Author

Dayanne M. Castro

New York University

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

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Systems Biology· 3 Genomics· 2

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Research Focus:gene regulatory network inference· 5 Approach:statistical model· 5 Organism:yeast· 3 Assay:ATAC-seq· 2 Assay:bulk RNA-seq· 2 Organism:mouse· 2 Assay:scRNA-seq· 2 Approach:benchmark study· 1

Frequent Co-Authors

Richard BonneauiD· 5 shared Aaron WattersiD· 3 shared Emily R. MiraldiiD· 3 shared Nicholas De VeauxiD· 3 shared Christopher A. JacksoniD· 2 shared Claudia Skok GibbsiD· 2 shared David GreshamiD· 2 shared Giuseppe-Antonio SaldiiD· 2 shared

Activity by Year

2019-2022 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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5 shared papersSystems Biology

Richard BonneauORCID

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5 papers

Recent·Most cited

High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0

Claudia Skok Gibbs, Christopher A. Jackson, Giuseppe-Antonio Saldi, Andreas Tjärnberg, Aashna Shah, Aaron Watters, Nicholas De Veaux, Konstantine Tchourine, Ren Yi, Tymor Hamamsy, Dayanne M. Castro, Nicholas John Carriero, Bram L. Gorissen, David Gresham, Emily R. Miraldi, Richard Bonneau

The paper introduces Inferelator 3.0, a tool for inferring gene regulatory networks from single-cell data, showing improved performance on yeast and mouse brain datasets.

Bioinformatics · Feb 17, 2022Peer-reviewedSystems Biology

Identification of new therapeutic targets in CRLF2-overexpressing B-ALL through discovery of2 TF-gene regulatory interactions.

Sana Badri, Beth A. Carella, Priscillia Lhoumaud, Dayanne M. Castro, Claudia Skok Gibbs, Ramya Raviram, Sonali Narang, Nikki Ann Evensen, Aaron Watters, William L. Carroll, Richard Bonneau, Jane A. Skok

The paper identifies new therapeutic targets in CRLF2-overexpressing B-ALL by analyzing TF-gene regulatory interactions using network inference and experimental validation.

bioRxiv · Sep 26, 2020 · PreprintGenomics

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

Christopher A. Jackson, Dayanne M. Castro, Giuseppe-Antonio Saldi, Richard Bonneau, David Gresham

The paper develops a method to reconstruct gene regulatory networks from scRNA-seq data by barcoding gene deletion mutants in budding yeast across diverse environments.

eLife · Jan 27, 2020Peer-reviewedGenomics

Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

Emily R. Miraldi, Maria V. Pokrovskii, Aaron Watters, Dayanne M. Castro, Nicholas De Veaux, Jason A. Hall, June‐Yong Lee, Maria Ciofani, Aviv Madar, Nick Carriero, Dan R. Littman, Richard Bonneau

The paper develops methods to infer transcriptional regulatory networks in Th17 cells using ATAC-seq and RNA-seq data, revealing new roles for transcription factors and their modules.

Genome Research · Mar 1, 2019Peer-reviewedSystems Biology

Multi-study inference of regulatory networks for more accurate models of gene regulation

Dayanne M. Castro, Nicholas De Veaux, Emily R. Miraldi, Richard Bonneau

This study improves gene regulatory network inference by integrating multiple datasets using multitask learning, showing better network reconstruction and robustness to dataset heterogeneity.

PLoS Computational Biology · Jan 24, 2019Peer-reviewedSystems Biology