Synthesis of high quality inorganic fullerene-like BN hollow spheres via a simple chemical routeElectronic supplementary information (ESI) available: XPS spectrum of as-prepared h-BN. See http://www.rsc.org/suppdata/cc/b3/b308264d/

2003 ◽  
pp. 2688 ◽  
Author(s):  
Xinjun Wang ◽  
Yi Xie ◽  
Qixun Guo
Keyword(s):  
Hollow Spheres ◽  
Supplementary Information ◽  
High Quality ◽  
Inorganic Fullerene ◽  
Simple Chemical

scholarly journals OpenBioLink: a benchmarking framework for large-scale biomedical link prediction

2020 ◽  
Vol 36 (13) ◽  
pp. 4097-4098 ◽  
Author(s):  
Anna Breit ◽  
Simon Ott ◽  
Asan Agibetov ◽  
Matthias Samwald
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
Source Code ◽  
Machine Learning Algorithms ◽  
Knowledge Networks ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Biomedical Knowledge ◽  
High Quality ◽  
Baseline Evaluation

Abstract Summary Recently, novel machine-learning algorithms have shown potential for predicting undiscovered links in biomedical knowledge networks. However, dedicated benchmarks for measuring algorithmic progress have not yet emerged. With OpenBioLink, we introduce a large-scale, high-quality and highly challenging biomedical link prediction benchmark to transparently and reproducibly evaluate such algorithms. Furthermore, we present preliminary baseline evaluation results. Availability and implementation Source code and data are openly available at https://github.com/OpenBioLink/OpenBioLink. Supplementary information Supplementary data are available at Bioinformatics online.

Top-Down Garbage Collector: a tool for selecting high-quality top-down proteomics mass spectra

2019 ◽  
Vol 35 (18) ◽  
pp. 3489-3490 ◽  
Author(s):  
Diogo B Lima ◽  
André R F Silva ◽  
Mathieu Dupré ◽  
Marlon D M Santos ◽  
Milan A Clasen ◽  
...  
Keyword(s):  
Quality Control ◽  
Mass Spectra ◽  
Rate Increase ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Top Down ◽  
High Quality ◽  
Garbage Collector ◽  
E Coli ◽  
Spectral Libraries

Abstract Motivation We present the first tool for unbiased quality control of top-down proteomics datasets. Our tool can select high-quality top-down proteomics spectra, serve as a gateway for building top-down spectral libraries and, ultimately, improve identification rates. Results We demonstrate that a twofold rate increase for two E. coli top-down proteomics datasets may be achievable. Availability and implementation http://patternlabforproteomics.org/tdgc, freely available for academic use. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Serpentine: a flexible 2D binning method for differential Hi-C analysis

2020 ◽  
Vol 36 (12) ◽  
pp. 3645-3651
Author(s):  
Lyam Baudry ◽  
Gaël A Millot ◽  
Agnes Thierry ◽  
Romain Koszul ◽  
Vittore F Scolari
Keyword(s):  
Deep Sequencing ◽  
Low Noise ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Fractal Nature ◽  
Contact Map ◽  
Signal To Noise ◽  
High Quality ◽  
Contact Maps ◽  
Contact Frequency

Abstract Motivation Hi-C contact maps reflect the relative contact frequencies between pairs of genomic loci, quantified through deep sequencing. Differential analyses of these maps enable downstream biological interpretations. However, the multi-fractal nature of the chromatin polymer inside the cellular envelope results in contact frequency values spanning several orders of magnitude: contacts between loci pairs separated by large genomic distances are much sparser than closer pairs. The same is true for poorly covered regions, such as repeated sequences. Both distant and poorly covered regions translate into low signal-to-noise ratios. There is no clear consensus to address this limitation. Results We present Serpentine, a fast, flexible procedure operating on raw data, which considers the contacts in each region of a contact map. Binning is performed only when necessary on noisy regions, preserving informative ones. This results in high-quality, low-noise contact maps that can be conveniently visualized for rigorous comparative analyses. Availability and implementation Serpentine is available on the PyPI repository and https://github.com/koszullab/serpentine; documentation and tutorials are provided at https://serpentine.readthedocs.io/en/latest/. Supplementary information Supplementary data are available at Bioinformatics online.

Bringing data from curated pathway resources to Cytoscape with OmniPath

2019 ◽  
Vol 36 (8) ◽  
pp. 2632-2633 ◽  
Author(s):  
Francesco Ceccarelli ◽  
Denes Turei ◽  
Attila Gabor ◽  
Julio Saez-Rodriguez
Keyword(s):  
Source Code ◽  
Large Body ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Network Resources ◽  
High Quality ◽  
Comprehensive Collection ◽  
Intuitive Interface ◽  
Growing Network

Abstract Summary Multiple databases provide valuable information about curated pathways and other resources that can be used to build and analyze networks. OmniPath combines 61 (and continuously growing) network resources into a comprehensive collection, with over 120 000 interactions. We present here the OmniPath App, a Cytoscape plugin to flexibly import data from OmniPath via a simple and intuitive interface. Thus, it makes possible to directly access the large body of high-quality knowledge provided by OmniPath within Cytoscape for inspection and further use with other tools. Availability and implementation The OmniPath App has been developed for Cytoscape 3 in the Java programing language. The latest source code and the plugin can be found at: https://github.com/saezlab/Omnipath_Cytoscape and http://apps.cytoscape.org/apps/omnipath, respectively. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals LRez: C ++ API and toolkit for analyzing and managing Linked-Reads data

