bluuebunny/crossref_metadata_2025 huggingface dataset:
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quantum well waveguide optical isolator with wide wavelength operational bandwidth
Authors: Debnath M. C. , Zayets V. , Ando K.
Published: July 2007

The authors developed a magneto-optical (Cd,Mn)Te∕(Cd,Zn)Te quantum well waveguide and demonstrated that the Faraday rotation in the waveguide was slightly dependent on the wavelength. For a waveguide with a quantum well width of 20Å, constant Faraday rotation and a small amount of wavelength dispersion were simultaneously achieved in a wide 25nm wavelength range at room temperature. Thus the broadband waveguide optical isolator is operated over a wide bandwidth of 25nm. This result shows that it is feasible to monolithically integrate an optical isolator made of diluted magnetic semiconductor with semiconductor optoelectronic devices.
Laser microfixation of highly ordered J aggregates on a glass substrate
Authors: Tanaka Yoshito , Yoshikawa Hiroyuki , Asahi Tsuyoshi , Masuhara Hiroshi
Published: July 2007

The authors have developed a laser microfixation technique of J aggregates from pseudoisocyanine solution onto a glass substrate. By focusing a near-infrared (NIR) laser beam into the solution near the glass substrate, conglomerate J aggregates are deposited at the focal point and fixed on the substrate. Fixed J aggregates show a narrower fluorescence band than the deposited one without laser focusing, indicating that highly ordered J aggregates are selectively confined in the laser focus due to optical trapping. Furthermore, they have demonstrated that the orientation of the transition dipole in fixed J aggregates is controlled by the polarization direction of the NIR laser beam.
Photocatalytic nanoparticle deposition on LiNbO3 nanodomain patterns via photovoltaic effect
Authors: Liu Xiaoyan , Kitamura Kenji , Terabe Kazuya , Hatano Hideki , Ohashi Naoki
Published: July 2007

Metallic nanoparticle deposition from an aqueous solution onto the +Z surfaces of LiNbO3 nanodomain patterns has been demonstrated. The distribution of the selectively deposited Ag particles was uniform and their sizes were 2–10nm both in the diameter and height. The authors have inferred the mechanism by which the photovoltaic effect causes Ag particles to be deposited selectively on the +Z surfaces. The photovoltaic current density and the ferroelectric crystal thickness are found to be fundamental parameters of the photocatalytic deposition.
Probing the internal geometry of a woven composite during deformation using an x-ray microdiffraction imaging technique
Authors: Davies Richard J. , Riekel Christian , Bennett James A. , Eichhorn Stephen J. , Young Robert J.
Published: July 2007

X-ray diffraction is an important tool for studying multiphase materials because it can resolve parameters from each phase independently. When coupled with a high-flux, microfocussed x-ray beam, scanning microdiffraction experiments are possible. This letter reports on the use of this technique to image a fiber reinforced composite with a complex woven lamina geometry. These systems are difficult to study with other experimental techniques because the fibers are inaccessible and the matrix is often opaque. However, microfocused x-ray diffraction reveals how macroscopic load affects the weave microgeometry by reorienting the embedded fibers.
Geiger-mode operation of back-illuminated GaN avalanche photodiodes
Authors: Pau J. L. , McClintock R. , Minder K. , Bayram C. , Kung P. , Razeghi M. , Muñoz E. , Silversmith D.
Published: July 2007

The authors report the Geiger-mode operation of back-illuminated GaN avalanche photodiodes. The devices were fabricated on transparent AlN templates specifically for back illumination in order to enhance hole-initiated multiplication. The spectral response in Geiger-mode operation was analyzed under low photon fluxes. Single photon detection capabilities were demonstrated in devices with areas ranging from 225to14063μm2. Single photon detection efficiency of 20% and dark count rate <10kHz were achieved in the smallest devices.
Beyond the Forest and the Trees: Local and Global Interference in Hierarchical Visual Stimuli Containing Three Levels
Authors: Rijpkema Mark , van Aalderen Sandra , Schwarzbach Jens , Verstraten Frans A J
Published: July 2007

Our visual world can be thought of as organised in a hierarchical manner. Studies on hierarchical letter stimuli (a large letter composed of smaller letters) suggest that processing of a visual scene is global to local, a phenomenon known as the global-precedence effect. Elaborating on this global-to-local hypothesis we tested whether global interference will increase with increasing level of globality. For this, we used three-level hierarchical letter stimuli with a global, middle, and local level. When attending to the local level of the stimulus, only the middle level showed an interference effect, whereas the global level did not interfere at all. We argue that, considering the perceptual and attentional contributions to this effect, the hypothesis of global-to-local processing of a visual scene may only hold within a limited spatial attentional window.
Shape from Shading from Images Rendered with Various Surface Types and Light Fields
Authors: Khang Byung-Geun , Koenderink Jan J , Kappers Astrid M L
Published: July 2007

