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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 (covered here), cancerology, and …

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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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Choosing the right name for a bioinformatics tool

The name of your bioinformatics tool is the icing on the cake. You should be very attentive and feel concerned regarding this feature. While having a great name does not assure you to stand out, a bad appellation often results in a failure to reach your audience. The do’s and don’ts Don’t take a name that already exists Your tool is unique. As such, it deserves to have a sole name. Don’t try to copy the name of a famous tool or your favorite one. It can be detrimental for you. Indeed, people tend to compare products with each other, especially if they have the same title. They can be confused about the function of your tool.   OMICtools contains …

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Improve your differential gene expression analysis with EPEE

Differential gene expression is highly common. The standard paradigm is to quantify the significance of differences in the individual gene expression values, commonly known as differential expression (DE) analysis. One opportunity to improve the widely used DE analysis is to incorporate known gene regulation relationships. Without regulatory knowledge, DE methods cannot discover any perturbation/regulation events due to post-transcriptional and/or translational mechanisms. To address this challenge, Murat Cobanoglu and his colleagues from Lyda Hill Department of Bioinformatics have developed EPEE. Here, they present their tool and its main features. EPEE features To more accurately analyze differential gene expression data, we need algorithms that account for differential regulation (DR). However, most currently existing DR methods do not strictly integrate existing knowledge of transcriptional regulation …

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Evaluate the quality of your read alignment with GeneQC

RNA-sequencing has replaced gene array and is now the leading technology in gene expression analysis. After a sequencing step, reads need to be mapped to a reference genome. However, this step is not perfect and errors can impact all downstream analyses. To address this issue, Adam McDermaid and colleagues have developed GeneQC, a tool to evaluate the quality of read alignment. Here, he describes GeneQC and its features. Quality control of read alignment One of the main benefits of using modern RNA-Sequencing (RNA-Seq) technology is the more accurate gene expression estimations compared with previous generations of expression data, such as the microarray. However, numerous issues can result in the possibility that an RNA-Seq read can be mapped to multiple locations …

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