Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Bioinformatics
Title: QuIN: A Web Server for Querying and Visualizing Chromatin Interaction Networks
Abstract: Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. AVAILABILITY: QuIN’s web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.
Extracted statement — deposits: QuIN
Research field: Bioinformatics
Title: Cost-effectiveness of dog rabies vaccination programs in East Africa
Abstract: Background Dog rabies annually causes 24,000–70,000 deaths globally. We built a spreadsheet tool, RabiesEcon, to aid public health officials to estimate the cost-effectiveness of dog rabies vaccination programs in East Africa. Methods RabiesEcon uses a mathematical model of dog-dog and dog-human rabies transmission to estimate dog rabies cases averted, the cost per human rabies death averted and cost per year of life gained (YLG) due to dog vaccination programs (US 2015 dollars). We used an East African human population of 1 million (approximately 2/3 living in urban setting, 1/3 rural). We considered, using data from the literature, three vaccination options; no vaccination, annual vaccination of 50% of dogs and 20% of dogs vaccinated semi-annually. We assessed 2 transmission scenarios: low (1.2 dogs infected per infectious dog) and high (1.7 dogs infected). We also examined the impact of annually vaccinating 70% of all dogs (World Health Organization recommendation for dog rabies elimination). Results Without dog vaccination, over 10 years there would a total of be approximately 44,000–65,000 rabid dogs and 2,100–2,900 human deaths. Annually vaccinating 50% of dogs results in 10-year reductions of 97% and 75% in rabid dogs (low and high transmissions scenarios, respectively), approximately 2,000–1,600 human deaths averted, and an undiscounted cost-effectiveness of $451-$385 per life saved. Semi-annual vaccination of 20% of dogs results in in 10-year reductions of 94% and 78% in rabid dogs, and approximately 2,000–1,900 human deaths averted, and cost $404-$305 per life saved. In the low transmission scenario, vaccinating either 50% or 70% of dogs eliminated dog rabies. Results were most sensitive to dog birth rate and the initial rate of dog-to-dog transmission (Ro). Conclusions Dog rabies vaccination programs can control, and potentially eliminate, dog rabies. The frequency and coverage of vaccination programs, along with the level of dog rabies transmission, can affect the cost-effectiveness of such programs. RabiesEcon can aid both the planning and assessment of dog rabies vaccination programs.
Extracted statement — creates: RabiesEcon
Research field: Bioinformatics
Title: Cost-effectiveness of dog rabies vaccination programs in East Africa
Abstract: Background Dog rabies annually causes 24,000–70,000 deaths globally. We built a spreadsheet tool, RabiesEcon, to aid public health officials to estimate the cost-effectiveness of dog rabies vaccination programs in East Africa. Methods RabiesEcon uses a mathematical model of dog-dog and dog-human rabies transmission to estimate dog rabies cases averted, the cost per human rabies death averted and cost per year of life gained (YLG) due to dog vaccination programs (US 2015 dollars). We used an East African human population of 1 million (approximately 2/3 living in urban setting, 1/3 rural). We considered, using data from the literature, three vaccination options; no vaccination, annual vaccination of 50% of dogs and 20% of dogs vaccinated semi-annually. We assessed 2 transmission scenarios: low (1.2 dogs infected per infectious dog) and high (1.7 dogs infected). We also examined the impact of annually vaccinating 70% of all dogs (World Health Organization recommendation for dog rabies elimination). Results Without dog vaccination, over 10 years there would a total of be approximately 44,000–65,000 rabid dogs and 2,100–2,900 human deaths. Annually vaccinating 50% of dogs results in 10-year reductions of 97% and 75% in rabid dogs (low and high transmissions scenarios, respectively), approximately 2,000–1,600 human deaths averted, and an undiscounted cost-effectiveness of $451-$385 per life saved. Semi-annual vaccination of 20% of dogs results in in 10-year reductions of 94% and 78% in rabid dogs, and approximately 2,000–1,900 human deaths averted, and cost $404-$305 per life saved. In the low transmission scenario, vaccinating either 50% or 70% of dogs eliminated dog rabies. Results were most sensitive to dog birth rate and the initial rate of dog-to-dog transmission (Ro). Conclusions Dog rabies vaccination programs can control, and potentially eliminate, dog rabies. The frequency and coverage of vaccination programs, along with the level of dog rabies transmission, can affect the cost-effectiveness of such programs. RabiesEcon can aid both the planning and assessment of dog rabies vaccination programs.
