bluuebunny/crossref_metadata_2025_split huggingface dataset:
Constructing modular and universal single molecule tension sensor using protein G to study mechano-sensitive receptors
Authors: Wang Xuefeng , Rahil Zainab , Li Isaac T. S. , Chowdhury Farhan , Leckband Deborah E. , Chemla Yann R. , Ha Taekjip 
Published: February 2016

AbstractRecently a variety of molecular force sensors have been developed to study cellular forces acting through single mechano-sensitive receptors. A common strategy adopted is to attach ligand molecules on a surface through engineered molecular tethers which report cell-exerted tension on receptor-ligand bonds. This approach generally requires chemical conjugation of the ligand to the force reporting tether which can be time-consuming and labor-intensive. Moreover, ligand-tether conjugation can severely reduce the activity of protein ligands. To address this problem, we developed a Protein G (ProG)-based force sensor in which force-reporting tethers are conjugated to ProG instead of ligands. A recombinant ligand fused with IgG-Fc is conveniently assembled with the force sensor through ProG:Fc binding, therefore avoiding ligand conjugation and purification processes. Using this approach, we determined that molecular tension on E-cadherin is lower than dsDNA unzipping force (nominal value: 12 pN) during initial cadherin-mediated cell adhesion, followed by an escalation to forces higher than 43 pN (nominal value). This approach is highly modular and potentially universal as we demonstrate using two additional receptor-ligand interactions, P-selectin & PSGL-1 and Notch & DLL1.
Characterization of DWARF14 Genes in Populus
Authors: Zheng Kaijie , Wang Xiaoping , Weighill Deborah A. , Guo Hao-Bo , Xie Meng , Yang Yongil , Yang Jun , Wang Shucai , Jacobson Daniel A. , Guo Hong , Muchero Wellington , Tuskan Gerald A. , Chen Jin-Gui 
Published: February 2016

AbstractStrigolactones are a new class of plant hormones regulating shoot branching and symbiotic interactions with arbuscular mycorrhizal fungi. Studies of branching mutants in herbaceous plants have identified several key genes involved in strigolactone biosynthesis or signaling. The strigolactone signal is perceived by a member of the α/β-fold hydrolase superfamily, known as DWARF14 (D14). However, little is known about D14 genes in the woody perennial plants. Here we report the identification of D14 homologs in the model woody plant Populus trichocarpa. We showed that there are two D14 homologs in P. trichocarpa, designated as PtD14a and PtD14b that are over 95% similar at the amino acid level. Expression analysis indicated that the transcript level of PtD14a is generally more abundant than that of PtD14b. However, only PtD14a was able to complement Arabidopsis d14 mutants, suggesting that PtD14a is the functional D14 ortholog. Amino acid alignment and structural modeling revealed substitutions of several highly conserved amino acids in the PtD14b protein including a phenylalanine near the catalytic triad of D14 proteins. This study lays a foundation for further characterization of strigolactone pathway and its functions in the woody perennial plants.
Reference frames for reaching when decoupling eye and target position in depth and direction
Authors: Bosco A. , Breveglieri R. , Hadjidimitrakis K. , Galletti C. , Fattori P. 
Published: February 2016

AbstractSpatial representations in cortical areas involved in reaching movements were traditionally studied in a frontoparallel plane where the two-dimensional target location and the movement direction were the only variables to consider in neural computations. No studies so far have characterized the reference frames for reaching considering both depth and directional signals. Here we recorded from single neurons of the medial posterior parietal area V6A during a reaching task where fixation point and reaching targets were decoupled in direction and depth. We found a prevalent mixed encoding of target position, with eye-centered and spatiotopic representations differently balanced in the same neuron. Depth was stronger in defining the reference frame of eye-centered cells, while direction was stronger in defining that of spatiotopic cells. The predominant presence of various typologies of mixed encoding suggests that depth and direction signals are processed on the basis of flexible coordinate systems to ensure optimal motor response.
Prenatal inflammation-induced NF-κB dyshomeostasis contributes to renin-angiotensin system over-activity resulting in prenatally programmed hypertension in offspring
Authors: Deng Youcai , Deng Yafei , He Xiaoyan , Chu Jianhong , Zhou Jianzhi , Zhang Qi , Guo Wei , Huang Pei , Guan Xiao , Tang Yuan , Wei Yanling , Zhao Shanyu , Zhang Xingxing , Wei Chiming , Namaka Michael , Yi Ping , Yu Jianhua , Li Xiaohui 
Published: February 2016

