bluuebunny/crossref_metadata_2025 huggingface dataset:
Tachyons in general relativity
Authors: Schwartz Charles
Published: May 2011

We consider the motion of tachyons (faster-than-light particles) in the framework of general relativity. An important feature is the large contribution of low energy tachyons to the energy-momentum tensor. We also calculate the gravitational field produced by tachyons in particular geometric arrangements; and it appears that there could be self-cohering bundles of such matter. This leads us to suggest that such theoretical ideas might be relevant to major problems (dark matter and dark energy) in current cosmological models.
Diffractive optical elements based on plasmonic metamaterials
Authors: Walther B. , Helgert C. , Rockstuhl C. , Pertsch T.
Published: May 2011

The dispersive properties of plasmonic metamaterials and the ability to tailor their complex transmission strongly suggest their use in versatile optical elements. Here we introduce the idea of such an application in diffractive elements and describe, as a proof-of-principle, two numerical implementations of computer-generated holograms at visible wavelengths that are based on fishnet metamaterials. These holograms consist of large arrays of metamaterial unit cells which have locally varying geometrical parameters into which the desired far-field optical response is encoded. We describe the entire design process for such holograms, discuss their efficiency and critically assess their limitations.
Femtosecond Z-scan measurement of third-order nonlinear refractive indices of BaMgF4
Authors: Chen Junjie , Chen Xianfeng , Wu Anhua , Li Hongjun , Zheng Yuanlin , Ma Yanzhi , Jiang Linwen , Xu Jun
Published: May 2011

The BaMgF4 single crystal is grown by Bridgman method. The third-order nonlinear refractive indices along three crystallographic axes are determined by Z-scan technique with femtosecond laser. The largest one that has a value of 2.35×10−18 m2/W is along the c-axis and the corresponding third-order nonlinear susceptibility is 1.24×10−12 esu. This value is compared with LiNbO3 through self-phase modulation effect. Furthermore, the mechanism and the possible applications of the relatively large third-order nonlinear refractive indices are also discussed at last.
Determination of microdroplet contact angles using electrically driven droplet oscillations
Authors: Karadag Y. , Jonáš A. , Tasaltin N. , Kiraz A.
Published: May 2011

Oscillatory deformations of micrometer-sized NaCl-water droplets by an AC electric field are used for contact angle measurements on superhydrophobic surfaces. Contact angles are determined from the dependence of the lowest-order resonant frequency of the electrically driven droplet oscillations on the droplet size. The resonant frequency and size of a droplet are found using whispering gallery mode spectroscopy. Measurements are compared with those performed with direct mechanical driving of the droplets using a piezoelectric transducer, and a good agreement is found. The demonstrated contact angle measurement method can be readily integrated into the planar architecture of microfluidic chips.
Metallic and insulating adsorbates on graphene
Authors: McCreary K. M. , Pi K. , Kawakami R. K.
Published: May 2011

We directly compare the effect of metallic titanium (Ti) and insulating titanium dioxide (TiO2) on the transport properties of single layer graphene. The deposition of Ti results in substantial n-type doping and a reduction in graphene mobility by charged impurity scattering. Subsequent exposure to oxygen largely reduces the doping and scattering by converting Ti into TiO2. In addition, we observe evidence for short-range scattering by TiO2 impurities. These results illustrate the contrasting scattering mechanisms for identical spatial distributions of metallic and insulating adsorbates.
High power Sb-free quantum cascade laser emitting at 3.3 μm above 350 K
Authors: Bismuto A. , Beck M. , Faist J.
Published: May 2011

The design and implementation of a short wavelength strain-compensated quantum cascade laser based on In0.72Ga0.28As/In0.52Al0.48As–AlAs on InP is presented. We demonstrate watt-level room temperature emission at 3.3 μm. Lasers operate in pulsed mode above 350 K. Threshold current densities of 3.6 kA/cm2 and slope efficiencies of more than 600 mW/A are observed at room temperature. The laser performance is comparable with Sb-containing quantum cascade lasers.
Diversification history and hybridisation of Dacrydium (Podocarpaceae) in remote Oceania
Authors: Keppel Gunnar , Prentis Peter , Biffin Ed , Hodgskiss Paul , Tuisese Susana , Tuiwawa Marika V. , Lowe Andrew J.
Published: May 2011