Extracted statement — uses: RabiesEcon
Research field: Bioinformatics
Title: Redirector: Designing Cell Factories by Reconstructing the Metabolic Objective
Abstract: Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production.
Extracted statement — creates: Redirector
Research field: Bioinformatics
Title: Redirector: Designing Cell Factories by Reconstructing the Metabolic Objective
Abstract: Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production.
Extracted statement — deposits: Redirector
Research field: Bioinformatics
Title: Redirector: Designing Cell Factories by Reconstructing the Metabolic Objective
Abstract: Advances in computational metabolic optimization are required to realize the full potential of new in vivo metabolic engineering technologies by bridging the gap between computational design and strain development. We present Redirector, a new Flux Balance Analysis-based framework for identifying engineering targets to optimize metabolite production in complex pathways. Previous optimization frameworks have modeled metabolic alterations as directly controlling fluxes by setting particular flux bounds. Redirector develops a more biologically relevant approach, modeling metabolic alterations as changes in the balance of metabolic objectives in the system. This framework iteratively selects enzyme targets, adds the associated reaction fluxes to the metabolic objective, thereby incentivizing flux towards the production of a metabolite of interest. These adjustments to the objective act in competition with cellular growth and represent up-regulation and down-regulation of enzyme mediated reactions. Using the iAF1260 E. coli metabolic network model for optimization of fatty acid production as a test case, Redirector generates designs with as many as 39 simultaneous and 111 unique engineering targets. These designs discover proven in vivo targets, novel supporting pathways and relevant interdependencies, many of which cannot be predicted by other methods. Redirector is available as open and free software, scalable to computational resources, and powerful enough to find all known enzyme targets for fatty acid production.
Extracted statement — uses: Redirector
Research field: Bioinformatics
Title: Biotea-2-Bioschemas, facilitating structured markup for semantically annotated scholarly publications
Abstract: The total number of scholarly publications grows day by day, making it necessary to explore and use simple yet effective ways to expose their metadata. Schema.org supports adding structured metadata to web pages via markup, making it easier for data providers but also for search engines to provide the right search results. Bioschemas is based on the standards of schema.org, providing new types, properties and guidelines for metadata, i.e., providing metadata profiles tailored to the Life Sciences domain. Here we present our proposed contribution to Bioschemas (from the project “Biotea”), which supports metadata contributions for scholarly publications via profiles and web components. Biotea comprises a semantic model to represent publications together with annotated elements recognized from the scientific text; our Biotea model has been mapped to schema.org following Bioschemas standards.
Extracted statement — Related Resource: schema.org
Research field: Bioinformatics
Title: scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data
Abstract: Abstract Single-cell RNA sequencing (scRNA-seq) technology allows researchers to profile the transcriptomes of thousands of cells simultaneously. Protocols that incorpo-rate both designed and random barcodes have greatly increased the throughput of scRNA-seq, but give rise to a more complex data structure. There is a need for new tools that can handle the various barcoding strategies used by different protocols and exploit this information for quality assessment at the sample-level and provide effective visualization of these results in preparation for higher-level analyses. To this end, we developed scPipe , a R/Bioconductor package that integrates barcode demultiplexing, read alignment, UMI-aware gene-level quantification and quality control of raw sequencing data generated by multiple 3-prime-end sequencing protocols that include CEL-seq, MARS-seq, Chromium 10X and Drop-seq. scPipe produces a count matrix that is essential for downstream analysis along with an HTML report that summarises data quality. These results can be used as input for downstream analyses including normalization, visualization and statistical testing. scPipe performs this processing in a few simple R commands, promoting reproducible analysis of single-cell data that is compatible with the emerging suite of scRNA-seq analysis tools available in R/Bioconductor. The scPipe R package is available for download from <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://www.bioconductor.org/packages/scPipe">https://www.bioconductor.org/packages/scPipe</jats:ext-link>.
