bluuebunny/crossref_metadata_2025 huggingface dataset:
Cell separation using tilted-angle standing surface acoustic waves
Authors: Ding Xiaoyun , Peng Zhangli , Lin Sz-Chin Steven , Geri Michela , Li Sixing , Li Peng , Chen Yuchao , Dao Ming , Suresh Subra , Huang Tony Jun
Published: August 2014

Significance

We have developed a unique approach for the separation of particles and biological cells through standing surface acoustic waves oriented at an optimum angle to the fluid flow direction in a microfluidic device. This experimental setup, optimized by systematic analyses, has been used to demonstrate effective separation based on size, compressibility, and mechanical properties of particles and cells. The potential of this method for biological–biomedical applications was demonstrated through the example of isolating MCF-7 breast cancer cells from white blood cells. The method offers a possible route for label-free particle or cell separation for many applications in research, disease diagnosis, and drug-efficacy assessment.
Directed evolution of a far-red fluorescent rhodopsin
Authors: McIsaac R. Scott , Engqvist Martin K. M. , Wannier Timothy , Rosenthal Adam Z. , Herwig Lukas , Flytzanis Nicholas C. , Imasheva Eleonora S. , Lanyi Janos K. , Balashov Sergei P. , Gradinaru Viviana , Arnold Frances H.
Published: August 2014

Significance

Archaerhodopsin-3 (Arch) is an integral membrane protein that can function as a genetically encoded fluorescent indicator of membrane voltage in neurons. The ability to visualize changes in membrane voltage is of great interest as a readout for neuronal activity. Published variants of this protein, however, are too dim to enable wide-field imaging of cell populations. We used directed evolution to increase the absolute brightness of Arch as a reporter for optogenetics research and live-cell imaging. This study establishes that introducing mutations around the retinal Schiff-base linkage and screening for increased fluorescence is an effective strategy for generating bright rhodopsin variants. At least some mutations discovered in one rhodopsin (
Gloeobacter violaceus
rhodopsin) can be transferred to another (Arch) to increase fluorescence.
Structural basis for the fast self-cleavage reaction catalyzed by the twister ribozyme
Authors: Eiler Daniel , Wang Jimin , Steitz Thomas A.
Published: August 2014

Significance

Twister is a small self-cleaving ribozyme similar in size to the hammerhead ribozyme but uses an orthogonal fold for a similar catalytic rate constant. However, the mechanistic source of the catalytic rate increase generated by twister was unknown. We present crystal structures of twister from
Orzyza sativa
as well as a twister sequence from an organism that has not been cultured in isolation and identify RNA nucleotides that are vital for self-cleavage, suggest their catalytic roles, and update twister’s conserved secondary structure model.
Mapping the epistemic landscape in innovation workshops
Authors: Landgrebe Jeanette , Heinemann Trine
Published: August 2014

This article addresses the epistemic domain of adult make-believe activities in innovation workshops. In particular, we demonstrate how adults initiate imaginary transformations of objects while displaying an orientation to a general order of make-believe in which everyone has equal epistemic rights, and how this can be displayed both verbally and nonverbally. This distribution of equal rights is only overridden by external or locally derived roles, and once invoked they override the general preference for epistemic symmetry, after which interlocutors orient to establishing epistemic congruence, despite the obvious presence of epistemic asymmetry.
Passive transfer of modest titers of potent and broadly neutralizing anti-HIV monoclonal antibodies block SHIV infection in macaques
Authors: Shingai Masashi , Donau Olivia K. , Plishka Ronald J. , Buckler-White Alicia , Mascola John R. , Nabel Gary J. , Nason Martha C. , Montefiori David , Moldt Brian , Poignard Pascal , Diskin Ron , Bjorkman Pamela J. , Eckhaus Michael A. , Klein Florian , Mouquet Hugo , Cetrulo Lorenzi Julio Cesar , Gazumyan Anna , Burton Dennis R. , Nussenzweig Michel C. , Martin Malcolm A. , Nishimura Yoshiaki
Published: August 2014

It is widely appreciated that effective human vaccines directed against viral pathogens elicit neutralizing antibodies (NAbs). The passive transfer of anti–HIV-1 NAbs conferring sterilizing immunity to macaques has been used to determine the plasma neutralization titers, which must be present at the time of exposure, to prevent acquisition of SIV/HIV chimeric virus (SHIV) infections. We administered five recently isolated potent and broadly acting anti-HIV neutralizing monoclonal antibodies (mAbs) to rhesus macaques and challenged them intrarectally 24 h later with either of two different R5-tropic SHIVs. By combining the results obtained from 60 challenged animals, we determined that the protective neutralization titer in plasma preventing virus infection in 50% of the exposed monkeys was relatively modest (∼1:100) and potentially achievable by vaccination.
Distinguished conjugacy classes and elliptic Weyl group elements
Authors: Lusztig G.
Published: August 2014