We examined evolutionary relationships, hybridisation and genetic diversity in species of Dacrydium (Podocarpaceae) in Remote Oceania, where it is restricted to New Caledonia and Fiji. We used cpDNA sequence (trnL–trnF) data to construct a phylogeny and estimate taxon divergence by using a relaxed molecular clock approach. The phylogeny was verified using allozymes, which were also used to investigate genetic diversity of all species and the hybridisation dynamics of two endangered species, D. guillauminii and D. nidulum. Our results suggested that Dacrydium species in Remote Oceania form a monophyletic group that arose and diversified within the last 20 million years through long-distance dispersal and a range of speciation mechanisms. Whereas we detected no hybridisation between the Fijian species D. nausoriense and D. nidulum, we confirmed hybridisation between D. guillauminii and D. araucarioides in New Caledonia and determined introgression to be assymetric from the widespread D. araucarioides into the rare, restricted-range species D. guillauminii. In addition, D. guillauminii had lower genetic diversity than did the other species of Dacrydium studied, which had genetic diversity similar to that of other gymnosperms. Our results provided evidence for the recent and complex diversification of Dacrydium in Remote Oceania. In addition, low genetic diversity of and introgression from D. araucarioides, are of grave concern for the conservation of D. guillauminii.
Evaluation of different methods for assessing the reproductive mode of weeping lovegrass plants, Eragrostis curvula (Schrad.) Nees
Authors: Meier Mauro , Zappacosta Diego , Selva Juan Pablo , Pessino Silvina , Echenique Viviana
Published: May 2011

Weeping lovegrass is a forage grass cultivated in semiarid regions of the world that reproduces mainly by apomixis (diplospory), a process that involves the formation of asexual seeds and bypasses the processes of meiosis and fertilisation. The aim of this work was to evaluate and compare different techniques (cytoembryology, callose deposition, flow cytometry and progeny tests) to determine the reproductive mode of weeping lovegrass. Typical sexual and apomictic processes were clearly differentiated using cytoembryology, and different callose deposition patterns were observed in sexual and apomictic genotypes. Previous studies indicated that presence of callose on the cell wall of the megaspore mother cell is associated only with sexual processes. Nevertheless, our results also found callose deposition in apomictic genotypes, although clearly different from the pattern found in sexual processes, allowing discrimination between sexual and apomictic plants. Flow cytometry seed screening using individual seeds did not differentiate between sexual and apomictic plants as the embryo : endosperm DNA content ratio was similar in sexual and apomictic plants. Progeny tests using molecular markers showed uniform patterns in offspring from apomictic plants and variable patterns among the progeny of sexual plants. The results obtained from cytological studies and progeny tests were similar, indicating that both methods provide useful tools for determination of reproductive mode. However, the callose test with aniline blue was faster and easier to use than other techniques.
Abiotic factors modulate phenotypic plasticity in an apomictic shrub [Miconia albicans (SW.) Triana] along a soil fertility gradient in a Neotropical savanna
Authors: Bedetti Cibele S. , Aguiar Débora B. , Jannuzzi Maria C. , Moura Maria Z. D. , Silveira Fernando A. O.
Published: May 2011

Phenotypic plasticity is an important means by which plants cope with environmental heterogeneity; therefore, understanding variation in plant traits in heterogeneous habitats is important to predict responses to changing environments. In this study, we examined the patterns of intraspecific variation in leaf traits of Miconia albicans (Melastomataceae), a widespread, obligatory apomictic shrub, across a soil fertility gradient in the Cerrado (Brazilian savanna). We predicted high plasticity because selection favours high phenotypic plasticity in asexual populations with low genetic variability. Leaves were sampled in campo sujo (grassland), cerrado (savanna) and cerradão (woodland) in south-eastern Brazil during both dry and rainy seasons to calculate leaf area, specific leaf area, leaf tissue thickness, trichome and stomata density. We found significant between-season variation in leaf traits, indicating that the production of season-specific leaves is a strategy to cope with the strong seasonality. Both multivariate analysis and the relative distance plasticity index indicate lower plasticity during the dry season, especially under shade. Our results show that the phenotypic plasticity can be modulated by changes in abiotic factors and the combination of shade and drought can limit the expression of phenotypic plasticity.
Molecular genetic variation in a widespread forest tree species Eucalyptus obliqua (Myrtaceae) on the island of Tasmania
Authors: Bloomfield Justin A. , Nevill Paul , Potts Brad M. , Vaillancourt René E. , Steane Dorothy A.
Published: May 2011

Eucalyptus obliqua L’Hér. is widespread across south-eastern Australia. On the island of Tasmania it has a more-or-less continuous distribution across its range and it dominates much of the wet sclerophyll forest managed for forestry purposes. To understand better the distribution of genetic variation in these native forests we examined nuclear microsatellite diversity in 432 mature individuals from 20 populations of E. obliqua across Tasmania, including populations from each end of three locally steep environmental gradients. In addition, chloroplast microsatellite loci were assessed in 297 individuals across 31 populations. Nuclear microsatellite diversity values in E. obliqua were high (average HE = 0.80) and inbreeding coefficients low (average F = 0.02) within these populations. The degree of differentiation between populations was very low (FST = 0.015). No significant microsatellite differentiation could be found across three locally steep environmental gradients, even though there is significant genetic differentiation in quantitative traits. This suggests that the observed quantitative variation is maintained by natural selection. Population differentiation based on chloroplast haplotypes was high (GST = 0.69) compared with that based on nuclear microsatellites, suggesting that pollen-mediated gene flow is >150 times the level of seed-mediated gene flow in this animal-pollinated species; hence, pollen is likely to be the main mode of gene flow countering selection along local environmental gradients. Implications of these results for silvicultural practices are discussed.
Expertise and Gambling: Using Type 2 Signal Detection Theory to Investigate Differences between Regular Gamblers and Nongamblers
Authors: Lueddeke Sara E. , Higham Philip A.
Published: May 2011

