Source data of CSF sTREM2 analyses
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens (human)
SUBMITTER:
PROVIDER: S-BSST954 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:MotivationIn this paper, we present an open source package with the latest release of Evolutionary-based BIClustering (EBIC), a next-generation biclustering algorithm for mining genetic data. The major contribution of this paper is adding a full support for multiple graphics processing units (GPUs) support, which makes it possible to run efficiently large genomic data mining analyses. Multiple enhancements to the first release of the algorithm include integration with R and Bioconductor, and an option to exclude missing values from the analysis.ResultsEvolutionary-based BIClustering was applied to datasets of different sizes, including a large DNA methylation dataset with 436 444 rows. For the largest dataset we observed over 6.6-fold speedup in computation time on a cluster of eight GPUs compared to running the method on a single GPU. This proves high scalability of the method.Availability and implementationThe latest version of EBIC could be downloaded from http://github.com/EpistasisLab/ebic. Installation and usage instructions are also available online.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6736067 | biostudies-literature
Project description:Single-cell RNA sequencing data require several processing procedures to arrive at interpretable results. While commercial platforms can serve as "one-stop shops" for data analysis, they relinquish the flexibility required for customized analyses and are often inflexible between experimental systems. For instance, there is no universal solution for the discrimination of informative or uninformative encapsulated cellular material; thus, pipeline flexibility takes priority. Here, we demonstrate a full data analysis pipeline, constructed modularly from open-source software, including tools that we have contributed. For complete details on the use and execution of this protocol, please refer to Petukhov et al. (2018), Heiser et al. (2020), and Heiser and Lau (2020).
| S-EPMC8082116 | biostudies-literature
Project description:Given trained models from multiple source domains, how can we predict the labels of unlabeled data in a target domain? Unsupervised multi-source domain adaptation (UMDA) aims for predicting the labels of unlabeled target data by transferring the knowledge of multiple source domains. UMDA is a crucial problem in many real-world scenarios where no labeled target data are available. Previous approaches in UMDA assume that data are observable over all domains. However, source data are not easily accessible due to privacy or confidentiality issues in a lot of practical scenarios, although classifiers learned in source domains are readily available. In this work, we target data-free UMDA where source data are not observable at all, a novel problem that has not been studied before despite being very realistic and crucial. To solve data-free UMDA, we propose DEMS (Data-free Exploitation of Multiple Sources), a novel architecture that adapts target data to source domains without exploiting any source data, and estimates the target labels by exploiting pre-trained source classifiers. Extensive experiments for data-free UMDA on real-world datasets show that DEMS provides the state-of-the-art accuracy which is up to 27.5% point higher than that of the best baseline.
| S-EPMC8270218 | biostudies-literature
Project description:We report evidence of a novel pathogenetic mechanism in which thyroid hormone dysregulation contributes to dementia in elderly persons. Two single nucleotide polymorphisms (SNPs) on chromosome 12p12 were the initial foci of our study: rs704180 and rs73069071. These SNPs were identified by separate research groups as risk alleles for non-Alzheimer's neurodegeneration. We found that the rs73069071 risk genotype was associated with hippocampal sclerosis (HS) pathology among people with the rs704180 risk genotype (National Alzheimer's Coordinating Center/Alzheimer's Disease Genetic Consortium data; n = 2113, including 241 autopsy-confirmed HS cases). Furthermore, both rs704180 and rs73069071 risk genotypes were associated with widespread brain atrophy visualized by MRI (Alzheimer's Disease Neuroimaging Initiative data; n = 1239). In human brain samples from the Braineac database, both rs704180 and rs73069071 risk genotypes were associated with variation in expression of ABCC9, a gene which encodes a metabolic sensor protein in astrocytes. The rs73069071 risk genotype was also associated with altered expression of a nearby astrocyte-expressed gene, SLCO1C1. Analyses of human brain gene expression databases indicated that the chromosome 12p12 locus may regulate particular astrocyte-expressed genes induced by the active form of thyroid hormone, triiodothyronine (T3). This is informative biologically, because the SLCO1C1 protein transports thyroid hormone into astrocytes from blood. Guided by the genomic data, we tested the hypothesis that altered thyroid hormone levels could be detected in cerebrospinal fluid (CSF) obtained from persons with HS pathology. Total T3 levels in CSF were elevated in HS cases (p < 0.04 in two separately analyzed groups), but not in Alzheimer's disease cases, relative to controls. No change was detected in the serum levels of thyroid hormone (T3 or T4) in a subsample of HS cases prior to death. We conclude that brain thyroid hormone perturbation is a potential pathogenetic factor in HS that may also provide the basis for a novel CSF-based clinical biomarker.
