Author

Maria Katherine Mejía‐Guerra

Cornell UniversityORCID 0000-0002-2791-182XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Assay:whole-genome sequencing· 2 Assay:ChIP-seq· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Organism:non-human primate· 1

Frequent Co-Authors

Edward S. BuckleriD· 2 shared Brian C. PetersoniD· 1 shared Brian E. ScheffleriD· 1 shared Caird E. RexroadiD· 1 shared David TzengiD· 1 shared Dongyan ZhaoiD· 1 shared Gary BurriD· 1 shared Geoffrey C. WaldbieseriD· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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2 shared papersMolecular Biology

Edward S. BucklerORCID

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3 papers

Recent·Most cited

The generation of the first chromosome-level de-novo genome assembly and the development and validation of a 50K SNP array for North American Atlantic salmon

Guangtu Gao, Geoffrey C. Waldbieser, Ramey C. Youngblood, Dongyan Zhao, Michael Pietrak, Melissa S. Allen, Jason A. Stannard, John T. Buchanan, Roseanna L. Long, Melissa Milligan, Gary Burr, Maria Katherine Mejía‐Guerra, Moira J. Sheehan, Brian E. Scheffler, Caird E. Rexroad, , Yniv Palti

The paper generates a chromosome-level de-novo genome assembly and develops a 50K SNP array for North American Atlantic salmon, revealing karyotype differences due to chromosomal fissions and fusions.

G3 Genes Genomes Genetics · Jun 19, 2023Peer-reviewedGenomics

The transcription regulatory code of a maize leaf

Xiaoyu Tu, María Katherine Mejía-Guerra, José A. Valdes Franco, David Tzeng, Po-Yu Chu, Xiuru Dai, Pinghua Li, Edward S. Buckler, Silin Zhong

This study uses ChIP-seq to map TF binding in maize leaves, reconstructs a regulatory network covering 77% of expressed genes, and shows its conserved evolution across species.

bioRxiv · Jan 7, 2020 · PreprintMolecular Biology

Evolutionarily informed deep learning methods: Predicting transcript abundance from DNA sequence

Jacob D. Washburn, Maria Katherine Mejía‐Guerra, Guillaume P. Ramstein, Karl A. G. Kremling, Ravi Valluru, Edward S. Buckler, Hai Wang

The paper develops deep learning methods that incorporate evolutionary relationships to predict transcript abundance from DNA sequence, achieving auROC values between 0.72 and 0.94.

Proceedings of the National Academy of Sciences · Mar 6, 2019Peer-reviewedMolecular Biology