We define and study a correspondence between the set of distinguishedG0G^0-conjugacy classes in a fixed connected component of a reductive groupGG(withG0G^0almost simple) and the set of (twisted) elliptic conjugacy classes in the Weyl group. We also prove a homogeneity property related to this correspondence.
Lambda: the local aligner for massive biological data
Authors: Hauswedell Hannes , Singer Jochen , Reinert Knut
Published: August 2014

Motivation: Next-generation sequencing technologies produce unprecedented amounts of data, leading to completely new research fields. One of these is metagenomics, the study of large-size DNA samples containing a multitude of diverse organisms. A key problem in metagenomics is to functionally and taxonomically classify the sequenced DNA, to which end the well-known BLAST program is usually used. But BLAST has dramatic resource requirements at metagenomic scales of data, imposing a high financial or technical burden on the researcher. Multiple attempts have been made to overcome these limitations and present a viable alternative to BLAST.

Results: In this work we present Lambda, our own alternative for BLAST in the context of sequence classification. In our tests, Lambda often outperforms the best tools at reproducing BLAST’s results and is the fastest compared with the current state of the art at comparable levels of sensitivity.

Availability and implementation: Lambda was implemented in the SeqAn open-source C++ library for sequence analysis and is publicly available for download at http://www.seqan.de/projects/lambda.

Contact:  hannes.hauswedell@fu-berlin.de

Supplementary information:  Supplementary data are available at Bioinformatics online.
Fiona: a parallel and automatic strategy for read error correction
Authors: Schulz Marcel H. , Weese David , Holtgrewe Manuel , Dimitrova Viktoria , Niu Sijia , Reinert Knut , Richard Hugues
Published: August 2014

Motivation: Automatic error correction of high-throughput sequencing data can have a dramatic impact on the amount of usable base pairs and their quality. It has been shown that the performance of tasks such as de novo genome assembly and SNP calling can be dramatically improved after read error correction. While a large number of methods specialized for correcting substitution errors as found in Illumina data exist, few methods for the correction of indel errors, common to technologies like 454 or Ion Torrent, have been proposed.

Results: We present Fiona, a new stand-alone read error–correction method. Fiona provides a new statistical approach for sequencing error detection and optimal error correction and estimates its parameters automatically. Fiona is able to correct substitution, insertion and deletion errors and can be applied to any sequencing technology. It uses an efficient implementation of the partial suffix array to detect read overlaps with different seed lengths in parallel. We tested Fiona on several real datasets from a variety of organisms with different read lengths and compared its performance with state-of-the-art methods. Fiona shows a constantly higher correction accuracy over a broad range of datasets from 454 and Ion Torrent sequencers, without compromise in speed.

Conclusion: Fiona is an accurate parameter-free read error–correction method that can be run on inexpensive hardware and can make use of multicore parallelization whenever available. Fiona was implemented using the SeqAn library for sequence analysis and is publicly available for download at http://www.seqan.de/projects/fiona.

Contact: mschulz@mmci.uni-saarland.de or hugues.richard@upmc.fr

Supplementary information:  Supplementary data are available at Bioinformatics online.
FastHap: fast and accurate single individual haplotype reconstruction using fuzzy conflict graphs
Authors: Mazrouee Sepideh , Wang Wei
Published: August 2014

Motivation: Understanding exact structure of an individual’s haplotype plays a significant role in various fields of human genetics. Despite tremendous research effort in recent years, fast and accurate haplotype reconstruction remains as an active research topic, mainly owing to the computational challenges involved. Existing haplotype assembly algorithms focus primarily on improving accuracy of the assembly, making them computationally challenging for applications on large high-throughput sequence data. Therefore, there is a need to develop haplotype reconstruction algorithms that are not only accurate but also highly scalable.

Results: In this article, we introduce FastHap, a fast and accurate haplotype reconstruction approach, which is up to one order of magnitude faster than the state-of-the-art haplotype inference algorithms while also delivering higher accuracy than these algorithms. FastHap leverages a new similarity metric that allows us to precisely measure distances between pairs of fragments. The distance is then used in building the fuzzy conflict graphs of fragments. Given that optimal haplotype reconstruction based on minimum error correction is known to be NP-hard, we use our fuzzy conflict graphs to develop a fast heuristic for fragment partitioning and haplotype reconstruction.

Availability: An implementation of FastHap is available for sharing on request.

