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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