Shape constancy is referred to as the tendency for the perceived shape of an object to remain unchanged even under changed viewing and illumination conditions. We investigated, in two experiments, whether shape constancy would hold for images of 3-D solid objects defined by shading only, whose renderings differed in terms of surface material type (bi-directional reflectance distribution functions), light field, light direction, shape, and specularity. Observers were presented with the image of a sphere or an ellipsoid and required to set perceived orientation and cross-section profile on designated points of the image. Results showed that shape judgments varied with all the aforementioned variables except specularity. Shape estimates were more precise with specular than asperity scattering surfaces, collimated than hemispherical diffuse lighting conditions, lower than higher elevations, spherical than ellipsoidal shapes, but not different between surfaces having differing specularity. These results suggest that shape judgments are made largely on the basis of the overall intensity distribution of shading, and that the portions of intensity distribution that are due to nonstructural variables such as surface material type or light field are not excluded in the process of shape estimation, as if being due to structural components. It is concluded that little constancy is expected in the perception of shape from shading.
Effects of polyploidy on photosynthetic properties and anatomy in leaves of Phlox drummondii
Authors: Vyas Poonam , Bisht Madho Singh , Miyazawa Shin-Ichi , Yano Satoshi , Noguchi Ko , Terashima Ichiro , Funayama-Noguchi Sachiko
Published: July 2007

Polyploidy affects photosynthesis by causing changes in morphology, anatomy and biochemistry. However, in newly developed polyploids, the genome may be unstable. In this study, diploid (2×) and synthetic autotetraploids in initial (4×-C0) and 11th generations (4×-C11) of Phlox drummondii Hook were used to study the effects of chromosome doubling and genome stabilisation on leaf photosynthesis and anatomical properties. The light-saturated photosynthetic rate on a leaf area basis at 360 µmol CO2 mol–1 air (A360) was highest in 4×-C11 leaves, intermediate in 4×-C0 leaves, and lowest in 2× leaves. Rubisco amounts, CO2-saturated photosynthetic rate at 1200 µmol CO2 mol–1 air at PPFD of 1000 µmol m–2 s–1 (A1200, representing the capacity for RuBP regeneration), cumulative surface areas of chloroplasts facing intercellular spaces (Sc), all expressed on a leaf area basis, were all higher in 4× leaves than in 2× leaves, and stomatal conductance (gs) at 360 µmol CO2 mol–1 air was only higher in the 4×-C11 leaves. A360 for the 4×-C11 leaves was greater than that in the 4×-C0 leaves despite having similar amounts of Rubisco. This was presumably associated with a greater RuBP regeneration capacity, as well as an increase in Sc and gs, which would increase the CO2 concentration of Rubisco. These results indicate that the higher rate of photosynthesis in 4×-C11 leaves was not an immediate outcome of chromosome doubling; rather, it was due to adjustment and adaptation during the process of genome stabilisation.
Reconstruction of highly heterogeneous gene-content evolution across the three domains of life
Authors: Iwasaki Wataru , Takagi Toshihisa
Published: July 2007

Motivation: Reconstruction of gene-content evolutionary history is fundamental in studying the evolution of genomes and biological systems. To reconstruct plausible evolutionary history, rates of gene gain/loss should be estimated by considering the high level of heterogeneity: e.g. genome duplication and parasitization, respectively, result in high rates of gene gain and loss. Gene-content evolution reconstruction methods that consider this heterogeneity and that are both effective in estimating the rates of gene gain and loss and sufficiently efficient to analyze abundant genomic data had not been developed.

Results: An effective and efficient method for reconstructing heterogeneous gene-content evolution was developed. This method comprises analytically integrable modeling of gene-content evolution, analytical formulation of expectation-maximization and efficient calculation of marginal likelihood using an inside-outside-like algorithm. Simulation tests on the scale of hundreds of genomes showed that both the gene gain/loss rates and evolutionary history were effectively estimated within a few days of computational time. Subsequently, this algorithm was applied to an actual data set of nearly 200 genomes to reconstruct the heterogeneous gene-content evolution across the three domains of life. The reconstructed history, which contained several features consistent with biological observations, showed that the trends of gene-content evolution were not only drastically different between prokaryotes and eukaryotes, but were highly variable within each form of life. The results suggest that heterogeneity should be considered in studies of the evolution of gene content, genomes and biological systems.

Availability: An R script that implements the algorithm is available upon request.