Contact: sepideh@cs.ucla.edu
Broad-Enrich: functional interpretation of large sets of broad genomic regions
Authors: Cavalcante Raymond G. , Lee Chee , Welch Ryan P. , Patil Snehal , Weymouth Terry , Scott Laura J. , Sartor Maureen A.
Published: August 2014

Motivation: Functional enrichment testing facilitates the interpretation of Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) data in terms of pathways and other biological contexts. Previous methods developed and used to test for key gene sets affected in ChIP-seq experiments treat peaks as points, and are based on the number of peaks associated with a gene or a binary score for each gene. These approaches work well for transcription factors, but histone modifications often occur over broad domains, and across multiple genes.

Results: To incorporate the unique properties of broad domains into functional enrichment testing, we developed Broad-Enrich, a method that uses the proportion of each gene’s locus covered by a peak. We show that our method has a well-calibrated false-positive rate, performing well with ChIP-seq data having broad domains compared with alternative approaches. We illustrate Broad-Enrich with 55 ENCODE ChIP-seq datasets using different methods to define gene loci. Broad-Enrich can also be applied to other datasets consisting of broad genomic domains such as copy number variations.

Availability and implementation:  http://broad-enrich.med.umich.edu for Web version and R package.

Contact:  sartorma@umich.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
Modeling DNA methylation dynamics with approaches from phylogenetics
Authors: Capra John A. , Kostka Dennis
Published: August 2014

Motivation: Methylation of CpG dinucleotides is a prevalent epigenetic modification that is required for proper development in vertebrates. Genome-wide DNA methylation assays have become increasingly common, and this has enabled characterization of DNA methylation in distinct stages across differentiating cellular lineages. Changes in CpG methylation are essential to cellular differentiation; however, current methods for modeling methylation dynamics do not account for the dependency structure between precursor and dependent cell types.

Results: We developed a continuous-time Markov chain approach, based on the observation that changes in methylation state over tissue differentiation can be modeled similarly to DNA nucleotide changes over evolutionary time. This model explicitly takes precursor to descendant relationships into account and enables inference of CpG methylation dynamics. To illustrate our method, we analyzed a high-resolution methylation map of the differentiation of mouse stem cells into several blood cell types. Our model can successfully infer unobserved CpG methylation states from observations at the same sites in related cell types (90% correct), and this approach more accurately reconstructs missing data than imputation based on neighboring CpGs (84% correct). Additionally, the single CpG resolution of our methylation dynamics estimates enabled us to show that DNA sequence context of CpG sites is informative about methylation dynamics across tissue differentiation. Finally, we identified genomic regions with clusters of highly dynamic CpGs and present a likely functional example. Our work establishes a framework for inference and modeling that is well suited to DNA methylation data, and our success suggests that other methods for analyzing DNA nucleotide substitutions will also translate to the modeling of epigenetic phenomena.

Availability and implementation: Source code is available at www.kostkalab.net/software.

Contact: tony.capra@vanderbilt.edu or kostka@pitt.edu
Alignment-free protein interaction network comparison
Authors: Ali Waqar , Rito Tiago , Reinert Gesine , Sun Fengzhu , Deane Charlotte M.
Published: August 2014

Motivation: Biological network comparison software largely relies on the concept of alignment where close matches between the nodes of two or more networks are sought. These node matches are based on sequence similarity and/or interaction patterns. However, because of the incomplete and error-prone datasets currently available, such methods have had limited success. Moreover, the results of network alignment are in general not amenable for distance-based evolutionary analysis of sets of networks. In this article, we describe Netdis, a topology-based distance measure between networks, which offers the possibility of network phylogeny reconstruction.

Results: We first demonstrate that Netdis is able to correctly separate different random graph model types independent of network size and density. The biological applicability of the method is then shown by its ability to build the correct phylogenetic tree of species based solely on the topology of current protein interaction networks. Our results provide new evidence that the topology of protein interaction networks contains information about evolutionary processes, despite the lack of conservation of individual interactions. As Netdis is applicable to all networks because of its speed and simplicity, we apply it to a large collection of biological and non-biological networks where it clusters diverse networks by type.

Availability and implementation: The source code of the program is freely available at http://www.stats.ox.ac.uk/research/proteins/resources.

Contact:  w.ali@stats.ox.ac.uk

Supplementary information:  Supplementary data are available at Bioinformatics online.
Identifying transcription factor complexes and their roles
Authors: Will Thorsten , Helms Volkhard
Published: August 2014

Motivation: Eukaryotic gene expression is controlled through molecular logic circuits that combine regulatory signals of many different factors. In particular, complexation of transcription factors (TFs) and other regulatory proteins is a prevailing and highly conserved mechanism of signal integration within critical regulatory pathways and enables us to infer controlled genes as well as the exerted regulatory mechanism. Common approaches for protein complex prediction that only use protein interaction networks, however, are designed to detect self-contained functional complexes and have difficulties to reveal dynamic combinatorial assemblies of physically interacting proteins.

