Bioinformatics RSS : Gourt

Bioimage informatics: a new area of engineering biology

In recent years, the deluge of complicated molecular and cellular microscopic images creates compelling challenges for the image computing community. There has been an increasing focus on developing novel image processing, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems. This emerging new area of bioinformatics can be called ‘bioimage informatics’. This article reviews the advances of this field from several aspects, including applications, key techniques, available tools and resources. Application examples such as high-throughput/high-content phenotyping and atlas building for model organisms demonstrate the importance of bioimage informatics. The essential techniques to the success of these applications, such as bioimage feature identification, segmentation and tracking, registration, annotation, mining, image data management and visualization, are further summarized, along with a brief overview of the available bioimage databases, analysis tools and other resources. Contact: pengh@janelia.hhmi.org Supplementary information: Supplementary data are available at Bioinformatics online.

PRIMEGENS-v2: genome-wide primer design for analyzing DNA methylation patterns of CpG islands

Motivation: DNA methylation plays important roles in biological processes and human diseases, especially cancers. High-throughput bisulfite genomic sequencing based on new generation of sequencers, such as the 454-sequencing system provides an efficient method for analyzing DNA methylation patterns. The successful implementation of this approach depends on the use of primer design software capable of performing genome-wide scan for optimal primers from in silico bisulfite-treated genome sequences. We have developed a method, which fulfills this requirement and conduct primer design for sequences including regions of given promoter CpG islands. Results: The developed method has been implemented using the C and JAVA programming languages. The primer design results were tested in the PCR experiments of 96 selected human DNA sequences containing CpG islands in the promoter regions. The results indicate that this method is efficient and reliable for designing sequence-specific primers. Availability: The sequence-specific primer design for DNA meth-ylated sequences including CpG islands has been integrated into the second version of PRIMEGENS as one of the primer design features. The software is freely available for academic use at http://digbio.missouri.edu/primegens/. Contact: xudong@missouri.edu

Discovering regulatory motifs in the Plasmodium genome using comparative genomics

Motivation: Understanding gene regulation in Plasmodium, the causative agent of malaria, is an important step in deciphering its complex life cycle as well as leading to possible new targets for therapeutic applications. Very little is known about gene regulation in Plasmodium, and in particular, few regulatory elements have been identified. Such discovery has been significantly hampered by the high A-T content of some of the genomes of Plasmodium species, as well as the challenge in associating discovered regulatory elements to gene regulatory cascades due to Plasmodium's complex life cycle. Results: We report a new method of using comparative genomics to systematically discover motifs in Plasmodium without requiring any functional data. Different from previous methods, our method does not depend on sequence alignments, and thus is particularly suitable for highly divergent genomes. We applied our method to discovering regulatory motifs between the human parasite, P.falciparum, and its rodent-infectious relative, P.yoelii. We also tested our procedure against comparisons between P.falciparum and the primate-infectious, P.knowlesi. Our computational effort leads to an initial catalog of 38 distinct motifs, corresponding to over 16 200 sites in the Plasmodium genome. The functionality of these motifs was further supported by their defined distribution within the genome as well as a correlation with gene expression patterns. This initial map provides a systematic view of gene regulation in Plasmodium, which can be refined as additional genomes become available. Availability: The new algorithm, named motif discovery using orthologous sequences (MDOS), is available at http://www.ics.uci.edu/~xhx/project/mdos/. Contact: xhx@ics.uci.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Context-dependent DNA recognition code for C2H2 zinc-finger transcription factors

Motivation: Modeling and identifying the DNA-protein recognition code is one of the most challenging problems in computational biology. Several quantitative methods have been developed to model DNA-protein interactions with specific focus on the C2H2 zinc-finger proteins, the largest transcription factor family in eukaryotic genomes. In many cases, they performed well. But the overall the predictive accuracy of these methods is still limited. One of the major reasons is all these methods used weight matrix models to represent DNA-protein interactions, assuming all base-amino acid contacts contribute independently to the total free energy of binding. Results: We present a context-dependent model for DNA–zinc-finger protein interactions that allows us to identify inter-positional dependencies in the DNA recognition code for C2H2 zinc-finger proteins. The degree of non-independence was detected by comparing the linear perceptron model with the non-linear neural net (NN) model for their predictions of DNA–zinc-finger protein interactions. This dependency is supported by the complex base-amino acid contacts observed in DNA–zinc-finger interactions from structural analyses. Using extensive published qualitative and quantitative experimental data, we demonstrated that the context-dependent model developed in this study can significantly improves predictions of DNA binding profiles and free energies of binding for both individual zinc fingers and proteins with multiple zinc fingers when comparing to previous positional-independent models. This approach can be extended to other protein families with complex base-amino acid residue interactions that would help to further understand the transcriptional regulation in eukaryotic genomes. Availability:The software implemented as c programs and are available by request. http://ural.wustl.edu/softwares.html Contact: stormo@ural.wustl.edu

