Tamer Kahveci

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.

Variable length queries for time series data

Finding similar patterns in a time sequence is a well-studied problem. Most of the current techniques work well for queries of a prespecified length, but not for variable length queries. We propose a new indexing technique that works well for variable length queries. The central idea is to store index structures at different resolutions for a given dataset. The resolutions are based on wavelets. For a given query, a number of subqueries at different resolutions are generated. The ranges of the subqueries are progressively refined based on results from previous subqueries. Our experiments show that the total cost for our method is 4 to 20 times less than the current techniques including linear scan. Because of the need to store information at multiple resolution levels, the storage requirement of our method could potentially be large. In the second part of the paper we show how the index information can be compressed with minimal information loss. According to our experimental results, even after compressing the size of the index to one fifth, the total cost of our method is 3 to 15 times less than the current techniques.

A role for the universal Kae1/Qri7/YgjD (COG0533) family in tRNA modification.

The YgjD/Kae1 family (COG0533) has been on the top‐10 list of universally conserved proteins of unknown function for over 5 years. It has been linked to DNA maintenance in bacteria and mitochondria and transcription regulation and telomere homeostasis in eukaryotes, but its actual function has never been found. Based on a comparative genomic and structural analysis, we predicted this family was involved in the biosynthesis of N6‐threonylcarbamoyl adenosine, a universal modification found at position 37 of tRNAs decoding ANN codons. This was confirmed as a yeast mutant lacking Kae1 is devoid of t6A. t6A− strains were also used to reveal that t6A has a critical role in initiation codon restriction to AUG and in restricting frameshifting at tandem ANN codons. We also showed that YaeZ, a YgjD paralog, is required for YgjD function in vivo in bacteria. This work lays the foundation for understanding the pleiotropic role of this universal protein family.

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning framework and use both supervised and unsupervised methods to predict regulatory edges by integrating transcription factor (TF) binding, evolutionarily conserved sequence motifs, gene expression, and chromatin modification data sets as input features. Applying these methods to Drosophila melanogaster, we predict ∼300,000 regulatory edges in a network of ∼600 TFs and 12,000 target genes. We validate our predictions using known regulatory interactions, gene functional annotations, tissue-specific expression, protein-protein interactions, and three-dimensional maps of chromosome conformation. We use the inferred network to identify putative functions for hundreds of previously uncharacterized genes, including many in nervous system development, which are independently confirmed based on their tissue-specific expression patterns. Last, we use the regulatory network to predict target gene expression levels as a function of TF expression, and find significantly higher predictive power for integrative networks than for motif or ChIP-based networks. Our work reveals the complementarity between physical evidence of regulatory interactions (TF binding, motif conservation) and functional evidence (coordinated expression or chromatin patterns) and demonstrates the power of data integration for network inference and studies of gene regulation at the systems level.

Similarity searching for multi-attribute sequences

We investigate the problem of searching similar multiattribute time sequences. Such sequences arise naturally in a number of medical, financial, video, weather forecast, and stock market databases where more than one attribute is of interest at a time instant. We first solve the simple case in which the distance is defined as the Euclidean distance. Later we extend it to shift and scale invariance. We formulate a new symmetric scale and shift invariant notion of distance for such sequences. We also propose a new index structure that transforms the data sequences and clusters them according to their shiftings and scalings. This clustering improves the efficiency considerably. According to our experiments with real and synthetic datasets, the index structures performance is 5 to 45 times better than competing techniques, the exact speedup based on other optimizations such as caching and replication.

Scalable Steady State Analysis of Boolean Biological Regulatory Networks

Background Computing the long term behavior of regulatory and signaling networks is critical in understanding how biological functions take place in organisms. Steady states of these networks determine the activity levels of individual entities in the long run. Identifying all the steady states of these networks is difficult due to the state space explosion problem. Methodology In this paper, we propose a method for identifying all the steady states of Boolean regulatory and signaling networks accurately and efficiently. We build a mathematical model that allows pruning a large portion of the state space quickly without causing any false dismissals. For the remaining state space, which is typically very small compared to the whole state space, we develop a randomized traversal method that extracts the steady states. We estimate the number of steady states, and the expected behavior of individual genes and gene pairs in steady states in an online fashion. Also, we formulate a stopping criterion that terminates the traversal as soon as user supplied percentage of the results are returned with high confidence. Conclusions This method identifies the observed steady states of boolean biological networks computationally. Our algorithm successfully reported the G1 phases of both budding and fission yeast cell cycles. Besides, the experiments suggest that this method is useful in identifying co-expressed genes as well. By analyzing the steady state profile of Hedgehog network, we were able to find the highly co-expressed gene pair GL1-SMO together with other such pairs. Availability Source code of this work is available at http://bioinformatics.cise.ufl.edu/palSteady.html twocolumnfalse]

