bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fast Virtual Stenting with Active Contour Models in Intracranical Aneurysm
Authors: Zhong Jingru , Long Yunling , Yan Huagang , Meng Qianqian , Zhao Jing , Zhang Ying , Yang Xinjian , Li Haiyun 
Published: February 2016

AbstractIntracranial stents are becoming increasingly a useful option in the treatment of intracranial aneurysms (IAs). Image simulation of the releasing stent configuration together with computational fluid dynamics (CFD) simulation prior to intervention will help surgeons optimize intervention scheme. This paper proposed a fast virtual stenting of IAs based on active contour model (ACM) which was able to virtually release stents within any patient-specific shaped vessel and aneurysm models built on real medical image data. In this method, an initial stent mesh was generated along the centerline of the parent artery without the need for registration between the stent contour and the vessel. Additionally, the diameter of the initial stent volumetric mesh was set to the maximum inscribed sphere diameter of the parent artery to improve the stenting accuracy and save computational cost. At last, a novel criterion for terminating virtual stent expanding that was based on the collision detection of the axis aligned bounding boxes was applied, making the stent expansion free of edge effect. The experiment results of the virtual stenting and the corresponding CFD simulations exhibited the efficacy and accuracy of the ACM based method, which are valuable to intervention scheme selection and therapy plan confirmation.
Homology-driven assembly of NOn-redundant protEin sequence sets (NOmESS) for mass spectrometry
Authors: Temu Tikira , Mann Matthias , Räschle Markus , Cox Jürgen 
Published: February 2016

Abstract
Summary: To enable mass spectrometry (MS)-based proteomic studies with poorly characterized organisms, we developed a computational workflow for the homology-driven assembly of a non-redundant reference sequence dataset. In the automated pipeline, translated DNA sequences (e.g. ESTs, RNA deep-sequencing data) are aligned to those of a closely related and fully sequenced organism. Representative sequences are derived from each cluster and joined, resulting in a non-redundant reference set representing the maximal available amino acid sequence information for each protein. We here applied NOmESS to assemble a reference database for the widely used model organism Xenopus laevis and demonstrate its use in proteomic applications.
Availability and implementation: NOmESS is written in C#. The source code as well as the executables can be downloaded from http://www.biochem.mpg.de/cox. Execution of NOmESS requires BLASTp and cd-hit in addition.
Contact: cox@biochem.mpg.de
Supplementary information: Supplementary data are available at Bioinformatics online.
Vasohibins: new transglutaminase-like cysteine proteases possessing a non-canonical Cys-His-Ser catalytic triad
Authors: Sanchez-Pulido Luis , Ponting Chris P. 
Published: February 2016

Abstract
Summary: Vasohibin-1 and Vasohibin-2 regulate angiogenesis, tumour growth and metastasis. Their molecular functions, however, were previously unknown, in large part owing to their perceived lack of homology to proteins of known structure and function. To identify their functional amino acids and domains, their molecular activity and their evolutionary history, we undertook an in-depth analysis of Vasohibin sequences. We find that Vasohibin proteins are previously undetected members of the transglutaminase-like cysteine protease superfamily, and all possess a non-canonical Cys-His-Ser catalytic triad. We further propose a calcium-dependent activation mechanism for Vasohibin proteins. These findings can now be used to design constructs for protein structure determination and to develop enzyme inhibitors as angiogenic regulators to treat metastasis and tumour growth.
Contact: luis.sanchezpulido@dpag.ox.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
Sampletrees and Rsampletrees: sampling gene genealogies conditional on SNP genotype data
Authors: Burkett Kelly M. , McNeney Brad , Graham Jinko 
Published: February 2016

Abstract
Summary: The program sampletrees is a Markov chain Monte Carlo sampler of gene genealogies conditional on either phased or unphased SNP genotype data. The companion program Rsampletrees is for pre- and post-processing of sampletrees files, including setting up the files for sampletrees and storing and plotting the output of a sampletrees run.
Availability and implementation: sampletrees is implemented in C ++. The source code, documentation and test files are available at http://stat.sfu.ca/statgen/research/sampletrees.html. The R package Rsampletrees is available on CRAN http://cran.r-project.org/web/packages/Rsampletrees/index.html.
Contact: kburkett@uottawa.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
Multi-omics enrichment analysis using the GeneTrail2 web service
Authors: Stöckel Daniel , Kehl Tim , Trampert Patrick , Schneider Lara , Backes Christina , Ludwig Nicole , Gerasch Andreas , Kaufmann Michael , Gessler Manfred , Graf Norbert , Meese Eckart , Keller Andreas , Lenhof Hans-Peter 
Published: February 2016

