Design considerations for workflow management systems use in production genomics research and the clinic

Ahmed, Azza E.; Allen, Joshua; Bhat, Tajesvi; Burra, Prakruthi; Fliege, Christina; Hart, Steven N.; Heldenbrand, Jacob R; Hudson, Matthew E.; Istanto, Dave Deandre; Kalmbach, Michael; Kapraun, Gregory D.; Kendig, Katherine; Kendzior, Matthew; Klee, Eric W.; Mattson, Nate; Roß, Christian; Sharif, Sami M.; Venkatakrishnan, Ramshankar; Fadlelmola, Faisal M.; Mainzer, Liudmila Sergeevna

doi:10.1038/s41598-021-99288-8

Cited by 10 publications

(8 citation statements)

References 85 publications

(88 reference statements)

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“…However, it is important to realize that workflow managers are evolving rapidly and that these reviews can become outdated quickly. Nonetheless, the workflow management system of choice depends on the specific use case, for which recommendations have previously been described [ 56 ].…”

Section: Interoperabilitymentioning

confidence: 99%

Ten quick tips for building FAIR workflows

de Visser,

Johansson,

Kulkarni

et al. 2023

PLoS Comput Biol

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Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows—systematic executions of a series of computational tools—is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

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Section: Interoperabilitymentioning

confidence: 99%

Ten quick tips for building FAIR workflows

de Visser,

Johansson,

Kulkarni

et al. 2023

PLoS Comput Biol

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“…Finally, the record_workflow_link used to denote a public link to a repository containing the workflow that ran the tool. For example, workflow scripts encoded in CWL, WDL, Nextflow, Snakemake are often stored in a public repository s GitHub or DockerHub [23][24][25] . In summary, these fields can be used to record provenance information matrices or individual annotations and will aide in reproducibility of single-cell data Implementation of MAMS in a portable format In order to facilitate the adoption of MAMS, we developed a simple list-like structure that can be used to MAMS metadata fields for matrices in a dataset.…”

Section: Provenance Related Metadata For Of Analysis Of Matricesmentioning

confidence: 99%

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

Wang

Sarfraz

Teh

et al. 2023

Preprint

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A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex use cases to characterize the types of feature-observation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple list-like schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

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“…For example, PCA is currently limited to datasets with 1000–2000 entries, which is considered ‘large’ ( Vogt and Tacke 2001a ; Rachakonda et al, 2016a ), but is probably small compared to the anticipated results of high-throughput experiments and the content of community-based databases. Therefore, efficient ways will be required to handle ‘big data’, such as healthcare patient data ( Ahmed et al, 2021a ; Dong et al, 2021b ).…”

Section: Modeling Approachesmentioning

confidence: 99%

The use of predictive models to develop chromatography-based purification processes

Bernau

Knödler

Emonts

et al. 2022

Front. Bioeng. Biotechnol.

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Chromatography is the workhorse of biopharmaceutical downstream processing because it can selectively enrich a target product while removing impurities from complex feed streams. This is achieved by exploiting differences in molecular properties, such as size, charge and hydrophobicity (alone or in different combinations). Accordingly, many parameters must be tested during process development in order to maximize product purity and recovery, including resin and ligand types, conductivity, pH, gradient profiles, and the sequence of separation operations. The number of possible experimental conditions quickly becomes unmanageable. Although the range of suitable conditions can be narrowed based on experience, the time and cost of the work remain high even when using high-throughput laboratory automation. In contrast, chromatography modeling using inexpensive, parallelized computer hardware can provide expert knowledge, predicting conditions that achieve high purity and efficient recovery. The prediction of suitable conditions in silico reduces the number of empirical tests required and provides in-depth process understanding, which is recommended by regulatory authorities. In this article, we discuss the benefits and specific challenges of chromatography modeling. We describe the experimental characterization of chromatography devices and settings prior to modeling, such as the determination of column porosity. We also consider the challenges that must be overcome when models are set up and calibrated, including the cross-validation and verification of data-driven and hybrid (combined data-driven and mechanistic) models. This review will therefore support researchers intending to establish a chromatography modeling workflow in their laboratory.

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Design considerations for workflow management systems use in production genomics research and the clinic

Cited by 10 publications

References 85 publications

Ten quick tips for building FAIR workflows

Ten quick tips for building FAIR workflows

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

The use of predictive models to develop chromatography-based purification processes

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