| S-EPMC5154615 | biostudies-literature
Project description:Although classically associated with myelopoiesis, granulocyte-macrophage colony-stimulating factor (GM-CSF) is being increasingly recognized for its potential role in innate resistance against tuberculosis (TB). While the GM-CSF is produced by a variety of host cells, including conventional and non-conventional T cells, macrophages, alveolar epithelial cells, the cell population that promotes GM-CSF mediated innate protection against Mycobacterium tuberculosis infection remains unclear. This is because studies related to the role of GM-CSF so far have been carried out in murine models of experimental TB, which is inherently susceptible to TB as compared to humans, who exhibit a resolution of infection in majority of cases. We found a significantly higher amount of GM-CSF production by human macrophages, compared to mouse macrophages, after infection with M. tuberculosis in vitro. The higher levels of GM-CSF produced by human macrophages were also directly correlated with their increased life span and ability to control M. tuberculosis infection. Other evidence from recent studies also support that M. tuberculosis infected human macrophages display heterogeneity in their antibacterial capacity, and cells with increased expression of genes involved in GM-CSF signaling pathway can control intracellular M. tuberculosis growth more efficiently. Collectively, these emerging evidence indicate that GM-CSF produced by lung resident macrophages could be vital for the host resistance against M. tuberculosis infection in humans. Identification of GM-CSF dependent key cellular pathways/processes that mediate intracellular host defense can lay the groundwork for the development of novel host directed therapies against TB as well as other intracellular infections.
| S-EPMC7390890 | biostudies-literature
Project description: Not available
| S-BSST1371 | biostudies-other
Project description:BACKGROUND:Genome projects and multiomics experiments generate huge volumes of data that must be stored, mined, and transformed into useful knowledge. All this information is supposed to be accessible and, if possible, browsable afterwards. Computational biologists have been dealing with this scenario for more than a decade and have been implementing software and databases to meet this challenge. The GMOD's (Generic Model Organism Database) biological relational database schema, known as Chado, is one of the few successful open source initiatives; it is widely adopted and many software packages are able to connect to it. FINDINGS:We have been developing an open source software package named Machado, a genomics data integration framework implemented in Python, to enable research groups to both store and visualize genomics data. The framework relies on the Chado database schema and, therefore, should be very intuitive for current developers to adopt it or have it running on top of already existing databases. It has several data-loading tools for genomics and transcriptomics data and also for annotation results from tools such as BLAST, InterproScan, OrthoMCL, and LSTrAP. There is an API to connect to JBrowse, and a web visualization tool is implemented using Django Views and Templates. The Haystack library integrated with the ElasticSearch engine was used to implement a Google-like search, i.e., single auto-complete search box that provides fast results and filters. CONCLUSION:Machado aims to be a modern object-relational framework that uses the latest Python libraries to produce an effective open source resource for genomics research.
| S-EPMC7490629 | biostudies-literature
Project description:The construction of data center network by applying SDN technology has become a hot research topic. The SDN architecture has innovatively separated the control plane from the data plane which makes the network more software-oriented and agile. Moreover, it provides virtual multi-tenancy, effective scheduling resources and centralized control strategies to meet the demand for cloud computing data center. However, the explosion of network information is facing severe challenges for SDN controller. The flow storage and lookup mechanisms based on TCAM device have led to the restriction of scalability, high cost and energy consumption. In view of this, a source-controlled data center network (SCDCN) model is proposed herein. The SCDCN model applies a new type of source routing address named the vector address (VA) as the packet-switching label. The VA completely defines the communication path and the data forwarding process can be finished solely relying on VA. There are four advantages in the SCDCN architecture. 1) The model adopts hierarchical multi-controllers and abstracts large-scale data center network into some small network domains that has solved the restriction for the processing ability of single controller and reduced the computational complexity. 2) Vector switches (VS) developed in the core network no longer apply TCAM for table storage and lookup that has significantly cut down the cost and complexity for switches. Meanwhile, the problem of scalability can be solved effectively. 3) The SCDCN model simplifies the establishment process for new flows and there is no need to download flow tables to VS. The amount of control signaling consumed when establishing new flows can be significantly decreased. 4) We design the VS on the NetFPGA platform. The statistical results show that the hardware resource consumption in a VS is about 27% of that in an OFS.
| S-EPMC5362056 | biostudies-literature
Project description:BackgroundWith the decreasing cost of sequencing and the rapid developments in genomics technologies and protocols, the need for validated bioinformatics software that enables efficient large-scale data processing is growing.FindingsHere we present GenPipes, a flexible Python-based framework that facilitates the development and deployment of multi-step workflows optimized for high-performance computing clusters and the cloud. GenPipes already implements 12 validated and scalable pipelines for various genomics applications, including RNA sequencing, chromatin immunoprecipitation sequencing, DNA sequencing, methylation sequencing, Hi-C, capture Hi-C, metagenomics, and Pacific Biosciences long-read assembly. The software is available under a GPLv3 open source license and is continuously updated to follow recent advances in genomics and bioinformatics. The framework has already been configured on several servers, and a Docker image is also available to facilitate additional installations.ConclusionsGenPipes offers genomics researchers a simple method to analyze different types of data, customizable to their needs and resources, as well as the flexibility to create their own workflows.
| S-EPMC6559338 | biostudies-literature