AbstractStudies involving the use of prenatally programmed hypertension have been shown to potentially contribute to prevention of essential hypertension (EH). Our previous research has demonstrated that prenatal inflammatory stimulation leads to offspring’s aortic dysfunction and hypertension in pregnant Sprague-Dawley rats challenged with lipopolysaccharide (LPS). The present study found that prenatal LPS exposure led to NF-κB dyshomeostasis from fetus to adult, which was characterized by PI3K-Akt activation mediated degradation of IκBα protein and impaired NF-κB self-negative feedback loop mediated less newly synthesis of IκBα mRNA in thoracic aortas (gestational day 20, postnatal week 7 and 16). Prenatal or postnatal exposure of the IκBα degradation inhibitor, pyrollidine dithiocarbamate, effectively blocked NF-κB activation, endothelium dysfunction and renin-angiotensin system (RAS) over-activity in thoracic aortas, resulting in reduced blood pressure in offspring that received prenatal exposure to LPS. Surprisingly, NF-κB dyshomeostasis and RAS over-activity were only found in thoracic aortas but not in superior mesenteric arteries. Collectively, our data demonstrate that the early life NF-κB dyshomeostasis induced by prenatal inflammatory exposure plays an essential role in the development of EH through triggering RAS over-activity. We conclude that early life NF-κB dyshomeostasis is a key predictor of EH and thus, NF-κB inhibition represents an effective interventional strategy for EH prevention.
3D Printing Surgical Implants at the clinic: A Experimental Study on Anterior Cruciate Ligament Reconstruction
Authors: Liu An , Xue Guang-huai , Sun Miao , Shao Hui-feng , Ma Chi-yuan , Gao Qing , Gou Zhong-ru , Yan Shi-gui , Liu Yan-ming , He Yong 
Published: February 2016

AbstractDesktop three-dimensional (3D) printers (D3DPs) have become a popular tool for fabricating personalized consumer products, favored for low cost, easy operation and other advantageous qualities. This study focused on the potential for using D3DPs to successfully, rapidly and economically print customized implants at medical clinics. An experiment was conducted on a D3DP-printed anterior cruciate ligament surgical implant using a rabbit model. A well-defined, orthogonal, porous PLA screw-like scaffold was printed, then coated with hydroxyapatite (HA) to improve its osteoconductivity. As an internal fixation as well as an ideal cell delivery system, the osteogenic scaffold loaded with mesenchymal stem cells (MSCs) were evaluated through bothin vitroandin vivotests to observe bone-ligament healing via cell therapy. The MSCs suspended in Pluronic F-127 hydrogel on PLA/HA screw-like scaffold showed the highest cell proliferation and osteogenesisin vitro.In vivoassessment of rabbit anterior cruciate ligament models for 4 and 12 weeks showed that the PLA/HA screw-like scaffold loaded with MSCs suspended in Pluronic F-127 hydrogel exhibited significant bone ingrowth and bone-graft interface formation within the bone tunnel. Overall, the results of this study demonstrate that fabricating surgical implants at the clinic (fab@clinic) with D3DPs can be feasible, effective and economical.
OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data
Authors: Leng Ning , Choi Jeea , Chu Li-Fang , Thomson James A. , Kendziorski Christina , Stewart Ron 
Published: February 2016

Abstract
Summary: A recent article identified an artifact in multiple single-cell RNA-seq (scRNA-seq) datasets generated by the Fluidigm C1 platform. Specifically, Leng et al. showed significantly increased gene expression in cells captured from sites with small or large plate output IDs. We refer to this artifact as an ordering effect (OE). Including OE genes in downstream analyses could lead to biased results. To address this problem, we developed a statistical method and software called OEFinder to identify a sorted list of OE genes. OEFinder is available as an R package along with user-friendly graphical interface implementations which allows users to check for potential artifacts in scRNA-seq data generated by the Fluidigm C1 platform.
Availability and implementation: OEFinder is freely available at https://github.com/lengning/OEFinder
Contact: rstewart@morgridge.org or lengning1@gmail.com
Supplementary information: Supplementary data are available at Bioinformatics online.
MMseqs software suite for fast and deep clustering and searching of large protein sequence sets
Authors: Hauser Maria , Steinegger Martin , Söding Johannes 
Published: February 2016

