bluuebunny/crossref_metadata_2025_split huggingface dataset:
Drug response prediction by inferring pathway-response associations with kernelized Bayesian matrix factorization
Authors: Ammad-ud-din Muhammad , Khan Suleiman A. , Malani Disha , Murumägi Astrid , Kallioniemi Olli , Aittokallio Tero , Kaski Samuel 
Published: September 2016

Abstract

Motivation
A key goal of computational personalized medicine is to systematically utilize genomic and other molecular features of samples to predict drug responses for a previously unseen sample. Such predictions are valuable for developing hypotheses for selecting therapies tailored for individual patients. This is especially valuable in oncology, where molecular and genetic heterogeneity of the cells has a major impact on the response. However, the prediction task is extremely challenging, raising the need for methods that can effectively model and predict drug responses.

Results
In this study, we propose a novel formulation of multi-task matrix factorization that allows selective data integration for predicting drug responses. To solve the modeling task, we extend the state-of-the-art kernelized Bayesian matrix factorization (KBMF) method with component-wise multiple kernel learning. In addition, our approach exploits the known pathway information in a novel and biologically meaningful fashion to learn the drug response associations. Our method quantitatively outperforms the state of the art on predicting drug responses in two publicly available cancer datasets as well as on a synthetic dataset. In addition, we validated our model predictions with lab experiments using an in-house cancer cell line panel. We finally show the practical applicability of the proposed method by utilizing prior knowledge to infer pathway-drug response associations, opening up the opportunity for elucidating drug action mechanisms. We demonstrate that pathway-response associations can be learned by the proposed model for the well-known EGFR and MEK inhibitors.

Availability and implementation
The source code implementing the method is available at http://research.cs.aalto.fi/pml/software/cwkbmf/.

Contacts
muhammad.ammad-ud-din@aalto.fi or samuel.kaski@aalto.fi

Supplementary information
Supplementary data are available at Bioinformatics online.
Patterns of amino acid conservation in human and animal immunodeficiency viruses
Authors: Voitenko Olga S , Dhroso Andi , Feldmann Anna , Korkin Dmitry , Kalinina Olga V 
Published: September 2016

Abstract

Motivation
Due to their high genomic variability, RNA viruses and retroviruses present a unique opportunity for detailed study of molecular evolution. Lentiviruses, with HIV being a notable example, are one of the best studied viral groups: hundreds of thousands of sequences are available together with experimentally resolved three-dimensional structures for most viral proteins. In this work, we use these data to study specific patterns of evolution of the viral proteins, and their relationship to protein interactions and immunogenicity.

Results
We propose a method for identification of two types of surface residues clusters with abnormal conservation: extremely conserved and extremely variable clusters. We identify them on the surface of proteins from HIV and other animal immunodeficiency viruses. Both types of clusters are overrepresented on the interaction interfaces of viral proteins with other proteins, nucleic acids or low molecular-weight ligands, both in the viral particle and between the virus and its host. In the immunodeficiency viruses, the interaction interfaces are not more conserved than the corresponding proteins on an average, and we show that extremely conserved clusters coincide with protein–protein interaction hotspots, predicted as the residues with the largest energetic contribution to the interaction. Extremely variable clusters have been identified here for the first time. In the HIV-1 envelope protein gp120, they overlap with known antigenic sites. These antigenic sites also contain many residues from extremely conserved clusters, hence representing a unique interacting interface enriched both in extremely conserved and in extremely variable clusters of residues. This observation may have important implication for antiretroviral vaccine development.

Availability and Implementation
A Python package is available at https://bioinf.mpi-inf.mpg.de/publications/viral-ppi-pred/

Contact
voitenko@mpi-inf.mpg.de or kalinina@mpi-inf.mpg.de

Supplementary information
Supplementary data are available at Bioinformatics online.
A probabilistic model for detecting rigid domains in protein structures
Authors: Nguyen Thach , Habeck Michael 
Published: September 2016

Abstract

Motivation
Large-scale conformational changes in proteins are implicated in many important biological functions. These structural transitions can often be rationalized in terms of relative movements of rigid domains. There is a need for objective and automated methods that identify rigid domains in sets of protein structures showing alternative conformational states.

