We are happy to announce the latest update of OmicsBox 3.2. “I am particularly happy to announce that with our latest release, our team managed to create a straightforward yet sophisticated solution for the challenging domain of Single Cell Transcriptomics. This marks a significant enhancement to the suite of complex analysis scenarios OmicsBox simplifies, including also Long-Read Transcriptomics, a field
OmicsBox’s Single Cell Data Analysis encompasses a comprehensive range of steps, spanning read quantification, pre-processing, clustering, differential expression analysis, and even advanced analyses like trajectory analysis and functional enrichment. This blog post will focus on general trajectory inference using Monocle3 within OmicsBox. Pseudotime Analysis with Monocle3 Monocle3, developed by the Trapnell lab, is a scRNA-Seq data analysis toolkit written in
Empowering Researchers with Advanced End-to-End Analysis Tools for Single-Cell and Genetic Variation Studies. We’re thrilled to announce the much-anticipated arrival of OmicsBox 3.1, a release that brings significant improvements to single-cell data analysis with UMI-based technology support and introduces a functional enrichment tool for genetic variation analysis. It also enhances OmicsBox’s long-read transcriptome analysis options, making it a one-of-a-kind solution.
Once again we are pleased to announce the latest update of OmicsBox 3.0. The whole BioBam team has worked to bring the new version of the BioBam bioinformatics solution to life. We proudly present the Genetic Variation Module as the highlight of OmicsBox 3.0. This module enables variant calling, filtering, and annotation, as well as the association of genetic variations
We are happy to announce the latest update of OmicsBox 2.2. After several months of intensive work we are finally ready to present the new version of BioBam’s bioinformatics solution. OmicsBox 2.2 comes with new Single Cell and Long Read Data Analysis Features and many other platform improvements. OmicsBox continues improving to become the most efficient, powerful and user-friendly bioinformatics
The differential expression (DE) analysis has been used in bulk RNA-seq analysis for many years. It allows us to statistically measure changes in gene expression levels between different groups. With bulk RNA-seq analysis many cells are sequenced at the same time, so gene expression levels are commonly measured at the tissue level. Thus, the differences between samples and conditions are
Transcriptome sequencing (RNA-Seq) produces big amounts of valuable information on all transcribed elements in the genome. With RNA-Seq, researchers can interrogate the transcriptome to profile gene expression, uncover alternative splicing, and identify novel and discard aberrant transcripts and coding variants, among others. The resulting BAM Files are the source of information for all proceeding step
Why Differential Expression Analysis with Single-cell RNA-seq data? The differential expression (DE) analysis has been used in bulk RNA-seq analysis for many years. It statistically measures changes in gene expression levels between different groups. With bulk RNA-seq analysis many cells are sequenced at the same time, so gene expression levels are commonly measured at the tissue level. Thus, the differences
Specific tools for a new generation of sequencing Third Generation Sequencing (TGS) is getting a notable increase in relevance by surpassing some of the limitations of NGS technologies. In that sense, the main feature of TGS is the capacity to produce much longer reads than NGS. These long reads facilitate the assembly of complex genomes and the study of alternative