AbstractMotivation: Gene set analysis has revolutionized the interpretation of high-throughput transcriptomic data. Nowadays, with comprehensive studies that measure multiple -omics from the same sample, powerful tools for the integrative analysis of multi-omics datasets are required.Results: Here, we present GeneTrail2, a web service allowing the integrated analysis of transcriptomic, miRNomic, genomic and proteomic datasets. It offers multiple statistical tests, a large number of predefined reference sets, as well as a comprehensive collection of biological categories and enables direct comparisons between the computed results. We used GeneTrail2 to explore pathogenic mechanisms of Wilms tumors. We not only succeeded in revealing signaling cascades that may contribute to the malignancy of blastemal subtype tumors but also identified potential biomarkers for nephroblastoma with adverse prognosis. The presented use-case demonstrates that GeneTrail2 is well equipped for the integrative analysis of comprehensive -omics data and may help to shed light on complex pathogenic mechanisms in cancer and other diseases.Availability and implementation: GeneTrail2 can be freely accessed under https://genetrail2.bioinf.uni-sb.de.Contact: dstoeckel@bioinf.uni-sb.deSupplementary information: Supplementary data are available at Bioinformatics online.
Orthogonal matrix factorization enables integrative analysis of multiple RNA binding proteins
Authors: Stražar Martin , Žitnik Marinka , Zupan Blaž , Ule Jernej , Curk Tomaž 
Published: February 2016

Abstract
Motivation: RNA binding proteins (RBPs) play important roles in post-transcriptional control of gene expression, including splicing, transport, polyadenylation and RNA stability. To model protein–RNA interactions by considering all available sources of information, it is necessary to integrate the rapidly growing RBP experimental data with the latest genome annotation, gene function, RNA sequence and structure. Such integration is possible by matrix factorization, where current approaches have an undesired tendency to identify only a small number of the strongest patterns with overlapping features. Because protein–RNA interactions are orchestrated by multiple factors, methods that identify discriminative patterns of varying strengths are needed.
Results: We have developed an integrative orthogonality-regularized nonnegative matrix factorization (iONMF) to integrate multiple data sources and discover non-overlapping, class-specific RNA binding patterns of varying strengths. The orthogonality constraint halves the effective size of the factor model and outperforms other NMF models in predicting RBP interaction sites on RNA. We have integrated the largest data compendium to date, which includes 31 CLIP experiments on 19 RBPs involved in splicing (such as hnRNPs, U2AF2, ELAVL1, TDP-43 and FUS) and processing of 3’UTR (Ago, IGF2BP). We show that the integration of multiple data sources improves the predictive accuracy of retrieval of RNA binding sites. In our study the key predictive factors of protein–RNA interactions were the position of RNA structure and sequence motifs, RBP co-binding and gene region type. We report on a number of protein-specific patterns, many of which are consistent with experimentally determined properties of RBPs.
Availability and implementation: The iONMF implementation and example datasets are available at https://github.com/mstrazar/ionmf.
Contact: tomaz.curk@fri.uni-lj.si
Supplementary information: Supplementary data are available at Bioinformatics online.
Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer
Authors: Young Jonathan H. , Peyton Michael , Seok Kim Hyun , McMillan Elizabeth , Minna John D. , White Michael A. , Marcotte Edward M. 
Published: February 2016

AbstractMotivation: Novel approaches are needed for discovery of targeted therapies for non-small-cell lung cancer (NSCLC) that are specific to certain patients. Whole genome RNAi screening of lung cancer cell lines provides an ideal source for determining candidate drug targets.Results: Unsupervised learning algorithms uncovered patterns of differential vulnerability across lung cancer cell lines to loss of functionally related genes. Such genetic vulnerabilities represent candidate targets for therapy and are found to be involved in splicing, translation and protein folding. In particular, many NSCLC cell lines were especially sensitive to the loss of components of the LSm2-8 protein complex or the CCT/TRiC chaperonin. Different vulnerabilities were also found for different cell line subgroups. Furthermore, the predicted vulnerability of a single adenocarcinoma cell line to loss of the Wnt pathway was experimentally validated with screening of small-molecule Wnt inhibitors against an extensive cell line panel.Availability and implementation: The clustering algorithm is implemented in Python and is freely available at https://bitbucket.org/youngjh/nsclc_paper.Contact: marcotte@icmb.utexas.edu or jon.young@utexas.eduSupplementary information: Supplementary data are available at Bioinformatics online.
MTG2: an efficient algorithm for multivariate linear mixed model analysis based on genomic information
Authors: Lee S. H. , van der Werf J. H. J. 
Published: February 2016