Results
We present a probabilistic model for detecting rigid-body movements in protein structures. Our model aims to approximate alternative conformational states by a few structural parts that are rigidly transformed under the action of a rotation and a translation. By using Bayesian inference and Markov chain Monte Carlo sampling, we estimate all parameters of the model, including a segmentation of the protein into rigid domains, the structures of the domains themselves, and the rigid transformations that generate the observed structures. We find that our Gibbs sampling algorithm can also estimate the optimal number of rigid domains with high efficiency and accuracy. We assess the power of our method on several thousand entries of the DynDom database and discuss applications to various complex biomolecular systems.

Availability and Implementation
The Python source code for protein ensemble analysis is available at: https://github.com/thachnguyen/motion_detection

Contact
mhabeck@gwdg.de
PEPSI-Dock: a detailed data-driven protein–protein interaction potential accelerated by polar Fourier correlation
Authors: Neveu Emilie , Ritchie David W , Popov Petr , Grudinin Sergei 
Published: September 2016

Abstract

Motivation
Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential.

Results
First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein–protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5–15 min on a modern laptop and can easily be extended to other types of interactions.

Availability and Implementation
https://team.inria.fr/nano-d/software/PEPSI-Dock.

Contact
sergei.grudinin@inria.fr
SWORD—a highly efficient protein database search
Authors: Vaser Robert , Pavlović Dario , Šikić Mile 
Published: September 2016

Abstract

Motivation
Protein database search is one of the fundamental problems in bioinformatics. For decades, it has been explored and solved using different exact and heuristic approaches. However, exponential growth of data in recent years has brought significant challenges in improving already existing algorithms. BLAST has been the most successful tool for protein database search, but is also becoming a bottleneck in many applications. Due to that, many different approaches have been developed to complement or replace it. In this article, we present SWORD, an efficient protein database search implementation that runs 8–16 times faster than BLAST in the sensitive mode and up to 68 times faster in the fast and less accurate mode. It is designed to be used in nearly all database search environments, but is especially suitable for large databases. Its sensitivity exceeds that of BLAST for majority of input datasets and provides guaranteed optimal alignments.

Availability and Implementation
Sword is freely available for download from https://github.com/rvaser/sword

Contact
robert.vaser@fer.hr and mile.sikic@fer.hr

Supplementary information
Supplementary data are available at Bioinformatics online.
ModuleAlign: module-based global alignment of protein–protein interaction networks
Authors: Hashemifar Somaye , Ma Jianzhu , Naveed Hammad , Canzar Stefan , Xu Jinbo 
Published: September 2016

Abstract

Motivation
As an increasing amount of protein–protein interaction (PPI) data becomes available, their computational interpretation has become an important problem in bioinformatics. The alignment of PPI networks from different species provides valuable information about conserved subnetworks, evolutionary pathways and functional orthologs. Although several methods have been proposed for global network alignment, there is a pressing need for methods that produce more accurate alignments in terms of both topological and functional consistency.

Results
In this work, we present a novel global network alignment algorithm, named ModuleAlign, which makes use of local topology information to define a module-based homology score. Based on a hierarchical clustering of functionally coherent proteins involved in the same module, ModuleAlign employs a novel iterative scheme to find the alignment between two networks. Evaluated on a diverse set of benchmarks, ModuleAlign outperforms state-of-the-art methods in producing functionally consistent alignments. By aligning Pathogen–Human PPI networks, ModuleAlign also detects a novel set of conserved human genes that pathogens preferentially target to cause pathogenesis.

Availability
http://ttic.uchicago.edu/∼hashemifar/ModuleAlign.html

Contact
canzar@ttic.edu or j3xu.ttic.edu

Supplementary information
Supplementary data are available at Bioinformatics online.
Genome wide predictions of miRNA regulation by transcription factors
Authors: Ruffalo Matthew , Bar-Joseph Ziv 
Published: September 2016

AbstractMotivationReconstructing regulatory networks from expression and interaction data is a major goal of systems biology. While much work has focused on trying to experimentally and computationally determine the set of transcription-factors (TFs) and microRNAs (miRNAs) that regulate genes in these networks, relatively little work has focused on inferring the regulation of miRNAs by TFs. Such regulation can play an important role in several biological processes including development and disease. The main challenge for predicting such interactions is the very small positive training set currently available. Another challenge is the fact that a large fraction of miRNAs are encoded within genes making it hard to determine the specific way in which they are regulated.ResultsTo enable genome wide predictions of TF–miRNA interactions, we extended semi-supervised machine-learning approaches to integrate a large set of different types of data including sequence, expression, ChIP-seq and epigenetic data. As we show, the methods we develop achieve good performance on both a labeled test set, and when analyzing general co-expression networks. We next analyze mRNA and miRNA cancer expression data, demonstrating the advantage of using the predicted set of interactions for identifying more coherent and relevant modules, genes, and miRNAs. The complete set of predictions is available on the supporting website and can be used by any method that combines miRNAs, genes, and TFs.Availability and ImplementationCode and full set of predictions are available from the supporting website: http://cs.cmu.edu/~mruffalo/tf-mirna/.Contactzivbj@cs.cmu.eduSupplementary informationSupplementary data are available at Bioinformatics online.
Extending partial haplotypes to full genome haplotypes using chromosome conformation capture data
Authors: Ben-Elazar Shay , Chor Benny , Yakhini Zohar 
Published: September 2016

