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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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Choose and use the right license for your software tool

Whether you are a software developer wishing to distribute your tool, or a user/programmer in need to use and/or modify a tool, you are confronted to licensing. Software licenses govern the use or redistribution of a software under specific laws. Two common categories for software under copyright law (which grants the licensee specific rights) are proprietary software and free and open source software (FOSS). The main difference between proprietary and FOSS license is the granting of rights to modify and re-use a software by a customer: FOSS software licenses both rights to the customer, while proprietary software does not license these rights (Wikipedia).   Software licenses on OMICtools   Following the general tendency to develop more open-source software, more than …

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Credit your work and enter to win a pass for a congress

You designed a tool or performed specific tasks in software development? Spread the word! Credit your expertise with badges and enter to win a pass for a congress (and others exciting prizes). To participate, all you need to do is badge your contribution on the bioinformatics tools we reference.  Don’t hang around, the contest ends the 11th May. How does the contest work? Sign in to your account (or sign up for free if you aren’t already a member). Find the tools you designed (or contributed to design): use the search engine and filters to rapidly locate your project Click on the “set my contributions” button  to add badges Why are badges important? Badges are the best way to earn …

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Explore gene-expression datasets with the Omics Dashboard

The advent of omics technologies has fostered the generation of a flood of complex, high-resolution datasets, the analysis of which remains a major hurdle and requires conversion into actionable biological knowledge. To address this challenge, Peter Karp and his colleagues from the SRI Bioinformatics Research Group have developed the Omics Dashboard within the BioCyc.org website. Here, they present their tool and its main features. The Omics Dashboard The Dashboard provides a multi-level visual read out of an expression dataset, from the cellular level to the gene level.  The user can probe their data in a fast and intuitive manner to gain a deep understanding of the data at multiple biological levels.   At the highest echelon (Figure 1) the Dashboard provides …

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Best bioinformatics software for MS-based proteomics analysis

Proteomics are the next step after genomics and transcriptomics to study biological systems. However, analyzing the proteome is much more difficult than the genome or transcriptome, because each cell expresses its own set of proteins. Mass spectrometry (MS) has emerged as the most important and popular tool to identify, characterize, and quantify proteins and their post-translational modifications with high throughput and on a large scale (Zhang et al.). To help you perform your experiments in the best conditions, we asked the OMICtools community to choose the best MS-based untargeted proteomics analysis tools. LC-MS based untargeted proteomics Two-dimension liquid-chromatography (LC) coupled with mass-spectrometry (LC-MS) is the leading technology for high-throughput proteomics. LC is used to separate proteins from different samples in parallel, and …

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Semantic Web in life sciences

  The exponential growth of the number of biological studies over the past decade has created an enormous challenge to make effective use of the accumulated information. Owing to this size, simple web-style text search engines are often not yielding the best results and a lot of important information remains buried in the masses of text (Doms, 2005). Semantic technologies have been introduced for better question answering and faster literature exploration (Doms, 2008). They use ontologies to give an overview over large query results and guide scientists for discoveries.   Starting your research in biology but don’t know which platform to use to find impactful studies? Here is a list of useful resources you will love using to retrieve articles …

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Search and find olive oil compounds with OliveNet

Olive oil is now widely recognized as a key component of the health benefits provided by the “Mediterranean diet”. However, the exact composition and biological activity of olive oil and its compounds are still being investigated. OliveNet aims at gathering all information on olive oil compounds in a knowledgebase. Here, they briefly present their database and its features.   The benefits of olive oil   The health benefits of a Mediterranean diet rich in extra-virgin olive oil was illustrated by the classical Ancel Keys Seven Countries Study and more recently the methodical, multicentre Prevention with Mediterranean (PREDIMED), trial in Spain. In the context of health benefits the initial focus was on the major mono- and poly-unsaturated fatty acids, and accumulated …

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Decipher sequencing data with file formats for NGS

Next-generation sequencing (NGS) technologies generate tremendous amount of data. A typical human genome consists of ~3 billion base pairs to be sequenced. A critical step in NGS is to extract the information, stock it and transmit it in an easy-to-use and lightweight way. Why need file format? Coded in bits, a human genome would “only” weight about 700 MB (Reid Robison). However, sequencers generate short reads that redundantly span the sequence and then need to be aligned to a reference genome. Moreover, since sequencing is not perfect, every base has a score attached to it to evaluate the quality of sequencing. Thus, file formats have been developed to code a maximum of this information in a minimum of space. Here …

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