Results: We developed the novel algorithm DACO that combines protein–protein interaction networks and domain–domain interaction networks with the cluster-quality metric cohesiveness. The metric is locally maximized on the holistic level of protein interactions, and connectivity constraints on the domain level are used to account for the exclusive and thus inherently combinatorial nature of the interactions within such assemblies. When applied to predicting TF complexes in the yeast Saccharomyces cerevisiae, the proposed approach outperformed popular complex prediction methods by far. Furthermore, we were able to assign many of the predictions to target genes, as well as to a potential regulatory effect in agreement with literature evidence.

Availability and implementation: A prototype implementation is freely available at https://sourceforge.net/projects/dacoalgorithm/.

Contact:  volkhard.helms@bioinformatik.uni-saarland.de

Supplementary information:  Supplementary data are available at Bioinformatics online.
HubAlign: an accurate and efficient method for global alignment of protein–protein interaction networks
Authors: Hashemifar Somaye , Xu Jinbo
Published: August 2014

Motivation: High-throughput experimental techniques have produced a large amount of protein–protein interaction (PPI) data. The study of PPI networks, such as comparative analysis, shall benefit the understanding of life process and diseases at the molecular level. One way of comparative analysis is to align PPI networks to identify conserved or species-specific subnetwork motifs. A few methods have been developed for global PPI network alignment, but it still remains challenging in terms of both accuracy and efficiency.

Results: This paper presents a novel global network alignment algorithm, denoted as HubAlign, that makes use of both network topology and sequence homology information, based upon the observation that topologically important proteins in a PPI network usually are much more conserved and thus, more likely to be aligned. HubAlign uses a minimum-degree heuristic algorithm to estimate the topological and functional importance of a protein from the global network topology information. Then HubAlign aligns topologically important proteins first and gradually extends the alignment to the whole network. Extensive tests indicate that HubAlign greatly outperforms several popular methods in terms of both accuracy and efficiency, especially in detecting functionally similar proteins.

Availability: HubAlign is available freely for non-commercial purposes at http://ttic.uchicago.edu/∼hashemifar/software/HubAlign.zip

Contact:  jinboxu@gmail.com

Supplementary information:  Supplementary data are available at Bioinformatics online.
Experimental design schemes for learning Boolean network models
Authors: Atias Nir , Gershenzon Michal , Labazin Katia , Sharan Roded
Published: August 2014

Motivation: A holy grail of biological research is a working model of the cell. Current modeling frameworks, especially in the protein–protein interaction domain, are mostly topological in nature, calling for stronger and more expressive network models. One promising alternative is logic-based or Boolean network modeling, which was successfully applied to model signaling regulatory circuits in human. Learning such models requires observing the system under a sufficient number of different conditions. To date, the amount of measured data is the main bottleneck in learning informative Boolean models, underscoring the need for efficient experimental design strategies.

Results: We developed novel design approaches that greedily select an experiment to be performed so as to maximize the difference or the entropy in the results it induces with respect to current best-fit models. Unique to our maximum difference approach is the ability to account for all (possibly exponential number of) Boolean models displaying high fit to the available data. We applied both approaches to simulated and real data from the EFGR and IL1 signaling systems in human. We demonstrate the utility of the developed strategies in substantially improving on a random selection approach. Our design schemes highlight the redundancy in these datasets, leading up to 11-fold savings in the number of experiments to be performed.

Availability and implementation: Source code will be made available upon acceptance of the manuscript.

Contact:  roded@post.tau.ac.il
Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis
Authors: Khan Suleiman A. , Virtanen Seppo , Kallioniemi Olli P. , Wennerberg Krister , Poso Antti , Kaski Samuel
Published: August 2014

Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.

Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.

Availability and implementation: Source Code implementing the method is available at: http://research.ics.aalto.fi/mi/software/GFAsparse

Contact:  suleiman.khan@aalto.fi or samuel.kaski@aalto.fi

Supplementary Information:  Supplementary data are available at Bioinformatics online.
Assessing the local structural quality of transmembrane protein models using statistical potentials (QMEANBrane)
Authors: Studer Gabriel , Biasini Marco , Schwede Torsten
Published: August 2014

Motivation: Membrane proteins are an important class of biological macromolecules involved in many cellular key processes including signalling and transport. They account for one third of genes in the human genome and >50% of current drug targets. Despite their importance, experimental structural data are sparse, resulting in high expectations for computational modelling tools to help fill this gap. However, as many empirical methods have been trained on experimental structural data, which is biased towards soluble globular proteins, their accuracy for transmembrane proteins is often limited.

