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Best bioinformatics software for RNA-seq quality control

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 prepare and analyse your RNA-seq experiments under the best conditions, we have launched a new series of surveys focused on the best tools for each fundamental step of an RNA-seq experiment. Starting your analysis with quality control The first step in the analysis of an RNA-seq experiment is quality control. This crucial step will ensure that your data are of the best quality to perform the subsequent steps of your analysis. Quality control usually includes sequence quality, sequencing depth, reads duplication rates (clonal reads), …

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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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MEDLINE queries made easy with the MEDOC tool

MEDOC (MEdline DOwnloading Contrivance) is a Python program designed to download data from MEDLINE on an FTP and to load all extracted information into a local MySQL database, thus making MEDLINE search easy. MEDLINE, the biomedical data keeper Since MEDLINE’s database has been released almost 50 years ago, the number of indexed publications rose from 1 million in 1970 to 27 millions this year. The aim of this repository is to facilitate the access to the scientific literature for everyone. The NIH (National Institute of Health, USA) also provides a powerful search engine, which allows to query this database throught the well-know web interface PubMed. This search engine supports complex queries by using logical operators (OR, AND) and indexes different text …

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Best bioinformatics software for gene clustering

  Clustering is a fundamental step in the analysis of biological and omics data. It is used to construct groups of objects (genes, proteins) with related function, expression patterns, or known to interact together. In microarrays or RNA-seq experiments, gene clustering is often associated with heatmap representation for data visualization.   Choosing the right clustering tool for your analysis   Many clustering methods and algorithms have been developed and are classified into partitioning (k-means), hierarchical (connectivity-based), density-based, model-based and graph-based approaches. To help you choose between all the existing clustering tools, we asked OMICtools community to choose the best software. Here are the top 3 tools, chosen by 23 voters.   1. ClustEval 2. Babelomics 3. Altanalyze First place for …

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Best bioinformatics software for venn diagram

Venn diagrams are very simple, yet incredibly useful tools used to show all logical relations between finite collections of different sets of data. In Venn diagrams, sets of data are often represented as overlapping circles. Data that are shared between two different sets will reside at the intersection, while unique data remain outside the intersection. Venn diagrams in biology In biology and omics, Venn diagrams can be used for a variety of purposes, such as the comparison of different lists of genes or proteins (generally 2 or 3) to identify similarities and represent them in two dimensions. Most softwares allow easy extraction of the data, and let you customize the diagrams.   Continuing our series of data visualization tools, OMICtools …

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Single-cell RNA sequencing in immunology

Single-cell RNA sequencing (scRNA-seq) has revolutionized the study of the immune system and now has a wide range of applications in immunology. This technology spans the whole genome and provides an unbiased gene expression profile of individual cells. Bulk vs single-cell RNA-seq Traditional bulk RNA-seq 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, which is a hallmark of adaptive immune cell subsets such as B and T lymphocytes. The need to identify new and discrete immune cell populations and to understand molecular changes that occur at the single cell level …

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Selecting and preparing proteins for virtual screening

Leandro Radusky and his team have come up with a new online web service, LigQ, that allows fast and efficient identification of potential binders to a desired target before starting a virtual screening procedure. Here, they describe the functionality of the LigQ tool and discuss how you can use it to select and prepare your proteins for virtual screening. The need for compound selection before virtual screening A major aspect of drug discovery involves the identification of new compounds that are able to bind a protein and control its activity. In silico virtual screening is one of the most powerful and widely used techniques to search for lead compounds that bind to a protein of interest with moderate to high affinity. …

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Best bioinformatics software for heatmap generation

Heatmaps are one of the most commonly used representation tools for synthesis of complex pools of data. By coding numerical values into colors, heatmaps enable quick representation of quantitative differences in expression levels of biological data. Using heatmaps to visualize your data Heatmaps are particularly useful for analysis of gene expression microarray data. Most heatmap representations are also combined with clustering methods to group genes and/or samples based on their expression patterns. Each gene is represented as a row and is color-coded to represent the intensity of its variation (either positive or negative) relative to a reference value. Biological samples are represented as columns in the grid. To evaluate the OMICtools series of data visualization tools, we asked members of the …

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