3 min read

Active Motif & OnRamp Announce Partnership

By Jeremy Davis-Turak on Dec 12, 2018 7:37:00 AM

OnRamp’s ROSALIND and Active Motif offer an improved ChIP-Seq data analysis experience for researchers with state-of-the-art dynamic track plots and advanced collaboration capabilities.

FOR IMMEDIATE RELEASE

San Diego, California – December 12, 2018 - OnRamp BioInformatics, a genomics company focused on scientist-friendly bioinformatics software solutions, today announced they are working with Active Motif to improve accuracies in data analysis for ChIP-Seq experiments.

OnRamp’s flagship software, ROSALIND, provides a point-and-click experience to speed up and simplify the process of genomic data analysis from experiment setup to QC and interactive data visualization to interpretation. With this integration, researchers will now benefit from an optimized analysis that includes the unique characteristics of Active Motif kits and antibodies, including Unique Molecular Identifiers (UMI) and spike-in solutions.

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Topics: Big Data Personalized Medicine Cancer BioInformatics Biology Rosalind advaita
4 min read

Decoding Human Health

By Jeremy Davis-Turak on May 3, 2018 9:10:20 AM

OnRamp and Advaita announce a Strategic Partnership to bring collaboration and widespread accessibility to genomic data analysis

San Diego, CA – May 3, 2018 OnRamp Bioinformatics, Inc., a genomics company providing the premier scientist-focused data analysis platform, and Advaita BioInformatics, a leader in personalized medicine and interpretation of Next-Gen Sequencing data, announced they are partnering to provide a comprehensive research experience from sample to interpretation with a seamless handoff between systems.  

OnRamp.Bio’s flagship product, ROSALIND™, enables researchers, drug developers and bench scientists to analyze raw genomics data by providing a transformative experience through point-and-click experiment set up, interactive data visualization and interpretation. This new approach increases productivity by freeing up time for the bioinformatician to focus on more challenging workloads, while making bioinformatic analysis more accessible for the scientist to do more discovery with their data.

Topics: Big Data Personalized Medicine Cancer BioInformatics Biology Rosalind advaita