datreant: persistent, Pythonic trees for heterogeneous data

Dotson, David; Seyler, Sean L.; Linke, Max; Gowers, Richard; Beckstein, Oliver

doi:10.25080/majora-629e541a-007

Cited by 14 publications

(11 citation statements)

References 6 publications

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“…With the exception of the compressed FASTQ files, all data (VCFs, images, MIC metadata, genetic variants and catalogue predictions) were aggregated and stored in a hierarchical file system using the Python datreant 1.0.2 module (55) which allowed for data discovery, tagging and filtering. Updates were performed by inhouse Python scripts.…”

Section: Methodsmentioning

confidence: 99%

Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis

Fowler

2021

Preprint

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Drug susceptibility testing of M. tuberculosis is rooted in a binary susceptible/resistant paradigm. There are considerable advantages in measuring the minimum inhibitory concentrations (MICs) of a panel of drugs for an isolate, including quantifying the magnitude of effect conferred by genetic variants and being able to identify isolates with elevated MICs that can still be treated with standard therapy. It is necessary, however, to measure the epidemiological cutoff values (ECOFF/ECVs) to permit comparison with qualitative data. Here we present ECOFF/ECVs for 13 anti-TB compounds, including bedaquiline and delamanid, derived from 20,637 clinical isolates collected by 14 laboratories based in 11 countries on five continents. Each isolate was incubated for 14 days on a dry 96-well broth microdilution plate and then read. Resistance to the majority of the drugs due to prior exposure is expected and the MIC distributions for many of the compounds are complex and therefore a phenotypically wild-type population could not be defined. Since a majority of samples also underwent genetic sequencing, we defined a genotypically wild-type population and measured the MIC of the 99th percentile by direct measurement and via fitting a Gaussian using interval regression. The proposed ECOFF/ECV values were then validated by comparing to the MIC distributions of high-confidence genetic variants that confer resistance and to qualitative drug susceptibility tests obtained via Mycobacterial Growth Indicator Tube and the Microscopic-Observation Drug-Susceptibility assay.

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Section: Methodsmentioning

confidence: 99%

Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis

Fowler

2021

Preprint

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“…In addition five further values of ΔΔ G tmp for the F99Y mutation were calculated using simulations two orders of magnitude longer (25 ns). To cope with the resulting very large numbers of molecular dynamics simulations, all data were stored in a file hierarchy and tagged using the datreant Python module [ 36 ]. All simulation data were then parsed and alchemical free energies were calculated as a function of simulation time t using a purpose-written Python class.…”

Section: Methodsmentioning

confidence: 99%

How quickly can we predict trimethoprim resistance using alchemical free energy methods?

Fowler

2020

Interface Focus.

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The emergence of antimicrobial resistance threatens modern medicine and necessitates more personalized treatment of bacterial infections. Sequencing the whole genome of the pathogen(s) in a clinical sample offers one way to improve clinical microbiology diagnostic services, and has already been adopted for tuberculosis in some countries. A key weakness of a genetics clinical microbiology is it cannot return a result for rare or novel genetic variants and therefore predictive methods are required. Non-synonymous mutations in the S. aureus dfrB gene can be successfully classified as either conferring resistance (or not) by calculating their effect on the binding free energy of the antibiotic, trimethoprim. The underlying approach, alchemical free energy methods, requires large numbers of molecular dynamics simulations to be run. We show that a large number ( N = 15) of binding free energies calculated from a series of very short (50 ps) molecular dynamics simulations are able to satisfactorily classify all seven mutations in our clinically derived testset. A result for a single mutation could therefore be returned in less than an hour, thereby demonstrating that this or similar methods are now sufficiently fast and reproducible for clinical use.

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“…In order to support generic file-based workflows, the signac data model makes minimal assumptions about how these workflows generate and operate on the data; signac manages the file paths, but the underlying files are stored directly on the file system without modification or serialization. This design ensures that existing tools may interact with a signac data repository without the need to serialize or convert existing file formats, an advantage shared by solutions like datreant [17]. Conversely, this design distinguishes signac from more domain-specific solutions that make certain assumptions about data schema and format, such as DCMS [18] and the AiiDA infrastructure [19].…”

Section: Workflowmentioning

confidence: 99%

Simple data and workflow management with the signac framework

Adorf

Dodd

Ramasubramani

et al. 2018

Computational Materials Science

113

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Researchers in the field of materials science, chemistry, and computational physics are regularly posed with the challenge of managing large and heterogeneous data spaces. The amount of data increases in lockstep with computational efficiency multiplied by the amount of available computational resources, which shifts the bottleneck in the scientific process from data acquisition to data processing and analysis. We present a framework designed to aid in the integration of various specialized data formats, tools and workflows. The signac framework provides all basic components required to create a well-defined and thus collectively accessible and searchable data space, simplifying data access and modification through a homogeneous data interface that is largely agnostic to the data source, i.e., computation or experiment. The framework's data model is designed to not require absolute commitment to the presented implementation, simplifying adaption into existing data sets and workflows. This approach not only increases the efficiency with which scientific results can be produced, but also significantly lowers barriers for collaborations requiring shared data access.

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datreant: persistent, Pythonic trees for heterogeneous data

Cited by 14 publications

References 6 publications

Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis

Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis

How quickly can we predict trimethoprim resistance using alchemical free energy methods?

Simple data and workflow management with the signac framework

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