Contact information
Research groups
Lance Hentges
DPhil
Postdoctoral Scientist
Background
My primary research interest is understanding genome regulation through the application of machine learning techniques. Both the complicated relationships between elements and the abundance of genomics data make machine learning, and in particular deep learning, well suited to this work. I actively develop and maintain software using these methods with the aim to improve traditional bioinformatics analysis, create new techniques, and make in silico predictions when bench work is infeasible or impossible. This includes LanceOtron, a tool developed by myself and colleagues, which applies a neural network to classify signal quality of chromatin profiling assays such as ATAC-seq, ChIP-seq, and DNase-seq.
Recent publications
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GTAC enables parallel genotyping of multiple genomic loci with chromatin accessibility profiling in single cells.
Journal article
Turkalj S. et al, (2023), Cell Stem Cell, 30, 722 - 740.e11
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Direct correction of haemoglobin E β-thalassaemia using base editors.
Journal article
Badat M. et al, (2023), Nat Commun, 14
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LanceOtron: a deep learning peak caller for genome sequencing experiments.
Journal article
Hentges LD. et al, (2022), Bioinformatics
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Genotyping of Multiple Genomic Loci with Chromatin Accessibility Profiling in Single Cells Links Clonal Hierarchy with Epigenetic Variation in Acute Myeloid Leukemia
Conference paper
Turkalj S. et al, (2022), BLOOD, 140, 1193 - 1194
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Defining genome architecture at base-pair resolution.
Journal article
Hua P. et al, (2021), Nature, 595, 125 - 129