Author

Rachel L. Goldfeder

The Jackson Laboratory for Genomic MedicineORCID 0000-0002-0997-4448Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Genetic & Genomic Medicine· 1 Genomics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:statistical model· 2 Research Focus:variant calling· 2 Assay:whole-genome sequencing· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Euan Angus AshleyiD· 2 shared Alexey V. DaniloviD· 1 shared Alyssa C. LauiD· 1 shared Charles Curnin· 1 shared Chia‐Lin WeiiD· 1 shared Daryl M. WaggottiD· 1 shared Devon E. BonneriD· 1 shared Gabriel Eduardo RechiD· 1 shared

Activity by Year

2016-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBioinformatics

Euan Angus AshleyORCID

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3 papers

Recent·Most cited

Global DNA methylomes reveal oncogenic-associated 5-hydroxylmethylated cytosine (5hmC) signatures in the cell-free DNA of cancer patients

Gabriel Eduardo Rech, Alyssa C. Lau, Rachel L. Goldfeder, Rahul Maurya, Alexey V. Danilov, Chia‐Lin Wei

This study identifies oncogenic-associated 5hmC signatures in cfDNA from cancer patients and validates their consistency with tumor-specific patterns.

medRxiv · Jan 9, 2025 · PreprintGenetic & Genomic Medicine

Machine learning-based detection of insertions and deletions in the human genome

Charles Curnin, Rachel L. Goldfeder, Shruti Marwaha, Devon E. Bonner, Daryl M. Waggott, Undiagnosed Diseases Network, Matthew Thomas Wheeler, Euan Angus Ashley

This paper introduces machine learning tools for improved indel detection in the human genome, achieving high precision and recall in clinical applications.

bioRxiv · Dec 25, 2019 · PreprintBioinformatics

A precision metric for clinical genome sequencing

Rachel L. Goldfeder, Euan A. Ashley

The paper introduces a precision metric to evaluate clinical genome sequencing technologies and shows that increased whole genome coverage improves performance for disease-causing genes.

bioRxiv · May 24, 2016 · PreprintGenomics