B metadata-massive datasets upload-2021-11-1
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Thermo-Fisher Q-Orbitrap MS/MS
ORGANISM(S): Eupatorium Fortunei Turcz.,syringa Oblata Lindl.,taraxacum
SUBMITTER: 
Sunshine
PROVIDER: MSV000088291 | MassIVE | Sun Oct 31 20:48:00 GMT 2021
REPOSITORIES: MassIVE
Similar Datasets
Project description:Screening and identifying PAs of chinese medicines based on UHPLC-Q-Exactive mass spectrometry
2021-10-31 | MSV000088291 | GNPS
Project description:SummaryEstimating linkage disequilibrium (LD) is essential for a wide range of summary statistics-based association methods for genome-wide association studies. Large genetic datasets, e.g. the TOPMed WGS project and UK Biobank, enable more accurate and comprehensive LD estimates, but increase the computational burden of LD estimation. Here, we describe emeraLD (Efficient Methods for Estimation and Random Access of LD), a computational tool that leverages sparsity and haplotype structure to estimate LD up to 2 orders of magnitude faster than current tools.Availability and implementationemeraLD is implemented in C++, and is open source under GPLv3. Source code and documentation are freely available at http://github.com/statgen/emeraLD.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC6298049 | biostudies-literature
Project description:We have created a statistically grounded tool for determining the correlation of genomewide data with other datasets or known biological features, intended to guide biological exploration of high-dimensional datasets, rather than providing immediate answers. The software enables several biologically motivated approaches to these data and here we describe the rationale and implementation for each approach. Our models and statistics are implemented in an R package that efficiently calculates the spatial correlation between two sets of genomic intervals (data and/or annotated features), for use as a metric of functional interaction. The software handles any type of pointwise or interval data and instead of running analyses with predefined metrics, it computes the significance and direction of several types of spatial association; this is intended to suggest potentially relevant relationships between the datasets.The package, GenometriCorr, can be freely downloaded at http://genometricorr.sourceforge.net/. Installation guidelines and examples are available from the sourceforge repository. The package is pending submission to Bioconductor.
| S-EPMC3364938 | biostudies-literature
Project description:Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations becomes large. There is a burgeoning literature on approaches for analyzing large spatial datasets. In this article, we propose a divide-and-conquer strategy within the Bayesian paradigm. We partition the data into subsets, analyze each subset using a Bayesian spatial process model and then obtain approximate posterior inference for the entire dataset by combining the individual posterior distributions from each subset. Importantly, as often desired in spatial analysis, we offer full posterior predictive inference at arbitrary locations for the outcome as well as the residual spatial surface after accounting for spatially oriented predictors. We call this approach "Spatial Meta-Kriging" (SMK). We do not need to store the entire data in one processor, and this leads to superior scalability. We demonstrate SMK with various spatial regression models including Gaussian processes and tapered Gaussian processes. The approach is intuitive, easy to implement, and is supported by theoretical results presented in the supplementary material available online. Empirical illustrations are provided using different simulation experiments and a geostatistical analysis of Pacific Ocean sea surface temperature data.
| S-EPMC6474670 | biostudies-literature
Project description:BackgroundHigh throughput single-cell transcriptomic technology produces massive high-dimensional data, enabling high-resolution cell type definition and identification. To uncover the expressional patterns beneath the big data, a transcriptional landscape searching algorithm at a single-cell level is desirable.ResultsWe explored the feasibility of using DenseFly algorithm for cell searching on scRNA-seq data. DenseFly is a locality sensitive hashing algorithm inspired by the fruit fly olfactory system. The experiments indicate that DenseFly outperforms the baseline methods FlyHash and SimHash in classification tasks, and the performance is robust to dropout events and batch effects.ConclusionWe developed a method for mapping cells across scRNA-seq datasets based on the DenseFly algorithm. It can be an efficient tool for cell atlas searching.
| S-EPMC7739457 | biostudies-literature
Project description:BackgroundThe spectrum of somatic single-nucleotide variants in cancer genomes often reflects the signatures of multiple distinct mutational processes, which can provide clinically actionable insights into cancer etiology. Existing software tools for identifying and evaluating these mutational signatures do not scale to analyze large datasets containing thousands of individuals or millions of variants.ResultsWe introduce Helmsman, a program designed to perform mutation signature analysis on arbitrarily large sequencing datasets. Helmsman is up to 300 times faster than existing software. Helmsman's memory usage is independent of the number of variants, resulting in a small enough memory footprint to analyze datasets that would otherwise exceed the memory limitations of other programs.ConclusionsHelmsman is a computationally efficient tool that enables users to evaluate mutational signatures in massive sequencing datasets that are otherwise intractable with existing software. Helmsman is freely available at https://github.com/carjed/helmsman .
| S-EPMC6263557 | biostudies-literature
Project description:Diffuse gliomas (DGs) are the most common and lethal primary neoplasms in the central nervous system. The latest 2021 WHO Classification of Tumors of the Central Nervous System (CNS) was published in 2021, immensely changing the approach to diagnosis and decision making. As a part of the Chinese Glioma Genome Atlas (CGGA) project, our aim was to provide genomic profiling of gliomas in a Chinese cohort. Two hundred eighty six gliomas with different grades were collected over the last decade. Using the Illumina HiSeq platform, over 75.8 million high-quality 150 bp paired-end reads were generated per sample, yielding a total of 43.4 billion reads. We also collected each patient’s clinical and pathological information and used it to annotate their genetic data. This dataset provides an important reference for researchers and will significantly advance our understanding of gliomas.
2022-10-25 | GSE216383 | GEO
Project description:BackgroundThe Distributed Annotation System (DAS) allows merging of DNA sequence annotations from multiple sources and provides a single annotation view. A straightforward way to establish a DAS annotation server is to use the "Lightweight DAS" server (LDAS). Onto this type of server, annotations can be uploaded as flat text files in a defined format. The popular Ensembl ContigView uses the same format for the transient upload and display of user data.ResultsIn order to easily generate LDAS upload files we developed a software tool that is accessible via a web-interface http://atgc.lirmm.fr/eldasionator.html. Users can submit their DNA sequences of interest. Our program (i) aligns these sequences to the reference sequences of Ensembl, (ii) determines start and end positions of each sequence on the reference sequence, and (iii) generates a formatted annotation file. This file can be used to load any LDAS annotation server or it can be uploaded to the Ensembl ContigView.ConclusionThe eL-DASionator is an on-line tool that is intended for life-science researchers with little bioinformatics background. It conveniently generates LDAS upload files, and makes it possible to generate annotations in a standard format that permits comfortable sharing of this data.
| S-EPMC416658 | biostudies-literature