Artificial neural network for prediction of antigenic activity for a major conformational epitope in the hepatitis C virus NS3 protein

Motivation: Insufficient knowledge of general principles for accurate quantitative inference of biological properties from sequences is a major obstacle in the rationale design of proteins with predetermined activities. Due to this deficiency, protein engineering frequently relies on the use of computational approaches focused on the identification of quantitative structure–activity relationship (SAR) for each specific task. In the current article, a computational model was developed to define SAR for a major conformational antigenic epitope of the hepatitis C virus (HCV) non-structural protein 3 (NS3) in order to facilitate a rationale design of HCV antigens with improved diagnostically relevant properties. Results: We present an artificial neural network (ANN) model that connects changes in the antigenic properties and structure of HCV NS3 recombinant proteins representing all 6 HCV genotypes. The ANN performed quantitative predictions of the enzyme immunoassay (EIA) Signal/Cutoff (S/Co) profiles from sequence information alone with 89.8% accuracy. Amino acid positions and physicochemical factors strongly associated with the HCV NS3 antigenic properties were identified. The positions most significantly contributing to the model were mapped on the NS3 3D structure. The location of these positions validates the major associations found by the ANN model between antigenicity and structure of the HCV NS3 proteins. Availability: Matlab code is available at the following URL address: http://bio-ai.myeweb.net/box_widget.html Contact: jlara@cdc.gov; yek0@cdc.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Accelerating and focusing protein-protein docking correlations using multi-dimensional rotational FFT generating functions

Motivation: Predicting how proteins interact at the molecular level is a computationally intensive task. Many protein docking algorithms begin by using fast Fourier transform (FFT) correlation techniques to find putative rigid body docking orientations. Most such approaches use 3D Cartesian grids and are therefore limited to computing three dimensional (3D) translational correlations. However, translational FFTs can speed up the calculation in only three of the six rigid body degrees of freedom, and they cannot easily incorporate prior knowledge about a complex to focus and hence further accelerate the calculation. Furthemore, several groups have developed multi-term interaction potentials and others use multi-copy approaches to simulate protein flexibility, which both add to the computational cost of FFT-based docking algorithms. Hence there is a need to develop more powerful and more versatile FFT docking techniques. Results: This article presents a closed-form 6D spherical polar Fourier correlation expression from which arbitrary multi-dimensional multi-property multi-resolution FFT correlations may be generated. The approach is demonstrated by calculating 1D, 3D and 5D rotational correlations of 3D shape and electrostatic expansions up to polynomial order L=30 on a 2 GB personal computer. As expected, 3D correlations are found to be considerably faster than 1D correlations but, surprisingly, 5D correlations are often slower than 3D correlations. Nonetheless, we show that 5D correlations will be advantageous when calculating multi-term knowledge-based interaction potentials. When docking the 84 complexes of the Protein Docking Benchmark, blind 3D shape plus electrostatic correlations take around 30 minutes on a contemporary personal computer and find acceptable solutions within the top 20 in 16 cases. Applying a simple angular constraint to focus the calculation around the receptor binding site produces acceptable solutions within the top 20 in 28 cases. Further constraining the search to the ligand binding site gives up to 48 solutions within the top 20, with calculation times of just a few minutes per complex. Hence the approach described provides a practical and fast tool for rigid body protein-protein docking, especially when prior knowledge about one or both binding sites is available. Availability: http://www.csd.abdn.ac.uk/hex/ Contact: d.w.ritchie@abdn.ac.uk

Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data

Motivation: Unraveling the transcriptional regulatory program mediated by transcription factors (TFs) is a fundamental objective of computational biology, yet still remains a challenge. Method: Here, we present a new methodology that integrates microarray and TF binding data for unraveling transcriptional regulatory networks. The algorithm is based on a two-stage constrained matrix decomposition model. The model takes into account the non-linear structure in gene expression data, particularly in the TF-target gene interactions and the combinatorial nature of gene regulation by TFs. The gene expression profile is modeled as a linear weighted combination of the activity profiles of a set of TFs. The TF activity profiles are deduced from the expression levels of TF target genes, instead directly from TFs themselves. The TF-target gene relationships are derived from ChIP-chip and other TF binding data. The proposed algorithm can not only identify transcriptional modules, but also reveal regulatory programs of which TFs control which target genes in which specific ways (either activating or inhibiting). Results: In comparison with other methods, our algorithm identifies biologically more meaningful transcriptional modules relating to specific TFs. We applied the new algorithm on yeast cell cycle and stress response data. While known transcriptional regulations were confirmed, novel TF-gene interactions were predicted and provide new insights into the regulatory mechanisms of the cell. Contact: zhanmi@mail.nih.gov Supplementary information: Supplementary data are available at Bioinformatics online.

Automated annotation of Drosophila gene expression patterns using a controlled vocabulary

Motivation: Regulation of gene expression in space and time directs its localization to a specific subset of cells during development. Systematic determination of the spatiotemporal dynamics of gene expression plays an important role in understanding the regulatory networks driving development. An atlas for the gene expression patterns of fruit fly Drosophila melanogaster has been created by whole-mount in situ hybridization, and it documents the dynamic changes of gene expression pattern during Drosophila embryogenesis. The spatial and temporal patterns of gene expression are integrated by anatomical terms from a controlled vocabulary linking together intermediate tissues developed from one another. Currently, the terms are assigned to patterns manually. However, the number of patterns generated by high-throughput in situ hybridization is rapidly increasing. It is, therefore, tempting to approach this problem by employing computational methods. Results: In this article, we present a novel computational framework for annotating gene expression patterns using a controlled vocabulary. In the currently available high-throughput data, annotation terms are assigned to groups of patterns rather than to individual images. We propose to extract invariant features from images, and construct pyramid match kernels to measure the similarity between sets of patterns. To exploit the complementary information conveyed by different features and incorporate the correlation among patterns sharing common structures, we propose efficient convex formulations to integrate the kernels derived from various features. The proposed framework is evaluated by comparing its annotation with that of human curators, and promising performance in terms of F1 score has been reported. Contact: jieping.ye@asu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Classification with reject option in gene expression data

Motivation: The classification methods typically used in bioinformatics classify all examples, even if the classification is ambiguous, for instance, when the example is close to the separating hyperplane in linear classification. For medical applications, it may be better to classify an example only when there is a sufficiently high degree of accuracy, rather than classify all examples with decent accuracy. Moreover, when all examples are classified, the classification rule has no control over the accuracy of the classifier; the algorithm just aims to produce a classifier with the smallest error rate possible. In our approach, we fix the accuracy of the classifier and thereby choose a desired risk of error. Results: Our method consists of defining a rejection region in the feature space. This region contains the examples for which classification is ambiguous. These are rejected by the classifier. The accuracy of the classifier becomes a user-defined parameter of the classification rule. The task of the classification rule is to minimize the rejection region with the constraint that the error rate of the classifier be bounded by the chosen target error. This approach is also used in the feature-selection step. The results computed on both synthetic and real data show that classifier accuracy is significantly improved. Availability: Companion Website. http://gsp.tamu.edu/Publications/rejectoption/ Contact: edward@ece.tamu.edu, hanczar_blaise@yahoo.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Multimarker analysis and imputation of multiple platform pooling-based genome-wide association studies