Results: We developed a local model quality estimation method for membrane proteins (‘QMEANBrane’) by combining statistical potentials trained on membrane protein structures with a per-residue weighting scheme. The increasing number of available experimental membrane protein structures allowed us to train membrane-specific statistical potentials that approach statistical saturation. We show that reliable local quality estimation of membrane protein models is possible, thereby extending local quality estimation to these biologically relevant molecules.

Availability and implementation: Source code and datasets are available on request.

Contact:  torsten.schwede@unibas.ch

Supplementary Information:  Supplementary data are available at Bioinformatics online.
PconsFold: improved contact predictions improve protein models
Authors: Michel Mirco , Hayat Sikander , Skwark Marcin J. , Sander Chris , Marks Debora S. , Elofsson Arne
Published: August 2014

Motivation: Recently it has been shown that the quality of protein contact prediction from evolutionary information can be improved significantly if direct and indirect information is separated. Given sufficiently large protein families, the contact predictions contain sufficient information to predict the structure of many protein families. However, since the first studies contact prediction methods have improved. Here, we ask how much the final models are improved if improved contact predictions are used.

Results: In a small benchmark of 15 proteins, we show that the TM-scores of top-ranked models are improved by on average 33% using PconsFold compared with the original version of EVfold. In a larger benchmark, we find that the quality is improved with 15–30% when using PconsC in comparison with earlier contact prediction methods. Further, using Rosetta instead of CNS does not significantly improve global model accuracy, but the chemistry of models generated with Rosetta is improved.

Availability: PconsFold is a fully automated pipeline for ab initio protein structure prediction based on evolutionary information. PconsFold is based on PconsC contact prediction and uses the Rosetta folding protocol. Due to its modularity, the contact prediction tool can be easily exchanged. The source code of PconsFold is available on GitHub at https://www.github.com/ElofssonLab/pcons-fold under the MIT license. PconsC is available from http://c.pcons.net/.

Contact:  arne@bioinfo.se

Supplementary information:  Supplementary data are available at Bioinformatics online.
CRISPRstrand: predicting repeat orientations to determine the crRNA-encoding strand at CRISPR loci
Authors: Alkhnbashi Omer S. , Costa Fabrizio , Shah Shiraz A. , Garrett Roger A. , Saunders Sita J. , Backofen Rolf
Published: August 2014

Motivation: The discovery of CRISPR-Cas systems almost 20 years ago rapidly changed our perception of the bacterial and archaeal immune systems. CRISPR loci consist of several repetitive DNA sequences called repeats, inter-spaced by stretches of variable length sequences called spacers. This CRISPR array is transcribed and processed into multiple mature RNA species (crRNAs). A single crRNA is integrated into an interference complex, together with CRISPR-associated (Cas) proteins, to bind and degrade invading nucleic acids. Although existing bioinformatics tools can recognize CRISPR loci by their characteristic repeat-spacer architecture, they generally output CRISPR arrays of ambiguous orientation and thus do not determine the strand from which crRNAs are processed. Knowledge of the correct orientation is crucial for many tasks, including the classification of CRISPR conservation, the detection of leader regions, the identification of target sites (protospacers) on invading genetic elements and the characterization of protospacer-adjacent motifs.

Results: We present a fast and accurate tool to determine the crRNA-encoding strand at CRISPR loci by predicting the correct orientation of repeats based on an advanced machine learning approach. Both the repeat sequence and mutation information were encoded and processed by an efficient graph kernel to learn higher-order correlations. The model was trained and tested on curated data comprising >4500 CRISPRs and yielded a remarkable performance of 0.95 AUC ROC (area under the curve of the receiver operator characteristic). In addition, we show that accurate orientation information greatly improved detection of conserved repeat sequence families and structure motifs. We integrated CRISPRstrand predictions into our CRISPRmap web server of CRISPR conservation and updated the latter to version 2.0.

Availability: CRISPRmap and CRISPRstrand are available at http://rna.informatik.uni-freiburg.de/CRISPRmap.

Contact: backofen@informatik.uni-freiburg.de

Supplementary information:  Supplementary data are available at Bioinformatics online.
ASTRAL: genome-scale coalescent-based species tree estimation
Authors: Mirarab S. , Reaz R. , Bayzid Md. S. , Zimmermann T. , Swenson M. S. , Warnow T.
Published: August 2014

Motivation: Species trees provide insight into basic biology, including the mechanisms of evolution and how it modifies biomolecular function and structure, biodiversity and co-evolution between genes and species. Yet, gene trees often differ from species trees, creating challenges to species tree estimation. One of the most frequent causes for conflicting topologies between gene trees and species trees is incomplete lineage sorting (ILS), which is modelled by the multi-species coalescent. While many methods have been developed to estimate species trees from multiple genes, some which have statistical guarantees under the multi-species coalescent model, existing methods are too computationally intensive for use with genome-scale analyses or have been shown to have poor accuracy under some realistic conditions.

