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, DNA methylation, RNA-seq, proteomics, lipidomics and metabolomics in large-scale human populations. The lab is interested in integrating and mining the different types of data to understand the genomic
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: multi-omics (e.g., RNA-seq, DNA methylation, proteomics), single-cell technologies (CyTOF, scRNA-seq), structural biology and computational immunology approaches. The successful candidate will support the
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. Essential Duties and Responsibilities Analyze large-scale next-generation sequencing (NGS) datasets, including whole genome/exome sequencing, RNA-seq, and DNA methylation data. Develop and implement state
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shapes stem and root parasite response to diverse hosts -The role of RNA-directed DNA methylation in the host–parasitic plant arms race -How horizontal gene transfer from host plants contributes
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involve conducting bioinformatic analyses of existing RNA-sequencing and whole-genome DNA methylation datasets from wild and hatchery fish populations. Depending on securing additional funding, there may
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heterogeneous multi-omics datasets. Integrative Data Analysis: Perform and lead analysis of large-scale multi-omics datasets, including RNA/DNA sequencing, methylation, and metabolomics. Method Development
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transcriptional activity in relation to DNA repair dynamics. -Investigate m6A RNA methylation patterns and the role of METTL3 and m6A reader proteins in the DNA damage response. -Manipulate gene expression using
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, flow cytometry, Mass spectrometry, immunofluorescence staining, Co-IP) Chromatin immunoprecipitation (followed by qPCR and sequencing) Epigenetic analysis (DNA methylation arrays, histone modification
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software. PREFERRED QUALIFICATIONS: Experience and expertise in analysis of high-dimensional genomic data, including DNA methylation. Experience and expertise in analysis of longitudinal data Experience and
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epidemiologic methods for environmental ‘omics and related software packages, including R, SAS, Python or equivalent. Previous experience analyzing large DNA methylation datasets, bioinformatics, and use