Contact: iwasaki@cb.k.u-tokyo.ac.jp
Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants
Authors: Danziger Samuel A. , Zeng Jue , Wang Ying , Brachmann Rainer K. , Lathrop Richard H.
Published: July 2007

Motivation: Many biomedical projects would benefit from reducing the time and expense of in vitro experimentation by using computer models for in silico predictions. These models may help determine which expensive biological data are most useful to acquire next. Active Learning techniques for choosing the most informative data enable biologists and computer scientists to optimize experimental data choices for rapid discovery of biological function. To explore design choices that affect this desirable behavior, five novel and five existing Active Learning techniques, together with three control methods, were tested on 57 previously unknown p53 cancer rescue mutants for their ability to build classifiers that predict protein function. The best of these techniques, Maximum Curiosity, improved the baseline accuracy of 56–77%. This article shows that Active Learning is a useful tool for biomedical research, and provides a case study of interest to others facing similar discovery challenges.
A physical model for tiling array analysis
Authors: Chung Ho-Ryun , Kostka Dennis , Vingron Martin
Published: July 2007

Motivation: Chromatin immunoprecipitation (ChIP) is a powerful experimental approach to identify in vivo binding sites of sequence-specific transcription factors (TFs). These experiments are designed to specifically enrich DNA fragments that are bound to the TF. Tiling arrays have become more and more popular for the identification of these DNA fragments. However, many studies showed that only a fraction of the identified DNA fragments contains bona fide binding sites for the TF, suggesting that indirect binding mechanisms play a very important role. We explored the possibility that the lack of binding sites can also be explained by problems in identifying ChIP-enriched DNA fragments from the measured intensities.

Results: We derived a physical model that explains some (but not all) variation of the measured probe intensities of Affymetrix tilling arrays. We used the physical model to estimate the probe-specific behavior and corrected for it. Subsequently, we developed a method to identify ChIP-enriched DNA fragments. We termed it physical model for tiling array analysis (PMT). We applied PMT to the data of ChIP-chip experiments interrogating chromosome 21 and 22 of the human genome for binding of the TFs MYC, SP1 and P53. Almost all regions recovered by PMT showed evidence for sequence-specific binding of the TFs.

Contact: chung@molgen.mpg.de
SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments
Authors: Ourfali Oved , Shlomi Tomer , Ideker Trey , Ruppin Eytan , Sharan Roded
Published: July 2007

Motivation: The complex program of gene expression allows the cell to cope with changing genetic, developmental and environmental conditions. The accumulating large-scale measurements of gene knockout effects and molecular interactions allow us to begin to uncover regulatory and signaling pathways within the cell that connect causal to affected genes on a network of physical interactions.

Results: We present a novel framework, SPINE, for Signaling-regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene is knocked out and as a result multiple genes change their expression levels. To this end, an integrated network of protein–protein and protein-DNA interactions is constructed, and signaling pathways connecting the causal gene to the affected genes are searched for in this network. The reconstruction problem is translated into that of assigning an activation/repression attribute with each protein so as to explain (in expectation) a maximum number of the knockout effects observed. We provide an integer programming formulation for the latter problem and solve it using a commercial solver.

We validate the method by applying it to a yeast subnetwork that is involved in mating. In cross-validation tests, SPINE obtains very high accuracy in predicting knockout effects (99%). Next, we apply SPINE to the entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall, we are able to infer 861 paths with confidence and assign effects to 183 genes. The predicted effects are found to be in high agreement with current biological knowledge.

Availability: The algorithm and data are available at http://cs.tau.ac.il/~roded/SPINE.html

Contact: roded@post.tau.ac.il
Genomic characterization of perturbation sensitivity
Authors: Ohn Jung Hun , Kim Jihun , Kim Ju Han
Published: July 2007

Motivation: In determining the function of a gene, it provides much information to observe the changes in a biological system after disruption of the gene of interest through its knockout. Thanks to the microarray technology, it is now possible to profile transcriptional changes of the whole genome, thus differentiating genes that are significantly affected by the knockout. Based on microarray experiments of hundreds of different knockouts, we assigned the so called, ‘Perturbation Sensitivity’, to the Saccharomyces cerevisiae genome by the frequency of significant changes in the transcript level in hundreds of knockout conditions. Biologically, it reflects the degree of a gene's sensitivity to external perturbations.

Results: Through gradually enriching gene sets with more perturbation sensitive genes, we show that perturbation sensitive genes are usually not essential and their coding proteins have fewer physical interaction partners and more transcription factors bind to their upstream sequences. And the two extreme gene groups, perturbation sensitive versus perturbation resistant, have mutually exclusive functional annotations.

contact: juhan@snu.ac.kr
Annotating gene function by combining expression data with a modular gene network
Authors: Shiga Motoki , Takigawa Ichigaku , Mamitsuka Hiroshi
Published: July 2007

Motivation: A promising and reliable approach to annotate gene function is clustering genes not only by using gene expression data but also literature information, especially gene networks.

Results: We present a systematic method for gene clustering by combining these totally different two types of data, particularly focusing on network modularity, a global feature of gene networks. Our method is based on learning a probabilistic model, which we call a hidden modular random field in which the relation between hidden variables directly represents a given gene network. Our learning algorithm which minimizes an energy function considering the network modularity is practically time-efficient, regardless of using the global network property. We evaluated our method by using a metabolic network and microarray expression data, changing with microarray datasets, parameters of our model and gold standard clusters. Experimental results showed that our method outperformed other four competing methods, including k-means and existing graph partitioning methods, being statistically significant in all cases. Further detailed analysis showed that our method could group a set of genes into a cluster which corresponds to the folate metabolic pathway while other methods could not. From these results, we can say that our method is highly effective for gene clustering and annotating gene function.

