Biomarker Discovery in Serum from Patients with Carotid Atherosclerosis

www.karger.com/cee This is an Open Access article licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License (www.karger.com/OA-license), applicable to the online version of the article only. Distribution for non-commercial purposes only

Graph-based methods for large-scale protein classification and orthology inference

The quest for understanding how proteins evolve and function has been a prominent and costly human endeavor. With advances in genomics and use of bioinformatics tools, the diversity of proteins in present day genomes can now be studied more efficiently than ever before. This thesis describes computational methods suitable for large-scale protein classification of many proteomes of diverse species. Specifically, we focus on methods that combine unsupervised learning (clustering) techniques with the knowledge of molecular phylogenetics, particularly that of orthology. In chapter 1 we introduce the biological context of protein structure, function and evolution, review the state-of-the-art sequence-based protein classification methods, and then describe methods used to validate the predictions. Finally, we present the outline and objectives of this thesis. Evolutionary (phylogenetic) concepts are instrumental in studying subjects as diverse as the diversity of genomes, cellular networks, protein structures and functions, and functional genome annotation. In particular, the detection of orthologous proteins (genes) across genomes provides reliable means to infer biological functions and processes from one organism to another. Chapter 2 evaluates the available computational tools, such as algorithms and databases, used to infer orthologous relationships between genes from fully sequenced genomes. We discuss the main caveats of large-scale orthology detection in general as well as the merits and pitfalls of each method in particular. We argue that establishing true orthologous relationships requires a phylogenetic approach which combines both trees and graphs (networks), reliable species phylogeny, genomic data for more than two species, and an insight into the processes of molecular evolution. Also proposed is a set of guidelines to aid researchers in selecting the correct tool. Moreover, this review motivates further research in developing reliable and scalable methods for functional and phylogenetic classification of large protein collections. Chapter 3 proposes a framework in which various protein knowledge-bases are combined into unique network of mappings (links), and hence allows comparisons to be made between expert curated and fully-automated protein classifications from a single entry point. We developed an integrated annotation resource for protein orthology, ProGMap (Protein Group Mappings, http://www.bioinformatics.nl/progmap), to help researchers and database annotators who often need to assess the coherence of proposed annotations and/or group assignments, as well as users of high throughput methodologies (e.g., microarrays or proteomics) who deal with partially annotated genomic data. ProGMap is based on a non-redundant dataset of over 6.6 million protein sequences which is mapped to 240,000 protein group descriptions collected from UniProt, RefSeq, Ensembl, COG, KOG, OrthoMCL-DB, HomoloGene, TRIBES and PIRSF using a fast and fully automated sequence-based mapping approach. The ProGMap database is equipped with a web interface that enables queries to be made using synonymous sequence identifiers, gene symbols, protein functions, and amino acid or nucleotide sequences. It incorporates also services, namely BLAST similarity search and QuickMatch identity search, for finding sequences similar (or identical) to a query sequence, and tools for presenting the results in graphic form. Graphs (networks) have gained an increasing attention in contemporary biology because they have enabled complex biological systems and processes to be modeled and better understood. For example, protein similarity networks constructed of all-versus-all sequence comparisons are frequently used to delineate similarity groups, such as protein families or orthologous groups in comparative genomics studies. Chapter 4.1 presents a benchmark study of freely available graph software used for this purpose. Specifically, the computational complexity of the programs is investigated using both simulated and biological networks. We show that most available software is not suitable for large networks, such as those encountered in large-scale proteome analyzes, because of the high demands on computational resources. To address this, we developed a fast and memory-efficient graph software, netclust (http://www.bioinformatics.nl/netclust/), which can scale to large protein networks, such as those constructed of millions of proteins and sequence similarities, on a standard computer. An extended version of this program called Multi-netclust is presented in chapter 4.2. This tool that can find connected clusters of data presented by different network data sets. It uses user-defined threshold values to combine the data sets in such a way that clusters connected in all or in either of the networks can be retrieved efficiently. Automated protein sequence clustering is an important task in genome annotation projects and phylogenomic studies. During the past years, several protein clustering programs have been developed for delineating protein families or orthologous groups from large sequence collections. However, most of these programs have not been benchmarked systematically, in particular with respect to the trade-off between computational complexity and biological soundness. In chapter 5 we evaluate three best known algorithms on different protein similarity networks and validation (or 'gold' standard) data sets to find out which one can scale to hundreds of proteomes and still delineate high quality similarity groups at the minimum computational cost. For this, a reliable partition-based approach was used to assess the biological soundness of predicted groups using known protein functions, manually curated protein/domain families and orthologous groups available in expert-curated databases. Our benchmark results support the view that a simple and computationally cheap method such as netclust can perform similar to and in cases even better than more sophisticated, yet much more costly methods. Moreover, we introduce an efficient graph-based method that can delineate protein orthologs of hundreds of proteomes into hierarchical similarity groups de novo. The validity of this method is demonstrated on data obtained from 347 prokaryotic proteomes. The resulting hierarchical protein classification is not only in agreement with manually curated classifications but also provides an enriched framework in which the functional and evolutionary relationships between proteins can be studied at various levels of specificity. Finally, in chapter 6 we summarize the main findings and discuss the merits and shortcomings of the methods developed herein. We also propose directions for future research. The ever increasing flood of new sequence data makes it clear that we need improved tools to be able to handle and extract relevant (orthological) information from these protein data. This thesis summarizes these needs and how they can be addressed by the available tools, or be improved by the new tools that were developed in the course of this research. <br/

