Biochemistry and Molecular Biology
Penn State Science
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Shaun Mahony

Shaun Mahony

Main Content

  • Assistant Professor of Biochemistry & Molecular Biology
404 South Frear Laboratory
University Park, PA 16802
Phone: (814) 865-3008

Research Interests

Computational biology and regulatory genomics.

 

Graduate Programs

BGBMMBMCIBS

Research Summary

Our research aims to understand where transcription factors (TFs) bind in the genome, and what they do once they get there. There are many forces that can affect a TF’s choice of binding targets once it is introduced into the nucleus. The inherent DNA-binding preference of the protein will specify the sites that could potentially be bound, but the vast majority of high-affinity sequences will not be occupied by the TF in any given cell type. Binding selectivity is thus determined by the regulatory environment of the cell: chromatin accessibility, interactions with co-factors, DNA methylation, and histone post-translational modifications all play roles in specifying the TF’s binding sites. These forces are context-specific, which allows the same TF to target different binding sites in different cell types. However, a TF’s choice of binding targets is only part of the equation; many bound sites do not seem to directly affect gene expression. We understand little about how enhancers can regulate genes that are thousands, sometimes millions, of bases away on the genome.

Fortunately, regulatory genomics assays based on high-throughput sequencing are giving us unprecedented insight into the regulatory environment of the cell. ChIP-seq and ChIP-exo allow us to profile TF and histone modification occupancy at high resolution over the entire genome. RNA-seq lets us profile the global transcriptional activity. ATAC-seq profiles the genome-wide accessibility landscape, while assays such as ChIA-PET and Hi-C open a window on the three-dimensional architecture of the genome.

We aim to integrate these various genomic data types to understand context-specific transcription factor activities. We deploy a wide range of machine learning approaches to aid in this goal, including neural networks, generative models, and dimensionality reduction approaches.

Selected Publications

  • B Aydin, A Kakumanu, M Rossillo, M Moreno-Estelles, G Garipler, N Ringstad, N Flames, S Mahony†, EO Mazzoni† (2019). Proneural factors Ascl1 and Neurog2 contribute to neuronal subtype identities by establishing distinct chromatin landscapes. Nature Neuroscience 35(6):903-913 († corresponding authors)
  • N Yamada, WKM Lai, N Farrell, BF Pugh, S Mahony (2019). Characterizing protein-DNA binding event subtypes in ChIP-exo data. Bioinformatics 35(6):903-913
  • A Kakumanu, S Velasco, EO Mazzoni, S Mahony (2017). Deconvolving sequence features that discriminate between overlapping regulatory annotations. PLoS Computational Biology 13(10):e1005795
  • L Rieber, S Mahony (2017). miniMDS: 3D structural inference from high-resolution Hi-C data. Bioinformatics 33 (14):i261-i266
  • S Velasco*, MM Ibrahim*, A Kakumanu*, G Garipler, B Aydin, MA Al-Sayegh, A Hirsekorn, F Abdul-Rahman, R Satija, U Ohler†, S Mahony†, EO Mazzoni† (2017). A multi-step transcriptional and chromatin state cascade underlies motor neuron programming. Cell Stem Cell 20(2):205-217.e8 (* equal contribution, † corresponding authors)
  • M Iwafuchi-Doi, G Donahue, A Kakumanu, JA Watts, S Mahony, BF Pugh, D Lee, KH Kaestner, KS Zaret (2016) The pioneer transcription factor FoxA maintains an accessible nucleosome configuration at enhancers for tissue-specific gene activation. Molecular Cell 62(1): 72-91
  • C Ariyachet, A Tovaglieri, G Xiang, J Lu, MS Shah, CA Richmond, C Verbeke, DA Melton, BZ Stanger, D Mooney, RA Shivdasani, S Mahony, Q Xia, DT Breault, Q Zhou (2016). Engineered stomach tissues as a renewable source of functional beta-cells for blood glucose regulation. Cell Stem Cell 18(3):410-421
  • S Mahony*&, MD Edwards&, EO Mazzoni, RI Sherwood, A Kakumanu, CA Morrison, H Wichterle, DK Gifford* (2014). An integrated model of multiple-condition ChIP-seq data reveals predeterminants of Cdx2 binding. PLoS Computational Biology 10(3):e1003501 (& equal contribution, *corresponding authors)
  • EO Mazzoni*, S Mahony*, M Closser, CA Morrison, S Nedelec, DJ Williams, D An, DK Gifford, H Wichterle (2013). Synergistic binding of transcription factors to cell-specific enhancers programs motor neuron identity. Nature Neuroscience 16(9):1219-1227 (* equal contribution) 
  • EO Mazzoni*, S Mahony*, M Peljto*, T Patel, SR Thornton, S McCuine, C Reeder, LA Boyer, RA Young, DK Gifford, H Wichterle (2013). Saltatory remodeling of Hox chromatin in response to rostrocaudal patterning signals . Nature Neuroscience 16(9):1191-1198 (* equal contribution) 
  • Y Guo, S Mahony*, DK Gifford* (2012). High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints. PLoS Computational Biology 8(8):e1002638. (* corresponding authors)
  • S Mahony*, EO Mazzoni*, S McCuine, RA Young, H Wichterle, DK Gifford (2011). Ligand-dependent dynamics of retinoic acid receptor binding during early neurogenesis. Genome Biology 12(1):R2. (* equal contribution)
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