Browsing by Subject "Bioinformatics"

Now showing 1 - 20 of 28

Open Access
AirLift: a fast and comprehensive technique for remapping alignments between reference genomes
(IEEE, 2024-08-19) Kim, Jeremie S.; Firtina, Can; Cavlak, Meryem Banu; Çalı, Damla Şenol; Hajinazar, Nastaran; Alser, Mohammed; Alkan, Can; Mutlu, Onur
AirLift is the first read remapping tool that enables users to quickly and comprehensively map a read set, that had been previously mapped to one reference genome, to another similar reference. Users can then quickly run a downstream analysis of read sets for each latest reference release. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces the overall execution time to remap read sets between two reference genome versions by up to 27.4×. We validate our remapping results with GATK and find that AirLift provides high accuracy in identifying ground truth SNP/INDEL variants
Open Access
Algorithms for effective querying of compound graph-based pathway databases
(BioMed Central Ltd., 2009-11-16) Doğrusöz, Uğur; Çetintaş, Ahmet; Demir, Emek; Babur, Özgün
Background: Graph-based pathway ontologies and databases are widely used to represent data about cellular processes. This representation makes it possible to programmatically integrate cellular networks and to investigate them using the well-understood concepts of graph theory in order to predict their structural and dynamic properties. An extension of this graph representation, namely hierarchically structured or compound graphs, in which a member of a biological network may recursively contain a sub-network of a somehow logically similar group of biological objects, provides many additional benefits for analysis of biological pathways, including reduction of complexity by decomposition into distinct components or modules. In this regard, it is essential to effectively query such integrated large compound networks to extract the sub-networks of interest with the help of efficient algorithms and software tools. Results: Towards this goal, we developed a querying framework, along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, that is applicable to all sorts of graph-based pathway databases, from PPIs (protein-protein interactions) to metabolic and signaling pathways. The framework is unique in that it can account for compound or nested structures and ubiquitous entities present in the pathway data. In addition, the queries may be related to each other through "AND" and "OR" operators, and can be recursively organized into a tree, in which the result of one query might be a source and/or target for another, to form more complex queries. The algorithms were implemented within the querying component of a new version of the software tool PATIKAweb (Pathway Analysis Tool for Integration and Knowledge Acquisition) and have proven useful for answering a number of biologically significant questions for large graph-based pathway databases. Conclusion: The PATIKA Project Web site is http://www.patika.org. PATIKAweb version 2.1 is available at http://web.patika.org. © 2009 Dogrusoz et al; licensee BioMed Central Ltd.
Open Access
Analyzing causal relationships in proteomic profiles using CausalPath
(Cell Press, 2021-12-17) Luna, A.; Siper, M. C.; Korkut, A.; Durupinar, F.; Aslan, J. E.; Sander, C.; Demir, E.; Babur, O.; Doğrusöz, Uğur
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset.
Open Access
Building and improving reference genome assemblies
(IEEE, 2017-01) Steinberg, K. M.; Schneider, V. A.; Alkan, Can; Montague, M. J.; Warren, W. C.; Church, D. M.; Wilson, R. K.
A genome sequence assembly provides the foundation for studies of genotypic and phenotypic variation, genome structure, and evolution of the target organism. In the past four decades, there has been a surge of new sequencing technologies, and with these developments, computational scientists have developed new algorithms to improve genome assembly. Here we discuss the relationship between sequencing technology improvements and assembly algorithm development and how these are applied to extend and improve human and nonhuman genome assemblies. © 1963-2012 IEEE.
Open Access
Can you really anonymize the donors of genomic data in today’s digital world?
(Springer, 2016-09) Alser, Mohammed; Almadhoun, Nour; Nouri, Azita; Alkan, Can; Ayday, Erman
The rapid progress in genome sequencing technologies leads to availability of high amounts of genomic data. Accelerating the pace of biomedical breakthroughs and discoveries necessitates not only collecting millions of genetic samples but also granting open access to genetic databases. However, one growing concern is the ability to protect the privacy of sensitive information and its owner. In this work, we survey a wide spectrum of cross-layer privacy breaching strategies to human genomic data (using both public genomic databases and other public non-genomic data). We outline the principles and outcomes of each technique, and assess its technological complexity and maturation. We then review potential privacy-preserving countermeasure mechanisms for each threat. © Springer International Publishing Switzerland 2016.