Abstract
Summary: We have developed an algorithm for genetic analysis of complex traits using genome-wide SNPs in a linear mixed model framework. Compared to current standard REML software based on the mixed model equation, our method is substantially faster. The advantage is largest when there is only a single genetic covariance structure. The method is particularly useful for multivariate analysis, including multi-trait models and random regression models for studying reaction norms. We applied our proposed method to publicly available mice and human data and discuss the advantages and limitations.
Availability and implementation: MTG2 is available in https://sites.google.com/site/honglee0707/mtg2.
Contact: hong.lee@une.edu.au
Supplementary information: Supplementary data are available at Bioinformatics online.
EPI-peptide designer: a tool for designing peptide ligand libraries based on epitope–paratope interactions
Authors: Viart B. , Dias-Lopes C. , Kozlova E. , Oliveira C. F. B. , Nguyen C. , Neshich G. , Chávez-Olórtegui C. , Molina F. , Felicori L. F. 
Published: February 2016

Abstract
Motivation: Antibodies are an important class of biological drugs, but with limitations, such as inadequate pharmacokinetics, adverse immunogenicity and high production costs. Synthetic peptides for the desired target represent an important alternative to antibodies. However, no computational tool exists to guide the design of these peptides.
Results: To identify the interacting residues in a given antibody–antigen (Ab–Ag) interface we used Interface Interacting Residue (I2R), a selection method based on computed molecular interactions. The aggregation of all the molecular interactions between epitope and paratope residues allowed us to transform the 3D Ab–Ag complex structures into interface graphs. Based on these data and the probability of molecular interaction we developed EPI-Peptide Designer tool that uses predicted paratope residues for an epitope of interest to generate targeted peptide ligand libraries. EPI-Peptide Designer successfully predicted 301 peptides able to bind to LiD1 target protein (65% of the experimentally tested peptides), an enrichment of 22% compared to randomly generated peptides. This tool should enable the development of a new generation of synthetic interacting peptides that could be very useful in the biosensor, diagnostic and therapeutic fields.
Availability and implementation: All software developed in this work are available at http://www.biocomp.icb.ufmg.br/biocomp/
Contact: liza@icb.ufmg.br
Supplementary information: Supplementary data are available at Bioinformatics online.
GeneValidator: identify problems with protein-coding gene predictions
Authors: Drăgan Monica-Andreea , Moghul Ismail , Priyam Anurag , Bustos Claudio , Wurm Yannick 
Published: February 2016

Abstract
Summary: Genomes of emerging model organisms are now being sequenced at very low cost. However, obtaining accurate gene predictions remains challenging: even the best gene prediction algorithms make substantial errors and can jeopardize subsequent analyses. Therefore, many predicted genes must be time-consumingly visually inspected and manually curated. We developed GeneValidator (GV) to automatically identify problematic gene predictions and to aid manual curation. For each gene, GV performs multiple analyses based on comparisons to gene sequences from large databases. The resulting report identifies problematic gene predictions and includes extensive statistics and graphs for each prediction to guide manual curation efforts. GV thus accelerates and enhances the work of biocurators and researchers who need accurate gene predictions from newly sequenced genomes.
Availability and implementation: GV can be used through a web interface or in the command-line. GV is open-source (AGPL), available at https://wurmlab.github.io/tools/genevalidator.
Contact: y.wurm@qmul.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
Inclusion of dyad-repeat pattern improves topology prediction of transmembrane β-barrel proteins
Authors: Hayat Sikander , Peters Christoph , Shu Nanjiang , Tsirigos Konstantinos D. , Elofsson Arne 
Published: February 2016

Abstract
Summary: Accurate topology prediction of transmembrane β-barrels is still an open question. Here, we present BOCTOPUS2, an improved topology prediction method for transmembrane β-barrels that can also identify the barrel domain, predict the topology and identify the orientation of residues in transmembrane β-strands. The major novelty of BOCTOPUS2 is the use of the dyad-repeat pattern of lipid and pore facing residues observed in transmembrane β-barrels. In a cross-validation test on a benchmark set of 42 proteins, BOCTOPUS2 predicts the correct topology in 69% of the proteins, an improvement of more than 10% over the best earlier method (BOCTOPUS) and in addition, it produces significantly fewer erroneous predictions on non-transmembrane β-barrel proteins.
Availability and implementation: BOCTOPUS2 webserver along with full dataset and source code is available at http://boctopus.bioinfo.se/
Contact: arne@bioinfo.se
Supplementary information: Supplementary data are available at Bioinformatics online.
Quantitative trait association study for mean telomere length in the South Asian genomes
Authors: Hakobyan Anna , Nersisyan Lilit , Arakelyan Arsen 
Published: February 2016