Distance-based clustering of CGH data

MOTIVATION We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples. The goal is to develop a systematic way of placing patients with similar CGH imbalance profiles into the same cluster. Our expectation is that patients with the same cancer types will generally belong to the same cluster as their underlying CGH profiles will be similar. RESULTS We focus on distance-based clustering strategies. We do this in two steps. (1) Distances of all pairs of CGH samples are computed. (2) CGH samples are clustered based on this distance. We develop three pairwise distance/similarity measures, namely raw, cosine and sim. Raw measure disregards correlation between contiguous genomic intervals. It compares the aberrations in each genomic interval separately. The remaining measures assume that consecutive genomic intervals may be correlated. Cosine maps pairs of CGH samples into vectors in a high-dimensional space and measures the angle between them. Sim measures the number of independent common aberrations. We test our distance/similarity measures on three well known clustering algorithms, bottom-up, top-down and k-means with and without centroid shrinking. Our results show that sim consistently performs better than the remaining measures. This indicates that the correlation of neighboring genomic intervals should be considered in the structural analysis of CGH datasets. The combination of sim with top-down clustering emerged as the best approach. AVAILABILITY All software developed in this article and all the datasets are available from the authors upon request. CONTACT juliu@cise.ufl.edu.

SubMAP: aligning metabolic pathways with subnetwork mappings

We consider the problem of aligning two metabolic pathways Unlike traditional approaches, we do not restrict the alignment to one-to-one mappings between the molecules of the input pathways We follow the observation that in nature different organisms can perform the same or similar functions through different sets of reactions and molecules The number and the topology of the molecules in these alternative sets often vary from one organism to another In other words, given two metabolic pathways of arbitrary topology, we would like to find a mapping that maximizes the similarity between the molecule subsets of query pathways of size at most a given integer k We transform this problem into an eigenvalue problem The solution to this eigenvalue problem produces alternative mappings in the form of a weighted bipartite graph We then convert this graph to a vertex weighted graph The maximum weight independent subset of this new graph is the alignment that maximizes the alignment score while ensuring consistency We call our algorithm SubMAP (Subnetwork Mappings in Alignment of Pathways) We evaluate its accuracy and performance on real datasets Our experiments demonstrate that SubMAP can identify biologically relevant mappings that are missed by traditional alignment methods and it is scalable for real size metabolic pathways. Availability: Our software and source code in C++ is available at http://bioinformatics.cise.ufl.edu/SubMAP.html

AN ITERATIVE ALGORITHM FOR METABOLIC NETWORK-BASED DRUG TARGET IDENTIFICATION

Post-genomic advances in bioinformatics have refined drug-design strategies, by focusing on the reduction of serious side-effects through the identification of enzymatic targets. We consider the problem of identifying the enzymes (i.e., drug targets), whose inhibition will stop the production of a given target set of compounds, while eliminating minimal number of non-target compounds. An exhaustive evaluation of all possible enzyme combinations to find the optimal solution subset may become computationally infeasible for very large metabolic networks. We propose a scalable iterative algorithm which computes a sub-optimal solution within reasonable time-bounds. Our algorithm is based on the intuition that we can arrive at a solution close to the optimal one by tracing backward from the target compounds. It evaluates immediate precursors of the target compounds and iteratively moves backwards to identify the enzymes whose inhibition will stop the production of the target compounds while incurring minimum side-effects. We show that our algorithm converges to a sub-optimal solution within a finite number of such iterations. Our experiments on the E. Coli metabolic network show that the average accuracy of our method deviates from that of the exhaustive search only by 0.02%. Our iterative algorithm is highly scalable. It can solve the problem for the entire metabolic network of Escherichia Coli in less than 10 seconds.

Mining metabolic networks for optimal drug targets.

Recent advances in bioinformatics promote drug-design methods that aim to reduce side-effects. Efficient computational methods are required to identify the optimal enzyme-combination (i.e., drug targets) whose inhibition, will achieve the required effect of eliminating a given target set of compounds, while incurring minimal side-effects. We formulate the optimal enzyme-combination identification problem as an optimization problem on metabolic networks. We define a graph based computational damage model that encapsulates the impact of enzymes onto compounds in metabolic networks. We develop a branch-and-bound algorithm, named OPMET, to explore the search space dynamically. We also develop two filtering strategies to prune the search space while still guaranteeing an optimal solution. They compute an upper bound to the number of target compounds eliminated and a lower bound to the side-effect respectively. Our experiments on the human metabolic network demonstrate that the proposed algorithm can accurately identify the target enzymes for known successful drugs in the literature. Our experiments also show that OPMET can reduce the total search time by several orders of magnitude as compared to the exhaustive search.