Open Access
Characterization of local SARS-CoV-2 isolates and pathogenicity in IFNAR−/- mice
(Elsevier, 2020) Hanifehnezhad, A.; Kehribar, Ebru Şahin; Öztop, S.; Sheraz, Ali; Kasırga, Serkan; Ergünay, K.; Önder, S.; Yılmaz, E.; Engin, D.; Oğuzoğlu, T. Ç.; Şeker, Urartu Özgür Şafak; Yılmaz, E.; Özkul, A.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recently a global pandemic with unprecedented public health, economic and social impact. The development of effective mitigation strategies, therapeutics and vaccines relies on detailed genomic and biological characterization of the regional viruses. This study was carried out to isolate SARS-CoV-2 viruses circulating in Anatolia, and to investigate virus propagation in frequently-used cells and experimental animals. We obtained two SARS-CoV-2 viruses from nasopharngeal swabs of confirmed cases in Vero E6 cells, visualized the virions using atomic force and scanning electron microscopy and determined size distribution of the particles. Viral cytopathic effects on Vero E6 cells were initially observed at 72 h post-inoculation and reached 90% of the cells on the 5th day. The isolates displayed with similar infectivity titers, time course and infectious progeny yields. Genome sequencing revealed the viruses to be well-conserved, with less than 1% diversity compared to the prototype virus. The analysis of the viral genomes, along with the available 62 complete genomes from Anatolia, showed limited diversity (up to 0.2% on deduced amino acids) and no evidence of recombination. The most prominent sequence variation was observed on the spike protein, resulting in the substitution D614G, with a prevalence of 56.2%. The isolates produced non-fatal infection in the transgenic type I interferon knockout (IFNAR−/-) mice, with varying neutralizing antibody titers. Hyperemia, regional consolidation and subpleural air accumulation was observed on necropsy, with similar histopathological and immunohistochemistry findings in the lungs, heart, stomach, intestines, liver, spleen and kidneys. Peak viral loads were detected in the lungs, with virus RNA present in the kidneys, jejunum, liver, spleen and heart. In conclusion, we characterized two local isolates, investigated in vitro growth dynamics in Vero E6 cells and identified IFNAR−/− mice as a potential animal model for SARS-CoV-2 experiments.
Open Access
Examining the annealing schedules for RNA design algorithm
(IEEE, 2016-07) Erhan, H. E.; Sav, Sinem; Kalashnikov, S.; Tsang, H. H.
RNA structures are important for many biological processes in the cell. One important function of RNA are as catalytic elements. Ribozymes are RNA sequences that fold to form active structures that catalyze important chemical reactions. The folded structure for these RNA are very important; only specific conformations maintain these active structures, so it is very important for RNA to fold in a specific way. The RNA design problem describes the prediction of an RNA sequence that will fold into a given RNA structure. Solving this problem allows researchers to design RNA; they can decide on what folded secondary structure is required to accomplish a task, and the algorithm will give them a primary sequence to assemble. However, there are far too many possible primary sequence combinations to test sequentially to see if they would fold into the structure. Therefore we must employ heuristics algorithms to attempt to solve this problem. This paper introduces SIMARD, an evolutionary algorithm that uses an optimization technique called simulated annealing to solve the RNA design problem. We analyzes three different cooling schedules for the annealing process: 1) An adaptive cooling schedule, 2) a geometric cooling schedule, and 3) a geometric cooling schedule with warm up. Our results show that an adaptive annealing schedule may not be more effective at minimizing the Hamming distance between the target structure and our folded sequence's structure when compared with geometric schedules. The results also show that warming up in a geometric cooling schedule may be useful for optimizing SIMARD. © 2016 IEEE.
