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'''"[[Journal:Global data quality assessment and the situated nature of “best” research practices in biology|Global data quality assessment and the situated nature of “best” research practices in biology]]"'''
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig4 Auer CytometryPartA2018 93-7.jpg|240px]]</div>
'''"[[Journal:ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis|ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis]]"'''


This paper reflects on the relation between international debates around data quality assessment and the diversity characterizing research practices, goals and environments within the life sciences. Since the emergence of molecular approaches, many biologists have focused their research, and related methods and instruments for data production, on the study of genes and genomes. While this trend is now shifting, prominent institutions and companies with stakes in molecular biology continue to set standards for what counts as "good science" worldwide, resulting in the use of specific data production technologies as proxy for assessing data quality. This is problematic considering (1) the variability in research cultures, goals and the very characteristics of biological systems, which can give rise to countless different approaches to knowledge production; and (2) the existence of research environments that produce high-quality, significant datasets despite not availing themselves of the latest technologies. Ethnographic research carried out in such environments evidences a widespread fear among researchers that providing extensive information about their experimental set-up will affect the perceived quality of their data, making their findings vulnerable to criticisms by better-resourced peers. These fears can make scientists resistant to sharing data or describing their provenance. ('''[[Journal:Global data quality assessment and the situated nature of “best” research practices in biology|Full article...]]''')<br />
Multicolor image analysis finds its applications in a broad range of biological studies. Specifically, multiplex [[wikipedia:Fluorescence in situ hybridization|fluorescence ''in situ'' hybridization]] (M‐FISH) for chromosome painting facilitates the analysis of individual chromosomes in complex metaphase spreads and is widely used to detect both numerical and structural aberrations. While this is well established for human and mouse [[wikipedia:Karyotype|karyotypes]], for which species sophisticated software and analysis tools are available, other organisms and species are less well served. Commercially available software is proprietary and not easily adaptable to other karyotypes. Therefore, a publicly available open-source software that combines flexibility and customizable functionalities is needed. Here we present such a tool, called “ChromaWizard,” which is based on popular scientific image analysis libraries (OpenCV, scikit‐image, and NumPy). We demonstrate its functionality on the example of primary Chinese hamster (''Cricetulus griseus'') fibroblasts metaphase spreads and on Chinese hamster ovary cell lines, known for their large number of chromosomal rearrangements. ('''[[Journal:ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis|Full article...]]''')<br />
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Revision as of 22:58, 24 February 2020

Fig4 Auer CytometryPartA2018 93-7.jpg

"ChromaWizard: An open-source image analysis software for multicolor fluorescence in situ hybridization analysis"

Multicolor image analysis finds its applications in a broad range of biological studies. Specifically, multiplex fluorescence in situ hybridization (M‐FISH) for chromosome painting facilitates the analysis of individual chromosomes in complex metaphase spreads and is widely used to detect both numerical and structural aberrations. While this is well established for human and mouse karyotypes, for which species sophisticated software and analysis tools are available, other organisms and species are less well served. Commercially available software is proprietary and not easily adaptable to other karyotypes. Therefore, a publicly available open-source software that combines flexibility and customizable functionalities is needed. Here we present such a tool, called “ChromaWizard,” which is based on popular scientific image analysis libraries (OpenCV, scikit‐image, and NumPy). We demonstrate its functionality on the example of primary Chinese hamster (Cricetulus griseus) fibroblasts metaphase spreads and on Chinese hamster ovary cell lines, known for their large number of chromosomal rearrangements. (Full article...)

Recently featured:

▪ Haves and have nots must find a better way: The case for open scientific hardware
▪ CytoConverter: A web-based tool to convert karyotypes to genomic coordinates
▪ Implementing a novel quality improvement-based approach to data quality monitoring and enhancement in a multipurpose clinical registry
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