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Best bioinformatics software for mass-cytometry analysis

Conventional flow cytometry is limited by the number of fluorochromes and cell markers that can be targeted at once. Overcoming this limitation, mass-cytometry combines mass-spectrometry and flow cytometry by using metal-conjugated antibodies to label cellular proteins and extend the number of markers to be targeted.   A combination of spectrometry and cytometry   In mass-cytometry, antibodies are conjugated with isotopically pure elements, and these antibodies are used to label cellular proteins. Cells are nebulized and sent through an argonplasma, which ionizes the metal-conjugated antibodies. The metal signals are then analyzed by a time-of-flight mass spectrometer. The approach overcomes limitations of spectral overlap in flow cytometry by utilizing discrete isotopes as a reporter system instead of traditional fluorophores which have broad emission spectra.   Due to its numerous advantages (minimal …

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DataOnTools #5: Tools in the literature

Scientific communications and publications are the best way to promote your work – whether you are a tool developer or a bioinformatician conducting analysis. In this article, we analyze tools publications to explore a new aspect of the evolution of the field of bioinformatics. Tool publications and citation In the OMICtools database, nearly eight out of ten have been published in a peer-reviewed journal, with 41.7% of all tools cited at least once, while 36.0% have never been cited in the literature (Figure 1a). Among those with a PMID, about half of them have never been cited in the literature, and the other half has been cited at least once. This is of importance because citations are the best way …

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DataOnTools #4: Evolution of tool specifications

When it comes to developing a bioinformatics software tool, many different languages can be used. Moreover, developers might have to choose on which operating systems among the most used they want their creation to be run. Finally, the target audience of the software (personal use, free distribution or commercial distribution) may influence the usage of the software (web interface, desktop, etc.).   Users can run bioinformatics software tools either on the web, locally on a desktop or server, or both. While tools that can be used on the web could be expected to be more common, reflecting the need for user-friendliness for less-skilled users, we in fact found that more than 69% of the 20,918 tools registered as software are …

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DataOnTools #3: Economics of tool development

Like any other research field, bioinformatics and software development are highly depend on funding. By extracting grants and other funding sources from tool publication, we provide a new insight on the economics of tool development.   Funding is a critical aspect of tool development. We ranked the sources of funding agencies associated with a total of 12,761 published tools to assess the top 20 tool-funding agencies worldwide (Figure 1). Nearly half of all published tools were funded by the National Institutes of Health (NIH) or the National Science Foundation (NSF), both US-based agencies.     Countries’ themselves take a critical role in funding research through national grants. Indeed figure 2 shows a high correlation (r=0.81; p<0.0001) between a country’s research …

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DataOnTools #2: Tool development and collaboration worldwide

Bioinformatics tools are developed by research laboratories, university, and private companies that often publish their work in scientific journals. In fact, nearly 80% of all tools registered on OMICtools have a PMID, and thus are accompanied with a scientific publication. By tracking authors affiliations, we provide a unique perspective on worldwide tool development and collaboration between countries.   Tool development worldwide   Tool development and publication is dominated by the USA, with 30% of published tools originating from US institutions (Figure 1a and b).     Most leading tool-developing institutions are hosted by European and American continents, with 18 out of the top 20 tool-developing institutes and universities located in the USA, United Kingdom, Canada, or rest of Europe (Figure …

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DataOnTools #1: A comprehensive data analysis of bioinformatics tools

Ever wondered how many tools are released each year? What country or institute produce the most tool? Or what are the most used programming languages? To answer these burning questions, and a lot more, we are launching a new series of articles, DataOnTools, providing a wide range of data on bioinformatics tools evolution and current status. Most of these data representation will be freely accessible and reusable.   DataOnTools, everything you always wanted to know about bioinformatics tools   Just like the data they are built to analyze, bioinformatics tools are growing at an exponential pace. The origin of bioinformatics tools is well documented and can be traced back to the late 60s. However, documenting the evolution of tools in …

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Visualize differential gene expression with ViDGER

Differential gene expression (DGE) analysis is one of the most common applications of RNA-seq data. This process allows for the elucidation of differentially expressed genes (DEGs) across two or more conditions. Interpretation of the DGE results can be non-intuitive and time-consuming due to the variety of formats based on the tool of choice and the numerous pieces of information provided in these results files. To address this challenge, Adam McDermaid and his colleagues from South Dakota State University have developed ViDGER. Here, they present their tool and its main features.   Interpretation and Visualization of Differential Gene Expression through ViDGER   One of the most straightforward ways to gain a broader understanding of the tens-of-thousands of pieces of information generated …

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Best bioinformatics software for venn diagram

Venn diagrams are very simple, yet incredibly useful tools used to show all logical relations between finite collections of different sets of data. In Venn diagrams, sets of data are often represented as overlapping circles. Data that are shared between two different sets will reside at the intersection, while unique data remain outside the intersection. Venn diagrams in biology In biology and omics, Venn diagrams can be used for a variety of purposes, such as the comparison of different lists of genes or proteins (generally 2 or 3) to identify similarities and represent them in two dimensions. Most softwares allow easy extraction of the data, and let you customize the diagrams.   Continuing our series of data visualization tools, OMICtools …

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