Management of parallel I/O.

DIACONESCU, Luca

doi:10.22215/etd/1997-03755

Cited by 3 publications

(3 citation statements)

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“…Create per-well mean aggregated profiles using the pycytominer 34 command `collate.py` If you have run your CellProfiler pipeline in a cluster computing environment, you can also use this same script to aggregate multiple CSV files into a single SQLite database before aggregation using the cytominer-database 35 package. This step can take 8-16 hours per plate per plate to run; if running multiple plates, it is strongly recommended to use parallel 36 or a similar tool to parallelize profile creation. 47.…”

Section: Creation Normalization and Feature Reduction Of Per-well Pro...mentioning

confidence: 99%

Optimizing the Cell Painting assay for image-based profiling

et al. 2023

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In image-based profiling, software extracts thousands of morphological features of cells from multi-channel fluorescence microscopy images, yielding single-cell profiles that can be used for basic research and drug discovery. Powerful applications have been proven, including clustering chemical and genetic perturbations based on their similar morphological impact, identifying disease phenotypes by observing differences in profiles between healthy and diseased cells, and predicting assay outcomes using machine learning, among many others. Here we provide an updated protocol for the most popular assay for image-based profiling, Cell Painting. Introduced in 2013, it uses six stains imaged in five channels and labels eight diverse components of the cell: DNA, cytoplasmic RNA, nucleoli, actin, golgi apparatus, plasma membrane, endoplasmic reticulum, and mitochondria. The original protocol was updated in 2016 based on several years' experience running it at two sites, after optimizing it by visual stain quality. Here we describe the work of the Joint Undertaking for Morphological Profiling (JUMP) Cell Painting Consortium, aiming to improve upon the assay via quantitative optimization, based on the measured ability of the assay to detect morphological phenotypes and group similar perturbations together. We find that the assay gives very robust outputs despite a variety of changes to the protocol and that two vendors' dyes work equivalently well. We present Cell Painting version 3, in which some steps are simplified and several stain concentrations can be reduced, saving costs. Cell culture and image acquisition take 1-2 weeks for a typically sized batch of 20 or fewer plates; feature extraction and data analysis take an additional 1-2 weeks..

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Section: Creation Normalization and Feature Reduction Of Per-well Pro...mentioning

confidence: 99%

Optimizing the Cell Painting assay for image-based profiling

et al. 2023

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“…To avoid a batch effect, the 663 samples were sequenced in the same batch. GNU Parallel v20180722 65 was used for parallelized preprocessing during bioinformatics analysis. The adapter sequences were trimmed by BBDuk (BBTools v38.19) using the default setting except for the following parameters: "ktrim=r k=23 mink=11 hdist=1 hdist2=0 ptpe tbo".…”

Section: Metagenomics Sequencing and Data Processing For Fecal Samplesmentioning

confidence: 99%

Co-localization of antibiotic resistance genes is widespread in the infant gut microbiome and associates with an immature gut microbial composition

Brejnrod

Trivedi

et al. 2023

Preprint

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Even in the absence of antibiotic exposure, the gut microbiome of infants has been shown to contain numerous antibiotic resistance genes (ARGs), but the mechanism for this remains unclear. In environmental bacteria, the selective advantage of ARGs can be increased through co-localization with genes such as other ARGs, biocide resistance genes, metal resistance genes, and virulence genes. However, this phenomenon is unknown from the human gut microbiome during early life despite frequent exposures to biocides and metals from an early age. Here, we conducted a comprehensive analysis of genetic co-localization of resistance genes in a cohort of 662 Danish children and examined the association between such co-localization and environmental factors as well as gut microbial maturation. Our study showed that co-localization of ARGs with other resistance and virulence genes is common in the early gut microbiome and is associated with gut bacteria that are indicative of low maturity, especially E. coli. The most common forms of co-localization involved tetracycline and fluoroquinolone resistance genes located near other ARGs, and, on plasmids, co-localization predominantly occurred in the form of class 1 integrons. Antibiotic use caused a short-term increase in mobile ARGs, while non-mobile ARGs showed no significant change. Finally, we found that a high abundance of virulence genes was associated with low gut microbial maturity and that virulence genes showed even higher potential for mobility than ARGs. Our study highlights important constraints that need to be considered when developing strategies to combat ARG dissemination.

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“…We used the Armadillo c++ linear algebra library to perform matrix multiplication [51,52]. We used GNU parallel to run many batches of simulations at once [61]. We used the FlyBase sequence coordinates converter to convert assembly 5 base pair coordinates to assembly 6 [62].…”

Section: External Tools and Librariesmentioning

confidence: 99%

Inferring multi-locus selection in admixed populations

Ayala

Corbett-Detig

2023

Preprint

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Admixture, the exchange of genetic information between distinct source populations, is thought to be a major source of adaptive novelty. Unlike mutation events, which periodically generate single alleles, admixture can introduce many selected alleles simultaneously. As such, the effects of linkage between selected alleles may be especially pronounced in admixed populations. However, existing tools for identifying selected mutations within admixed populations only account for selection at a single site, overlooking phenomena such as interference among proximal selected alleles. Here, we develop and extensively validate a method for identifying and quantifying the individual effects of multiple linked selected sites on a chromosome in admixed populations. Our approach numerically calculates the expected local ancestry landscape in an admixed population for a given multi-locus selection model, and then maximizes the likelihood of the model. After applying this method to admixed populations of Drosophila melanogaster, we found that the impacts between linked sites may be an important contributor to natural selection in admixed populations. Furthermore, for the situations we considered, the selection coefficients and number of selected sites are overestimated in analyses that do not consider the effects of linkage among selected sites. Our results imply that linkage among selected sites may be an important evolutionary force in admixed populations. This tool provides a powerful generalized method to investigate these crucial phenomena in diverse populations.

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Management of parallel I/O.

Cited by 3 publications

References 0 publications

Optimizing the Cell Painting assay for image-based profiling

Optimizing the Cell Painting assay for image-based profiling

Co-localization of antibiotic resistance genes is widespread in the infant gut microbiome and associates with an immature gut microbial composition

Inferring multi-locus selection in admixed populations

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