Contact: shiga@kuicr.kyoto-u.ac.jp
Prediction of DNA-binding residues from sequence
Authors: Ofran Yanay , Mysore Venkatesh , Rost Burkhard
Published: July 2007

Motivation: Thousands of proteins are known to bind to DNA; for most of them the mechanism of action and the residues that bind to DNA, i.e. the binding sites, are yet unknown. Experimental identification of binding sites requires expensive and laborious methods such as mutagenesis and binding essays. Hence, such studies are not applicable on a large scale. If the 3D structure of a protein is known, it is often possible to predict DNA-binding sites in silico. However, for most proteins, such knowledge is not available.

Results: It has been shown that DNA-binding residues have distinct biophysical characteristics. Here we demonstrate that these characteristics are so distinct that they enable accurate prediction of the residues that bind DNA directly from amino acid sequence, without requiring any additional experimental or structural information. In a cross-validation based on the largest non-redundant dataset of high-resolution protein–DNA complexes available today, we found that 89% of our predictions are confirmed by experimental data. Thus, it is now possible to identify DNA-binding sites on a proteomic scale even in the absence of any experimental data or 3D-structural information.

Availability: http://cubic.bioc.columbia.edu/services/disis

Contact: yo135@columbia.edu
Different mechanistic requirements for prokaryotic and eukaryotic chaperonins: a lattice study
Authors: Jacob Etai , Horovitz Amnon , Unger Ron
Published: July 2007

Motivation: The folding of many proteins in vivo and in vitro is assisted by molecular chaperones. A well-characterized molecular chaperone system is the chaperonin GroEL/GroES from Escherichia coli which has a homolog found in the eukaryotic cytosol called CCT. All chaperonins have a ring structure with a cavity in which the substrate protein folds. An interesting difference between prokaryotic and eukaryotic chaperonins is in the nature of the ATP-mediated conformational changes that their ring structures undergo during their reaction cycle. Prokaryotic chaperonins are known to exhibit a highly cooperative concerted change of their cavity surface while in eukaryotic chaperonins the change is sequential. Approximately 70% of proteins in eukaryotic cells are multi-domain whereas in prokaryotes single-domain proteins are more common. Thus, it was suggested that the different modes of action of prokaryotic and eukaryotic chaperonins can be explained by the need of eukaryotic chaperonins to facilitate folding of multi-domain proteins.

Results: Using a 2D square lattice model, we generated two large populations of single-domain and double-domain substrate proteins. Chaperonins were modeled as static structures with a cavity wall with which the substrate protein interacts. We simulated both concerted and sequential changes of the cavity surfaces and demonstrated that folding of single-domain proteins benefits from concerted but not sequential changes whereas double-domain proteins benefit also from sequential changes. Thus, our results support the suggestion that the different modes of allosteric switching of prokaryotic and eukaryotic chaperonin rings have functional implications as it enables eukaryotic chaperonins to better assist multi-domain protein folding.
Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles
Authors: Sysi-Aho Marko , Vehtari Aki , Velagapudi Vidya R. , Westerbacka Jukka , Yetukuri Laxman , Bergholm Robert , Taskinen Marja-Riitta , Yki-Järvinen Hannele , Orešič Matej
Published: July 2007

Motivation: Serum lipids have been traditionally studied in the context of lipoprotein particles. Today's emerging lipidomics technologies afford sensitive detection of individual lipid molecular species, i.e. to a much greater detail than the scale of lipoproteins. However, such global serum lipidomic profiles do not inherently contain any information on where the detected lipid species are coming from. Since it is too laborious and time consuming to routinely perform serum fractionation and lipidomics analysis on each lipoprotein fraction separately, this presents a challenge for the interpretation of lipidomic profile data. An exciting and medically important new bioinformatics challenge today is therefore how to build on extensive knowledge of lipid metabolism at lipoprotein levels in order to develop better models and bioinformatics tools based on high-dimensional lipidomic data becoming available today.

Results: We developed a hierarchical Bayesian regression model to study lipidomic profiles in serum and in different lipoprotein classes. As a background data for the model building, we utilized lipidomic data for each of the lipoprotein fractions from 5 subjects with metabolic syndrome and 12 healthy controls. We clustered the lipid profiles and applied a regression model within each cluster separately. We found that the amount of a lipid in serum can be adequately described by the amounts of lipids in the lipoprotein classes. In addition to improved ability to interpret lipidomic data, we expect that our approach will also facilitate dynamic modelling of lipid metabolism at the individual molecular species level.