Open Access
A framework for management of multiple views of cellular pathway graphs
(2003) Güleşir, Gürcan
The enhancements in genomic studies have given birth to the necessity of advanced techniques for storing, integrating and analyzing the accumulated data regarding molecular level cellular processes. Since this data is huge and complex, advanced visualization and complexity management techniques need to be developed to improve its understandability. In this thesis, we present a single subject - multiple view framework for manipulating complex pathway data, which is in the form of a directed graph. The framework facilitates visualization of potentially huge pathway data in possibly varying forms and sizes. While maintaining the subject data (i.e., pathway graph) and its views, the presented framework coordinates all the views using an observer software pattern. It ensures the validity and consistency of subject data across all views. Support for replication of biological data, which is desired to reduce complexity (i.e., high degree ubique pathway objects), is another benefit of our framework. Being a neatly modularized, isolated component of a functional pathway editor, this framework is distinguished from any other single subject - multiple view graph editing environment by addressing the domain specific needs of pathway informatics.
Open Access
GenASM: a high-performance, low-power approximate string matching acceleration framework for genome sequence analysis
(IEEE Computer Society, 2020) Şenol-Çalı, D.; Kalsi, G. S.; Bingöl, Zülal; Fırtına, C.; Subramanian, L.; Kim, J. S.; Ausavarungnirun, R.; Alser, M.; Gomez-Luna, J.; Boroumand, A.; Norion, A.; Scibisz, A.; Subramoneyon, S.; Alkan, Can; Ghose, S.; Mutlu, Onur
Genome sequence analysis has enabled significant advancements in medical and scientific areas such as personalized medicine, outbreak tracing, and the understanding of evolution. To perform genome sequencing, devices extract small random fragments of an organism's DNA sequence (known as reads). The first step of genome sequence analysis is a computational process known as read mapping. In read mapping, each fragment is matched to its potential location in the reference genome with the goal of identifying the original location of each read in the genome. Unfortunately, rapid genome sequencing is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data. A major contributor to this bottleneck is approximate string matching (ASM), which is used at multiple points during the mapping process. ASM enables read mapping to account for sequencing errors and genetic variations in the reads. We propose GenASM, the first ASM acceleration framework for genome sequence analysis. GenASM performs bitvectorbased ASM, which can efficiently accelerate multiple steps of genome sequence analysis. We modify the underlying ASM algorithm (Bitap) to significantly increase its parallelism and reduce its memory footprint. Using this modified algorithm, we design the first hardware accelerator for Bitap. Our hardware accelerator consists of specialized systolic-array-based compute units and on-chip SRAMs that are designed to match the rate of computation with memory capacity and bandwidth, resulting in an efficient design whose performance scales linearly as we increase the number of compute units working in parallel. We demonstrate that GenASM provides significant performance and power benefits for three different use cases in genome sequence analysis. First, GenASM accelerates read alignment for both long reads and short reads. For long reads, GenASM outperforms state-of-the-art software and hardware accelerators by 116× and 3.9×, respectively, while reducing power consumption by 37× and 2.7×. For short reads, GenASM outperforms state-of-the-art software and hardware accelerators by 111× and 1.9×. Second, GenASM accelerates pre-alignment filtering for short reads, with 3.7× the performance of a state-of-the-art pre-alignment filter, while reducing power consumption by 1.7× and significantly improving the filtering accuracy. Third, GenASM accelerates edit distance calculation, with 22-12501× and 9.3-400× speedups over the state-of-the-art software library and FPGA-based accelerator, respectively, while reducing power consumption by 548-582× and 67×. We conclude that GenASM is a flexible, high-performance, and low-power framework, and we briefly discuss four other use cases that can benefit from GenASM.
Open Access
Improving genome assemblies using multi-platform sequence data
(Springer, 2015-09) Kavak, P.; Ergüner, B.; Üstek, D.; Yüksel, B.; Saǧıroǧlu, M. Ş.; Güngör, T.; Alkan, Can
Accurate de novo assembly using short reads generated by next generation sequencing technologies is still an open problem. Although there are several assembly algorithms developed for data generated with different sequencing technologies, and some that can make use of hybrid data, the assemblies are still far from being perfect. There is still a need for computational approaches to improve draft assemblies. Here we propose a new method to correct assembly mistakes when there are multiple types of data generated using different sequencing technologies that have different strengths and biases. We exploit the assembly of highly accurate short reads to correct the contigs obtained from less accurate long reads. We apply our method to Illumina, 454, and Ion Torrent data, and also compare our results with existing hybrid assemblers, Celera and Masurca. © Springer International Publishing Switzerland 2016.