This paper presents an experimental investigation into how individuals make decisions under uncertainty when faced with different payout structures in the context of gambling. Type 2 signal detection theory was utilized to compare sensitivity to bias manipulations between regular nonproblem gamblers and nongamblers in a novel probability-based gambling task. The results indicated that both regular gamblers and nongamblers responded to the changes of rewards for correct responses (Experiment 1) and penalties for errors (Experiment 2) in setting their gambling criteria, but that regular gamblers were more sensitive to these manipulations of bias. Regular gamblers also set gambling criteria that were more optimal. The results are discussed in terms of an expertise-transference hypothesis.
Nowhere-harmonic colorings of graphs
Authors: Beck Matthias , Braun Benjamin
Published: May 2011

Proper vertex colorings of a graph are related to its boundary map

∂
1

\partial _1

, also called its signed vertex-edge incidence matrix. The vertex Laplacian of a graph,

L
=

∂
1

∂
1
t

L=\partial _1 \partial _1^t

, a natural extension of the boundary map, leads us to introduce nowhere-harmonic colorings and analogues of the chromatic polynomial and Stanley’s theorem relating negative evaluations of the chromatic polynomial to acyclic orientations. Further, we discuss several examples demonstrating that nowhere-harmonic colorings are more complicated from an enumerative perspective than proper colorings.
On the connectedness of the branch locus of the moduli space of Riemann surfaces of low genus
Authors: Bartolini Gabriel , Izquierdo Milagros
Published: May 2011

Let

g
g

be an integer

≥
3

\ge 3

and let

B

g

=
{
X
∈

M

g

|

A
u
t
(
X
)
≠

1
d

}

\mathcal {B}_g = \{X\in \mathcal {M}_g | Aut(X)\neq 1_d \}

, where

M

g

\mathcal {M}_g

denotes the moduli space of compact Riemann surfaces of genus

g
g

. Using uniformization of Riemann surfaces by Fuchsian groups and the equisymmetric stratification of the branch locus of the moduli space, we prove that the subloci corresponding to Riemann surfaces with automorphism groups isomorphic to cyclic groups of order 2 and 3 belong to the same connected component. We also prove the connectedness of

B

g

\mathcal {B}_g

for

g
=
5
,
6
,
7

g=5,6,7

and

8
8

with the exception of the isolated points given by Kulkarni.
Probabilistic divergence measures for detecting interspecies

recombination
Authors: Husmeier Dirk , Wright Frank
Published: May 2011

This paper proposes a graphical method for detecting interspecies recombination in multiple alignments of DNA sequences. A fixed-size window is moved along a given DNA sequence alignment. For every position, the marginal posterior probability over tree topologies is determined by means of a Markov chain Monte Carlo simulation. Two probabilistic divergence measures are plotted along the alignment, and are used to identify recombinant regions. The method is compared with established detection methods on a set of synthetic benchmark sequences and two real-world DNA sequence alignments.

Contact: dirk@bioss.ac.uk
SCOPE: a probabilistic model for scoring tandem mass spectra
against a peptide database
Authors: Bafna Vineet , Edwards Nathan
Published: May 2011

Proteomics, or the direct analysis of the expressed protein components of a cell, is critical to our understanding of cellular biological processes in normal and diseased tissue. A key requirement for its success is the ability to identify proteins in complex mixtures. Recent technological advances in tandem mass spectrometry has made it the method of choice for high-throughput identification of proteins. Unfortunately, the software for unambiguously identifying peptide sequences has not kept pace with the recent hardware improvements in mass spectrometry instruments. Critical for reliable high-throughput protein identification, scoring functions evaluate the quality of a match between experimental spectra and a database peptide. Current scoring function technology relies heavily on ad-hoc parameterization and manual curation by experienced mass spectrometrists. In this work, we propose a two-stage stochastic model for the observed MS/MS spectrum, given a peptide. Our model explicitly incorporates fragment ion probabilities, noisy spectra, and instrument measurement error. We describe how to compute this probability based score efficiently, using a dynamic programming technique. A prototype implementation demonstrates the effectiveness of the model.

Contact: Vineet.Bafna@Celera.Com
Integrating genomic homology into gene structure prediction
Authors: Korf Ian , Flicek Paul , Duan Daniel , Brent Michael R.
Published: May 2011

Twinscan is a new gene-structure prediction system that directly extends the probability model of Genscan, allowing it to exploit homology between two related genomes. Separate probability models are used for conservation in exons, introns, splice sites, and UTRs, reflecting the differences among their patterns of evolutionary conservation. Twinscan is specifically designed for the analysis of high-throughput genomic sequences containing an unknown number of genes. In experiments on high-throughput mouse sequences, using homologous sequences from the human genome, Twinscan shows notable improvement over Genscan in exon sensitivity and specificity and dramatic improvement in exact gene sensitivity and specificity. This improvement can be attributed entirely to modeling the patterns of evolutionary conservation in genomic sequence.

