Author

Peng Yin

Harvard UniversityORCID 0000-0002-4773-359XUnclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Peng Yin5 papers · Harvard University

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Genetics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 2 Approach:statistical model· 2 Assay:DNA methylation· 1 Approach:experimental assay· 1 Research Focus:genome assembly· 1 Assay:Hi-C· 1 Assay:imaging· 1 Organism:mouse· 1

Frequent Co-Authors

Brian J. BeliveauiD· 2 shared Chao-ting WuiD· 2 shared Guy NiriD· 2 shared Hiroshi M. SasakiiD· 2 shared Jocelyn Y. KishiiD· 2 shared Son C. NguyeniD· 2 shared Carl G. EbelingiD· 1 shared Cynthia Pérez EstradaiD· 1 shared

Activity by Year

2018-2018 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersGenetics

Brian J. BeliveauORCID

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Walking along chromosomes with super-resolution imaging, contact maps, and integrative modeling

Guy Nir, Irene Farabella, Cynthia Pérez Estrada, Carl G. Ebeling, Brian J. Beliveau, Hiroshi M. Sasaki, S. Dean Lee, Son C. Nguyen, Ruth B. McCole, Shyamtanu Chattoraj, Jelena Erceg, Jumana AlHaj Abed, Nuno M. C. Martins, Huy Q. Nguyen, Mohammed A. Hannan, , Chao-ting Wu

The paper uses super-resolution imaging and integrative modeling to achieve 10 kb resolution of chromosome folding.

PLoS Genetics · Dec 26, 2018Peer-reviewedGenetics

OligoMiner: A rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes

Brian J. Beliveau, Jocelyn Y. Kishi, Guy Nir, Hiroshi M. Sasaki, Sinem K. Saka, Son C. Nguyen, Chao-ting Wu, Peng Yin

OligoMiner designs genome-scale oligo FISH probes with customizable parameters and evaluates probe specificity using supervised learning, demonstrating scalability across model organisms.

Proceedings of the National Academy of Sciences · Feb 20, 2018Peer-reviewedBioinformatics