2021 ◽  
Author(s):  
Pierre Morisse ◽  
Claire Lemaitre ◽  
Fabrice Legeai
Keyword(s):  
Genome Assembly ◽  
Low Cost ◽  
Variant Calling ◽  
Supplementary Information ◽  
Supplementary Data ◽  
High Quality ◽  
Dna Molecule ◽  
Sequencing Technologies ◽  
Wide Range ◽  
Genomic Regions

Abstract Motivation Linked-Reads technologies combine both the high-quality and low cost of short-reads sequencing and long-range information, through the use of barcodes tagging reads which originate from a common long DNA molecule. This technology has been employed in a broad range of applications including genome assembly, phasing and scaffolding, as well as structural variant calling. However, to date, no tool or API dedicated to the manipulation of Linked-Reads data exist. Results We introduce LRez, a C ++ API and toolkit which allows easy management of Linked-Reads data. LRez includes various functionalities, for computing numbers of common barcodes between genomic regions, extracting barcodes from BAM files, as well as indexing and querying BAM, FASTQ and gzipped FASTQ files to quickly fetch all reads or alignments containing a given barcode. LRez is compatible with a wide range of Linked-Reads sequencing technologies, and can thus be used in any tool or pipeline requiring barcode processing or indexing, in order to improve their performances. Availability and implementation LRez is implemented in C ++, supported on Unix-based platforms, and available under AGPL-3.0 License at https://github.com/morispi/LRez, and as a bioconda module. Supplementary information Supplementary data are available at Bioinformatics Advances

scholarly journals Reconstructing signaling pathways using regular language constrained paths

2019 ◽  
Vol 35 (14) ◽  
pp. i624-i633 ◽  
Author(s):  
Mitchell J Wagner ◽  
Aditya Pratapa ◽  
T M Murali
Keyword(s):  
Signaling Pathways ◽  
Regular Language ◽  
Interaction Network ◽  
Automated Analysis ◽  
Supplementary Information ◽  
Natural Question ◽  
Supplementary Data ◽  
High Quality ◽  
Alternative Approaches ◽  
New Interactions

Abstract Motivation High-quality curation of the proteins and interactions in signaling pathways is slow and painstaking. As a result, many experimentally detected interactions are not annotated to any pathways. A natural question that arises is whether or not it is possible to automatically leverage existing pathway annotations to identify new interactions for inclusion in a given pathway. Results We present RegLinker, an algorithm that achieves this purpose by computing multiple short paths from pathway receptors to transcription factors within a background interaction network. The key idea underlying RegLinker is the use of regular language constraints to control the number of non-pathway interactions that are present in the computed paths. We systematically evaluate RegLinker and five alternative approaches against a comprehensive set of 15 signaling pathways and demonstrate that RegLinker recovers withheld pathway proteins and interactions with the best precision and recall. We used RegLinker to propose new extensions to the pathways. We discuss the literature that supports the inclusion of these proteins in the pathways. These results show the broad potential of automated analysis to attenuate difficulties of traditional manual inquiry. Availability and implementation https://github.com/Murali-group/RegLinker. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Estimation of Eosinophil Cells in Cord Blood with References Based on Blood in Adults via Bayesian Measurement Error Modeling

2019 ◽  
Author(s):  
Yu Jiang ◽  
Hongmei Zhang ◽  
Shan V Andrews ◽  
Hasan Arshad ◽  
Susan Ewart ◽  
...  
Keyword(s):  
Measurement Error ◽  
Cord Blood ◽  
Blood Cells ◽  
White Blood Cells ◽  
Error Modeling ◽  
Supplementary Information ◽  
Estimation Accuracy ◽  
Bioconductor Package ◽  
High Quality ◽  
Eosinophil Cell

Abstract Motivation Eosinophils are phagocytic white blood cells with a variety of roles in the immune system. In situations where actual counts are not available, high quality approximations of their cell proportions using indirect markers are critical. Results We develop a Bayesian measurement error model to estimate proportions of eosinophils in cord blood, using the cord blood DNA methylation profiles, based on markers of eosinophil cell heterogeneity in blood of adults. The proposed method can be directly extended to other cells across different reference panels. We demonstrate the method’s estimation accuracy using B cells and show that the findings support the proposed approach. The method has been incorporated into the estimateCellCounts function in the minfi package to estimate eosinophil cells proportions in cord blood. Availability estimateCellCounts function is implemented and available in Bioconductor package minfi. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Gcluster: a simple-to-use tool for visualizing and comparing genome contexts for numerous genomes

2020 ◽  
Vol 36 (12) ◽  
pp. 3871-3873 ◽  
Author(s):  
Xiangyang Li ◽  
Fang Chen ◽  
Yunpeng Chen
Keyword(s):  
Source Code ◽  
Gene Clusters ◽  
Genomic Region ◽  
Supplementary Information ◽  
Homologous Gene ◽  
High Quality ◽  
Large Numbers ◽  
Linear Maps ◽  
User Friendly

Abstract Motivation Comparing the organization of gene, gene clusters and their flanking genomic contexts is of critical importance to the determination of gene function and evolutionary basis of microbial traits. Currently, user-friendly and flexible tools enabling to visualize and compare genomic contexts for numerous genomes are still missing. Results We here present Gcluster, a stand-alone Perl tool that allows researchers to customize and create high-quality linear maps of the genomic region around the genes of interest across large numbers of completed and draft genomes. Importantly, Gcluster integrates homologous gene analysis, in the form of a built-in orthoMCL, and mapping genomes onto a given phylogeny to provide superior comparison of gene contexts. Availability and implementation Gcluster is written in Perl and released under GPLv3. The source code is freely available at https://github.com/Xiangyang1984/Gcluster and http://www.microbialgenomic.com/Gcluster_tool.html. Gcluster can also be installed through conda: ‘conda install -c bioconda gcluster’. Supplementary information Supplementary data are available at Bioinformatics online.

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