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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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Track and control your code with version control systems

Software codes are constantly evolving, become more complex as newer versions are released. But sometimes errors and bugs occur and being able to retrieve the latest stable version of your software code can be a life-saver. For this, most software code repositories are equipped with version control systems (VCS). Why do you need version control and how is it implemented? Software developers working in teams are continually writing new source code and changing existing source code. The code for a project is typically organized in a folder structure or “file tree”. One developer on the team may be working on a new feature while another developer fixes an unrelated bug by changing code, each developer may make their changes in …

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Your top 3 RNA-seq read alignment tools

RNA-sequencing (RNA-seq) is currently the leading technology for transcriptome analysis. RNA-seq has a wide range of applications, from the study of alternative gene splicing, post-transcriptional modifications, to comparison of relative gene expression between different biological samples.   To help you perform your RNA-seq experiments in the best conditions, we are continuing our series of surveys by asking you to choose your favorite analysis tools step by step. Mapping reads to reference genome  After a first step of quality control (previous blog post here), the next step in the analysis of your RNA-seq experiment is alignment of reads to a reference genome or a transcriptome database.   There are two types of aligners: Splice-unaware and splice-aware. Splice-unaware aligners are able to align continuous reads to a …

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Simulate immune responses to vaccines with C-IMMSIM

Because of the diversity of the immune repertoires, it is very challenging to predict the efficacy of a vaccine to properly stimulate all components of the immune system and ultimately protect against infectious diseases, that is it’s immunogenicity. To aid researchers in the design and set up of their vaccination/infection protocols, Dr. Filippo Castiglione and colleagues from the Institute for Applied Computing in Rome have developed C-IMMSIM. Here, he talks about the features and benefits of his tool. A novel and free tool to perform in silico experiments about vaccinations and/or infections To date, this is the only simulation tool for the immune response which combines epitope/peptide prediction algorithms with agent-based methodology to predict the follow-up of virtual infection experiments. …

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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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Identify and map genomic islands with xenoGI

Genomic islands (GI) are part of a genome that have been transferred horizontally beween organisms, typically bacteria. They code for many functions, such as symbiosis, pathogenesis, antibiotic resistance, etc. Determining GI is often performed by base composition analysis and phylogeny estimations.   Dr. Eliot Bush has developed xenoGI, a tool that proposes several features to aid identify islands of genes that entered via common horizontal transfer events, to map those events onto the phylogenetic tree, and more. Here, he briefly describes the features and benefits of his tool. Features and benefits of xenoGI Microbes have acquired many important traits through the horizontal transfer of genomic islands. Understanding the evolution of these traits often requires us to understand the adaptive path …

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Process 16S rRNA sequences with the sl1p tool

Advancing DNA sequencing technologies have encouraged a surge of microbiome studies. The microbiome, the set of microbes (bacteria, viruses, archaea) who live in a particular environmental niche, has been extensively studied, including in the context of human disease, changes in ecological environments, and progressive oxygen gradients in the deep sea. One of the most popular methods for these types of studies is the sequencing of segments of the 16S rRNA gene– a highly conserved gene among bacterial populations which allows researchers to identify the taxonomic diversity within a given bacterial niche. Drs. Whelan and Surette have recently come up with a new tool, sl1p, that helps automate the processing of 16S rRNA gene sequencing data and provides analyses which allow the …

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Analyse co-expression gene modules with CEMItool

  Identifying single changes in gene expression levels is a common analysis step after a microarray or RNA-Seq experiment. The expression levels of co-expressed genes can also be analyzed and visualized by gene co-expression networks (GCNs), which are undirected graphs used to represent co-expression relationships between pairs of genes across samples.   Dr. Helder Nakaya from Sao Paolo University has recently developed CEMItool, an easy-to-use method to automatically run gene co-expression analyses in R. Here, he describes the features provided by CEMItools. Analyse your transcriptomic data for co-expression modules The analysis of co-expression gene modules can help uncover the mechanisms underlying diseases and infection. CEMItool is a fast and easy-to-use Bioconductor package that unifies the discovery and the analysis of …

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