Galland, M. | Search bioRxiv

bioRxiv2021

PlotXpress, a webtool for normalization and visualization of reporter expression data

In molecular cell biology, reporter assays are frequently used to investigate gene expression levels. Reporter assays employ a gene that encodes a light-emitting protein, of which the luminescence is quantified as a proxy of gene expression. Commercial parties provide reporter assay kits that include protocols and specialized detection machinery. However, downstream analysis of the output data and their presentation are not standardized. We have developed plotXpress to fill this gap, providing a free, open-source platform for the semi-automated analysis and standardized visualisation of experimental gene reporter data. Users can upload raw luminescence data acquired from a reporter gene assay with an internal control. In plotXpress, the data is corrected for sample variation with the internal control and the average for each condition is calculated. When a reference condition is selected the fold change is calculated for all other conditions, based on the selected reference. The results are shown as dot plots with a statistical summary, which can be adjusted to create publication-grade plots without requiring coding skills. Altogether, plotXpress is an open-source, low-threshold, web-based tool, that promotes a standardized and reproducible analysis while providing an appealing visualization of reporter data. The webtool can be accessed at: https://huygens.science.uva.nl/PlotXpress/ O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=125 SRC="FIGDIR/small/451595v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@1b499aborg.highwire.dtl.DTLVardef@1d4d375org.highwire.dtl.DTLVardef@186bac1org.highwire.dtl.DTLVardef@4ed7bc_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2018

Building a local community of practice in scientific programming for Life Scientists

For most experimental biologists, handling the avalanche of data generated is similar to learning how to drive on your own. Although that might be doable, it is preferable and safer to learn programming and good practices together and preferably from experienced scientists. One way to achieve this is to build local communities of practice in scientific programming by bringing together life scientists that perform code-intensive research to spread know-how and good practices. The community will make sure that, for a given researcher working for an academic institution, their capacity to conduct data-intensive research will not be arbitrarily reliant on the presence of well-trained bioinformaticians in their vicinity.\n\nIn this paper, we propose a three-step field guide for building a local community of practice in scientific programming for life scientists. The first step is organizing a Carpentries programming workshop designed for researchers new to computational biology. However, while such workshops provide an immediate solution for learning, more regular long-term assistance is also needed. Researchers need persisting, local support to continue learning and solving programming issues that may hamper their research progress. The solution we describe here is to implement a Mozilla Study Group where researchers can meet-up and help each other in a \"safe-learning atmosphere\". We describe two real-life examples of building local communities of practice: one in the Netherlands at the Amsterdam Science Park and one in the United States at the University of Wisconsin-Madison. In order to help future researchers to build their own community of practice in scientific programming we discuss challenges and implemented solutions.\n\nWe believe that our local communities of practice will prove useful for other life scien-tists who want to set up similar support groups for researchers involved in scientific programming and data science.\n\nAuthor summaryIn this paper, we describe why and how to build a community of practice for life scientists that start to make more use of computers and programming in their research. A community of practice is a small group of scientists that meet regularly to help each other and promote good practices in scientific computing. While most life scientists are well-trained in the laboratory to conduct experiments, good practices with (big) datasets and their analysis are often missing. This paper proposes a field-guide on how to build such a community of practice at a local academic institution. Based on two real-life examples, challenges and related recommendations are provided. We believe that the current data deluge that life scientists will increasingly face can benefit from the implementation of these small communities. Good practices spread among experimental scientists will foster open, transparent, sound scientific results beneficial to society.

scientific communication and education↗