Günhan Gülsoy

Topologically associating domains are stable units of replication-timing regulation

Eukaryotic chromosomes replicate in a temporal order known as the replication-timing program. In mammals, replication timing is cell-type-specific with at least half the genome switching replication timing during development, primarily in units of 400–800 kilobases (‘replication domains’), whose positions are preserved in different cell types, conserved between species, and appear to confine long-range effects of chromosome rearrangements. Early and late replication correlate, respectively, with open and closed three-dimensional chromatin compartments identified by high-resolution chromosome conformation capture (Hi-C), and, to a lesser extent, late replication correlates with lamina-associated domains (LADs). Recent Hi-C mapping has unveiled substructure within chromatin compartments called topologically associating domains (TADs) that are largely conserved in their positions between cell types and are similar in size to replication domains. However, TADs can be further sub-stratified into smaller domains, challenging the significance of structures at any particular scale. Moreover, attempts to reconcile TADs and LADs to replication-timing data have not revealed a common, underlying domain structure. Here we localize boundaries of replication domains to the early-replicating border of replication-timing transitions and map their positions in 18 human and 13 mouse cell types. We demonstrate that, collectively, replication domain boundaries share a near one-to-one correlation with TAD boundaries, whereas within a cell type, adjacent TADs that replicate at similar times obscure replication domain boundaries, largely accounting for the previously reported lack of alignment. Moreover, cell-type-specific replication timing of TADs partitions the genome into two large-scale sub-nuclear compartments revealing that replication-timing transitions are indistinguishable from late-replicating regions in chromatin composition and lamina association and accounting for the reduced correlation of replication timing to LADs and heterochromatin. Our results reconcile cell-type-specific sub-nuclear compartmentalization and replication timing with developmentally stable structural domains and offer a unified model for large-scale chromosome structure and function.

Fine-scale chromatin interaction maps reveal the cis-regulatory landscape of human lincRNA genes

High-throughput methods based on chromosome conformation capture have greatly advanced our understanding of the three-dimensional (3D) organization of genomes but are limited in resolution by their reliance on restriction enzymes. Here we describe a method called DNase Hi-C for comprehensively mapping global chromatin contacts. DNase Hi-C uses DNase I for chromatin fragmentation, leading to greatly improved efficiency and resolution over that of Hi-C. Coupling this method with DNA-capture technology provides a high-throughput approach for targeted mapping of fine-scale chromatin architecture. We applied targeted DNase Hi-C to characterize the 3D organization of 998 large intergenic noncoding RNA (lincRNA) promoters in two human cell lines. Our results revealed that expression of lincRNAs is tightly controlled by complex mechanisms involving both super-enhancers and the Polycomb repressive complex. Our results provide the first glimpse of the cell type–specific 3D organization of lincRNA genes.

HIDEN: Hierarchical decomposition of regulatory networks

BackgroundTranscription factors regulate numerous cellular processes by controlling the rate of production of each gene. The regulatory relations are modeled using transcriptional regulatory networks. Recent studies have shown that such networks have an underlying hierarchical organization. We consider the problem of discovering the underlying hierarchy in transcriptional regulatory networks.ResultsWe first transform this problem to a mixed integer programming problem. We then use existing tools to solve the resulting problem. For larger networks this strategy does not work due to rapid increase in running time and space usage. We use divide and conquer strategy for such networks. We use our method to analyze the transcriptional regulatory networks of E. coli, H. sapiens and S. cerevisiae.ConclusionsOur experiments demonstrate that: (i) Our method gives statistically better results than three existing state of the art methods; (ii) Our method is robust against errors in the data and (iii) Our method’s performance is not affected by the different topologies in the data.

RINQ: Reference-based Indexing for Network Queries

We consider the problem of similarity queries in biological network databases. Given a database of networks, similarity query returns all the database networks whose similarity (i.e. alignment score) to a given query network is at least a specified similarity cutoff value. Alignment of two networks is a very costly operation, which makes exhaustive comparison of all the database networks with a query impractical. To tackle this problem, we develop a novel indexing method, named RINQ (Reference-based Indexing for Biological Network Queries). Our method uses a set of reference networks to eliminate a large portion of the database quickly for each query. A reference network is a small biological network. We precompute and store the alignments of all the references with all the database networks. When our database is queried, we align the query network with all the reference networks. Using these alignments, we calculate a lower bound and an approximate upper bound to the alignment score of each database network with the query network. With the help of upper and lower bounds, we eliminate the majority of the database networks without aligning them to the query network. We also quickly identify a small portion of these as guaranteed to be similar to the query. We perform pairwise alignment only for the remaining networks. We also propose a supervised method to pick references that have a large chance of filtering the unpromising database networks. Extensive experimental evaluation suggests that (i) our method reduced the running time of a single query on a database of around 300 networks from over 2 days to only 8 h; (ii) our method outperformed the state of the art method Closure Tree and SAGA by a factor of three or more; and (iii) our method successfully identified statistically and biologically significant relationships across networks and organisms. Contact: ggulsoy@cise.ufl.edu; tamer@cise.ufl.edu Supplementary information: Supplementary data are available at Bioinformatics online.