Summary: For many genome-wide association (GWA) studies individually genotyping one million or more SNPs provides a marginal increase in coverage at a substantial cost. Much of the information gained is redundant due to the correlation structure inherent in the human genome. Pooling-based GWA studies could benefit significantly by utilizing this redundancy to reduce noise, improve the accuracy of the observations and increase genomic coverage. We introduce a measure of correlation between individual genotyping and pooling, under the same framework that r2 provides a measure of linkage disequilibrium (LD) between pairs of SNPs. We then report a new non-haplotype multimarker multi-loci method that leverages the correlation structure between SNPs in the human genome to increase the efficacy of pooling-based GWA studies. We first give a theoretical framework and derivation of our multimarker method. Next, we evaluate simulations using this multimarker approach in comparison to single marker analysis. Finally, we experimentally evaluate our method using different pools of HapMap individuals on the Illumina 450S Duo, Illumina 550K and Affymetrix 5.0 platforms for a combined total of 1 333 631 SNPs. Our results show that use of multimarker analysis reduces noise specific to pooling-based studies, allows for efficient integration of multiple microarray platforms and provides more accurate measures of significance than single marker analysis. Additionally, this approach can be extended to allow for imputing the association significance for SNPs not directly observed using neighboring SNPs in LD. This multimarker method can now be used to cost-effectively complete pooling-based GWA studies with multiple platforms across over one million SNPs and to impute neighboring SNPs weighted for the loss of information due to pooling. Contact: dcraig@tgen.org Supplementary information: Supplementary data are available at Bioinformatics online.

Computational design of synthetic gene circuits with composable parts

Motivation: In principle, novel genetic circuits can be engineered using standard parts with well-understood functionalities. However, no model based on the simple composition of these parts has become a standard, mainly because it is difficult to define signal exchanges between biological units as unambiguously as in electrical engineering. Corresponding concepts and computational tools for easy circuit design in biology are missing. Results: Taking inspiration from (and slightly modifying) ideas in the ‘MIT Registry of Standard Biological Parts’, we developed a method for the design of genetic circuits with composable parts. Gene expression requires four kinds of signal carriers: RNA polymerases, ribosomes, transcription factors and environmental ‘messages’ (inducers or corepressors). The flux of each of these types of molecules is a quantifiable biological signal exchanged between parts. Here, each part is modeled independently by the ordinary differential equations (ODE) formalism and integrated into the software ProMoT (Process Modeling Tool). In this way, we realized a ‘drag and drop’ tool, where genetic circuits are built just by placing biological parts on a canvas and by connecting them through ‘wires’ that enable flow of signal carriers, as it happens in electrical engineering. Our simulations of well-known synthetic circuits agree well with published computational and experimental results. Availability: The code is available on request from the authors. Contact: mario.marchisio@bsse.ethz.ch Supplementary information: Supplementary data are available at Bioinformatics online.

Automated mapping of large-scale chromatin structure in ENCODE

Motivation: A recently developed DNaseI assay has given us our first genome-wide view of chromatin structure. In addition to cataloging DNaseI hypersensitive sites, these data allows us to more completely characterize overall features of chromatin accessibility. We employed a Bayesian hierarchical change-point model (CPM), a generalization of a hidden Markov Model (HMM), to characterize tiled microarray DNaseI sensitivity data available from the ENCODE project. Results: Our analysis shows that the accessibility of chromatin to cleavage by DNaseI is well described by a four state model of local segments with each state described by a continuous mixture of Gaussian variables. The CPM produces a better fit to the observed data than the HMM. The large posterior probability for the four-state CPM suggests that the data falls naturally into four classes of regions, which we call major and minor DNaseI hypersensitive sites (DHSs), regions of intermediate sensitivity, and insensitive regions. These classes agree well with a model of chromatin in which local disruptions (DHSs) are concentrated within larger domains of intermediate sensitivity, the accessibility islands. The CPM assigns 92% of the bases within the ENCODE regions to the insensitive regions. The 5.8% of the bases that are in regions of intermediate sensitivity are clearly enriched in functional elements, including genes and activating histone modifications, while the remaining 2.2% of the bases in hypersensitive regions are very strongly enriched in these elements. Availability: The CPM software is available upon request from the authors. Contact: jstam@stamlab.org; noble@gs.washington.edu; Charles_Lawrence@brown.edu Supplementary information: Supplementary data are available at Bioinformatics online. Source code is available at http://noble.gs.washington.edu/proj/segment.

