BioBam Releases OmicsBox 3.2: Single Cell Transcriptomics Made Easy

Providing Researchers with Comprehensive Tools for Advanced Bioinformatics Data Analysis

BOSTON — BioBam, a leading provider of bioinformatics solutions, is pleased to announce the launch of OmicsBox 3.2 at the BIO-IT World Expo in Boston. This release showcases BioBam’s latest advancements in computational biology and bioinformatics, with a special focus on Single Cell and Long-Read data analysis.

OmicsBox 3.2 features a comprehensive suite of tools for the functional analysis and visualization of state-of-the-art transcriptomics data analysis scenarios. These customizable options enable researchers to effectively interpret data, fostering insights and discoveries within complex genomics datasets.

Dr. Stefan Götz, CEO, stated, “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 still in the process of defining its analysis protocols and with OmicsBox already offering a broad array of options.”

Key highlights of this release include enhanced visualization and cell-type annotation features for Single Cell Transcriptomics data analysis. Single Cell data analysis, inherently exploratory, benefits greatly from the new annotation and visualization options, enabling the integration of various tools and datasets to unravel tissue complexities using approaches like trajectory analysis and differential expression analysis of cell groups.

As the Long-Read data analysis landscape continues to evolve, OmicsBox now introduces support for working with non-model species and generating high-quality annotations based on long reads, all within a unified platform. This includes reference-free isoform reconstruction and various transcript quantification tools.

The Genetic Variation Module has also seen several significant updates, including the capability to perform population structure analysis and additional tools for VCF data manipulation, filtering, phasing, and imputation. These enhancements culminate in a comprehensive toolkit for end-to-end genetic variation analysis.

About OmicsBox:

OmicsBox is an end-to-end bioinformatics solution for data analysis of genomes, transcriptomes, metagenomes, and genetic variation studies. The application is used by top private and public research institutions worldwide and allows researchers to easily process large and complex data sets, and streamline their analysis process. It is designed to be user-friendly, efficient, and with a powerful set of tools to extract biological insights from omics data.

The software is structured in different modules, each with a specific set of tools and functions designed to perform different types of analysis, such as de-novo genome assemblies, genetic variation analysis, differential expression analysis, and taxonomic classifications of microbiome data, including the functional interpretation as well as rich visualizations of results.

The functional analysis module, which includes the popular Blast2GO annotation methodology, makes OmicsBox particularly suited for non-model organism research. Over 25k scientific research citations demonstrate this. OmicsBox works out of the box on any standard PC or laptop with Windows, Linux, or Mac. You can also explore its features with a free trial, making it even more accessible for students and researchers.

About BioBam:

BioBam is a bioinformatics company that provides innovative software solutions to accelerate genomics research. The company is dedicated to developing user-friendly and powerful bioinformatics tools that simplify data analysis for researchers, empowering them to focus on data interpretation and explore new insights. BioBam aims to close the technology gap between state-of-the-art bioinformatics and applied genomics research, by transforming complex data analysis into intuitive and interactive tasks that facilitate scientific advancement.

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