Abstract

Motivation
Complex interactions among alleles often drive differences in inherited properties including disease predisposition. Isolating the effects of these interactions requires phasing information that is difficult to measure or infer. Furthermore, prevalent sequencing technologies used in the essential first step of determining a haplotype limit the range of that step to the span of reads, namely hundreds of bases. With the advent of pseudo-long read technologies, observable partial haplotypes can span several orders of magnitude more. Yet, measuring whole-genome-single-individual haplotypes remains a challenge. A different view of whole genome measurement addresses the 3D structure of the genome—with great development of Hi-C techniques in recent years. A shortcoming of current Hi-C, however, is the difficulty in inferring information that is specific to each of a pair of homologous chromosomes.

Results
In this work, we develop a robust algorithmic framework that takes two measurement derived datasets: raw Hi-C and partial short-range haplotypes, and constructs the full-genome haplotype as well as phased diploid Hi-C maps. By analyzing both data sets together we thus bridge important gaps in both technologies—from short to long haplotypes and from un-phased to phased Hi-C. We demonstrate that our method can recover ground truth haplotypes with high accuracy, using measured biological data as well as simulated data. We analyze the impact of noise, Hi-C sequencing depth and measured haplotype lengths on performance. Finally, we use the inferred 3D structure of a human genome to point at transcription factor targets nuclear co-localization.

Availability and Implementation
The implementation available at https://github.com/YakhiniGroup/SpectraPh.

Contact
zohar.yakhini@gmail.com

Supplementary information
Supplementary data are available at Bioinformatics online.
Edge-based sensitivity analysis of signaling networks by using Boolean dynamics
Authors: Trinh Hung-Cuong , Kwon Yung-Keun 
Published: September 2016

Abstract

Motivation
Biological networks are composed of molecular components and their interactions represented by nodes and edges, respectively, in a graph model. Based on this model, there were many studies with respect to effects of node-based mutations on the network dynamics, whereas little attention was paid to edgetic mutations so far.

Results
In this paper, we defined an edgetic sensitivity measure that quantifies how likely a converging attractor is changed by edge-removal mutations in a Boolean network model. Through extensive simulations based on that measure, we found interesting properties of highly sensitive edges in both random and real signaling networks. First, the sensitive edges in random networks tend to link two end nodes both of which are susceptible to node-knockout mutations. Interestingly, it was analogous to an observation that the sensitive edges in human signaling networks are likely to connect drug-target genes. We further observed that the edgetic sensitivity predicted drug-targets better than the node-based sensitivity. In addition, the sensitive edges showed distinguished structural characteristics such as a lower connectivity, more involving feedback loops and a higher betweenness. Moreover, their gene-ontology enrichments were clearly different from the other edges. We also observed that genes incident to the highly sensitive interactions are more central by forming a considerably large connected component in human signaling networks. Finally, we validated our approach by showing that most sensitive interactions are promising edgetic drug-targets in p53 cancer and T-cell apoptosis networks. Taken together, the edgetic sensitivity is valuable to understand the complex dynamics of signaling networks.

Contact
kwonyk@ulsan.ac.kr

Supplementary information
Supplementary data are available at Bioinformatics online.
On cross-conditional and fluctuation correlations in competitive RNA networks
Authors: Sánchez-Taltavull Daniel , MacLeod Matthew , Perkins Theodore J 
Published: September 2016

Abstract

Motivation
In competitive endogenous RNA (ceRNA) networks, different mRNAs targeted by the same miRNA can ‘cross-talk’ by absorbing miRNAs and relieving repression on the other mRNAs. This creates correlations in mRNA expression even without direct interaction. Most previous theoretical study of cross-talk has focused on correlations in stochastic fluctuations of mRNAs around their steady state values. However, the experimentally known examples of cross-talk do not involve single-cell fluctuations, but rather bulk tissue-level changes between conditions, such as due to differentiation or disease. In our study, we quantify for the first time both fluctuational and cross-conditional cross-talk in chemical kinetic models of miRNA–mRNA interaction networks. We study the parameter regions under which these different types of cross-talk arise, and how they are affected by network structure.