Contact: ikorf@cs.wustl.edu; pflicek@cs.wustl.edu; duan@cs.wustl.edu; brent@cs.wustl.edu
Prediction of the coupling specificity of G protein coupled
receptors to their G proteins
Authors: Möller Steffen , Vilo Jaak , Croning Michael D.R.
Published: May 2011

G protein coupled receptors (GPCRs) are found in great numbers in most eukaryotic genomes. They are responsible for sensing a staggering variety of structurally diverse ligands, with their activation resulting in the initiation of a variety of cellular signalling cascades. The physiological response that is observed following receptor activation is governed by the guanine nucleotide-binding proteins (G proteins) to which a particular receptor chooses to couple. Previous investigations have demonstrated that the specificity of the receptor-G protein interaction is governed by the intracellular domains of the receptor. Despite many studies it has proven very difficult to predict de novo , from the receptor sequence alone, the G proteins to which a GPCR is most likely to couple. We have used a data-mining approach, combining pattern discovery with membrane topology prediction, to find patterns of amino acid residues in the intracellular domains of GPCR sequences that are specific for coupling to a particular functional class of G proteins. A prediction system was then built, being based upon these discovered patterns. We can report this approach was successful in the prediction of G protein coupling specificity of unknown sequences. Such predictions should be of great use in providing in silico characterisation of newly cloned receptor sequences and for improving the annotation of GPCRs stored in protein sequence databases. Available at: http://www.ebi.ac.uk/~croning/coupling.html

Contact: croning@ebi.ac.uk
Joint modeling of DNA sequence and physical properties to

improve eukaryotic promoter recognition
Authors: Ohler Uwe , Niemann Heinrich , Liao Guo-chun , Rubin Gerald M.
Published: May 2011

We present an approach to integrate physical properties of DNA, such as DNA bendability or GC content, into our probabilistic promoter recognition system McPromoter. In the new model, a promoter is represented as a sequence of consecutive segments represented by joint likelihoods for DNA sequence and profiles of physical properties. Sequence likelihoods are modeled with interpolated Markov chains, physical properties with Gaussian distributions. The background uses two joint sequence/profile models for coding and non-coding sequences, each consisting of a mixture of a sense and an anti-sense submodel. On a large Drosophila test set, we achieved a reduction of about 30% of false positives when compared with a model solely based on sequence likelihoods.

Contact: Uwe.Ohler@informatik.uni-erlangen.de
Designing better phages
Authors: Skiena Steven S.
Published: May 2011

We propose a method to engineer the genome of bacteriophages to increase their effectiveness as antibacterial agents. Specifically, we exploit the redundancy of the triplet code to design genomes that avoid restriction sites while producing the same proteins as wild-type phages. We give an efficient algorithm to minimize the number of restriction sites against sets of cutter sequences, and demonstrate that that phage genomes can be significantly protected against surprisingly large sets of enzymes with no loss of function. Finally, we develop a model to explain why evolution has failed to eliminate many possible restriction sites despite selective pressure, thus motivating the need for genome-level sequence engineering.

Contact: skiena@cs.sunysb.edu
0j.py: a software tool for low complexity proteins and protein

domains
Authors: Wise Michael J.
Published: May 2011

Low complexity proteins and protein domains have sequences which appear highly non-random. Over the years, these sequences have been routinely filtered out during sequence similarity searches because interest has been focused on globular proteins, and inclusion of these domains can severely skew search results. However, early work on these proteins and more recent studies of the related area of repeated protein sequences suggests that low complexity protein domains have function and therefore are in need of further investigation. 0j.py is a new tool for demarcating low complexity protein domains more accurately than has been possible to date. The paper describes 0j.py and its use in revealing proteins with repeated and poly-amino-acid peptides. Statistical methods are then employed to to examine the distribution of these proteins across species, while keyword clustering is used to suggest roles performed by proteins through the use of low complexity domains.

Contact: M.Wise@ccsr.cam.ac.uk
Protein-protein interaction map inference using interacting

domain profile pairs
Authors: Wojcik Jérôme , Schächter Vincent
Published: May 2011

A number of predictive methods have been designed to predict protein interaction from sequence or expression data. On the experimental front, however, high-throughput proteomics technologies are starting to yield large volumes of protein-protein interaction data. High-quality experimental protein interaction maps constitute the natural dataset upon which to build interaction predictions. Thus the motivation to develop the first interaction-based protein interaction map prediction algorithm.