Contact: marko.sysi-aho@vtt.fi
Biases induced by pooling samples in microarray experiments
Authors: Mary-Huard Tristan , Daudin Jean-Jacques , Baccini Michela , Biggeri Annibale , Bar-Hen Avner
Published: July 2007

Motivation: If there is insufficient RNA from the tissues under investigation from one organism, then it is common practice to pool RNA. An important question is to determine whether pooling introduces biases, which can lead to inaccurate results. In this article, we describe two biases related to pooling, from a theoretical as well as a practical point of view.

Results: We model and quantify the respective parts of the pooling bias due to the log transform as well as the bias due to biological averaging of the samples. We also evaluate the impact of the bias on the statistical differential analysis of Affymetrix data.

Contact: maryhuar@inapg.fr
Kernel-based data fusion for gene prioritization
Authors: De Bie Tijl , Tranchevent Léon-Charles , van Oeffelen Liesbeth M. M. , Moreau Yves
Published: July 2007

Motivation: Hunting disease genes is a problem of primary importance in biomedical research. Biologists usually approach this problem in two steps: first a set of candidate genes is identified using traditional positional cloning or high-throughput genomics techniques; second, these genes are further investigated and validated in the wet lab, one by one. To speed up discovery and limit the number of costly wet lab experiments, biologists must test the candidate genes starting with the most probable candidates. So far, biologists have relied on literature studies, extensive queries to multiple databases and hunches about expected properties of the disease gene to determine such an ordering. Recently, we have introduced the data mining tool ENDEAVOUR (Aerts et al., 2006), which performs this task automatically by relying on different genome-wide data sources, such as Gene Ontology, literature, microarray, sequence and more.

Results: In this article, we present a novel kernel method that operates in the same setting: based on a number of different views on a set of training genes, a prioritization of test genes is obtained. We furthermore provide a thorough learning theoretical analysis of the method's guaranteed performance. Finally, we apply the method to the disease data sets on which ENDEAVOUR (Aerts et al., 2006) has been benchmarked, and report a considerable improvement in empirical performance.

Availability: The MATLAB code used in the empirical results will be made publicly available.

Contact: tijl.debie@gmail.com or yves.moreau@esat.kuleuven.be
Structural templates predict novel protein interactions and targets from pancreas tumour gene expression data
Authors: Dawelbait Gihan , Winter Christof , Zhang Yanju , Pilarsky Christian , Grützmann Robert , Heinrich Jörg-Christian , Schroeder Michael
Published: July 2007

Motivation: Pancreatic ductal adenocarcinoma (PDAC) eludes early detection and is characterized by its aggressiveness and resistance to current therapies. A number of gene expression screens have been carried out to identify genes differentially expressed in cancerous tissue. To identify molecular markers and suitable targets, these genes have been mapped to protein interactions to gain an understanding at systems level.

Results: Here, we take such a network-centric approach to pancreas cancer by re-constructing networks from known interactions and by predicting novel protein interactions from structural templates. The pathways we find to be largely affected are signal transduction, actin cytoskeleton regulation, cell growth and cell communication.

Our analysis indicates that the alteration of the calcium pathway plays an important role in pancreas-specific tumorigenesis. Furthermore, our structural prediction method identifies 40 novel interactions including the tissue factor pathway inhibitor 2 (TFPI2) interacting with the transmembrane protease serine 4 (TMPRSS4). Since TMPRSS4 is involved in metastasis formation, we hypothezise that the upregulation of TMPRSS4 and the downregulation of its predicted inhibitor TFPI2 plays an important role in this process. Moreover, we examine the potential role of BVDU (RP101) as an inhibitor of TMPRSS4. BDVU is known to support apoptosis and prevent the acquisition of chemoresistance. Our results suggest that BVDU might bind to the active site of TMPRSS4, thus reducing its assistance in metastasis.

Contact: ms@biotec.tu-dresden.de

Supplementary information: Supplementary data are available atBioinformatics online.
In search of lost introns
Authors: Csűrös Miklós , Holey J. Andrew , Rogozin Igor B.
Published: July 2007

Many fundamental questions concerning the emergence and subsequent evolution of eukaryotic exon–intron organization are still unsettled. Genome-scale comparative studies, which can shed light on crucial aspects of eukaryotic evolution, require adequate computational tools.

We describe novel computational methods for studying spliceosomal intron evolution. Our goal is to give a reliable characterization of the dynamics of intron evolution. Our algorithmic innovations address the identification of orthologous introns, and the likelihood-based analysis of intron data. We discuss a compression method for the evaluation of the likelihood function, which is noteworthy for phylogenetic likelihood problems in general. We prove that after O(nℓ) preprocessing time, subsequent evaluations take O(nℓ/log ℓ) time almost surely in the Yule–Harding random model of n-taxon phylogenies, where ℓ is the input sequence length.

