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Clustering redundant transcripts in OmicsBox with CD-HIT

De novo transcriptome assemblies are required to analyze RNA-seq data from a species for which there is no reference genome. However, with the advancement of next-generation sequencing technologies, the amount of available sequencing data is growing exponentially. Because of this, assembly algorithms often generate a large number of transcripts. Removing redundancy from such data could be crucial for reducing storage space,

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new version 5

Release Version 5.2

(Release Date: 02/08/2018) We are happy to announce that Blast2GO 5.2 has now been released. This update makes our bioinformatics tool for straightforward functional genomics even better.Read below to learn which features and improvements are part of this release. Main features: Venn Diagrams that are fully customizable and allows multiple list comparisons FastQ Pre-Processing (Adapters, Quality, Length) with multiple trimming options.

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How to create analysis workflows

OmicsBox/Blast2GO provides an interface to create, edit and run workflows based on the Common Workflow Language (CWL) specification. This interface allows to describe all analysis steps using the functions and tools offered by Blast2GO and connect them to perform a complete analysis in a single run.  This video shows step-by-step how to create a workflow from scratch, define the input

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Expression Estimation at Transcript-Level

The Transcript-level Quantification feature of OmicsBox/Blast2GO allows quantifying the gene and isoform expression of RNA-seq datasets. This video shows step-by-step how to create a count table of aligned sequencing reads and explains in detail the different concepts of expression quantification at transcript level. The application is based on the RSEM software package, which assigns reads to the isoforms they came

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Expression Quantification at Gene-Level

The “Create Count Table” feature of OmicsBox/Blast2GO allows quantifying the gene expression of RNA-seq datasets. This video shows step-by-step how to create a count table of raw reads and explains in detail different concepts of expression quantification. The available parameters are inspired by the popular HTSeq Python Package (reference below).

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Time-Course Differential Expression Analysis

The Time Course Expression Analysis tool allows performing a differential expression analysis of expression data arising from a time course RNA-seq experiment. This application is based on the maSigPro Bioconductor package, which implements a two-step regression strategy to detect genes with significant temporal expression changes and significant differences between experimental groups. This video shows the analysis of count data coming

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Pairwise Differential Expression Analysis

The Pairwise Differential Expression Analysis tool is designed to perform differential expression analysis of count data arising from an RNA-seq experiment. The application, which is based on the software package “edgeR”, allows the identification of differentially expressed genes between two experimental conditions by applying quantitative statistical methods. This video shows the performance of a pairwise differential expression analysis in which

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Time Course Expression Analysis

A simple use-case comparing OmicsBox with R chunks for Time Course Expression Analysis The Blast2GO feature “Time Course Expression Analysis” is designed to perform time-course expression analysis of count data arising from RNA-seq technology. Based on the software package ‘maSigPro’, which belongs to the Bioconductor project, this tool allows the detection of genomic features with significant temporal expression changes and

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Pairwise Differential Expression Analysis

A simple use-case comparing OmicsBox with R chunks The OmicsBox feature “Pairwise Differential Expression Analysis” is designed to perform differential expression analysis of count data arising from RNA-seq technology. This tool allows the identification of differential expressed genes considering two different conditions based on the software package ‘edgeR’, which belongs to the Bioconductor project. This use case shows the basic

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