Extracted statement — deposits: scPipe
Research field: Bioinformatics
Title: PoreWalker: A Novel Tool for the Identification and Characterization of Channels in Transmembrane Proteins from Their Three-Dimensional Structure
Abstract: Transmembrane channel proteins play pivotal roles in maintaining the homeostasis and responsiveness of cells and the cross-membrane electrochemical gradient by mediating the transport of ions and molecules through biological membranes. Therefore, computational methods which, given a set of 3D coordinates, can automatically identify and describe channels in transmembrane proteins are key tools to provide insights into how they function. Herein we present PoreWalker, a fully automated method, which detects and fully characterises channels in transmembrane proteins from their 3D structures. A stepwise procedure is followed in which the pore centre and pore axis are first identified and optimised using geometric criteria, and then the biggest and longest cavity through the channel is detected. Finally, pore features, including diameter profiles, pore-lining residues, size, shape and regularity of the pore are calculated, providing a quantitative and visual characterization of the channel. To illustrate the use of this tool, the method was applied to several structures of transmembrane channel proteins and was able to identify shape/size/residue features representative of specific channel families. The software is available as a web-based resource at http://www.ebi.ac.uk/thornton-srv/software/PoreWalker/.
Extracted statement — deposits: software
Research field: Bioinformatics
Title: ACME: Automated Cell Morphology Extractor for Comprehensive Reconstruction of Cell Membranes
Abstract: The quantification of cell shape, cell migration, and cell rearrangements is important for addressing classical questions in developmental biology such as patterning and tissue morphogenesis. Time-lapse microscopic imaging of transgenic embryos expressing fluorescent reporters is the method of choice for tracking morphogenetic changes and establishing cell lineages and fate maps in vivo. However, the manual steps involved in curating thousands of putative cell segmentations have been a major bottleneck in the application of these technologies especially for cell membranes. Segmentation of cell membranes while more difficult than nuclear segmentation is necessary for quantifying the relations between changes in cell morphology and morphogenesis. We present a novel and fully automated method to first reconstruct membrane signals and then segment out cells from 3D membrane images even in dense tissues. The approach has three stages: 1) detection of local membrane planes, 2) voting to fill structural gaps, and 3) region segmentation. We demonstrate the superior performance of the algorithms quantitatively on time-lapse confocal and two-photon images of zebrafish neuroectoderm and paraxial mesoderm by comparing its results with those derived from human inspection. We also compared with synthetic microscopic images generated by simulating the process of imaging with fluorescent reporters under varying conditions of noise. Both the over-segmentation and under-segmentation percentages of our method are around 5%. The volume overlap of individual cells, compared to expert manual segmentation, is consistently over 84%. By using our software (ACME) to study somite formation, we were able to segment touching cells with high accuracy and reliably quantify changes in morphogenetic parameters such as cell shape and size, and the arrangement of epithelial and mesenchymal cells. Our software has been developed and tested on Windows, Mac, and Linux platforms and is available publicly under an open source BSD license (https://github.com/krm15/ACME).
Extracted statement — deposits: software
Research field: Bioinformatics
Title: ESPRIT-Forest: Parallel clustering of massive amplicon sequence data in subquadratic time
Abstract: The rapid development of sequencing technology has led to an explosive accumulation of genomic sequence data. Clustering is often the first step to perform in sequence analysis, and hierarchical clustering is one of the most commonly used approaches for this purpose. However, it is currently computationally expensive to perform hierarchical clustering of extremely large sequence datasets due to its quadratic time and space complexities. In this paper we developed a new algorithm called ESPRIT-Forest for parallel hierarchical clustering of sequences. The algorithm achieves subquadratic time and space complexity and maintains a high clustering accuracy comparable to the standard method. The basic idea is to organize sequences into a pseudo-metric based partitioning tree for sub-linear time searching of nearest neighbors, and then use a new multiple-pair merging criterion to construct clusters in parallel using multiple threads. The new algorithm was tested on the human microbiome project (HMP) dataset, currently one of the largest published microbial 16S rRNA sequence dataset. Our experiment demonstrated that with the power of parallel computing it is now compu- tationally feasible to perform hierarchical clustering analysis of tens of millions of sequences. The software is available at http://www.acsu.buffalo.edu/∼yijunsun/lab/ESPRIT-Forest.html.
