Author

Kristiina Kuningas

University of Tartu

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Genetics· 1

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Assay:ATAC-seq· 1 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Assay:ChIP-seq· 1 Approach:deep learning· 1 Assay:DNA methylation· 1 Organism:human· 1 Assay:scATAC-seq· 1

Frequent Co-Authors

Anna ShcherbinaiD· 1 shared Anshul Bharat KundajeiD· 1 shared Anusri PampariiD· 1 shared Arwa S. Kathiria· 1 shared Evgeny Z. KvoniD· 1 shared Kaur AlasooiD· 1 shared Len A. PennacchioiD· 1 shared Michael KosickiiD· 1 shared

Activity by Year

2024-2024 · peak 1/yr

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10 top collaborators · 45 mutual co-author links

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1 shared paperGenetics

Anna ShcherbinaORCID

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1 paper

Recent·Most cited

ChromBPNet: bias factorized, base-resolution deep learning models of chromatin accessibility reveal cis-regulatory sequence syntax, transcription factor footprints and regulatory variants

Anusri Pampari, Anna Shcherbina, Evgeny Z. Kvon, Michael Kosicki, Surag Nair, Soumya Kundu, Arwa S. Kathiria, Viviana I. Risca, Kristiina Kuningas, Kaur Alasoo, William James Greenleaf, Len A. Pennacchio, Anshul Bharat Kundaje

Learning and removing Tn5/DNase-I sequence bias lets a small base-resolution CNN (ChromBPNet) recover TF motifs and footprints from accessibility data and rival Enformer at predicting chromatin QTL effects.

bioRxiv · Dec 25, 2024 · PreprintGenetics✦ In-depth digest