hpcng/singularity: Singularity 3.7.3

Kurtzer, Gregory M.; cclerget,; Bauer, Michael; Kaneshiro, Ian; Godlove, David

doi:10.5281/zenodo.4667718

Cited by 9 publications

(4 citation statements)

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“…To remove the dependence on the specific Linux environment, users can take advantage of Apptainer (formerly Singularity; https://github.com/apptainer/apptainer), a container platform designed for mobility of compute [42, 43]. With Apptainer, the FMAPB2 package can run on any modern Linux distribution that is installed on bare metal or a Virtual Machine in Windows or Mac.…”

Section: Resultsmentioning

confidence: 99%

Calculating Structure Factors of Protein Solutions by Atomistic Modeling of Protein-Protein Interactions

Qin,

Zhou

2024

Preprint

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We present a method, FMAPS(q), for calculating the structure factor, S(q), of a protein solution, by extending our fast Fourier transform-based modeling of atomistic protein-protein interactions (FMAP) approach. The interaction energy consists of steric, nonpolar attractive, and electrostatic terms that are additive among all pairs of atoms between two protein molecules. In the present version, we invoke the free-rotation approximation, such that the structure factor is given by the Fourier transform of the protein center-center distribution function gC(R). At low protein concentrations, gC(R) can be approximated as e-βW(R), where W(R) is the potential of mean force along the center-center distance R. We calculate W(R) using FMAPB2, a member of the FMAP class of methods that is specialized for the second virial coefficient [Qin and Zhou, J Phys Chem B 123 (2019) 8203-8215]. For higher protein concentrations, we obtain S(q) by a modified random-phase approximation, which is a perturbation around the steric-only energy function. Without adjusting any parameters, the calculated structure factors for lysozyme and bovine serum albumin at various ionic strengths, temperatures, and protein concentrations are all in reasonable agreement with those measured by small-angle X-ray or neutron scattering. This initial success motivates further developments, including removing approximations and parameterizing the interaction energy function.

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Section: Resultsmentioning

confidence: 99%

Calculating Structure Factors of Protein Solutions by Atomistic Modeling of Protein-Protein Interactions

Qin,

Zhou

2024

Preprint

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“…Software: This research has made use of NASA's Astrophysics Data System Bibliographic Services. NumPy (Harris et al 2020), SciPy (Virtanen et al 2020), Matplotlib (Hunter 2007), Singularity (Kurtzer et al 2017(Kurtzer et al , 2021, Docker (Merkel 2014), Astropy (Astropy Collaboration et al 2013Collaboration et al , 2018Collaboration et al , 2022, PopSyCLE (Lam et al 2020), Galaxia (Sharma et al 2011), SPISEA (Hosek et al 2020).…”

Section: Acknowledgmentsmentioning

confidence: 99%

Astrometric Microlensing by Primordial Black Holes with the Roman Space Telescope

Fardeen,

McGill,

Perkins

et al. 2024

ApJ

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Primordial black holes (PBHs) could explain some fraction of dark matter and shed light on many areas of early-Universe physics. Despite over half a century of research interest, a PBH population has so far eluded detection. The most competitive constraints on the fraction of dark matter comprised of PBHs (f DM) in the (10−9–10)M ⊙ mass ranges come from photometric microlensing and bound f DM ≲ 10−2–10−1. With the advent of the Roman Space Telescope with its submilliarcsecond astrometric capabilities and its planned Galactic Bulge Time Domain Survey (GBTDS), detecting astrometric microlensing signatures will become routine. Compared with photometric microlensing, astrometric microlensing signals are sensitive to different lens masses–distance configurations and contain different information, making it a complimentary lensing probe. At submilliarcsecond astrometric precision, astrometric microlensing signals are typically detectable at larger lens–source separations than photometric signals, suggesting a microlensing detection channel of pure astrometric events. We use a Galactic simulation to predict the number of detectable microlensing events during the GBTDS via this pure astrometric microlensing channel. Assuming an absolute astrometric precision floor for bright stars of 0.1 mas for the GBTDS, we find that the number of detectable events peaks at ≈103 f DM for a population of 1M ⊙ PBHs and tapers to ≈10f DM and ≈100f DM at 10−4 M ⊙ and 103 M ⊙, respectively. Accounting for the distinguishability of PBHs from stellar lenses, we conclude the GBTDS will be sensitive to a PBH population at f DM down to ≈10−1–10−3 for (10−1–102)M ⊙ likely yielding novel PBH constraints.

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“…GraffiTE organizes a complex collection of SVs and pangenome analysis software in order to identify, extract, and analyze genomic variants likely to represent mobile element insertion polymorphisms (pMEs). The pipeline is implemented in the Nextflow framework, and relies on an Apptainer container (formerly known as Singularity 48 ) to provide all the required dependencies.…”

Section: A Unified Framework To Study Te Insertion Polymorphismsmentioning

confidence: 99%

A Unified Framework to Analyze Transposable Element Insertion Polymorphisms using Graph Genomes

Groza,

Chen,

Wheeler

et al. 2023

Preprint

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Transposable Elements (TEs) are abundant and mobile repetitive DNA sequences evolving within and across their hosts' genomes. Active TEs cause insertion polymorphism and contribute to genomic diversity. Here, we present GraffiTE, a flexible and comprehensive pipeline for detecting and genotyping polymorphic mobile elements (pMEs). By integrating state-of-the-art SV detection algorithms and graph-genome frameworks, GraffiTE enables the accurate identification of pMEs from genomic assemblies and long-read as well as the precise genotyping of these variants using short- or long-read data. Performance evaluations using simulated and benchmark datasets demonstrate high precision and recall rates. Notably, we demonstrate the versatility of GraffiTE by analyzing the human reference pangenome, 30 Drosophila melanogaster genomes, and multiple cultivars of the emerging crop model Cannabis sativa, where pMEs are undocumented. These analyses reveal the landscapes of pMEs and their frequency variations across individuals, strains, and cultivars. GraffiTE provides a user-friendly interface, allowing non-expert users to perform comprehensive pME analyses, including in models with limited TE prior knowledge. The pipeline's extensible design and compatibility with various sequencing technologies make it a valuable integrative framework for studying TE dynamics and their impact on genome evolution. GraffiTE is freely available at https://github.com/cgroza/GraffiTE.

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hpcng/singularity: Singularity 3.7.3

Cited by 9 publications

References 0 publications

Calculating Structure Factors of Protein Solutions by Atomistic Modeling of Protein-Protein Interactions

Calculating Structure Factors of Protein Solutions by Atomistic Modeling of Protein-Protein Interactions

Astrometric Microlensing by Primordial Black Holes with the Roman Space Telescope

A Unified Framework to Analyze Transposable Element Insertion Polymorphisms using Graph Genomes

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