Extracted statement — deposits: software
Research field: Bioinformatics
Title: sourceR: Classification and source attribution of infectious agents among heterogeneous populations
Abstract: Zoonotic diseases are a major cause of morbidity, and productivity losses in both human and animal populations. Identifying the source of food-borne zoonoses (e.g. an animal reservoir or food product) is crucial for the identification and prioritisation of food safety interventions. For many zoonotic diseases it is difficult to attribute human cases to sources of infection because there is little epidemiological information on the cases. However, microbial strain typing allows zoonotic pathogens to be categorised, and the relative frequencies of the strain types among the sources and in human cases allows inference on the likely source of each infection. We introduce sourceR, an R package for quantitative source attribution, aimed at food-borne diseases. It implements a Bayesian model using strain-typed surveillance data from both human cases and source samples, capable of identifying important sources of infection. The model measures the force of infection from each source, allowing for varying survivability, pathogenicity and virulence of pathogen strains, and varying abilities of the sources to act as vehicles of infection. A Bayesian non-parametric (Dirichlet process) approach is used to cluster pathogen strain types by epidemiological behaviour, avoiding model overfitting and allowing detection of strain types associated with potentially high “virulence”. sourceR is demonstrated using Campylobacter jejuni isolate data collected in New Zealand between 2005 and 2008. Chicken from a particular poultry supplier was identified as the major source of campylobacteriosis, which is qualitatively similar to results of previous studies using the same dataset. Additionally, the software identifies a cluster of 9 multilocus sequence types with abnormally high ‘virulence’ in humans. sourceR enables straightforward attribution of cases of zoonotic infection to putative sources of infection. As sourceR develops, we intend it to become an important and flexible resource for food-borne disease attribution studies.
Extracted statement — deposits: sourceR
Research field: Bioinformatics
Title: Different Populations of Blacklegged Tick Nymphs Exhibit Differences in Questing Behavior That Have Implications for Human Lyme Disease Risk
Abstract: Animal behavior can have profound effects on pathogen transmission and disease incidence. We studied the questing (= host-seeking) behavior of blacklegged tick (Ixodes scapularis) nymphs, which are the primary vectors of Lyme disease in the eastern United States. Lyme disease is common in northern but not in southern regions, and prior ecological studies have found that standard methods used to collect host-seeking nymphs in northern regions are unsuccessful in the south. This led us to hypothesize that there are behavior differences between northern and southern nymphs that alter how readily they are collected, and how likely they are to transmit the etiological agent of Lyme disease to humans. To examine this question, we compared the questing behavior of I. scapularis nymphs originating from one northern (Lyme disease endemic) and two southern (non-endemic) US regions at field sites in Wisconsin, Rhode Island, Tennessee, and Florida. Laboratory-raised uninfected nymphs were monitored in circular 0.2 m2 arenas containing wooden dowels (mimicking stems of understory vegetation) for 10 (2011) and 19 (2012) weeks. The probability of observing nymphs questing on these stems (2011), and on stems, on top of leaf litter, and on arena walls (2012) was much greater for northern than for southern origin ticks in both years and at all field sites (19.5 times greater in 2011; 3.6–11.6 times greater in 2012). Our findings suggest that southern origin I. scapularis nymphs rarely emerge from the leaf litter, and consequently are unlikely to contact passing humans. We propose that this difference in questing behavior accounts for observed geographic differences in the efficacy of the standard sampling techniques used to collect questing nymphs. These findings also support our hypothesis that very low Lyme disease incidence in southern states is, in part, a consequence of the type of host-seeking behavior exhibited by southern populations of the key Lyme disease vector.
Extracted statement — uses: Stan