Finding conserved patterns in biological sequences, networks and genomes

Biological patterns are widely used for identifying biologically interesting regions within macromolecules, classifying biological objects, predicting functions and studying evolution. Good pattern finding algorithms will help biologists to formulate and validate hypotheses in an attempt to obtain important insights into the complex mechanisms of living things. In this dissertation, we aim to improve and develop algorithms for five biological pattern finding problems. For the multiple sequence alignment problem, we propose an alternative formulation in which a final alignment is obtained by preserving pairwise alignments specified by edges of a given tree. In contrast with traditional NPhard formulations, our preserving alignment formulation can be solved in polynomial time without using a heuristic, while having very good accuracy. For the path matching problem, we take advantage of the linearity of the query path to reduce the problem to finding a longest weighted path in a directed acyclic graph. We can find k paths with top scores in a network from the query path in polynomial time. As many biological pathways are not linear, our graph matching approach allows a non-linear graph query to be given. Our graph matching formulation overcomes the common weakness of previous approaches that there is no guarantee on the quality of the results. For the gene cluster finding problem, we investigate a formulation based on constraining the overall size of a cluster and develop statistical significance estimates that allow direct comparisons of clusters of different sizes. We explore both a restricted version which requires that orthologous genes are strictly ordered within each cluster, and the unrestricted problem that allows paralogous genes within a genome and clusters that may not appear in every genome. We solve the first problem in polynomial time and develop practical exact algorithms for the second one. In the gene cluster querying problem, based on a querying strategy, we propose an efficient approach for investigating clustering of related genes across multiple genomes for a given gene cluster. By analyzing gene clustering in 400 bacterial genomes, we show that our algorithm is efficient enough to study gene clusters across hundreds of genomes

Comparison of protein interaction networks reveals species conservation and divergence

BACKGROUND: Recent progresses in high-throughput proteomics have provided us with a first chance to characterize protein interaction networks (PINs), but also raised new challenges in interpreting the accumulating data. RESULTS: Motivated by the need of analyzing and interpreting the fast-growing data in the field of proteomics, we propose a comparative strategy to carry out global analysis of PINs. We compare two PINs by combining interaction topology and sequence similarity to identify conserved network substructures (CoNSs). Using this approach we perform twenty-one pairwise comparisons among the seven recently available PINs of E.coli, H.pylori, S.cerevisiae, C.elegans, D.melanogaster, M.musculus and H.sapiens. In spite of the incompleteness of data, PIN comparison discloses species conservation at the network level and the identified CoNSs are also functionally conserved and involve in basic cellular functions. We investigate the yeast CoNSs and find that many of them correspond to known complexes. We also find that different species harbor many conserved interaction regions that are topologically identical and these regions can constitute larger interaction regions that are topologically different but similar in framework. Based on the species-to-species difference in CoNSs, we infer potential species divergence. It seems that different species organize orthologs in similar but not necessarily the same topology to achieve similar or the same function. This attributes much to duplication and divergence of genes and their associated interactions. Finally, as the application of CoNSs, we predict 101 protein-protein interactions (PPIs), annotate 339 new protein functions and deduce 170 pairs of orthologs. CONCLUSION: Our result demonstrates that the cross-species comparison strategy we adopt is powerful for the exploration of biological problems from the perspective of networks

Effective Identification of Conserved Pathways in Biological Networks Using Hidden Markov Models

The advent of various high-throughput experimental techniques for measuring molecular interactions has enabled the systematic study of biological interactions on a global scale. Since biological processes are carried out by elaborate collaborations of numerous molecules that give rise to a complex network of molecular interactions, comparative analysis of these biological networks can bring important insights into the functional organization and regulatory mechanisms of biological systems.In this paper, we present an effective framework for identifying common interaction patterns in the biological networks of different organisms based on hidden Markov models (HMMs). Given two or more networks, our method efficiently finds the top matching paths in the respective networks, where the matching paths may contain a flexible number of consecutive insertions and deletions.Based on several protein-protein interaction (PPI) networks obtained from the Database of Interacting Proteins (DIP) and other public databases, we demonstrate that our method is able to detect biologically significant pathways that are conserved across different organisms. Our algorithm has a polynomial complexity that grows linearly with the size of the aligned paths. This enables the search for very long paths with more than 10 nodes within a few minutes on a desktop computer. The software program that implements this algorithm is available upon request from the authors

A Novel Framework for the Comparative Analysis of Biological Networks

Genome sequencing projects provide nearly complete lists of the individual components present in an organism, but reveal little about how they work together. Follow-up initiatives have deciphered thousands of dynamic and context-dependent interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Here, we present a novel framework for the alignment and comparative analysis of biological networks of arbitrary topology. Our strategy includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in the current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which vastly increases its performance with respect to existing tools. Finally, we illustrate the biological significance of the results through the identification of novel complex components and potential cases of cross-talk between pathways and alternative signaling routes

Unified Alignment of Protein-Protein Interaction Networks

Paralleling the increasing availability of protein-protein interaction (PPI) network data, several network alignment methods have been proposed. Network alignments have been used to uncover functionally conserved network parts and to transfer annotations. However, due to the computational intractability of the network alignment problem, aligners are heuristics providing divergent solutions and no consensus exists on a gold standard, or which scoring scheme should be used to evaluate them. We comprehensively evaluate the alignment scoring schemes and global network aligners on large scale PPI data and observe that three methods, HUBALIGN, L-GRAAL and NATALIE, regularly produce the most topologically and biologically coherent alignments. We study the collective behaviour of network aligners and observe that PPI networks are almost entirely aligned with a handful of aligners that we unify into a new tool, Ulign. Ulign enables complete alignment of two networks, which traditional global and local aligners fail to do. Also, multiple mappings of Ulign define biologically relevant soft clusterings of proteins in PPI networks, which may be used for refining the transfer of annotations across networks. Hence, PPI networks are already well investigated by current aligners, so to gain additional biological insights, a paradigm shift is needed. We propose such a shift come from aligning all available data types collectively rather than any particular data type in isolation from others

Scalable global alignment for multiple biological networks

<p>Abstract</p> <p>Background</p> <p>Advances in high-throughput technology has led to an increased amount of available data on protein-protein interaction (PPI) data. Detecting and extracting functional modules that are common across multiple networks is an important step towards understanding the role of functional modules and how they have evolved across species. A global protein-protein interaction network alignment algorithm attempts to find such functional orthologs across multiple networks.</p> <p>Results</p> <p>In this article, we propose a scalable global network alignment algorithm based on clustering methods and graph matching techniques in order to detect conserved interactions while simultaneously attempting to maximize the sequence similarity of nodes involved in the alignment. We present an algorithm for multiple alignments, in which several PPI networks are aligned. We empirically evaluated our algorithm on three real biological datasets with 6 different species and found that our approach offers a significant benefit both in terms of quality as well as speed over the current state-of-the-art algorithms.</p> <p>Conclusion</p> <p>Computational experiments on the real datasets demonstrate that our multiple network alignment algorithm is a more efficient and effective algorithm than the state-of-the-art algorithm, IsoRankN. From a qualitative standpoint, our approach also offers a significant advantage over IsoRankN for the multiple network alignment problem.</p