Synchronous versus asynchronous modeling of gene regulatory networks

Motivation: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene–gene, protein–protein and gene–protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes. Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1–Th2 cellular differentiation process. Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html. Contact: abhishek.garg@epfl.ch

Coherent coupling of feedback loops: a design principle of cell signaling networks

Motivation:It is widely accepted that cell signaling networks have been evolved to be robust against perturbations. To investigate the topological characteristics resulting in such robustness, we have examined large-scale signaling networks and found that a number of feedback loops are present mostly in coupled structures. In particular, the coupling was made in a coherent way implying that same types of feedback loops are interlinked together. Results: We have investigated the role of such coherently coupled feedback loops through extensive Boolean network simulations and found that a high proportion of coherent couplings can enhance the robustness of a network against its state perturbations. Moreover, we found that the robustness achieved by coherently coupled feedback loops can be kept evolutionarily stable. All these results imply that the coherent coupling of feedback loops might be a design principle of cell signaling networks devised to achieve the robustness. Contact: ckh@kaist.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.

BioBayes: A software package for Bayesian inference in systems biology

Motivation: There are several levels of uncertainty involved in the mathematical modelling of biochemical systems. There often may be a degree of uncertainty about the values of kinetic parameters, about the general structure of the model and about the behaviour of biochemical species which cannot be observed directly. The methods of Bayesian inference provide a consistent framework for modelling and predicting in these uncertain conditions. We present a software package for applying the Bayesian inferential methodology to problems in systems biology. Results: Described herein is a software package, BioBayes, which provides a framework for Bayesian parameter estimation and evidential model ranking over models of biochemical systems defined using ordinary differential equations. The package is extensible allowing additional modules to be included by developers. There are no other such packages available which provide this functionality. Availability: http://www.dcs.gla.ac.uk/BioBayes/ Contact: vvv@dcs.gla.ac.uk

PuReD-MCL: a graph-based PubMed document clustering methodology

Motivation: Biomedical literature is the principal repository of biomedical knowledge, with PubMed being the most complete database collecting, organizing and analyzing such textual knowledge. There are numerous efforts that attempt to exploit this information by using text mining and machine learning techniques. We developed a novel approach, called PuReD-MCL (Pubmed Related Documents-MCL), which is based on the graph clustering algorithm MCL and relevant resources from PubMed. Methods: PuReD-MCL avoids using natural language processing (NLP) techniques directly; instead, it takes advantage of existing resources, available from PubMed. PuReD-MCL then clusters documents efficiently using the MCL graph clustering algorithm, which is based on graph flow simulation. This process allows users to analyse the results by highlighting important clues, and finally to visualize the clusters and all relevant information using an interactive graph layout algorithm, for instance BioLayout Express 3D. Results: The methodology was applied to two different datasets, previously used for the validation of the document clustering tool TextQuest. The first dataset involves the organisms Escherichia coli and yeast, whereas the second is related to Drosophila development. PuReD-MCL successfully reproduces the annotated results obtained from TextQuest, while at the same time provides additional insights into the clusters and the corresponding documents. Availability: Source code in perl and R are available from http://tartara.csd.auth.gr/~theodos/ Contact: theodos@csd.auth.gr

PoooL: an efficient method for estimating haplotype frequencies from large DNA pools

Motivation: Pooling DNA is a cost-effective alternative to individual genotyping method. It is often used for initial screening in genome-wide association analysis. In some studies, large pools with sizes up to several hundreds were applied in order to significantly reduce genotyping cost. However, method for estimating haplotype frequencies from large DNA pools has not been available due to computational complexity involved. Methods: We propose a novel constrained EM algorithm, PoooL, to estimate frequencies of single-nucleotide polymorphism (SNP) haplotypes from DNA pools. A quantity called importance factor is introduced to measure the contribution of a haplotype to the likelihood. Under the assumption of asymptotic normality of the estimated allele frequencies and a system of linear constraints on haplotype frequencies the importance factor remains a constant in the iterative maximization process. The maximization problem in the EM algorithm is then formulated into a constrained maximum entropy model and solved by the improved iterative scaling method. Results: Simulation study shows that our algorithm can efficiently estimate haplotype frequencies from DNA pools with arbitrarily large sizes. The algorithm works equally well for large pools with sizes up to hundreds or thousands and for pools with sizes as small as one or two individuals. The computational complexity of the PoooL algorithm is independent of pool sizes, and the computational efficiency for large pools is thus substantially improved over existing estimating methods. Simulation results also show that the proposed method is robust to genotype errors and population admixture. Availability: http://staff.ustc.edu.cn/~ynyang/poool Contact: zhanghan@mail.ustc.edu.cn; ynyang@ustc.edu.cn

Spidermonkey: rapid detection of co-evolving sites using Bayesian graphical models