We illustrate the practicality of our methods by compiling and analyzing a data set involving 18 eukaryotes, which is more than in any other study to date. The study yields the surprising result that ancestral eukaryotes were fairly intron-rich. For example, the bilaterian ancestor is estimated to have had more than 90% as many introns as vertebrates do now.

Availability: The Java implementations of the algorithms are publicly available from the corresponding author's site http://www.iro.umontreal.ca/~csuros/introns/. Data are available on request.

Contact: csuros@iro.umontreal.ca
Adaptive torsion-angle quasi-statics: a general simulation method with applications to protein structure analysis and design
Authors: Rossi Romain , Isorce Mathieu , Morin Sandy , Flocard Julien , Arumugam Karthik , Crouzy Serge , Vivaudou Michel , Redon Stephane
Published: July 2007

Motivation: The cost of molecular quasi-statics or dynamics simulations increases with the size of the simulated systems, which is a problem when studying biological phenomena that involve large molecules over long time scales. To address this problem, one has often to either increase the processing power (which might be expensive), or make arbitrary simplifications to the system (which might bias the study).

Results: We introduce adaptive torsion-angle quasi-statics, a general simulation method able to rigorously and automatically predict the most mobile regions in a simulated system, under user-defined precision or time constraints. By predicting and simulating only these most important regions, the adaptive method provides the user with complete control on the balance between precision and computational cost, without requiring him or her to perform a priori, arbitrary simplifications. We build on our previous research on adaptive articulated-body simulation and show how, by taking advantage of the partial rigidification of a molecule, we are able to propose novel data structures and algorithms for adaptive update of molecular forces and energies. This results in a globally adaptive molecular quasi-statics simulation method. We demonstrate our approach on several examples and show how adaptive quasi-statics allows a user to interactively design, modify and study potentially complex protein structures.

Contact: stephane.redon@inria.fr
Identification of functional modules from conserved ancestral protein–protein interactions
Authors: Dutkowski Janusz , Tiuryn Jerzy
Published: July 2007

Motivation: The increasing availability of large-scale protein–protein interaction (PPI) data has fuelled the efforts to elucidate the building blocks and organization of cellular machinery. Previous studies have shown cross-species comparison to be an effective approach in uncovering functional modules in protein networks. This has in turn driven the research for new network alignment methods with a more solid grounding in network evolution models and better scalability, to allow multiple network comparison.

Results: We develop a new framework for protein network alignment, based on reconstruction of an ancestral PPI network. The reconstruction algorithm is built upon a proposed model of protein network evolution, which takes into account phylogenetic history of the proteins and the evolution of their interactions. The application of our methodology to the PPI networks of yeast, worm and fly reveals that the most probable conserved ancestral interactions are often related to known protein complexes. By projecting the conserved ancestral interactions back onto the input networks we are able to identify the corresponding conserved protein modules in the considered species. In contrast to most of the previous methods, our algorithm is able to compare many networks simultaneously. The performed experiments demonstrate the ability of our method to uncover many functional modules with high specificity.

Availability: Information for obtaining software and supplementary results are available at http://bioputer.mimuw.edu.pl/papers/cappi.

Contact: januszd@mimuw.edu.pl
Information theory applied to the sparse gene ontology annotation network to predict novel gene function
Authors: Tao Ying , Sam Lee , Li Jianrong , Friedman Carol , Lussier Yves A.
Published: July 2007

Motivation: Despite advances in the gene annotation process, the functions of a large portion of gene products remain insufficiently characterized. In addition, the in silico prediction of novel Gene Ontology (GO) annotations for partially characterized gene functions or processes is highly dependent on reverse genetic or functional genomic approaches. To our knowledge, no prediction method has been demonstrated to be highly accurate for sparsely annotated GO terms (those associated to fewer than 10 genes).

Results: We propose a novel approach, information theory-based semantic similarity (ITSS), to automatically predict molecular functions of genes based on existing GO annotations. Using a 10-fold cross-validation, we demonstrate that the ITSS algorithm obtains prediction accuracies (precision 97%, recall 77%) comparable to other machine learning algorithms when compared in similar conditions over densely annotated portions of the GO datasets. This method is able to generate highly accurate predictions in sparsely annotated portions of GO, where previous algorithms have failed. As a result, our technique generates an order of magnitude more functional predictions than previous methods. A 10-fold cross validation demonstrated a precision of 90% at a recall of 36% for the algorithm over sparsely annotated networks of the recent GO annotations (about 1400 GO terms and 11 000 genes in Homo sapiens). To our knowledge, this article presents the first historical rollback validation for the predicted GO annotations, which may represent more realistic conditions than more widely used cross-validation approaches. By manually assessing a random sample of 100 predictions conducted in a historical rollback evaluation, we estimate that a minimum precision of 51% (95% confidence interval: 43–58%) can be achieved for the human GO Annotation file dated 2003.