Open Access
Integrating biological pathways and genomic profiles with ChiBE 2
(2013) Çakır, Merve
Biological pathways store information about spatial and temporal organization of interactions taking place in an organism. They hold valuable information that can assist scientific community in understanding the details of a particular mechanism or deciphering the reasons of disruption when the system goes wrong. However, extracting knowledge from these pathways is not trivial as they can be huge and complicated. Additionally, simple visualization of pathways will only reveal limited knowledge, whereas their integration with experimental results can identify distinct and intriguing relationships. Therefore, it is critical to have tools that are specialized in analyzing and understanding biological pathways. ChiBE is one such tool that can visualize, manipulate and analyze pathway data stored in BioPAX format. While preparing the second version of the tool, there have been improvements regarding pathway searches, high throughput data integration, and database connections. Visual notation has also been updated in order to follow standards in visualizations defined by the SBGN community. Previously defined pathway query algorithms have been adapted to be compatible with the BioPAX model. New query types have also been designed to offer a wider range of options. With these queries, ChiBE now offers a variety of ways of pathway decomposition and thorough analysis of complex pathway views. There has also been improvements in integration of high throughput experimental results. To offer easy access to expression microarrays, a gateway to the GEO database has been added. The cBio Cancer Genomics Portal is also now reachable within ChiBE in order to obtain information about genomic status of various cancer cells. After simply asking for an identifier of a particular experiment, ChiBE retrieves the results from databases and then integrates them with the available pathway view through color codes. Furthermore, a connection to DAVID database is available, in case users want to annotate a list of genes with respect to biological terms associated with them. With these new features and improvements, ChiBE 2 has become a comprehensive tool that offers a wide range of analysis options with a genomics-oriented workflow to deepen our understanding of biological pathways.
Open Access
Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal
(American Association for the Advancement of Science (A A A S), 2013) Gao J.; Aksoy, B. A.; Dogrusoz, U.; Dresdner, G.; Gross, B.; Sumer, S. O.; Sun, Y.; Jacobsen, A.; Sinha, R.; Larsson, E.; Cerami, E.; Sander, C.; Schultz, N.
The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics. © 2013 American Association for the Advancement of Science.
Open Access
Investigation of multi-objective optimization criteria for RNA design
(IEEE, 2017-12) Hampson, D. J. D.; Sav, Sinem; Tsang, H. H.
RNA design is the inverse of RNA folding and it appears to be NP-hard. In RNA design, a secondary structure is given and the goal is to find a nucleotide sequence that will fold into this structure. To find such sequence(s) involves exploring the exponentially large sequence space. In literature, heuristic algorithms are the standard technique for tackling the RNA design. Heuristic algorithms enable effective and efficient exploration of the high-dimensional sequence-structure space when searching for candidates that fold into a given target structure. The main goal of this paper is to investigate the use of multi-objective criteria in SIMARD and Quality Pre-selection Strategy (QPS). The objectives that we optimize are Hamming distance (between designed structure and target structure) and thermodynamic free energy. We examine the different combinations of optimization criteria, and attempt to draw conclusions about the relationships between them. We find that energy is a poor primary objective but makes an excellent secondary objective. We also find that using multi-objective pre-selection produces viable solutions in far fewer steps than was previously possible with SIMARD. © 2016 IEEE.
