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Shaun Mahony

Associate Professor of Biochemistry and Molecular Biology
Shaun Mahony

Program or Departmental Affiliations

BMMB Graduate Program Bioinformatics and Genomics Program Computational, Bioinformatics, and Statistical Program
  Molecular, Cellular, and Integrative Biosciences Program  



Center for Eukaryotic Gene Regulation


Research Interest

Computational biology and regulatory genomics.


Research Summary

The Mahony lab at Penn State University is part of the Department of Biochemistry & Molecular Biology and the Center for Eukaryotic Gene Regulation. We are computational biologists who develop machine learning approaches for understanding gene regulation.

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

  • Direct prediction of regulatory elements from partial data without imputation
    Y Zhang & S Mahony
    PLoS Computational Biology (2019) 15(11):e1007399
  • Systematic integration of GATA transcription factors and epigenomes via IDEAS paints the regulatory landscape of hematopoietic cells
    RC Hardison, Y Zhang, CA Keller, G Xiang, EF Heuston, L An, J Lichtenberg, BM Giardine, D Bodine, S Mahony, Q Li, F Yue, MJ Weiss, GA Blobel, J Taylor, J Hughes, DR Higgs, B Göttgens.
    IUBMB Life (2019) (in press:  doi: 10.1002/iub.2195) [review article]
  • Sequence and chromatin determinants of transcription factor binding and the establishment of cell type-specific binding patterns
    D Srivastava & S Mahony
    BBA - Gene Regulatory Mechanisms (2019) 194443 [review article]
  • Joint inference and alignment of genome structures enables characterization of compartment-independent 3D relocalization across cell types
    L Rieber & S Mahony
    Epigenetics & Chromatin (2019) 12(1):61
  • Proneural factors Ascl1 and Neurog2 contribute to neuronal subtype identities by establishing distinct chromatin landscapes
    B Aydin, A Kakumanu, M Rossillo, M Moreno-Estelles, G Garipler, N Ringstad, N Flames, S Mahony*, EO Mazzoni*
    Nature Neuroscience (2019) 22(6):897-908 (*corresponding authors)
  • Characterizing protein-DNA binding event subtypes in ChIP-exo data
    N Yamada, WKM Lai, N Farrell, BF Pugh, S Mahony
    Bioinformatics (2019) 35(6):903-913
  • Widespread and precise reprogramming of yeast protein-genome interactions in response to heat shock
    V Vinayachandran, R Reja, MJ Rossi, B Park, L Rieber, C Mittal, S Mahony, BF Pugh
    Genome Research (2018) 28: 357-366
  • Deconvolving sequence features that discriminate between overlapping regulatory annotations
    A Kakumanu, S Velasco, EO Mazzoni, S Mahony
    PLoS Computational Biology (2017) 13(10):e1005795
  • miniMDS: 3D structural inference from high-resolution Hi-C data
    L Rieber, S Mahony
    Bioinformatics (2017) 33 (14): i261-i266
  • A multi-step transcriptional and chromatin state cascade underlies motor neuron programming
    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*
    Cell Stem Cell (2017) 20(2):205-217 (& equal contribution, *corresponding authors)
  • The pioneer transcription factor FoxA maintains an accessible nucleosome configuration at enhancers for tissue-specific gene activation
    M Iwafuchi-Doi, G Donahue, A Kakumanu, JA Watts, S Mahony, BF Pugh, D Lee, KH Kaestner, KS Zaret
    Molecular Cell (2016) 62(1): 72-91
  • Engineered stomach tissues as a renewable source of functional beta-cells for blood glucose regulation
    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
    Cell Stem Cell (2016) 18(3):410-421
  • Phenome-wide interaction study (PheWIS) in AIDS clinical trials group data (ACTG)
    SS Verma, AT Frase, A Verma, SA Pendergrass, S Mahony, DW Haas, MD Ritchie
    Pacific Symposium on Biocomputing (2016) 21:57-68
  • Genome-wide organization of GATA1 and TAL1 determined at high resolution
    GC Han, V Vinayachandran, A Bataille, B Park, KY Chan-Salis, CA Keller, M Long, S Mahony, RC Hardison, BF Pugh
    Molecular & Cell Biology (2015) 36(1):157-172
  • Protein-DNA binding in high resolution
    S Mahony, BF Pugh
    Critical Reviews in Biochemistry and Molecular Biology (2015) 50(4):269-283 [review article]
  • Gene co-regulation by Fezf2 selects neurotransmitter identity and connectivity of corticospinal neurons
    S Lodato, BJ Molyneax, E Zuccaro, LA Goff, H-H Chen, W Yuan, A Meleski, E Takahashi, S Mahony, JL Rinn, DK Gifford, P Arlotta
    Nature Neuroscience (2014) 17(8):1046-54
  • An integrated model of multiple-condition ChIP-seq data reveals predeterminants of Cdx2 binding
    S Mahony*&, MD Edwards&, EO Mazzoni, RI Sherwood, A Kakumanu, CA Morrison, H Wichterle, DK Gifford*
    PLoS Computational Biology (2014) 10(3):e1003501 (& equal contribution, *corresponding authors)
  • A Cdx4-Sall4 regulatory module controls the transition from mesoderm formation to embryonic hematopoiesis
    EJ Paik, S Mahony, RM White, EN Price, A DiBiase, B Dorjsuren, C Mosimann, AJ Davidson, DK Gifford, LI Zon
    Stem Cell Reports (2013) 1(5):425-436 