Availability: The program is available on request. The 97 732 positive predictions of novel gene annotations from the 2005 GO Annotation dataset and other supplementary information is available at http://phenos.bsd.uchicago.edu/ITSS/

Contact: Lussier@uchicago.edu

Supplementary information: Supplementary data are available atBioinformatics online.
Kinetics analysis methods for approximate folding landscapes
Authors: Tapia Lydia , Tang Xinyu , Thomas Shawna , Amato Nancy M.
Published: July 2007

Motivation: Protein motions play an essential role in many biochemical processes. Lab studies often quantify these motions in terms of their kinetics such as the speed at which a protein folds or the population of certain interesting states like the native state. Kinetic metrics give quantifiable measurements of the folding process that can be compared across a group of proteins such as a wild-type protein and its mutants.

Results: We present two new techniques, map-based master equation solution and map-based Monte Carlo simulation, to study protein kinetics through folding rates and population kinetics from approximate folding landscapes, models called maps. From these two new techniques, interesting metrics that describe the folding process, such as reaction coordinates, can also be studied. In this article we focus on two metrics, formation of helices and structure formation around tryptophan residues. These two metrics are often studied in the lab through circular dichroism (CD) spectra analysis and tryptophan fluorescence experiments, respectively. The approximated landscape models we use here are the maps of protein conformations and their associated transitions that we have presented and validated previously.

In contrast to other methods such as the traditional master equation and Monte Carlo simulation, our techniques are both fast and can easily be computed for full-length detailed protein models. We validate our map-based kinetics techniques by comparing folding rates to known experimental results. We also look in depth at the population kinetics, helix formation and structure near tryptophan residues for a variety of proteins.

Availability: We invite the community to help us enrich our publicly available database of motions and kinetics analysis by submitting to our server: http://parasol.tamu.edu/foldingserver/

Contact: amato@cs.tamu.edu
Optimized design and assessment of whole genome tiling arrays
Authors: Gräf Stefan , Nielsen Fiona G. G. , Kurtz Stefan , Huynen Martijn A. , Birney Ewan , Stunnenberg Henk , Flicek Paul
Published: July 2007

Motivation: Recent advances in microarray technologies have made it feasible to interrogate whole genomes with tiling arrays and this technique is rapidly becoming one of the most important high-throughput functional genomics assays. For large mammalian genomes, analyzing oligonucleotide tiling array data is complicated by the presence of non-unique sequences on the array, which increases the overall noise in the data and may lead to false positive results due to cross-hybridization. The ability to create custom microarrays using maskless array synthesis has led us to consider ways to optimize array design characteristics for improving data quality and analysis. We have identified a number of design parameters to be optimized including uniqueness of the probe sequences within the whole genome, melting temperature and self-hybridization potential.

Results: We introduce the uniqueness score, U, a novel quality measure for oligonucleotide probes and present a method to quickly compute it. We show that U is equivalent to the number of shortest unique substrings in the probe and describe an efficient greedy algorithm to design mammalian whole genome tiling arrays using probes that maximize U. Using the mouse genome, we demonstrate how several optimizations influence the tiling array design characteristics. With a sensible set of parameters, our designs cover 78% of the mouse genome including many regions previously considered ‘untilable’ due to the presence of repetitive sequence. Finally, we compare our whole genome tiling array designs with commercially available designs.

Availability: Source code is available under an open source license from http://www.ebi.ac.uk/~graef/arraydesign/

Contact: flicek@ebi.ac.uk
Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks
Authors: Lim Wei Keat , Wang Kai , Lefebvre Celine , Califano Andrea
Published: July 2007

Motivation: An increasingly common application of gene expression profile data is the reverse engineering of cellular networks. However, common procedures to normalize expression profiles generated using the Affymetrix GeneChips technology were originally developed for a rather different purpose, namely the accurate measure of differential gene expression between two or more phenotypes. As a result, current evaluation strategies lack comprehensive metrics to assess the suitability of available normalization procedures for reverse engineering and, in general, for measuring correlation between the expression profiles of a gene pair.

Results: We benchmark four commonly used normalization procedures (MAS5, RMA, GCRMA and Li-Wong) in the context of established algorithms for the reverse engineering of protein–protein and protein–DNA interactions. Replicate sample, randomized and human B-cell data sets are used as an input. Surprisingly, our study suggests that MAS5 provides the most faithful cellular network reconstruction. Furthermore, we identify a crucial step in GCRMA responsible for introducing severe artifacts in the data leading to a systematic overestimate of pairwise correlation. This has key implications not only for reverse engineering but also for other methods, such as hierarchical clustering, relying on accurate measurements of pairwise expression profile correlation. We propose an alternative implementation to eliminate such side effect.