A technique to predict protein-protein interaction maps across organisms is introduced, the ‘interaction-domain pair profile’ method. The method uses a high-quality protein interaction map with interaction domain information as input to predict an interaction map in another organism. It combines sequence similarity searches with clustering based on interaction patterns and interaction domain information. We apply this approach to the prediction of an interaction map of Escherichia coli from the recently published interaction map of the human gastric pathogen Helicobacter pylori. Results are compared with predictions of a second inference method based only on full-length protein sequence similarity — the “naive” method. The domain-based method is shown to i) eliminate a significant amount of false-positives of the naive method that are the consequences of multi-domain proteins; ii) increase the sensitivity compared to the naive method by identifying new potential interactions.

Availability: Contact the authors.

Contact: vschachter@hybrigenics.fr
CLIFF: clustering of high-dimensional microarray data via

iterative feature filtering using normalized cuts
Authors: Xing Eric P. , Karp Richard M.
Published: May 2011

We present CLIFF, an algorithm for clustering biological samples using gene expression microarray data. This clustering problem is difficult for several reasons, in particular the sparsity of the data, the high dimensionality of the feature (gene) space, and the fact that many features are irrelevant or redundant. Our algorithm iterates between two computational processes, feature filtering and clustering. Given a reference partition that approximates the correct clustering of the samples, our feature filtering procedure ranks the features according to their intrinsic discriminability, relevance to the reference partition, and irredundancy to other relevant features, and uses this ranking to select the features to be used in the following round of clustering. Our clustering algorithm, which is based on the concept of a normalized cut, clusters the samples into a new reference partition on the basis of the selected features. On a well-studied problem involving 72 leukemia samples and 7130 genes, we demonstrate that CLIFF outperforms standard clustering approaches that do not consider the feature selection issue, and produces a result that is very close to the original expert labeling of the sample set.

Contact: epxing@cs.berkeley.edu
Molecular classification of multiple tumor types
Authors: Yeang Chen-Hsiang , Ramaswamy Sridhar , Tamayo Pablo , Mukherjee Sayan , Rifkin Ryan M. , Angelo Michael , Reich Michael , Lander Eric , Mesirov Jill , Golub Todd
Published: May 2011

Using gene expression data to classify tumor types is a very promising tool in cancer diagnosis. Previous works show several pairs of tumor types can be successfully distinguished by their gene expression patterns (Golub et al. 1999, Ben-Dor et al. 2000, Alizadeh et al. 2000). However, the simultaneous classification across a heterogeneous set of tumor types has not been well studied yet. We obtained 190 samples from 14 tumor classes and generated a combined expression dataset containing 16063 genes for each of those samples. We performed multi-class classification by combining the outputs of binary classifiers. Three binary classifiers (k-nearest neighbors, weighted voting, and support vector machines) were applied in conjunction with three combination scenarios (one-vs-all, all-pairs, hierarchical partitioning). We achieved the best cross validation error rate of 18.75% and the best test error rate of 21.74% by using the one-vs-all support vector machine algorithm. The results demonstrate the feasibility of performing clinically useful classification from samples of multiple tumor types.

Contact: chyeang@mit.edu
Visualizing associations between genome sequences and gene
expression data using genome-mean expression profiles
Authors: Chiang Derek Y. , Brown Patrick O. , Eisen Michael B.
Published: May 2011

The combination of genome-wide expression patterns and full genome sequences offers a great opportunity to further our understanding of the mechanisms and logic of transcriptional regulation. Many methods have been described that identify sequence motifs enriched in transcription control regions of genes that share similar gene expression patterns. Here we present an alternative approach that evaluates the transcriptional information contained by specific sequence motifs by computing for each motif the mean expression profile of all genes that contain the motif in their transcription control regions. These genome-mean expression profiles (GMEP’s) are valuable for visualizing the relationship between genome sequences and gene expression data, and for characterizing the transcriptional importance of specific sequence motifs. Analysis of GMEP’s calculated from a dataset of 519 whole-genome microarray experiments in Saccharomyces cerevisiae show a significant correlation between GMEP’s of motifs that are reverse complements, a result that supports the relationship between GMEP’s and transcriptional regulation. Hierarchical clustering of GMEP’s identifies clusters of motifs that correspond to binding sites of well-characterized transcription factors. The GMEP’s of these clustered motifs have patterns of variation across conditions that reflect the known activities of these transcription factors.

Software that computed GMEP’s from sequence and gene expression data is available under the terms of the Gnu Public License from http://rana.lbl.gov/

Contact: mbeisen@lbl.gov
Using mixtures of common ancestors for estimating the
probabilities of discrete events in biological sequences
Authors: Eskin Eleazar , Grundy William N. , Singer Yoram
Published: May 2011

Accurately estimating probabilities from observations is important for probabilistic-based approaches to problems in computational biology. In this paper we present a biologically-motivated method for estimating probability distributions over discrete alphabets from observations using a mixture model of common ancestors. The method is an extension of substitution matrix-based probability estimation methods. In contrast to previous such methods, our method has a simple Bayesian interpretation and has the advantage over Dirichlet mixtures that it is both effective and simple to compute for large alphabets. The method is applied to estimate amino acid probabilities based on observed counts in an alignment and is shown to perform comparably to previous methods. The method is also applied to estimate probability distributions over protein families and improves protein classification accuracy.