Abstract
Motivation: Mean telomere length (MTL) is associated with cancers and age-related diseases, which necessitates identification of genomic and environmental factors that impact telomere length dynamics. Here, we present a pilot genome wide association (GWA) study for MTL in South Asian population using publicly available next generation whole genome sequences (WGS), both for MTL and genotype calculations.
Results: MTL in the studied population was not correlated with age, which is in accordance with previous reports. Further, we identified that individuals with Sikh religion had longer telomeres, which may be the result of complex interaction between genetic background and environmental factors. Finally, we identified 51 MTL-associated SNPs residing in five loci. The top ones were located in ADARB2 gene, which has previously been implicated with extreme old age.
Conclusion: Our results show that WGS data can be used in telomere length studies. In addition, we introduce novel loci implicated in MTL that may be worth considering in further telomere studies.
Contact: aarakelyan@sci.am
Supplementary information: Supplementary data are available at Bioinformatics online.
GiANT: gene set uncertainty in enrichment analysis
Authors: Schmid Florian , Schmid Matthias , Müssel Christoph , Sträng J. Eric , Buske Christian , Bullinger Lars , Kraus Johann M. , Kestler Hans A. 
Published: February 2016

Abstract
Summary: Over the past years growing knowledge about biological processes and pathways revealed complex interaction networks involving many genes. In order to understand these networks, analysis of differential expression has continuously moved from single genes towards the study of gene sets. Various approaches for the assessment of gene sets have been developed in the context of gene set analysis (GSA). These approaches are bridging the gap between raw measurements and semantically meaningful terms.
We present a novel approach for assessing uncertainty in the definition of gene sets. This is an essential step when new gene sets are constructed from domain knowledge or given gene sets are suspected to be affected by uncertainty. Quantification of uncertainty is implemented in the R-package GiANT. We also included widely used GSA methods, embedded in a generic framework that can readily be extended by custom methods. The package provides an easy to use front end and allows for fast parallelization.
Availability and implementation: The package GiANT is available on CRAN.
Contacts: hans.kestler@leibniz-fli.de or hans.kestler@uni-ulm.de
ELASPIC web-server: proteome-wide structure-based prediction of mutation effects on protein stability and binding affinity
Authors: Witvliet Daniel K. , Strokach Alexey , Giraldo-Forero Andrés Felipe , Teyra Joan , Colak Recep , Kim Philip M. 
Published: February 2016

Abstract
Summary: ELASPIC is a novel ensemble machine-learning approach that predicts the effects of mutations on protein folding and protein–protein interactions. Here, we present the ELASPIC webserver, which makes the ELASPIC pipeline available through a fast and intuitive interface. The webserver can be used to evaluate the effect of mutations on any protein in the Uniprot database, and allows all predicted results, including modeled wild-type and mutated structures, to be managed and viewed online and downloaded if needed. It is backed by a database which contains improved structural domain definitions, and a list of curated domain–domain interactions for all known proteins, as well as homology models of domains and domain–domain interactions for the human proteome. Homology models for proteins of other organisms are calculated on the fly, and mutations are evaluated within minutes once the homology model is available.
Availability and implementation: The ELASPIC webserver is available online at http://elaspic.kimlab.org.
Contact: pm.kim@utoronto.ca or pi@kimlab.org
Supplementary data: Supplementary data are available at Bioinformatics online.
Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method
Authors: Du Lei , Huang Heng , Yan Jingwen , Kim Sungeun , Risacher Shannon L. , Inlow Mark , Moore Jason H. , Saykin Andrew J. , Shen Li ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: February 2016

Abstract
Motivation: Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated.
Results: We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations.
Availability and implementation: The Matlab code and sample data are freely available at http://www.iu.edu/∼shenlab/tools/angscca/.
Contact: shenli@iu.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
BCFtools/RoH: a hidden Markov model approach for detecting autozygosity from next-generation sequencing data
Authors: Narasimhan Vagheesh , Danecek Petr , Scally Aylwyn , Xue Yali , Tyler-Smith Chris , Durbin Richard 
Published: February 2016

Abstract
Summary: Runs of homozygosity (RoHs) are genomic stretches of a diploid genome that show identical alleles on both chromosomes. Longer RoHs are unlikely to have arisen by chance but are likely to denote autozygosity, whereby both copies of the genome descend from the same recent ancestor. Early tools to detect RoH used genotype array data, but substantially more information is available from sequencing data. Here, we present and evaluate BCFtools/RoH, an extension to the BCFtools software package, that detects regions of autozygosity in sequencing data, in particular exome data, using a hidden Markov model. By applying it to simulated data and real data from the 1000 Genomes Project we estimate its accuracy and show that it has higher sensitivity and specificity than existing methods under a range of sequencing error rates and levels of autozygosity.
Availability and implementation: BCFtools/RoH and its associated binary/source files are freely available from https://github.com/samtools/BCFtools.
Contact: vn2@sanger.ac.uk or pd3@sanger.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.