Scaling Similarity Joins over Tree-Structured Data

Given a large collection of tree-structured objects (e.g., XML documents), the similarity join finds the pairs of objects that are similar to each other, based on a similarity threshold and a tree edit distance measure. The state-of-the-art similarity join methods compare simpler approximations of the objects (e.g., strings), in order to prune pairs that cannot be part of the similarity join result based on distance bounds derived by the approximations. In this paper, we propose a novel similarity join approach, which is based on the dynamic decomposition of the tree objects into subgraphs, according to the similarity threshold. Our technique avoids computing the exact distance between two tree objects, if the objects do not share at least one common subgraph. In order to scale up the join, the computed subgraphs are managed in a two-layer index. Our experimental results on real and synthetic data collections show that our approach outperforms the state-of-the-art methods by up to an order of magnitude.published_or_final_versio

Declarative Querying For Biological Sequences.

Life science research labs today manage increasing volumes of sequence data. Much of the data management and querying today is accomplished procedurally using Perl, Python, or Java programs that integrate data from different sources and query tools. The dangers of this procedural approach are well known to the database community-- a) severe limitations on the ability to rapidly express queries and b) inefficient query plans due to the lack of sophisticated optimization tools. This situation is likely to get worse with advances in high-throughput technologies that make it easier to quickly produce vast amounts of sequence data. The need for a declarative and efficient system to manage and query biological sequence data is urgent. To address this need, we designed the Periscope/SQ system. Periscope/SQ extends current relational systems to enable sophisticated queries on sequence data and can optimize and execute these queries efficiently. This thesis describes the problems that need to be solved to make it possible to build the Periscope/SQ system. First, we describe the algebraic framework which forms the backbone of Periscope/SQ. Second, we describe algorithms to construct large scale suffix tree indexes for efficiently answering sequence queries. Third, we describe techniques for selectivity estimation and optimization in the context of queries over biological sequences. Next, we demonstrate how some of the techniques developed for Periscope/SQ can be applied to produce a powerful mining algorithm that we call FLAME. Finally, we describe GeneFinder, a biological application built on top of Periscope/SQ. GeneFinder is currently being used to predict the targets of transcription factors. Today, genomic and proteomic sequences are the most abundantly available source of high-quality biological data. By making it possible to declaratively and efficiently query vast amount of sequence data, Periscope/SQ opens the door to vast improvements in the pace of bioinformatics research.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/55670/2/tatas_1.pd

Advances in dissimilarity-based data visualisation

Gisbrecht A. Advances in dissimilarity-based data visualisation. Bielefeld: Universitätsbibliothek Bielefeld; 2015

Predicting Flavonoid UGT Regioselectivity with Graphical Residue Models and Machine Learning.

Machine learning is applied to a challenging and biologically significant protein classification problem: the prediction of flavonoid UGT acceptor regioselectivity from primary protein sequence. Novel indices characterizing graphical models of protein residues are introduced. The indices are compared with existing amino acid indices and found to cluster residues appropriately. A variety of models employing the indices are then investigated by examining their performance when analyzed using nearest neighbor, support vector machine, and Bayesian neural network classifiers. Improvements over nearest neighbor classifications relying on standard alignment similarity scores are reported

A dynamical model of the distributed interaction of intracellular signals

A major goal of modern cell biology is to understand the regulation of cell behavior in the reductive terms of all the molecular interactions. This aim is made explicit by the assertion that understanding a cell\u27s response to stimuli requires a full inventory of details. Currently, no satisfactory explanation exists to explain why cells exhibit only a relatively small number of different behavioral modes. In this thesis, a discrete dynamical model is developed to study interactions between certain types of signaling proteins. The model is generic and connectionist in nature and incorporates important concepts from the biology. The emphasis is on examining dynamic properties that occur on short-term time scales and are independent of gene expression. A number of modeling assumptions are made. However, the framework is flexible enough to be extended in future studies. The dynamical states of the system are explored both computationally and analytically. Monte Carlo methods are used to study the state space of simulated networks over selected parameter regimes. Networks show a tendency to settle into fixed points or oscillations over a wide range of initial conditions. A genetic algorithm (GA) is also designed to explore properties of networks. It evolves a population of modeled cells, selecting and ranking them according to a fitness function, which is designed to mimic features of real biological evolution. An analogue of protein domain shuffling is used as the crossover operator and cells are reproduced asexually. The effects of changing the parameters of the GA are explored. A clustering algorithm is developed to test the effectiveness of the GA search at generating cells, which display a limited number of different behavioral modes. Stability properties of equilibrium states in small networks are analyzed. The ability to generalize these techniques to larger networks is discussed. Topological properties of networks generated by the GA are examined. Structural properties of networks are used to provide insight into their dynamic properties. The dynamic attractors exhibited by such signaling networks may provide a framework for understanding why cells persist in only a small number of stable behavioral modes