Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
Abstract Background Quantification of gene expression from RNA-seq data is a prerequisite for transcriptome analysis such as differential gene expression analysis and gene co-expression network construction. Individual RNA-seq experiments are larger and combining multiple experiments from sequence repositories can result in datasets with thousands of samples. Processing hundreds to thousands of RNA-seq data can result in challenges related to data management, access to sufficient computational resources, navigation of high-performance computing (HPC) systems, installation of required software dependencies, and reproducibility. Processing of larger and deeper RNA-seq experiments will become more common as sequencing technology matures. Results GEMmaker, is a nf-core compliant, Nextflow workflow, that quantifies gene expression from small to massive RNA-seq datasets. GEMmaker ensures results are highly reproducible through the use of versioned containerized software that can be executed on a single workstation, institutional compute cluster, Kubernetes platform or the cloud. GEMmaker supports popular alignment and quantification tools providing results in raw and normalized formats. GEMmaker is unique in that it can scale to process thousands of local or remote stored samples without exceeding available data storage. Conclusions Workflows that quantify gene expression are not new, and many already address issues of portability, reusability, and scale in terms of accessmore »Free, publicly-accessible full text available December 1, 2023
Abstract Magnetized plasma interactions are ubiquitous in astrophysical and laboratory plasmas. Various physical effects have been shown to be important within colliding plasma flows influenced by opposing magnetic fields, however, experimental verification of the mechanisms within the interaction region has remained elusive. Here we discuss a laser-plasma experiment whereby experimental results verify that Biermann battery generated magnetic fields are advected by Nernst flows and anisotropic pressure effects dominate these flows in a reconnection region. These fields are mapped using time-resolved proton probing in multiple directions. Various experimental, modelling and analytical techniques demonstrate the importance of anisotropic pressure in semi-collisional, high- β plasmas, causing a reduction in the magnitude of the reconnecting fields when compared to resistive processes. Anisotropic pressure dynamics are crucial in collisionless plasmas, but are often neglected in collisional plasmas. We show pressure anisotropy to be essential in maintaining the interaction layer, redistributing magnetic fields even for semi-collisional, high energy density physics (HEDP) regimes.Free, publicly-accessible full text available December 1, 2022
Gene Expression Matrices (GEMs) are a fundamental data type in the genomics domain. As the size and scope of genomics experiments increase, researchers are struggling to process large GEMs through downstream workflows with currently accepted practices. In this paper, we propose a methodology to reduce the size of GEMs using multiple approaches. Our method partitions data into discrete fields based on data type and employs state-of-the-art lossless and lossy compression algorithms to reduce the input data size. This work explores a variety of lossless and lossy compression methods to determine which methods work the best for each component of a GEM. We evaluate the accuracy of the compressed GEMs by running them through the Knowledge Independent Network Construction (KINC) workflow and comparing the quality of the resulting gene co-expression network with a lossless control to verify result fidelity. Results show that utilizing a combination of lossy and lossless compression results in compression ratios up to 9.77× on a Yeast GEM, while still preserving the biological integrity of the data. Usage of the compression methodology on the Cancer Cell Line Encyclopedia(CCLE) GEM resulted in compression ratios up to 9.26×. By using this methodology, researchers in the Genomics domain may be ablemore »