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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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Put bioinformatics tools in favorites and enter to win exciting prizes

Have you ever found a bioinformatics software or a database you wanted to use later, only to realize that you didn’t remember its name? Favorites (called bookmarks in most browsing activity) are a great way to save and organize tools so you can revisit them. Bookmark a maximum of tools and enter to win a set of headphones! Don’t hang around, the contest ends the 30th June. How does the contest work? Sign in to your account (or sign up for free if you aren’t already a member). Find your favorite tools Click the icon to put them in favorites Using favorites for better tool accessibility Favorites are the best way to save tools you judge useful for your data …

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A guide for protein structure prediction methods and software

To exert their biological functions, proteins fold into one or more specific conformations, dictated by complex and reversible non-covalent interactions. Determining the structure of a protein can be achieved by time-consuming and relatively expensive technics such as crystallography, nuclear-magnetic resonance spectroscopy, and dual polarization interferometry. Bioinformatics software have been developed to compute and predict protein structures based on their amino acid sequences.   A recap on protein structure   As an alternative to experimental technics, structure analysis and prediction tools help predict protein structure according to their amino-acid sequences. Solving the structure of a given protein is highly important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes). The field of computational …

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Best bioinformatics software for single-cell RNA sequencing

RNA-sequencing is often performed on well-identified groups of cells thought to be homogeneous. However, quantification of molecular changes is made by estimating the mean value from millions of cells and averaging the signal of individual cells, thus ignoring cell-to-cell heterogeneity. Single-cell RNA-sequencing (scRNAseq) enables to unravel the heterogeneity of cell genotype, phenotype, and function within a given subpopulation.   ScRNA-seq now has a wide variety of applications, and numerous tools were developed to analyze this new kind of sequencing data. To help you perform your experiments in the best conditions, we asked OMICtools members to choose their favorite scRNA-seq analysis tools.   Main applications for scRNA-sequencing   Single-cell RNA sequencing finds its main applications in immunology, cancerology, and the study …

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Test your microbiome analysis pipeline with PLuMA

Study of the microbiome and metagenomics are actively developing areas of research. In the meantime, analyzing microbiome data has become increasingly complex, with multiple tools required to be run sequentially. Running these so-called “pipelines” of tools can be challenging because of the diversity of coding languages and compatibility issues.   To overcome this problem, Trevor Cickovski and Giri Narasimhan from the Bioinformatics Research Group of the Florida International University have developed PLuMA, a Plugin-Based Microbiome Analysis lightweight back end pipeline that supports multiple dynamically loaded plugin extensions. Here, they describe their tool and its main features.   Plugin-Based Microbiome Analysis   If you are an algorithm developer who wants to prototype, test and debug a new pipeline stage in your …

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