TOPAC: ALIGNMENT OF GENE REGULATORY NETWORKS USING TOPOLOGY-AWARE COLORING

We consider the problem of finding a subnetwork in a given biological network (i.e. target network) that is most similar to a given small query network. We aim to find the optimal solution (i.e. the subnetwork with the largest alignment score) with a provable confidence bound. There is no known polynomial time solution to this problem in the literature. Alon et al. has developed a state-of-the-art coloring method that reduces the cost of this problem. This method randomly colors the target network prior to alignment for many iterations until a user-supplied confidence is reached. Here we develop a novel coloring method, named k-hop coloring (k is a positive integer), that achieves a provable confidence value in a small number of iterations without sacrificing the optimality. Our method considers the color assignments already made in the neighborhood of each target network node while assigning a color to a node. This way, it preemptively avoids many color assignments that are guaranteed to fail to produce the optimal alignment. We also develop a filtering method that eliminates the nodes that cannot be aligned without reducing the alignment score after each coloring instance. We demonstrate both theoretically and experimentally that our coloring method outperforms that of Alon et al., which is also used by a number network alignment methods, including QPath and QNet, by a factor of three without reducing the confidence in the optimality of the result. Our experiments also suggest that the resulting alignment method is capable of identifying functionally enriched regions in the target network successfully.

Finding steady states of large scale regulatory networks through partitioning

Identifying steady states that characterize the long term outcome of regulatory networks is crucial in understanding important biological processes such as cellular differentiation. Finding all possible steady states of regulatory networks is a computationally intensive task as it suffers from state space explosion problem. Here, we propose a method for finding steady states of large-scale Boolean regulatory networks. Our method exploits scale-freeness and weak connectivity of regulatory networks in order to speed up the steady state search through partitioning. In the trivial case where network has more than one component such that the components are disconnected from each other, steady states of each component are independent of those of the remaining components. When the size of at least one connected component of the network is still prohibitively large, further partitioning is necessary. In this case, we identify weakly dependent components (i.e., two components that have a small number of regulations from one to the other) and calculate the steady states of each such component independently. We then combine these steady states by taking into account the regulations connecting them. We show that this approach is much more efficient than calculating the steady states of the whole network at once when the number of edges connecting them is small. Since regulatory networks often have small in-degrees, this partitioning strategy can be used effectively in order to find their steady states. Our experimental results on real datasets demonstrate that our method leverages steady state identification to very large regulatory networks.

Topology aware coloring of gene regulatory networks

We consider the problem of finding a subnetwork in a given biological network (i.e., target network) that is the most similar to a given small query network. We aim to find the optimal solution (i.e., the subnetwork with the largest alignment score) with a provable confidence bound. There is no known polynomial time solution to this problem in the literature. Alon et al. has developed a state of the art coloring method that reduces the cost of this problem. This method randomly colors the target network prior to alignment for many iterations until a user supplied confidence is reached. Here we develop a novel coloring method, named k-hop coloring (k is a positive integer), that achieves a provable confidence value in a small number of iterations without sacrificing the optimality. Our method considers the color assignments already made in the neighborhood of each target network node while assigning a color to a node. This way, it preemptively avoids many color assignments that are guaranteed to fail to produce the optimal alignment. We demonstrate both theoretically and experimentally that our coloring method outperforms that of Alon et al. which is also used by a number network alignment methods including QPath and QNet by a factor of three without reducing the confidence in the optimality of the result.

Using DNase Hi-C techniques to map global and local three-dimensional genome architecture at high resolution

The folding and three-dimensional (3D) organization of chromatin in the nucleus critically impacts genome function. The past decade has witnessed rapid advances in genomic tools for delineating 3D genome architecture. Among them, chromosome conformation capture (3C)-based methods such as Hi-C are the most widely used techniques for mapping chromatin interactions. However, traditional Hi-C protocols rely on restriction enzymes (REs) to fragment chromatin and are therefore limited in resolution. We recently developed DNase Hi-C for mapping 3D genome organization, which uses DNase I for chromatin fragmentation. DNase Hi-C overcomes RE-related limitations associated with traditional Hi-C methods, leading to improved methodological resolution. Furthermore, combining this method with DNA capture technology provides a high-throughput approach (targeted DNase Hi-C) that allows for mapping fine-scale chromatin architecture at exceptionally high resolution. Hence, targeted DNase Hi-C will be valuable for delineating the physical landscapes of cis-regulatory networks that control gene expression and for characterizing phenotype-associated chromatin 3D signatures. Here, we provide a detailed description of method design and step-by-step working protocols for these two methods.

Inferring gene functions from metabolic reactions

Metabolic networks model the physiological processes that transform metabolites in organisms. A metabolic network is considered to be in steady state if the rate at which all such transformations remain unchanged. Analyzing steady states has been essential in understanding the contribution of individual molecules to long term characteristics of the underlying organism. In this paper, we develop a novel method to establish the relationship between the functions of genes that take part in a given metabolic network and the steady states of that network systematically. To do this, we first characterize the impact of each reaction on the steady states of the network. Then, using their impacts, we group every reaction in the network into clusters of genes with similar impacts. We conjecture that genes with similar impacts on the set of possible steady states tend to serve similar functions. Following from this conjecture, for each group we formed, we calculate the enrichment of each gene ontology (GO) term that exists for at least one gene in that group. Given a new gene with missing annotations in the network, we find the cluster that is closest to that gene in the steady state space. We predict the enriched GO terms of in that cluster as possible annotations to that gene. Our experiments demonstrate that enrichment values correlate highly with the actual GO terms of each reaction, and thus, our method can predict the GO terms of less known genes accurately.

Mining Biological Networks for Similar Patterns

In this chapter, we present efficient and accurate methods to analyze biological networks. Biological networks show how different biochemical entities interact with each other to perform vital functions for the survival of an organism. Three main types of biological networks are protein interaction networks, metabolic pathways and regulatory networks. In this work, we focus on alignment of metabolic networks.