Abstract
Motivation: Sequence databases are growing fast, challenging existing analysis pipelines. Reducing the redundancy of sequence databases by similarity clustering improves speed and sensitivity of iterative searches. But existing tools cannot efficiently cluster databases of the size of UniProt to 50% maximum pairwise sequence identity or below. Furthermore, in metagenomics experiments typically large fractions of reads cannot be matched to any known sequence anymore because searching with sensitive but relatively slow tools (e.g. BLAST or HMMER3) through comprehensive databases such as UniProt is becoming too costly.
Results: MMseqs (Many-against-Many sequence searching) is a software suite for fast and deep clustering and searching of large datasets, such as UniProt, or 6-frame translated metagenomics sequencing reads. MMseqs contains three core modules: a fast and sensitive prefiltering module that sums up the scores of similar k-mers between query and target sequences, an SSE2- and multi-core-parallelized local alignment module, and a clustering module.
In our homology detection benchmarks, MMseqs is much more sensitive and 4–30 times faster than UBLAST and RAPsearch, respectively, although it does not reach BLAST sensitivity yet. Using its cascaded clustering workflow, MMseqs can cluster large databases down to ∼30% sequence identity at hundreds of times the speed of BLASTclust and much deeper than CD-HIT and USEARCH. MMseqs can also update a database clustering in linear instead of quadratic time. Its much improved sensitivity-speed trade-off should make MMseqs attractive for a wide range of large-scale sequence analysis tasks.
Availability and implementation: MMseqs is open-source software available under GPL at https://github.com/soedinglab/MMseqs
Contact: martin.steinegger@mpibpc.mpg.de, soeding@mpibpc.mpg.de
Supplementary information: Supplementary data are available at Bioinformatics online.
lnCaNet: pan-cancer co-expression network for human lncRNA and cancer genes
Authors: Liu Yining , Zhao Min 
Published: February 2016

Abstract
Summary: Thousands of human long non-coding RNAs (lncRNAs) have been identified in cancers and played important roles in a wide range of tumorigenesis. However, the functions of vast majority of human lncRNAs are still elusive. Emerging studies revealed that the expression level of majority lncRNAs shows discordant expression pattern with their protein-coding gene neighbors in various model organisms. Therefore, it may be useful to infer lncRNAs’ potential biological function in cancer development by more comprehensive functional views of co-expressed cancer genes beyond mere physical proximity of genes. To this aim, we performed thorough searches and analyses of the interactions between lncRNA and non-neighboring cancer genes and provide a comprehensive co-expression data resource, LnCaNet. In current version, LnCaNet contains the pre-computed 8 494 907 significant co-expression pairs of 9641 lncRNAs and 2544 well-classified cancer genes in 2922 matched TCGA samples. In detail, we integrated 10 cancer gene lists from public database and calculate the co-expression with all the lncRNAs in 11 TCGA cancer types separately. Based on the resulted 110 co-expression networks, we identified 17 common regulatory pairs related to extracellular space shared in 11 cancers. We expect LnCaNet will enable researcher to explore lncRNA expression pattern, their affected cancer genes and pathways, biological significance in the context of specific cancer types and other useful annotation related to particular kind of lncRNA-cancer gene interaction.
Availability and implementation: http://lncanet.bioinfo-minzhao.org/
Contact: m.zhao@uq.edu.au
Supplementary information: Supplementary data are available at Bioinformatics online.
SIRAH tools: mapping, backmapping and visualization of coarse-grained models
Authors: Machado Matías R. , Pantano Sergio 
Published: February 2016