Contect: califano@c2b2.columbia.edu
Co-occurrence analysis of insertional mutagenesis data reveals cooperating oncogenes
Authors: de Ridder Jeroen , Kool Jaap , Uren Anthony , Bot Jan , Wessels Lodewyk , Reinders Marcel
Published: July 2007

Motivation: Cancers are caused by an accumulation of multiple independent mutations that collectively deregulate cellular pathways, e.g. such as those regulating cell division and cell-death. The publicly available Retroviral Tagged Cancer Gene Database (RTCGD) contains the data of many insertional mutagenesis screens, in which the virally induced mutations result in tumor formation in mice. The insertion loci therefore indicate the location of putative cancer genes. Additionally, the presence of multiple independent insertions within one tumor hints towards a cooperation between the insertionally mutated genes. In this study we focus on the detection of statistically significant co-mutations.

Results: We propose a two-dimensional Gaussian Kernel Convolution method (2DGKC), a computational technique that identifies the cooperating mutations in insertional mutagenesis data. We define the Common Co-occurrence of Insertions (CCI), signifying the co-mutations that are statistically significant across all different screens in the RTCGD. Significance estimates are made on multiple scales, and the results visualized in a scale space, thereby providing valuable extra information on the putative cooperation.

The multidimensional analysis of the insertion data results in the discovery of 86 statistically significant co-mutations, indicating the presence of cooperating oncogenes that play a role in tumor development. Since oncogenes may cooperate with several members of a parallel pathway, we combined the co-occurrence data with gene family information to find significant cooperations between oncogenes and families of genes. We show, for instance, the interchangeable cooperation of Myc insertions with insertions in the Pim family.

Availability: A list of the resulting CCIs is available at: http://ict.ewi.tudelft.nl/~jeroen/CCI/CCI_list.txt

Contact: m.j.t.reinders@tudelft.nl
Automated image analysis of protein localization in budding yeast
Authors: Chen Shann-Ching , Zhao Ting , Gordon Geoffrey J. , Murphy Robert F.
Published: July 2007

Motivation: The yeast Saccharomyces cerevisiae is the first eukaryotic organism to have its genome completely sequenced. Since then, several large-scale analyses of the yeast genome have provided extensive functional annotations of individual genes and proteins. One fundamental property of a protein is its subcellular localization, which provides critical information about how this protein works in a cell. An important project therefore was the creation of the yeast GFP fusion localization database by the University of California, San Francisco, USA (UCSF). This database provides localization data for 75% of the proteins believed to be encoded by the yeast genome. These proteins were classified into 22 distinct subcellular location categories by visual examination. Based on our past success at building automated systems to classify subcellular location patterns in mammalian cells, we sought to create a similar system for yeast.

Results: We developed computational methods to automatically analyze the images created by the UCSF yeast GFP fusion localization project. The system was trained to recognize the same location categories that were used in that study. We applied the system to 2640 images, and the system gave the same label as the previous assignments to 2139 images (81%). When only the highest confidence assignments were considered, 94.7% agreement was observed. Visual examination of the proteins for which the two approaches disagree suggests that at least some of the automated assignments may be more accurate. The automated method provides an objective, quantitative and repeatable assignment of protein locations that can be applied to new collections of yeast images (e.g. for different strains or the same strain under different conditions). It is also important to note that this performance could be achieved without requiring colocalization with any marker proteins.

Availability: The original images analyzed in this article are available at http://yeastgfp.ucsf.edu, and source code and results are available at http://murphylab.web.cmu.edu/software

Contact: murphy@cmu.edu
From protein microarrays to diagnostic antigen discovery: a study of the pathogen Francisella tularensis
Authors: Sundaresh Suman , Randall Arlo , Unal Berkay , Petersen Jeannine M. , Belisle John T. , Gill Hartley M. , Duffield Melanie , Titball Richard W. , Davies D. Huw , Felgner Philip L. , Baldi Pierre
Published: July 2007

Motivation: An important application of protein microarray data analysis is identifying a serodiagnostic antigen set that can reliably detect patterns and classify antigen expression profiles. This work addresses this problem using antibody responses to protein markers measured by a novel high-throughput microarray technology. The findings from this study have direct relevance to rapid, broad-based diagnostic and vaccine development.

Results: Protein microarray chips are probed with sera from individuals infected with the bacteria Francisella tularensis, a category A biodefense pathogen. A two-step approach to the diagnostic process is presented (1) feature (antigen) selection and (2) classification using antigen response measurements obtained from F.tularensis microarrays (244 antigens, 46 infected and 54 healthy human sera measurements). To select antigens, a ranking scheme based on the identification of significant immune responses and differential expression analysis is described. Classification methods including k-nearest neighbors, support vector machines (SVM) and k-Means clustering are applied to training data using selected antigen sets of various sizes. SVM based models yield prediction accuracy rates in the range of ∼90% on validation data, when antigen set sizes are between 25 and 50. These results strongly indicate that the top-ranked antigens can be considered high-priority candidates for diagnostic development.

Availability: All software programs are written in R and available at http://www.igb.uci.edu/index.php?page=tools and at http://www.r-project.org

Contact: pfbaldi@uci.edu

Supplementary information: Supplementary data are available at Bioinformatics online.