Open Access
k-Shell decomposition reveals structural properties of the gene coexpression network for neurodevelopment
(TÜBİTAK, 2017) Çiçek, A. Ercüment
Neurodevelopment is a dynamic and complex process, which involves interactions of thousands of genes. Understanding the mechanisms of brain development is important for uncovering the genetic architectures of neurodevelopmental disorders such as autism spectrum disorder and intellectual disability. The BrainSpan dataset is an important resource for studying the transcriptional mechanisms governing neurodevelopment. It contains RNA-seq and microarray data for 13 developmental periods in 8-16 brain regions. Various important studies used this dataset, in particular to generate gene coexpression networks. The topology of the BrainSpan gene coexpression network yielded various important gene clusters, which are found to play key roles in diseases. In this work, we analyze the topology of the BrainSpan gene coexpression network using the k-shell decomposition method. k-Shell decomposition is an unsupervised method to decompose a network into layers (shells) using the connectivity information and to detect a nucleus that is central to overall connectivity. Our results show that there are 267 layers in the BrainSpan gene coexpression network. The nucleus contains 2584 genes, which are related to chromatin modification function. We compared and contrasted the structure with the autonomous system level Internet. We found that despite similarities in percolation transition and crust size distribution, there are also differences: the BrainSpan coexpression network has a significantly large nucleus and only a very small number of genes need to access the nucleus first, to be able to connect to other genes in the crust above the nucleus. © TÜBİTAK.
Open Access
A layout algorithm for signaling pathways
(Elsevier, 2006-01-20) Genç, Burkay; Doğrusöz, Uğur
Visualization is crucial to the effective analysis of biological pathways. A poorly laid out pathway confuses the user, while a well laid out one improves the user's comprehension of the underlying biological phenomenon. We present a new, elegant algorithm for layout of biological signaling pathways. Our algorithm uses a force-directed layout scheme, taking into account directional and rectangular regional constraints enforced by different molecular interaction types and subcellular locations in a cell. The algorithm has been successfully implemented as part of a pathway visualization and analysis toolkit named Patika, and results with respect to computational complexity and quality of the layout have been found satisfactory. The algorithm may be easily adapted to be used in other applications with similar conventions and constraints as well. Patika version 1.0 beta is available upon request at http://www.patika.org. © 2004 Elsevier Inc. All rights reserved.
Open Access
A layout algorithm for undirected compound graphs
(Elsevier, 2009-03-15) Doğrusöz, Uğur; Giral, Erhan; Çetintaş, Ahmet; Civril, Ali; Demir, Emek
We present an algorithm for the layout of undirected compound graphs, relaxing restrictions of previously known algorithms in regards to topology and geometry. The algorithm is based on the traditional force-directed layout scheme with extensions to handle multi-level nesting, edges between nodes of arbitrary nesting levels, varying node sizes, and other possible application-specific constraints. Experimental results show that the execution time and quality of the produced drawings with respect to commonly accepted layout criteria are quite satisfactory. The algorithm has also been successfully implemented as part of a pathway integration and analysis toolkit named PATIKA, for drawing complicated biological pathways with compartmental constraints and arbitrary nesting relations to represent molecular complexes and various types of pathway abstractions. © 2008 Elsevier Inc. All rights reserved.
Open Access
Methods and tools for visualization and management of SBGN process description maps
(2014) Sarı, Mecit
Graphs are commonly used to model relational information in many areas such as relational databases, software engineering, biological and social networks. In visualization of graphs, automatic layout, interactive editing and complexity management of crowded graphs are essential for effective utilization of underlying information. Advances in graphical user interfaces have given rise and value to interactive editing and diagramming techniques in graph visualization. As the size of the information to be visualized vastly increased, it became harder to analyze such networks, making use of relational information needed to be acquired. To overcome this problem, sophisticated and domain-specific complexity management techniques should be provided. The Systems Biology Graphical Notation (SBGN) has been developed over a number of years by biochemists and computer scientists to standardize visual representation of biochemical and cellular processes. SBGN introduces a concrete, detailed set of symbols for scientists to represent network of interactions, in a way that is not open to more than one interpretation. It also describes the manner, in which such graphical information should be interpreted. The SBGN Process Description (PD) language shows how entities are influenced by processes, which are represented by several reaction types in a biological pathway. It can be used to show all the molecular interactions taking place in a network of biochemical entities, with the same entity appearing multiple times in the same diagram. We developed methods and tools to effectively visualize and manage SBGNPD diagrams. Specifically, we introduced new algorithms for proper management of complexity of large SBGN-PD diagrams. These algorithms strive to keep SBGN-PD diagrams intact as complexity management takes places. In addition, we provided software components and web-based tools that implement these methods. These tools use state-of-the-art web technologies and libraries.
