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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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Your top 3 MS-based proteomics analysis tools

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 OMICtools members to choose their favorite 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 is …

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Identifying the right bioinformatics tools for biological data

In the biology big data context, managing the amount and diversity of data that experiments produce is a challenging task. Depending on the scope of your research, you probably spend a lot of your time searching for the right bioinformatics tools.   Like most, you probably have a general idea of how to analyze your data and have used more than one tool. If you’re working in a computational biology laboratory, you’ve probably heard the question “What is the best software for genome sequence alignment?” or “What algorithm is the standard for sequence alignment in genetics?” While BLAST is probably the most popular tool for this, there are lots of other tools for mining, and aligning biological data.  So choosing the right solution for …

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Your top 3 RNA-seq quantification and differential expression tools

RNA-sequencing is progressively replacing microarrays for the study of transcriptomes, and comparison of gene expression. One advantage of this technique is the ability to identify and quantify the expression of isoforms and unknown transcripts.   To help you perform your experiments in the best conditions, we are closing our series of surveys on RNA-sequencing by asking OMICtools members to choose their favorite quantification and differential expression tools. RNA quantification and differential expression While microarrays produce a numerical estimate of the relative expression of genes across the genome, RNA-sequencing experiments rely on read-count distributions. After mapping reads to a reference genome, the expression level for each gene or isoform are estimated and normalized, and finally differentially-expressed genes are identified using statistical methods. …

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Share your best tips with protocol repositories

Research protocols are like good old recipes, they contain time-tested knowledge, and their secrets are not shared with anybody. However, due to the complexity of today’s experiments, it has become more important than ever for scientists to find and use the best protocols available. New omics technologies require precise and controlled sample preparation, and the smallest variation can lead to huge differences in the resulting output. The need for protocol repositories The “big data” has fostered the development of a number of repositories, for bioinformatics tools (OMICtools), software codes (GitHub), and peer-reviewed journals dedicated to protocols (Nature Protocols, JOVE). Following this trend, a number of initiative propose to store and reference research protocols on so-called protocol repositories. These repositories share …

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How documentation can improve your tools

Bioinformatics tools documentation and guidelines can come in handy when using a complicated piece of software. Yet, tools developers most often overlook the benefit of releasing documentation with their creations.   The main goal of tool documentation is to provide the basics of the software’s functionalities and guidelines on how to use it. Having such documentation will ensure great coverage and impact of your tool, as well as save you countless hours answering basic questions. Different types of research software documentation Software documentation can take various formats: Manuscript, usually the original publication describing the tool Readme, which contains basic instructions for installation and use of the software Quickstart, a step-by-step protocol for installation and use of the software Reference manual, …

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Map functional networks of ncRNAs with circlncRNAnet

Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) lack protein-coding potential but have nonetheless emerged as key determinants in gene regulation, acting to fine-tune transcriptional and signaling output. These noncoding RNA transcripts are known to affect expression of messenger RNAs (mRNAs) via epigenetic and post-transcriptional regulation. To fully capture, from a network perspective, the functional implications of lncRNAs or circRNAs of interest, Dr. Bertrand Chin-MingTan and his team have implemented an integrative bioinformatics approach to examine in silico the functional networks of non-coding RNAs. Here, they present their web server tool “circlncRNAnet” and discuss its main features. In-depth analyses of non-coding RNA biology The main purpose for implementing this web server is to provide biologists with a user-friendly, “one-stop” web tool …

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Predict transcription factor binding from DNase footprints with Sasquatch

Predicting the impact of regulatory sequence variation on transcription factor (TF) binding is an important challenge as the vast majority of disease associated SNPs are found in the non-coding genome (Vaquerizas, Kummerfeld, Teichmann, & Luscombe, 2009)⁠. Most existing approaches rely on large catalogs of cell type and TF specific functional annotations. As only a minority of TFs is well characterized (Maurano et al., 2015; Rockman & Kruglyak, 2006)⁠, identifying the relevant factors and probing them in the appropriate cell types represents a major limitation of TF centric approaches. With this in mind, Ron Schwessinger and colleagues from University of Oxford have developed the Sasquatch tool to use DNase footprinting data to estimate and visualize the effects of non-coding variants on …

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