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Articles by A. J. Hartemink
Total Records ( 3 ) for A. J. Hartemink
  X Guo and A. J. Hartemink

Motivation: Recent advances in high-throughput experimental techniques have yielded a large amount of data on protein–protein interactions (PPIs). Since these interactions can be organized into networks, and since separate PPI networks can be constructed for different species, a natural research direction is the comparative analysis of such networks across species in order to detect conserved functional modules. This is the task of network alignment.

Results: Most conventional network alignment algorithms adopt a node-then-edge-alignment paradigm: they first identify homologous proteins across networks and then consider interactions among them to construct network alignments. In this study, we propose an alternative direct-edge-alignment paradigm. Specifically, instead of explicit identification of homologous proteins, we directly infer plausibly alignable PPIs across species by comparing conservation of their constituent domain interactions. We apply our approach to detect conserved protein complexes in yeast–fly and yeast–worm PPI networks, and show that our approach outperforms two recent approaches in most alignment performance metrics.

Availability: Supplementary material and source code can be found at


  T Wasson and A. J. Hartemink

Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not. Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an "occupancy profile," a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding. Importantly, the sequence preferences our model takes as input are derived from in vitro experiments. This ensures that the calculated occupancy profiles are the result of the forces of competition represented explicitly in our model and the inherent sequence affinities of the constituent DBFs.

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