Results: We present ASTRAL, a fast method for estimating species trees from multiple genes. ASTRAL is statistically consistent, can run on datasets with thousands of genes and has outstanding accuracy—improving on MP-EST and the population tree from BUCKy, two statistically consistent leading coalescent-based methods. ASTRAL is often more accurate than concatenation using maximum likelihood, except when ILS levels are low or there are too few gene trees.

Availability and implementation: ASTRAL is available in open source form at https://github.com/smirarab/ASTRAL/. Datasets studied in this article are available at http://www.cs.utexas.edu/users/phylo/datasets/astral.

Contact:  warnow@illinois.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
Polytomy refinement for the correction of dubious duplications in gene trees
Authors: Lafond Manuel , Chauve Cedric , Dondi Riccardo , El-Mabrouk Nadia
Published: August 2014

Motivation: Large-scale methods for inferring gene trees are error-prone. Correcting gene trees for weakly supported features often results in non-binary trees, i.e. trees with polytomies, thus raising the natural question of refining such polytomies into binary trees. A feature pointing toward potential errors in gene trees are duplications that are not supported by the presence of multiple gene copies.

Results: We introduce the problem of refining polytomies in a gene tree while minimizing the number of created non-apparent duplications in the resulting tree. We show that this problem can be described as a graph-theoretical optimization problem. We provide a bounded heuristic with guaranteed optimality for well-characterized instances. We apply our algorithm to a set of ray-finned fish gene trees from the Ensembl database to illustrate its ability to correct dubious duplications.

Availability and implementation: The C++ source code for the algorithms and simulations described in the article are available at http://www-ens.iro.umontreal.ca/~lafonman/software.php.

Contact:  lafonman@iro.umontreal.ca or mabrouk@iro.umontreal.ca

Supplementary information:  Supplementary data are available at Bioinformatics online.
Drug susceptibility prediction against a panel of drugs using kernelized Bayesian multitask learning
Authors: Gönen Mehmet , Margolin Adam A.
Published: August 2014

Motivation: Human immunodeficiency virus (HIV) and cancer require personalized therapies owing to their inherent heterogeneous nature. For both diseases, large-scale pharmacogenomic screens of molecularly characterized samples have been generated with the hope of identifying genetic predictors of drug susceptibility. Thus, computational algorithms capable of inferring robust predictors of drug responses from genomic information are of great practical importance. Most of the existing computational studies that consider drug susceptibility prediction against a panel of drugs formulate a separate learning problem for each drug, which cannot make use of commonalities between subsets of drugs.

Results: In this study, we propose to solve the problem of drug susceptibility prediction against a panel of drugs in a multitask learning framework by formulating a novel Bayesian algorithm that combines kernel-based non-linear dimensionality reduction and binary classification (or regression). The main novelty of our method is the joint Bayesian formulation of projecting data points into a shared subspace and learning predictive models for all drugs in this subspace, which helps us to eliminate off-target effects and drug-specific experimental noise. Another novelty of our method is the ability of handling missing phenotype values owing to experimental conditions and quality control reasons. We demonstrate the performance of our algorithm via cross-validation experiments on two benchmark drug susceptibility datasets of HIV and cancer. Our method obtains statistically significantly better predictive performance on most of the drugs compared with baseline single-task algorithms that learn drug-specific models. These results show that predicting drug susceptibility against a panel of drugs simultaneously within a multitask learning framework improves overall predictive performance over single-task learning approaches.

Availability and implementation: Our Matlab implementations for binary classification and regression are available at https://github.com/mehmetgonen/kbmtl.

Contact:  mehmet.gonen@sagebase.org

Supplementary Information:  Supplementary data are available at Bioinformatics online.
Large-scale automated identification of mouse brain cells in confocal light sheet microscopy images
Authors: Frasconi Paolo , Silvestri Ludovico , Soda Paolo , Cortini Roberto , Pavone Francesco S. , Iannello Giulio
Published: August 2014

Motivation: Recently, confocal light sheet microscopy has enabled high-throughput acquisition of whole mouse brain 3D images at the micron scale resolution. This poses the unprecedented challenge of creating accurate digital maps of the whole set of cells in a brain.

Results: We introduce a fast and scalable algorithm for fully automated cell identification. We obtained the whole digital map of Purkinje cells in mouse cerebellum consisting of a set of 3D cell center coordinates. The method is accurate and we estimated an F1 measure of 0.96 using 56 representative volumes, totaling 1.09 GVoxel and containing 4138 manually annotated soma centers.