Spidermonkey is a new component of the Datamonkey suite of phylogenetic tools that provides methods for detecting coevolving sites from a multiple alignment of homologous nucleotide or amino acid sequences. It reconstructs the substitution history of the alignment by maximum likelihood-based phylogenetic methods, and then analyzes the joint distribution of substitution events using Bayesian graphical models to identify significant associations among sites. Availability: Spidermonkey is publicly available both as a web application at http://www.data-monkey.org and as a stand-alone component of the phylogenetic software package HyPhy, which is freely distributed on the web (http://www.hyphy.org) as precompiled binaries and open source. Contact: afpoon@ucsd.edu

iFoldRNA: three-dimensional RNA structure prediction and folding

Summary: Three-dimensional RNA structure prediction and folding is of significant interest in the biological research community. Here, we present iFoldRNA, a novel web-based methodology for RNA structure prediction with near atomic resolution accuracy and analysis of RNA folding thermodynamics. iFoldRNA rapidly explores RNA conformations using discrete molecular dynamics simulations of input RNA sequences. Starting from simplified linear-chain conformations, RNA molecules (<50 nt) fold to native-like structures within half an hour of simulation, facilitating rapid RNA structure prediction. All-atom reconstruction of energetically stable conformations generates iFoldRNA predicted RNA structures. The predicted RNA structures are within 2–5 Å root mean squre deviations (RMSDs) from corresponding experimentally derived structures. RNA folding parameters including specific heat, contact maps, simulation trajectories, gyration radii, RMSDs from native state, fraction of native-like contacts are accessible from iFoldRNA. We expect iFoldRNA will serve as a useful resource for RNA structure prediction and folding thermodynamic analyses. Availability: http://iFoldRNA.dokhlab.org. Contact: dokh@med.unc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

PIGS: automatic prediction of antibody structures

Summary: We describe a web server for the automatic prediction of immunoglobulin variable domains based on the canonical structure model. The server is user-friendly and flexible. It allows the user to select the templates for the frameworks and the loops using different strategies. The final output is a full-fledged 3D model of the variable domains of the target immunoglobulin. Availability: The server is openly accessible to academic users at the address: http://arianna.bio.uniroma1.it/pigs. It does not require registration and there is no limit to the number of sequences that can be submitted. Contact: anna.tramontano@uniroma1.it

TRITON: a graphical tool for ligand-binding protein engineering

Summary: The new version of the TRITON program provides user-friendly graphical tools for modeling protein mutants using the external program MODELLER and for docking ligands into the mutants using the external program AutoDock. TRITON can now be used to design ligand-binding proteins, to study protein–ligand binding mechanisms or simply to dock any ligand to a protein. Availability: Executable files of TRITON are available free of charge for academic users at http://ncbr.chemi.muni.cz/triton/ Contact: triton@chemi.muni.cz Supplementary information: Supplementary data are available at Bioinformatics online.

PathCluster: a framework for gene set-based hierarchical clustering

Motivation: Gene clustering and gene set-based functional analysis are widely used for the analysis of expression profiles. The development of a comprehensive method jointly combining the two methods would allow for greater biological insights. Results: We developed a software package, PathCluster for gene set-based clustering via an agglomerative hierarchical clustering algorithm. The distances between predefined gene sets are illustrated in a dendrogram in which the relationships between gene sets can be visually assessed. Valuable biological insights can be obtained according to the type of gene sets, e.g. coordinated action of molecular functions (functional gene sets) and putative motif synergy (promoter gene set) in a biological process. The combined use of gene sets further enables the interrogation of different biological themes and their putative relationships, such as function-versus-regulatory motif or drug-versus-function. PathCluster can also be used for knowledge-based sample partitioning or class categorization for clinical purposes. With extended applicability, PathCluster will facilitate the gleaning of meaningful biological insights and testable hypotheses in the contexts of given expression profiles. Availability: PathCluster executable files can be freely downloaded at http://www.systemsbiology.co.kr/PathCluster/. Contact: yejun@catholic.ac.kr

