Protocols.io: Virtual Communities for Protocol Development and Discussion

Teytelman, Lenny; Stoliartchouk, Alexei; Kindler, Lori; Hurwitz, Bonnie

doi:10.1371/journal.pbio.1002538

Cited by 61 publications

(64 citation statements)

References 6 publications

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“…This work highlights the importance of (1) fully recording the experimental design (especially the cell seeding density) and (2) fitting appropriate mathematical models for estimating the population growth rates. Not only will recording the experimental protocols (e.g., using Protocols.io [25]) help to reduce biological and technical variability, but it will also help ensure that we design and apply appropriate analyses to interpret the data. If this allows us to narrow the currently large range in reported cell proliferation and other phenotypic characteristics, then we can improve the quality of curated cell line resources that consistently record the phenotypes of well-characterized, standardized cell lines.…”

Section: Discussionmentioning

confidence: 99%

Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

Friedman

Macklin

2017

Preprint

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Abstract:High-throughput cell profiling experiments are characterizing cell phenotype under a broad variety of microenvironmental and therapeutic conditions. However, biological and technical variability are contributing to wide ranges of reported parameter values, even for standard cell lines grown in identical conditions. In this paper, we develop a mathematical model of cell proliferation assays that account for biological and technical variability and limitations of the experimental platforms, including (1) cell confluency effects, (2) biological variability and technical errors in pipetting, (3) biological variability in proliferation characteristics, (4) technical variability and uncertainty in measurement timing, (5) cell counting errors, and (6) the impact of limited temporal sampling. We use this model to create synthetic datasets with growth rates and measurement times typical of cancer cell cultures, and investigate the impact of the initial cell seeding density and the common practice of fitting exponential growth curves to three cell count measurements. We find that the combined sources of variability mask the sub-exponential growth characteristics of the synthetic datasets, and that researchers profiling the same cell lines under different seeding characteristics can find significant (p < 0.05) differences in the measured growth rates. Even seeding the cells at 1% of the confluent limit can cause significant (p < 0.05) differences in the measured growth rate from the ground truth. We explored the effect of reducing errors in each part of the virtual experimental system, and found the best improvements from reducing timing errors, reducing cell counting errors, or reducing the interval between measurements (to reduce the inaccuracy of the exponential growth assumption when fitting curves). Reducing biological variability and pipetting errors had the least impact, because any improvements are still masked by cell counting errors. We close with a discussion of recommended practices for high-throughput cell phenotyping and cell line identification systems.

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Section: Discussionmentioning

confidence: 99%

Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

Friedman

Macklin

2017

Preprint

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“…To further accelerate the pace of idea exchange among researchers, the EMS program supported the web-based open-access platform protocols.io [14] to establish a dedicated user group, ‘Protist Research to Optimize Tools in Genetics’ (PROT-G). This online group facilitated protocol sharing and discussions among the grantees and any other researchers interested in protist genetics.…”

Section: Building Communitymentioning

confidence: 99%

Strength in numbers: collaborative science for new experimental model systems

Waller

Cleves

Rubio-Brotons

et al. 2018

Preprint

Self Cite

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25Our current understanding of biology is heavily based on the contributions from a 26 small number of genetically tractable model organisms. Most eukaryotic phyla lack such 27 experimental models, and this limits our ability to explore the molecular mechanisms that 28 ultimately define their biology, ecology, and diversity. In particular, marine protists suffer 29 from a paucity of model organisms despite playing critical roles in global nutrient cycles, food 30 webs, and climate. To address this deficit, an initiative was launched in 2015 to foster 31 development of ecologically and taxonomically diverse marine protist genetic models. This 32 multifaceted, complex but important challenge required a highly collaborative community-33 based approach. Herein we describe this approach, the advances achieved, and the lessons 34 learned by participants in this novel community-based model for research.35 36

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“…Best practices, such as the use of version control (e.g., Git, SVN; Perez‐Riverol et al ) and documentation of the design and purpose of code (Wilson et al ), should become common practice and required for publication. Tools, such as protocols.io, promise user‐friendly means of generating and publishing such workflows (Teytelman et al ). Computational platforms Access to high‐performance computational (HPC) resources is central to the analysis of Big Data. Different types of data, analytical software, and algorithms have different limitations.…”

Section: Challenge 2 Data Handlingmentioning

confidence: 99%

Integrating “Big Data” into Aquatic Ecology: Challenges and Opportunities

Durden¹,

Luo²,

Alexander³

et al. 2017

Limnology and Oceanography Bulletin

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Got “Big Data”? Not sure how best to use it? Big Data is becoming an important facet of aquatic ecology, and researchers must learn to harness it to reap the rewards of using it. The benefits of using Big Data are many, and include advancements in scientific understanding at larger scales and higher resolution, applications to improving environmental management and policy, and public engagement. We aim to demystify the use of Big Data for individual scientists, and provide some food for thought for the aquatic ecology community on how to develop this sphere. To achieve this, we highlight six key challenges: (1) how to recognize if you have Big Data, (2) handling Big Data, (3) issues with classical analytical techniques, (4) verification of Big Data, (5) considerations for data sharing, and (6) community development of knowledge infrastructures. We then present approaches and tools which have been successfully applied to these challenges in aquatic ecology and other scientific fields.

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Protocols.io: Virtual Communities for Protocol Development and Discussion

Cited by 61 publications

References 6 publications

Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

Computational investigation of biological and technical variability in high throughput phenotyping and cell line identification

Strength in numbers: collaborative science for new experimental model systems

Integrating “Big Data” into Aquatic Ecology: Challenges and Opportunities

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