Spectronaut Archives - Biognosys

 

To celebrate Spectronaut, our software for DIA analysis, turning 10 we created a video series featuring some of the amazing team that made Spectronaut what it is today.

 

In our second video, we are looking back on some of the highlights and challenges of the Spectronaut journey so far with Oliver Rinner, Lukas Reiter Tejas Gandhi, and Oliver Bernhardt.

 

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There are many things we love about our flagship software Spectronaut®, ranging from its deep proteome coverage to its AI augmentation and seamless user experience.

Find out some of our team’s favorite features in our latest Spectronaut video.

 

 

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To celebrate Spectronaut, our software for DIA analysis, turning 10 we created a video series featuring some of the amazing team that made Spectronaut what it is today.

 

In the first video, our team explores how Spectronaut came to be and why researchers should choose it for their DIA analysis.

 

Watch the Entire Series

 

To celebrate Spectronaut, our software for DIA analysis, turning 10 we created a video series featuring some of the amazing team that made Spectronaut what it is today.

 

Curious to learn more about what Spectronaut 16 will bring? Find out what the team behind Spectronaut has to say about the upcoming version.

 

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Post-translational modifications (PTMs) can dramatically alter the function of proteins and have important roles in disease and health as biomarkers or pharmaceutical targets. An important aspect of PTMs is the relative rate of site occupancy or PTM stoichiometry, which can provide biological information irrespective of the underlying protein abundance.

In proteomics experiments where modifications are studied using an enrichment step, it is important to decouple the changes in PTM site quantity from the changes in protein abundance in the original sample. This is achieved by performing input (pre-enrichment samples) normalization of the PTM site quantity.

In this poster, we present the support for input normalization as well as improved PTM stoichiometry calculations in Spectronaut 20 for label-free DIA data. By using existing publicly available ground truth data, we validate the implementation of input normalization, as well as stoichiometry calculations based on an improved 2-points flyability ratio algorithm.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide. Although immunotherapy has improved the treatment of NSCLC, a significant number of patients still fail to respond or develop resistance.

Deep learning has become essential for modern mass spectrometry (MS) data analysis, but the development cycle is time-consuming and iterative. In many cases, this cycle fails, becoming tedious, convoluted, and
prone to errors due to the lack of specialized tools that optimally combine expert human control with process automation.
At Biognosys, we have developed a MLOps platform that meets these requirements: it minimizes human intervention, reserving it only for expert decision-making tasks and selecting the best models based on the application.

In January, we announced our new strategic partnership with Bruker to broaden access to our leading proteomics services & tools for biopharma and #biomarker customers.

In this exciting SpotLight episode, Biognosys’ CEO Oliver Rinner and Rohan Thakur, President of the Bruker Life-Science Mass Spectrometry division, discuss the many synergies of the new alliance and give an outlook of what the future might hold.

 

Recently, the library-free approach has become one of the methods of choice for the proteomic analysis of DIA data. It simplifies the workflow allowing users to skip a DDA acquisition for the generation of a library. However, the various library-free workflows available in the field are significantly more computationally expensive.

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