Results
We find that while a network may support both fluctuational and cross-conditional cross-talk, the parameter regimes under which each type of cross-talk tends to emerge are rather different. Consistent with previous studies, fluctuational cross-talk occurs when miRNA and mRNA expression levels are ‘balanced’, whereas cross-conditional cross-talk tends to emerge when average miRNA levels are high and average mRNA levels are low. Conversely, cross-conditional miRNA cross-talk—a little-discussed phenomenon—is greatest when miRNA levels are low and mRNA levels are high. We show that the parameter ranges where cross-talk is maximized can, to some degree, be predicted based on network structure. Indeed, we find that the dominant effect of network structure on correlations happens through the effect of network structure on the overall balance between miRNA and mRNA expression. However, it is not the only effect, as we find that the density of connections between miRNAs and mRNAs in larger networks increases cross-talk without altering the expression balance.

Conclusion
Our results deepen the theoretical understanding of cross-talk in ceRNA networks, and have implications for the experimental identification of ceRNA cross-talk phenomena.

Availability and Implementation
Simulation software available upon request.

Contact
tperkins@ohri.ca
Stronger sink demand for metabolites supports dominance of the apical bud in etiolated growth
Authors: Buskila Yossi , Sela Noa , Teper-Bamnolker Paula , Tal Iris , Shani Eilon , Weinstain Roy , Gaba Victor , Tam Yehudit , Lers Amnon , Eshel Dani 
Published: September 2016

Abstract
The potato tuber is a swollen underground stem that can sprout under dark conditions. Sprouting initiates in the tuber apical bud (AP), while lateral buds (LTs) are repressed by apical dominance (AD). Under conditions of lost AD, removal of tuber LTs showed that they partially inhibit AP growth only at the AD stage. Detached buds were inhibited by exogenous application of naphthaleneacetic acid (NAA), whereas 6-benzyladenine (6-BA) and gibberellic acid (GA 3 ) induced bud burst and elongation, respectively. NAA, applied after 6-BA or GA 3 , nullified the latters’ growth-stimulating effect in both the AP and LTs. GA 3 applied to the fifth-position LT was transported mainly to the tuber’s AP. GA 3 treatment also resulted in increased indole-3-acetic acid (IAA) concentration and cis-zeatin O-glucoside in the AP. In a tuber tissue strip that included two or three buds connected by the peripheral vascular system, treatment of a LT with GA 3 affected only the AP side of the strip, suggesting that the AP is the strongest sink for GA 3 , which induces its etiolated elongation. Dipping etiolated sprouts in labeled GA 3 showed specific accumulation of the signal in the AP. Transcriptome analysis of GA 3 ’s effect showed that genes related to the cell cycle, cell proliferation, and hormone transport are up-regulated in the AP as compared to the LT. Sink demand for metabolites is suggested to support AD in etiolated stem growth by inducing differential gene expression in the AP.
MutationalPatterns: comprehensive genome-wide analysis of mutational processes
Authors: Blokzijl Francis , Janssen Roel , van Boxtel Ruben , Cuppen Edwin 
Published: September 2016

AbstractBase substitution catalogs represent historical records of mutational processes that have been active in a system. Such processes can be distinguished by typical characteristics, like mutation type, sequence context, transcriptional and replicative strand bias, and distribution throughout the genome. MutationalPatterns is an R/Bioconductor package that characterizes this broad range of mutational patterns and potential relations with (epi-)genomic features. Furthermore, it offers an efficient method to quantify the contribution of known mutational signatures. Such analyses can be used to determine whether certain DNA repair mechanisms are perturbed and to further characterize the processes underlying known mutational signatures.
Microclimate and radiant energy measurements using a novel device for natural resource monitoring
Authors: Thomas Taylor , Wolf Adam 
Published: September 2016

ABSTRACTA novel device for microclimate and radiant energy monitoring of natural resources was compared to known reference instruments and sensors to gauge relative performance under field conditions in Nebraska and New Jersey, USA during summer 2016. For all measurements tested, including air temperature, relative humidity, barometric pressure, four component net radiation, and photosynthetically active radiation, the device reported values near or within the accuracy limits as stated by the reference instrument / sensor manufacturer.