  • Synergistic binding of transcription factors to cell-specific enhancers programs motor neuron identity
    EO Mazzoni*, S Mahony*, M Closser, CA Morrison, S Nedelec, DJ Williams, D An, DK Gifford, H Wichterle
    Nature Neuroscience (2013) 16(9):1219-1227   (* equal contribution)
  • Saltatory remodeling of Hox chromatin in response to rostrocaudal patterning signals
    EO Mazzoni*, S Mahony*, M Peljto*, T Patel, SR Thornton, S McCuine, C Reeder, LA Boyer, RA Young, DK Gifford, H Wichterle
    Nature Neuroscience (2013) 16(9):1191-1198   (* equal contribution)
  • A multi-parametric flow cytometric assay to analyze DNA-protein interactions
    M Arbab, S Mahony, H Cho, J Chick, PA Rolfe, J Van Hoff, V Morris, S Gygi, RL Maas, DK Gifford, R Sherwood
    Nucleic Acids Research (2013) 41(2):e38
  • High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints
    Y Guo, S Mahony*, DK Gifford*
    PLoS Computational Biology (2012) 8(8):e1002638  (* corresponding authors)
  • Embryonic stem cell based system for the discovery and mapping of developmental transcriptional programs
    EO Mazzoni, S Mahony, M Iacovino, CA Morrison, G Mountoufaris, M Closser, WA Whyte, RA Young, M Kyba, DK Gifford, H Wichterle
    Nature Methods (2011) 8(12):1056-1058
  • Large scale comparison of innate responses to viral and bacterial pathogens in mouse and macaque
    GE Zinman, R Brower-Sinning, CH Emeche, J Ernst, GT Huang, S Mahony, AJ Myers, DM O'Dee, JL Flynn, GJ Nau, TM Ross, RD Salter, PV Benos, Z Bar-Joseph, PA Morel
    PLoS ONE (2011) 6(7):e22401
  • Ligand-dependent dynamics of retinoic acid receptor binding during early neurogenesis
    S Mahony*, EO Mazzoni*, S McCuine, RA Young, H Wichterle, DK Gifford
    Genome Biology (2011) 12(1):R2  (* equal contribution)
  • Discovering homotypic binding events at high spatial resolution
    Y Guo, G Papachristoudis, RC Altshuler, GK Gerber, TS Jaakkola, DK Gifford, S Mahony*
    Bioinformatics (2010) 26(24):3028-3034 (* corresponding author)
  • Global control of motor neuron topography mediated by the repressive actions of a single Hox gene
    H Jung, J Lacombe, EO Mazzoni, KF Leim, J Grinstein, S Mahony, D Mukopadhyay, DK Gifford, RA Young, KV Anderson, H Wichterle, JS Dasen
    Neuron (2010) 67(5):781-796
  • Feed-forward regulation of a cell fate determinant by an RNA-binding protein generates asymmetry in yeast
    JJ Wolf, RD Dowell, S Mahony, M Rabani, DK Gifford, GR Fink
    Genetics (2010) 185:513-522
  • ORegAnno: an open-access community-driven resource for regulatory annotation
    OL Griffith, SB Montgomery…, S Mahony (7th of 29 authors)…, CM Bergman, SJM Jones
    Nucleic Acids Research (2008) 36:D107-D113
  • Combined analysis reveals a core set of cycling genes
    Y Lu, S Mahony, PV Benos, R Rosenfeld, I Simon, LL Breeden, Z Bar-Joseph
    Genome Biology (2007) 8(7):R146
  • Inferring protein-DNA dependencies using motif alignments and mutual information
    S Mahony, PE Auron, PV Benos
    Bioinformatics (2007) 23(13): i297-i304
  • Regulatory conservation of protein coding and miRNA genes in vertebrates: lessons from the opossum genome
    S Mahony, DL Corcoran, E Feingold, PV Benos
    Genome Biology (2007) 8(5):R84
  • Genome of the marsupial Monodelphis domestica reveals innovation in non-coding sequences
    TS Mikkelsen, MJ Wakefield..., S Mahony (37th of 64 authors)…, ES Lander, K Lindblad-Toh
    Nature (2007) 447:167-177
  • STAMP: a web tool for exploring DNA-binding motif similarities
    S Mahony, PV Benos
    Nucleic Acids Research (2007) 35:W253-W258
  • DNA familial binding profiles made easy: comparison of various motif alignment and clustering strategies
    S Mahony, PE Auron, PV Benos
    PLoS Computational Biology (2007) 3(3):e61
  • Gene prediction in metagenomic libraries using the self-organising map and high performance computing techniques
    N McCoy, S Mahony, A Golden
    Springer Lecture Notes in Bioinformatics (2007) 4360: 99-109
  • Self-organizing neural networks to support the discovery of DNA-binding motifs
    S Mahony, PV Benos, TJ Smith, A Golden
    Neural Networks (2006) 19 (6-7): 950-962
  • Reconstructing an ancestral mammalian immune supercomplex from a marsupial Major Histocompatibility Complex
    K Belov, JE Deakin, AT Papenfuss, ML Baker, SD Melman, HV Siddle, N Gouin, DL Goode, TJ Sargeant, MD Robinson, MJ Wakefield, S Mahony, JG Cross, PV Benos, PB Samollow, TP Speed, JA Graves, RD Miller
    PLoS Biology (2006) 4 (3): e46
  • Improved detection of DNA motifs using a self-organized clustering of familial binding profiles
    S Mahony, A Golden, TJ Smith, PV Benos
    Bioinformatics (2005) 21 (Suppl 1): i283-i291
  • Transcription factor binding site identification using the Self-Organizing Map
    S Mahony, D Hendrix, A Golden, TJ Smith, DS Rokhsar
    Bioinformatics (2005) 21(9): 1807-14
  • Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models
    S Mahony, JO McInerney, TJ Smith, A Golden
    BMC Bioinformatics (2004) 5:23