Contact: eeskin@cs.columbia.edu
Promoter prediction in the human genome
Authors: Hannenhalli Sridhar , Levy Samuel
Published: May 2011

Computational prediction of eukaryotic polII promoters has been one of the most elusive problems despite considerable effort devoted to the study. Researchers have looked for various types of signals around the transcriptional start site (TSS), viz. oligo-nucleotide statistics, potential binding sites for core factors, clusters of binding sites, proximity to CpG islands etc.. The proximity of CpG islands to gene starts is now a well established fact, although until recently, it was based on very little genomic data. In this work we explore the possibility of enhancing the promoter prediction accuracy by combining CpG island information with a few other, biologically motivated, seemingly independent signals, that cover most of the known knowledge. We benchmarked the method on a much larger genomic datasets compared to previous studies. We were able to improve slightly upon current prediction accuracy. Furthermore, we observe that CpG islands are the most dominant signals and the other signals do not improve the prediction. This suggests that the computational prediction of promoters for genes with no associated CpG-island (typically having tissue-specific expression) looking only at the immediate neighborhood of the TSS may not even be possible. We suggest some biological experiments and studies to better understand the biology of transcription.

Contact: Sridhar.Hannenhalli@celera.com; Samuel.Levy@celera.com
Disambiguating proteins, genes, and RNA in text: a machine
learning approach
Authors: Hatzivassiloglou Vasileios , Duboué Pablo
   A. , Rzhetsky Andrey
Published: May 2011

We present an automated system for assigning protein, gene, or mRNA class labels to biological terms in free text. Three machine learning algorithms and several extended ways for defining contextual features for disambiguation are examined, and a fully unsupervised manner for obtaining training examples is proposed. We train and evaluate our system over a collection of 9 million words of molecular biology journal articles, obtaining accuracy rates up to 85%.

Contact: vh@cs.columbia.edu
A variance-stabilizing transformation for gene-expression
microarray data
Authors: Durbin B.P. , Hardin J.S. , Hawkins D.M. , Rocke D.M.
Published: May 2011

Motivation: Standard statistical techniques often assume that data are normally distributed, with constant variance not depending on the mean of the data. Data that violate these assumptions can often be brought in line with the assumptions by application of a transformation. Gene-expression microarray data have a complicated error structure, with a variance that changes with the mean in a non-linear fashion. Log transformations, which are often applied to microarray data, can inflate the variance of observations near background.

Results: We introduce a transformation that stabilizes the variance of microarray data across the full range of expression. Simulation studies also suggest that this transformation approximately symmetrizes microarray data.

Contact: bpdurbin@wald.ucdavis.edu

Keywords: cDNA array; microarray; statistical analysis; transformation; normalization.
Microarray synthesis through multiple-use PCR primer design
Authors: Fernandes Rohan J. , Skiena Steven S.
Published: May 2011

A substantial percentage of the expense in constructing full-genome spotted microarrays comes from the cost of synthesizing the PCR primers to amplify the desired DNA. We propose a computationally-based method to substantially reduce this cost. Historically, PCR primers are designed so that each primer occurs uniquely in the genome. This condition is unnecessarily strong for selective amplification, since only the primer pair associated with each amplification need be unique. We demonstrate that careful design in a genome-level amplification project permits us to save the cost of several thousand primers over conventional approaches.

Contact: skiena@cs.sunysb.edu; rohan@cs.sunysb.edu

Keywords: PCR; primer design; microarray synthesis.
Discovering statistically significant biclusters in gene expression data
Authors: Tanay Amos , Sharan Roded , Shamir Ron
Published: May 2011

In gene expression data, a bicluster is a subset of the genes exhibiting consistent patterns over a subset of the conditions. We propose a new method to detect significant biclusters in large expression datasets. Our approach is graph theoretic coupled with statistical modelling of the data. Under plausible assumptions, our algorithm is polynomial and is guaranteed to find the most significant biclusters. We tested our method on a collection of yeast expression profiles and on a human cancer dataset. Cross validation results show high specificity in assigning function to genes based on their biclusters, and we are able to annotate in this way 196 uncharacterized yeast genes. We also demonstrate how the biclusters lead to detecting new concrete biological associations. In cancer data we are able to detect and relate finer tissue types than was previously possible. We also show that the method outperforms the biclustering algorithm of Cheng and Church (2000).

Contact: amos@tau.ac.il; roded@tau.ac.il; rshamir@tau.ac.il

Availability:

*These authors contributed equally to this work.
Co-clustering of biological networks and gene expression data
Authors: Hanisch Daniel , Zien Alexander , Zimmer Ralf , Lengauer Thomas
Published: May 2011

Motivation: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably.

Results: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.