Abstract
Summary: Coarse-grained (CG) models reduce the cost of molecular dynamics simulations keeping the essence of molecular interactions. Still, the diversity of CG representations (sizes, connectivity, naming, etc.) hampers the handling and visualization of such models. SIRAH Tools comprises a set of utilities to convert all-atoms coordinates to arbitrary residue-based CG schemes, write GROMACS’ topological information at any resolution into PSF format and a VMD plugin to visualize, analyze and retrieve pseudo-atomistic information from CG trajectories performed with the SIRAH force field. These tools facilitate the use of intricate CG force fields outside the small developer’s community.
Availability and implementation: Different utilities of SIRAH Tools are written in Perl, Tcl, or R. Documentation and source codes are freely distributed at http://www.sirahff.com.
Contact: mmachado@pasteur.edu.uy or spantano@pasteur.edu.uy
Library of binding protein scaffolds (LibBP): a computational platform for selection of binding protein scaffolds
Authors: Hong Seungpyo , Kim Dongsup 
Published: February 2016

Abstract
Motivation: Developments in biotechnology have enabled the in vitro evolution of binding proteins. The emerging limitations of antibodies in binding protein engineering have led to suggestions for other proteins as alternative binding protein scaffolds. Most of these proteins were selected based on human intuition rather than systematic analysis of the available data. To improve this strategy, we developed a computational framework for finding desirable binding protein scaffolds by utilizing protein structure and sequence information.
Results: For each protein, its structure and the sequences of evolutionarily-related proteins were analyzed, and spatially contiguous regions composed of highly variable residues were identified. A large number of proteins have these regions, but leucine rich repeats (LRRs), histidine kinase domains and immunoglobulin domains are predominant among them. The candidates suggested as new binding protein scaffolds include histidine kinase, LRR, titin and pentapeptide repeat protein.
Availability and implementation: The database and web-service are accessible via http://bcbl.kaist.ac.kr/LibBP.
Contact: kds@kaist.ac.kr
Supplementary data: Supplementary data are available at Bioinformatics online.
Alloscore: a method for predicting allosteric ligand–protein interactions
Authors: Li Shuai , Shen Qiancheng , Su Minyi , Liu Xinyi , Lu Shaoyong , Chen Zhongjie , Wang Renxiao , Zhang Jian 
Published: February 2016

Abstract
Summary: Allosteric ligands have increasingly gained attention as potential therapeutic agents due to their higher target selectivity and lower toxicity compared with classic orthosteric ligands. Despite the great interest in the development of allosteric drugs as a new tactic in drug discovery, the understanding of the ligand–protein interactions underlying allosteric binding represents a key challenge. Herein, we introduce Alloscore, a web server that predicts the binding affinities of allosteric ligand–protein interactions. This method exhibits prominent performance in describing allosteric binding and could be useful in allosteric virtual screening and the structural optimization of allosteric agonists/antagonists.
Availability and implementation: The Alloscore server and tutorials are freely available at http://mdl.shsmu.edu.cn/alloscore
Contact: jian.zhang@sjtu.edu.cn
Supplementary information: Supplementary data are available at Bioinformatics online.
BISQUE: locus- and variant-specific conversion of genomic, transcriptomic and proteomic database identifiers
Authors: Meyer Michael J. , Geske Philip , Yu Haiyuan 
Published: February 2016

Abstract
Summary: Biological sequence databases are integral to efforts to characterize and understand biological molecules and share biological data. However, when analyzing these data, scientists are often left holding disparate biological currency—molecular identifiers from different databases. For downstream applications that require converting the identifiers themselves, there are many resources available, but analyzing associated loci and variants can be cumbersome if data is not given in a form amenable to particular analyses. Here we present BISQUE, a web server and customizable command-line tool for converting molecular identifiers and their contained loci and variants between different database conventions. BISQUE uses a graph traversal algorithm to generalize the conversion process for residues in the human genome, genes, transcripts and proteins, allowing for conversion across classes of molecules and in all directions through an intuitive web interface and a URL-based web service.
Availability and implementation: BISQUE is freely available via the web using any major web browser (http://bisque.yulab.org/). Source code is available in a public GitHub repository (https://github.com/hyulab/BISQUE).
Contact: haiyuan.yu@cornell.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Predicting physiologically relevant SH3 domain mediated protein–protein interactions in yeast
Authors: Jain Shobhit , Bader Gary D. 
Published: February 2016

