Author

Dejun Lin

University of WashingtonPrimary ORCID profile (0000-0001-5618-7175 · 1 paper)Unclaimed ORCID profile · follow to get notified of new preprints

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Same name, identified by ORCID

Dejun LiniD1 paper · Nvidia (United States)

Biology Disciplines

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Bioinformatics· 2 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Assay:Hi-C· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:ChIP-seq· 1 Research Focus:dimensionality reduction· 1 Assay:DNA methylation· 1 Approach:experimental assay· 1 Organism:yeast· 1

Frequent Co-Authors

William Stafford NobleiD· 3 shared Cheng-Fu KaoiD· 1 shared Dakota R. Hunt· 1 shared Galip Gürkan YardımcıiD· 1 shared Jie Liu· 1 shared Justin Sanders· 1 shared Po-Yen Lin· 1 shared Sarah G. SwygertiD· 1 shared

Activity by Year

2018-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersBioinformatics

William Stafford NobleORCID

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Author profile →

3 papers

Recent·Most cited

Chromatin Fiber Folding Represses Transcription and Loop Extrusion in Quiescent Cells

Sarah G. Swygert, Dejun Lin, Stephanie Portillo‐Ledesma, Po-Yen Lin, Dakota R. Hunt, Cheng-Fu Kao, Tamar Schlick A, William Stafford Noble, Toshio Tsukiyama

The paper shows that chromatin fiber folding in quiescent yeast represses transcription and loop extrusion, with H4 tail modifications affecting fiber structure and function.

eLife · Nov 4, 2021Peer-reviewedMolecular Biology

HiCRep.py: Fast comparison of Hi-C contact matrices in Python

Dejun Lin, Justin Sanders, William Stafford Noble

HiCRep.py provides a faster Python implementation of HiCRep for comparing Hi-C contact matrices, demonstrating improved performance on high-resolution and single-cell Hi-C data.

Bioinformatics · Feb 9, 2021Peer-reviewedBioinformatics

Unsupervised embedding of single-cell Hi-C data

Jie Liu, Dejun Lin, Galip Gürkan Yardımcı, William Stafford Noble

This paper applies bulk Hi-C analysis methods to scHi-C data with unsupervised embedding, finding HiCRep with MDS outperforms other methods and is robust to low sequencing depth.

Bioinformatics · Apr 16, 2018Peer-reviewedBioinformatics