Contact: Daniel.Hanisch@scai.fhg.de

Keywords: gene expression; biological networks; metabolic networks; co-clustering; clustering.
The degenerate primer design problem
Authors: Linhart Chaim , Shamir Ron
Published: May 2011

A PCR primer sequence is called degenerate if some of its positions have several possible bases. The degeneracy of the primer is the number of unique sequence combinations it contains. We study the problem of designing a pair of primers with prescribed degeneracy that match a maximum number of given input sequences. Such problems occur when studying a family of genes that is known only in part, or is known in a related species. We prove that various simplified versions of the problem are hard, show the polynomiality of some restricted cases, and develop approximation algorithms for one variant. Based on these algorithms, we implemented a program called hyden for designing highly-degenerate primers for a set of genomic sequences. We report on the success of the program in an experimental scheme for identifying all human olfactory receptor (OR) genes. In that project, hyden was used to design primers with degeneracies up to 1010 that amplified with high specificity many novel genes of that family, tripling the number of OR genes known at the time.

Availability: Available on request from the authors.

Contact: chaiml@tau.ac.il; rshamir@tau.ac.il

Keywords: PCR primer design; degenerate primers; optimization; human olfactory subgenome; protein families.
Splicing graphs and EST assembly problem
Authors: Heber Steffen , Alekseyev Max , Sze Sing-Hoi , Tang Haixu , Pevzner Pavel A.
Published: May 2011

Motivation: The traditional approach to annotate alternative splicing is to investigate every splicing variant of the gene in a case-by-case fashion. This approach, while useful, has some serious shortcomings. Recent studies indicate that alternative splicing is more frequent than previously thought and some genes may produce tens of thousands of different transcripts. A list of alternatively spliced variants for such genes would be difficult to build and hard to analyse. Moreover, such a list does not show the relationships between different transcripts and does not show the overall structure of all transcripts. A better approach would be to represent all splicing variants for a given gene in a way that captures the relationships between different splicing variants.

Results: We introduce the notion of the splicing graph that is a natural and convenient representation of all splicing variants. The key difference with the existing approaches is that we abandon the linear (sequence) representation of each transcript and replace it with a graph representation where each transcript corresponds to a path in the graph. We further design an algorithm to assemble EST reads into the splicing graph rather than assembling them into each splicing variant in a case-by-case fashion.

Availability: http://www-cse.ucsd.edu/groups/bioinformatics/software.html

Contact: sheber@ucsd.edu

Keywords: EST assembly; splicing graph; alternative splicing
Discovering regulatory and signalling circuits in molecular
interaction networks
Authors: Ideker Trey , Ozier Owen , Schwikowski Benno , Siegel Andrew F.
Published: May 2011

Motivation: In model organisms such as yeast, large databases of protein–protein and protein-DNA interactions have become an extremely important resource for the study of protein function, evolution, and gene regulatory dynamics. In this paper we demonstrate that by integrating these interactions with widely-available mRNA expression data, it is possible to generate concrete hypotheses for the underlying mechanisms governing the observed changes in gene expression. To perform this integration systematically and at large scale, we introduce an approach for screening a molecular interaction network to identify active subnetworks, i.e., connected regions of the network that show significant changes in expression over particular subsets of conditions. The method we present here combines a rigorous statistical measure for scoring subnetworks with a search algorithm for identifying subnetworks with high score.

Results: We evaluated our procedure on a small network of 332 genes and 362 interactions and a large network of 4160 genes containing all 7462 protein–protein and protein-DNA interactions in the yeast public databases. In the case of the small network, we identified five significant subnetworks that covered 41 out of 77 (53%) of all significant changes in expression. Both network analyses returned several top-scoring subnetworks with good correspondence to known regulatory mechanisms in the literature. These results demonstrate how large-scale genomic approaches may be used to uncover signalling and regulatory pathways in a systematic, integrative fashion.

Availability: The methods presented in this paper are implemented in the Cytoscape software package which is available to the academic community at http://www.cytoscape.org.

Contact: trey@wi.mit.edu

Keywords: molecular interactions; gene expression; data integration; simulated annealing; Monte carlo methods.

*To whom correspondence should be addressed.
Minreg: Inferring an active regulator set
Authors: Pe'er Dana , Regev Aviv , Tanay Amos
Published: May 2011

Regulatory relations between genes are an important component of molecular pathways. Here, we devise a novel global method that uses a set of gene expression profiles to find a small set of relevant active regulators, identify the genes that they regulate, and automatically annotate them. We show that our algorithm is capable of handling a large number of genes in a short time and is robust to a wide range of parameters. We apply our method to a combined dataset of S. cerevisiae expression profiles, and validate the resulting model of regulation by cross-validation and extensive biological analysis of the selected regulators and their derived annotations.

Keywords: gene expression; gene regulation; gene networks; machine learning.
Marginalized kernels for biological sequences
Authors: Tsuda Koji , Kin Taishin , Asai Kiyoshi
Published: May 2011

Motivation: Kernel methods such as support vector machines require a kernel function between objects to be defined a priori. Several works have been done to derive kernels from probability distributions, e.g., the Fisher kernel. However, a general methodology to design a kernel is not fully developed.