Open Access
An ontology for computer-aided modeling of cellular processes
(2005-10) Emek, Demir
Cellular processes form the hardware layer of living organisms. Malfunctions in cellular processes are responsible for most of the currently incurable diseases. Not surprisingly, knowledge about cellular processes are growing at an enormous rate. However, today’s molecular biology suffers from lack of a formal representation system for cellular processes. Most of the knowledge is locked in literature, that are not accessible to computational analysis and modeling. Given the complexity of the system we are attacking, the need for a representation system and modeling tools for cellular processes are clear. In this dissertation, we describe an ontology for modeling processes. Our ontology possesses several unique features, including ability to represent abstractions and multiple levels of detail, cellular compartments and molecular states. Furthermore, it was designed to meet several user and system requirements, including ease of integration, querying, analysis and visualization. Based on this ontology we also implemented a set of software tools within the Patika project. Primary use cases of Patika are integration, querying and visualization, and we have obtained satisfactory results proving the feasibility of our ontology. Compared with existing alternative methods of representing and querying information about cellular processes, Patika provides several advantages, including a regular representation system, powerful querying options, an advanced visualization. Moreover Patika models can be analyzed by computational methods such as flux analysis or pathway activity inference. Although it has a more steep learning curve compared to existing ad hoc representation systems, we believe that tools like Patika will be essential for molecular biology research in the future.
Open Access
Patika Web : a Web service for accessing and visualizing pathway data in patika database
(2005) Erson, Emine Zeynep
After completion of Human Genome Project, there has been an exponential increase in the available biological data. Although there has been an enormous effort for creating ontologies, standards and tools, current bioinformatics infrastructure is far from coping with this data. The Patika Project aims to provide the community an integrated environment for modeling, analyzing and integrating cellular processes. Patika project develops software tools providing access, visualization and analysis on the data in Patika database. In this thesis, we present analysis, design and implementation of Patikaweb, a Web-service having a user-friendly interface without requiring any registrations, installations. To achieve an enhanced data analysis , Patikaweb provides a multiple-view schema , compartments and compound graphs for visualizing molecular complexes, pathways and black-box reactions. Querying component supports SQL-like queries and an array of graphtheoretic queries for finding feedback loops, common targets and regulators, or interesting subgraphs based on user’s genes of interest. Constructed models can be saved in XML, exported to standard formats such as BioPAX, SBML or converted to static images. A highly interactive and user friendly querying interface is supported with Patikaweb. Visual representation of complex information in pathway research is very important. The information should be presented with high coverage, while providing a user friendly interface. In this thesis we also present a new approach to visualize complex pathway information coping with the limitations introduced by ontology and graphical representation. Patikaweb ’s unique visualization and querying features fill an important gap in the pool of currently available tools and databases.
Open Access
PATIKAmad: putting microarray data into pathway context
(Wiley - V C H Verlag GmbH & Co. KGaA, 2008-06) Babur, Özgün; Colak, Recep; Demir, Emek; Doğrusöz, Uğur
High-throughput experiments, most significantly DNA microarrays, provide us with system-scale profiles. Connecting these data with existing biological networks poses a formidable challenge to uncover facts about a cell's proteome. Studies and tools with this purpose are limited to networks with simple structure, such as protein-protein interaction graphs, or do not go much beyond than simply displaying values on the network. We have built a microarray data analysis tool, named PATIKAmad, which can be used to associate microarray data with the pathway models in mechanistic detail, and provides facilities for visualization, clustering, querying, and navigation of biological graphs related with loaded microarray experiments. PATIKAmad is freely available to noncommercial users as a new module of PATIKAweb at http://web.patika.org. © 2008 Wiley-VCH Verlag GmbH & Co. KGaA.