Availability and implementation: Source code and its documentation are available at http://bcfind.dinfo.unifi.it/. The whole pipeline of methods is implemented in Python and makes use of Pylearn2 and modified parts of Scikit-learn. Brain images are available on request.

Contact: paolo.frasconi@unifi.it

Supplementary information: Supplementary data are available at Bioinformatics online.
The impact of incomplete knowledge on the evaluation of protein function prediction: a structured-output learning perspective
Authors: Jiang Yuxiang , Clark Wyatt T. , Friedberg Iddo , Radivojac Predrag
Published: August 2014

Motivation: The automated functional annotation of biological macromolecules is a problem of computational assignment of biological concepts or ontological terms to genes and gene products. A number of methods have been developed to computationally annotate genes using standardized nomenclature such as Gene Ontology (GO). However, questions remain about the possibility for development of accurate methods that can integrate disparate molecular data as well as about an unbiased evaluation of these methods. One important concern is that experimental annotations of proteins are incomplete. This raises questions as to whether and to what degree currently available data can be reliably used to train computational models and estimate their performance accuracy.

Results: We study the effect of incomplete experimental annotations on the reliability of performance evaluation in protein function prediction. Using the structured-output learning framework, we provide theoretical analyses and carry out simulations to characterize the effect of growing experimental annotations on the correctness and stability of performance estimates corresponding to different types of methods. We then analyze real biological data by simulating the prediction, evaluation and subsequent re-evaluation (after additional experimental annotations become available) of GO term predictions. Our results agree with previous observations that incomplete and accumulating experimental annotations have the potential to significantly impact accuracy assessments. We find that their influence reflects a complex interplay between the prediction algorithm, performance metric and underlying ontology. However, using the available experimental data and under realistic assumptions, our results also suggest that current large-scale evaluations are meaningful and almost surprisingly reliable.

Contact:  predrag@indiana.edu

Supplementary information:  Supplementary data are available at Bioinformatics online.
Fast randomization of large genomic datasets while preserving alteration counts
Authors: Gobbi Andrea , Iorio Francesco , Dawson Kevin J. , Wedge David C. , Tamborero David , Alexandrov Ludmil B. , Lopez-Bigas Nuria , Garnett Mathew J. , Jurman Giuseppe , Saez-Rodriguez Julio
Published: August 2014

Motivation: Studying combinatorial patterns in cancer genomic datasets has recently emerged as a tool for identifying novel cancer driver networks. Approaches have been devised to quantify, for example, the tendency of a set of genes to be mutated in a ‘mutually exclusive’ manner. The significance of the proposed metrics is usually evaluated by computing P-values under appropriate null models. To this end, a Monte Carlo method (the switching-algorithm) is used to sample simulated datasets under a null model that preserves patient- and gene-wise mutation rates. In this method, a genomic dataset is represented as a bipartite network, to which Markov chain updates (switching-steps) are applied. These steps modify the network topology, and a minimal number of them must be executed to draw simulated datasets independently under the null model. This number has previously been deducted empirically to be a linear function of the total number of variants, making this process computationally expensive.

Results: We present a novel approximate lower bound for the number of switching-steps, derived analytically. Additionally, we have developed the R package BiRewire, including new efficient implementations of the switching-algorithm. We illustrate the performances of BiRewire by applying it to large real cancer genomics datasets. We report vast reductions in time requirement, with respect to existing implementations/bounds and equivalent P-value computations. Thus, we propose BiRewire to study statistical properties in genomic datasets, and other data that can be modeled as bipartite networks.

Availability and implementation: BiRewire is available on BioConductor at http://www.bioconductor.org/packages/2.13/bioc/html/BiRewire.html

Contact:  iorio@ebi.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.
RidgeRace: ridge regression for continuous ancestral character estimation on phylogenetic trees
Authors: Kratsch Christina , McHardy Alice C.
Published: August 2014

Motivation: Ancestral character state reconstruction describes a set of techniques for estimating phenotypic or genetic features of species or related individuals that are the predecessors of those present today. Such reconstructions can reach into the distant past and can provide insights into the history of a population or a set of species when fossil data are not available, or they can be used to test evolutionary hypotheses, e.g. on the co-evolution of traits. Typical methods for ancestral character state reconstruction of continuous characters consider the phylogeny of the underlying data and estimate the ancestral process along the branches of the tree. They usually assume a Brownian motion model of character evolution or extensions thereof, requiring specific assumptions on the rate of phenotypic evolution.

Results: We suggest using ridge regression to infer rates for each branch of the tree and the ancestral values at each inner node. We performed extensive simulations to evaluate the performance of this method and have shown that the accuracy of its reconstructed ancestral values is competitive to reconstructions using other state-of-the-art software. Using a hierarchical clustering of gene mutation profiles from an ovarian cancer dataset, we demonstrate the use of the method as a feature selection tool.