Results: We propose a reasonable way of designing a kernel when objects are generated from latent variable models (e.g., HMM). First of all, a joint kernel is designed for complete data which include both visible and hidden variables. Then a marginalized kernel for visible data is obtained by taking the expectation with respect to hidden variables. We will show that the Fisher kernel is a special case of marginalized kernels, which gives another viewpoint to the Fisher kernel theory. Although our approach can be applied to any object, we particularly derive several marginalized kernels useful for biological sequences (e.g., DNA and proteins). The effectiveness of marginalized kernels is illustrated in the task of classifying bacterial gyrase subunit B (gyrB) amino acid sequences.

Contact: koji.tsuda@aist.go.jp

Keywords: kernel design; marginalized kernels; the Fisher kernel; biological sequence classification; string kernels.
Efficient multiple genome alignment
Authors: Höhl Michael , Kurtz Stefan , Ohlebusch Enno
Published: May 2011

Motivation: To allow a direct comparison of the genomic DNA sequences of sufficiently similar organisms, there is an urgent need for software tools that can align more than two genomic sequences.

Results: We developed new algorithms and a software tool ‘Multiple Genome Aligner’ (MGA for short) that efficiently computes multiple genome alignments of large, closely related DNA sequences. For example, it can align 85% percent of the complete genomes of six human adenoviruses (average length 35305 bp.) in 159 seconds. An alignment of 74% of the complete genomes of three of strains of E. coli (lengths: 5528445; 5498450; 4639221~bp.) is produced in 30 minutes.

Availability: The software MGA is available free of charge for non-commercial research institutions. For details see http://bibiserv.techfak.uni-bielefeld.de/mga/

Contact: kurtz@techfak.uni-bielefeld.deenno@techfak.uni-bielefeld.de

Keywords: genome comparison; multiple alignment; efficient algorithms; graph algorithms; suffix trees.
PseudoViewer: automatic visualization of RNA pseudoknots
Authors: Han Kyungsook , Lee Yujin , Kim Wootaek
Published: May 2011

Motivation: Several algorithms have been developed for drawing RNA secondary structures, however none of these can be used to draw RNA pseudoknot structures. In the sense of graph theory, a drawing of RNA secondary structures is a tree, whereas a drawing of RNA pseudoknots is a graph with inner cycles within a pseudoknot as well as possible outer cycles formed between a pseudoknot and other structural elements. Thus, RNA pseudoknots are more difficult to visualize than RNA secondary structures. Since no automatic method for drawing RNA pseudoknots exists, visualizing RNA pseudoknots relies on significant amount of manual work and does not yield satisfactory results. The task of visualizing RNA pseudoknots by hand becomes more challenging as the size and complexity of the RNA pseudoknots increase.

Results: We have developed a new representation and an algorithm for drawing H-type pseudoknots with RNA secondary structures. Compared to existing representations of H-type pseudoknots, the new representation ensures uniform and clear drawings with no edge crossing for any H-type pseudoknots. To the best of our knowledge, this is the first algorithm for automatically drawing RNA pseudoknots with RNA secondary structures. The algorithm has been implemented in a Java program, which can be executed on any computing system. Experimental results demonstrate that the algorithm generates an aesthetically pleasing drawing of all H-type pseudoknots. The results have also shown that the drawing has high readability, enabling the user to quickly and easily recognize the whole RNA structure as well as the pseudoknots themselves.

Availability: All algorithms are available on request from the corresponding author.

Contact: khan@inha.ac.kr

Keywords: RNA structure; H-type pseudoknot; visualization, graph drawing.

*To whom correspondence should be addressed.
A powerful non-homology method for the prediction of
operons in prokaryotes
Authors: Moreno-Hagelsieb Gabriel , Collado-Vides Julio
Published: May 2011

Motivation: The prediction of the transcription unit organization of genomes is an important clue in the inference of functional relationships of genes, the interpretation and evaluation of transcriptome experiments, and the overall inference of the regulatory networks governing the expression of genes in response to the environment. Though several methods have been devised to predict operons, most need a high characterization of the genome analysed. Log-likelihoods derived from inter-genic distance distributions work surprisingly well to predict operons in Escherichia coli and are available for any genome as soon as the gene sets are predicted.

Results: Here we provide evidence that the very same method is applicable to any prokaryotic genome. First, the method has the same efficiency when evaluated using a collection of experimentally known operons of Bacillus subtilis. Second, operons among most if not all prokaryotes seem to have the same tendencies to keep short distances between their genes, the most frequent distances being the overlaps of four and one base pairs. The universality of this structural feature allows us to predict the organization of transcription units in all prokaryotes. Third, predicted operons contain a higher proportion of genes with related phylogenetic profiles and conservation of adjacency than predicted borders of transcription units.

Supplementary information: Additional materials and graphs, are available at: http://www.cifn.unam.mx/moreno/pub/TUpredictions/

Contact: moreno@cifn.unam.mx

Keywords: functional genomics; comparative genomics; operon prediction; operon structure.

*To whom correspondence should be addressed.