Robert Shoemaker
University of California, San Diego
17 Papers
28 Citations
Robert Shoemaker is an academic researcher from University of California, San Diego. The author has contributed to research in topics: DNA methylation & Epigenomics. The author has an hindex of 9, co-authored 16 publications. Previous affiliations of Robert Shoemaker include University of Pennsylvania.
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Papers
Targeted bisulfite sequencing reveals changes in DNA methylation associated with nuclear reprogramming
Jie Deng,Robert Shoemaker,Bin Xie,Athurva Gore,Emily M LeProust,Jessica Antosiewicz-Bourget,Dieter Egli,Nimet Maherali,In-Hyun Park,Junying Yu,George Q. Daley,Kevin Eggan,Konrad Hochedlinger,James A. Thomson,Wei Li Wang,Yuan Gao,Kun Zhang +16 more
TL;DR: A method to specifically capture an arbitrary subset of genomic targets for single-molecule bisulfite sequencing for digital quantification of DNA methylation at single-nucleotide resolution is reported.
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Allele-specific methylation is prevalent and is contributed by CpG-SNPs in the human genome
TL;DR: A chromosome-wide survey of ASM was carried out across 16 human pluripotent and adult cell lines using Illumina bisulfite sequencing and a potential role for CpG-SNP is suggested in connecting genetic variation with the epigenome.
Library-free methylation sequencing with bisulfite padlock probes
TL;DR: Improved bisulfite padlock probes (BSPPs) with a design algorithm to generate efficient padlocked probes are reported, a library-free protocol that dramatically reduces sample-preparation cost and time and is compatible with automation, and an efficient bioinformatics pipeline to accurately obtain both methylation levels and genotypes from sequencing of bisulfITE-converted DNA.
A Novel Knowledge-Driven Systems Biology Approach for Phenotype Prediction upon Genetic Intervention
TL;DR: A novel knowledge-driven systems biology method that utilizes qualitative knowledge to construct a Dynamic Bayesian network (DBN) to represent the biological network underlying a specific phenotype and can predict phenotypic traits upon genetic interventions and perturbing in the network is applied.
An integrated approach to identifying cis-regulatory modules in the human genome.
TL;DR: A hidden Markov model (HMM) is proposed to incorporate sequence motif information, TF-DNA interaction data and chromatin modification patterns to precisely identify cis-regulatory modules (CRMs) in eukaryotic genomes.