Abstract
Motivation: Many intracellular signaling processes are mediated by interactions involving peptide recognition modules such as SH3 domains. These domains bind to small, linear protein sequence motifs which can be identified using high-throughput experimental screens such as phage display. Binding motif patterns can then be used to computationally predict protein interactions mediated by these domains. While many protein–protein interaction prediction methods exist, most do not work with peptide recognition module mediated interactions or do not consider many of the known constraints governing physiologically relevant interactions between two proteins.
Results: A novel method for predicting physiologically relevant SH3 domain-peptide mediated protein–protein interactions in S. cerevisae using phage display data is presented. Like some previous similar methods, this method uses position weight matrix models of protein linear motif preference for individual SH3 domains to scan the proteome for potential hits and then filters these hits using a range of evidence sources related to sequence-based and cellular constraints on protein interactions. The novelty of this approach is the large number of evidence sources used and the method of combination of sequence based and protein pair based evidence sources. By combining different peptide and protein features using multiple Bayesian models we are able to predict high confidence interactions with an overall accuracy of 0.97.
Availability and implementation: Domain-Motif Mediated Interaction Prediction (DoMo-Pred) command line tool and all relevant datasets are available under GNU LGPL license for download from http://www.baderlab.org/Software/DoMo-Pred. The DoMo-Pred command line tool is implemented using Python 2.7 and C ++.
Contact: gary.bader@utoronto.ca
Supplementary information: Supplementary data are available at Bioinformatics online.
Computational approaches to define a human milk metaglycome
Authors: Agravat Sanjay B. , Song Xuezheng , Rojsajjakul Teerapat , Cummings Richard D. , Smith David F. 
Published: February 2016

Abstract
Motivation: The goal of deciphering the human glycome has been hindered by the lack of high-throughput sequencing methods for glycans. Although mass spectrometry (MS) is a key technology in glycan sequencing, MS alone provides limited information about the identification of monosaccharide constituents, their anomericity and their linkages. These features of individual, purified glycans can be partly identified using well-defined glycan-binding proteins, such as lectins and antibodies that recognize specific determinants within glycan structures.
Results: We present a novel computational approach to automate the sequencing of glycans using metadata-assisted glycan sequencing, which combines MS analyses with glycan structural information from glycan microarray technology. Success in this approach was aided by the generation of a ‘virtual glycome’ to represent all potential glycan structures that might exist within a metaglycomes based on a set of biosynthetic assumptions using known structural information. We exploited this approach to deduce the structures of soluble glycans within the human milk glycome by matching predicted structures based on experimental data against the virtual glycome. This represents the first meta-glycome to be defined using this method and we provide a publically available web-based application to aid in sequencing milk glycans.
Availability and implementation: http://glycomeseq.emory.edu
Contact: sagravat@bidmc.harvard.edu
Supplementary information: Supplementary data are available at Bioinformatics online.
Measuring the spatial correlations of protein binding sites
Authors: Wei Yingying , Wu Hao 
Published: February 2016

Abstract
Motivation: Understanding the interactions of different DNA binding proteins is a crucial first step toward deciphering gene regulatory mechanism. With advances of high-throughput sequencing technology such as ChIP-seq, the genome-wide binding sites of many proteins have been profiled under different biological contexts. It is of great interest to quantify the spatial correlations of the binding sites, such as their overlaps, to provide information for the interactions of proteins. Analyses of the overlapping patterns of binding sites have been widely performed, mostly based on ad hoc methods. Due to the heterogeneity and the tremendous size of the genome, such methods often lead to biased even erroneous results.
Results: In this work, we discover a Simpson’s paradox phenomenon in assessing the genome-wide spatial correlation of protein binding sites. Leveraging information from publicly available data, we propose a testing procedure for evaluating the significance of overlapping from a pair of proteins, which accounts for background artifacts and genome heterogeneity. Real data analyses demonstrate that the proposed method provide more biologically meaningful results.
Availability and implementation: An R package is available at http://www.sta.cuhk.edu.hk/YWei/ChIPCor.html.
Contacts: ywei@sta.cuhk.edu.hk or hao.wu@emory.edu.
Supplementary information: Supplementary data are available at Bioinformatics online.