Availability and implementation: The algorithm described here is implemented in C++ as a stand-alone program, and the source code is freely available at http://algbio.cs.uni-duesseldorf.de/software/RidgeRace.tar.gz.

Contact:  mchardy@hhu.de

Supplementary information:  Supplementary data are available at Bioinformatics online.
Probabilistic single-individual haplotyping
Authors: Kuleshov Volodymyr
Published: August 2014

Motivation: Accurate haplotyping—determining from which parent particular portions of the genome are inherited—is still mostly an unresolved problem in genomics. This problem has only recently started to become tractable, thanks to the development of new long read sequencing technologies. Here, we introduce ProbHap, a haplotyping algorithm targeted at such technologies. The main algorithmic idea of ProbHap is a new dynamic programming algorithm that exactly optimizes a likelihood function specified by a probabilistic graphical model and which generalizes a popular objective called the minimum error correction. In addition to being accurate, ProbHap also provides confidence scores at phased positions.

Results: On a standard benchmark dataset, ProbHap makes 11% fewer errors than current state-of-the-art methods. This accuracy can be further increased by excluding low-confidence positions, at the cost of a small drop in haplotype completeness.

Availability: Our source code is freely available at: https://github.com/kuleshov/ProbHap.

Contact: kuleshov@stanford.edu
The role of the reflection fraction in constraining black hole spin
Authors: Dauser T. , García J. , Parker M. L. , Fabian A. C. , Wilms J.
Published: August 2014

In many active galaxies, the X-ray reflection features from the innermost regions of the accretion disc are relativistically distorted. This distortion allows us to measure parameters of the black hole such as its spin. The ratio in flux between the direct and the reflected radiation, the so-called reflection fraction, is determined directly from the geometry and location of primary source of radiation. We calculate the reflection fraction in the lamp post geometry in order to determine its maximal possible value for a given value of black hole spin. We show that high reflection fractions in excess of two are only possible for rapidly rotating black holes, suggesting that the high spin sources produce the strongest relativistic reflection features. Using simulations we show that taking this constraint into account does significantly improve the determination of the spin values. We make software routines for the most popular X-ray data analysis packages available that incorporate these additional constraints.
Sustained UK marine observations. Where have we been? Where are we now? Where are we going?
Authors: Owens Nicholas J. P.
Published: August 2014

This introduction traces the earliest interaction of ancient humans with their marine environment, through marine explorations in the Middle Ages and Renaissance, to the development of early marine science in the Enlightenment. This sets the scene for how marine observations developed in the modern era and explains the status of today's marine observation networks. The paper concludes with an assessment of the future needs and constraints of sustained marine observation networks and suggests the lessons from a long history might be the key to the future.
Sea level: measuring the bounding surfaces of the ocean
Authors: Tamisiea Mark E. , Hughes Chris W. , Williams Simon D. P. , Bingley Richard M.
Published: August 2014

The practical need to understand sea level along the coasts, such as for safe navigation given the spatially variable tides, has resulted in tide gauge observations having the distinction of being some of the longest instrumental ocean records. Archives of these records, along with geological constraints, have allowed us to identify the century-scale rise in global sea level. Additional data sources, particularly satellite altimetry missions, have helped us to better identify the rates and causes of sea-level rise and the mechanisms leading to spatial variability in the observed rates. Analysis of all of the data reveals the need for long-term and stable observation systems to assess accurately the regional changes as well as to improve our ability to estimate future changes in sea level. While information from many scientific disciplines is needed to understand sea-level change, this review focuses on contributions from geodesy and the role of the ocean's bounding surfaces: the sea surface and the Earth's crust.
DISEASES: Text mining and data integration of disease–gene associations
Authors: Pletscher-Frankild Sune , Pallejà Albert , Tsafou Kalliopi , Binder Janos X. , Jensen Lars Juhl
Published: August 2014

Text mining is a flexible technology that can be applied to numerous different tasks in biology and medicine. We present a system for extracting disease–gene associations from biomedical abstracts. The system consists of a highly efficient dictionary-based tagger for named entity recognition of human genes and diseases, which we combine with a scoring scheme that takes into account co-occurrences both within and between sentences. We show that this approach is able to extract half of all manually curated associations with a false positive rate of only 0.16%. Nonetheless, text mining should not stand alone, but be combined with other types of evidence. For this reason, we have developed the DISEASES resource, which integrates the results from text mining with manually curated disease–gene associations, cancer mutation data, and genome-wide association studies from existing databases. The DISEASES resource is accessible through a user-friendly web interface athttp://diseases.jensenlab.org/, where the text-mining software and all associations are also freely available for download.