Sircah: a tool for the detection and visualization of alternative transcripts

Summary: Sircah is a flexible tool for the detection, analysis and visualization of alternative transcripts. It takes as input gene models or spliced alignments and creates a database of alternative transcription events: alternative transcription initiation and polyadenylation, alternative 3' and 5' splice-site usage, skipped exons and retained introns. The results can be visualized in a variety of ways, allowing the creation of publication quality images. Availability: The Sircah is available for download under a creative commons license along with additional documentation and a tutorial from http://www.bork.embl.de/Sircah. Contact: bork@embl.de

Watermarking sexually reproducing diploid organisms

Summary: DNA watermarks are used for hiding messages or for authenticating genetically modified organisms. Recently, we presented an algorithm called DNA-Crypt for generating DNA-based watermarks that can be integrated into the genome by using the characteristics of the degenerative genetic code. DNA-Crypt generates the watermark by replacing single bases and thus creating synonymous codons that encrypt the hidden information. Mutations within the integrated DNA sequence can be corrected using several mutation correction codes, to keep the hidden information intact. This method has successfully been tested in asexually replicating organisms like bacteria or yeast, where the watermark is duplicated with every cell division. It has been shown that DNA watermarks produced by DNA-Crypt do not influence the transcription or translation of a protein. In sexually reproducing diploid organisms, additional problems can occur, e.g. recombination events can destroy hidden information. Using population predictions as well as statistical analyses we identified a coupled Y-chromosomal/mitochondrial DNA watermarking procedure as the most appropriate for diploid organisms. We developed a mitochondria adapted version of DNA-Crypt, which is called Project Mito that can be used in combination with the original program. Availability: http://www.uni-muenster.de/Biologie.NeuroVer/Tumorbiologie/DNA-Crypt/index.html Contact: barneko@uni-muenster.de Password: WWUTB Requirements: Java 5.0 or higher Supplementary information: Supplementary data are available at Bioinformatics online.

Comparing simulation results of SBML capable simulators

Motivation: Simulations are an essential tool when analyzing biochemical networks. Researchers and developers seeking to refine simulation tools or develop new ones would benefit greatly from being able to compare their simulation results. Summary: We present an approach to compare simulation results between several SBML capable simulators and provide a website for the community to share simulation results. Availability: The website with simulation results and additional material can be found under: http://sys-bio.org/sbwWiki/compare. The software used to generate the simulation results is available on the website for download. Contact: fbergman@u.washington.edu

mlegp: statistical analysis for computer models of biological systems using R

Summary: Gaussian processes (GPs) are flexible statistical models commonly used for predicting output from complex computer codes. As such, GPs are well suited for the analysis of computer models of biological systems, which have been traditionally difficult to analyze due to their high-dimensional, non-linear and resource-intensive nature. We describe an R package, mlegp, that fits GPs to computer model outputs and performs sensitivity analysis to identify and characterize the effects of important model inputs. Availability: http://www.biomath.org/mlegp Contact: kdorman@iastate.edu Supplementary information: See http://www.biomath.org/mlegp for a user manual and examples.

Yale Image Finder (YIF): a new search engine for retrieving biomedical images

Summary: Yale Image Finder (YIF) is a publicly accessible search engine featuring a new way of retrieving biomedical images and associated papers based on the text carried inside the images. Image queries can also be issued against the image caption, as well as words in the associated paper abstract and title. A typical search scenario using YIF is as follows: a user provides few search keywords and the most relevant images are returned and presented in the form of thumbnails. Users can click on the image of interest to retrieve the high resolution image. In addition, the search engine will provide two types of related images: those that appear in the same paper, and those from other papers with similar image content. Retrieved images link back to their source papers, allowing users to find related papers starting with an image of interest. Currently, YIF has indexed over 140 000 images from over 34 000 open access biomedical journal papers. Availability: http://krauthammerlab.med.yale.edu/imagefinder/ Contact: michael.krauthammer@yale.edu

Semantic reclassification of the UMLS concepts

Summary: Accurate semantic classification is valuable for text mining and knowledge-based tasks that perform inference based on semantic classes. To benefit applications using the semantic classification of the Unified Medical Language System (UMLS) concepts, we automatically reclassified the concepts based on their lexical and contextual features. The new classification is useful for auditing the original UMLS semantic classification and for building biomedical text mining applications. Availability: http://www.dbmi.columbia.edu/~juf7002/reclassify_production Contact: fan@dbmi.columbia.edu Supplementary information: Supplementary data is available at http://www.dbmi.columbia.edu/~juf7002/reclassify_production.