How do you make sense of huge amounts of proteomics data?
- Seamlessly structure and integrate all of proteomics data
- Derive maximum value from in-house and external projects and resources.
- Enhance scientific knowledge and insight with proteomics data linked to entities – genes, proteins, cell lines, samples – which are then available across the Platform.
Can you simply and seamlessly integrate Mass Spectrometry data formats?
- Parse, integrate and standardise all popular mass spectrometry data formats into the system using one seamless process.
- Import data using an easy-to-use web interface, or via command line or API.
- Quality assurance reporting is instantly generated during integration, providing full visibility on data quality
- Fully federated and audit logging for processing and integration parameters, enabling re-use and enhancing efficiency.
Contact a Scientist
Discuss your proteomics data challenges with one of our scientists
Can you interrogate proteomics data at all levels?
- Dive more deeply into your proteomics data to gain maximum insight from key experiments and imported resources.
- Identify peptides of interest with just a few clicks – Combine filters or sorting according to user-defined parameters such as peptide length, PSM counts or modifications.
- Simplify the demanding task of complex pattern matching on peptide sequences using the platform’s regular expression generator.
- Drill down into data even further, e.g.list all the occurrences of identified peptides in detailed views, in the context of related experimental data, such as R/T, m/z, peak area and source files.
- Visualise more gene-centric analyses by setting up a list of all peptides alongside their linked genes.
Focus on the Data that Matters
Keep track of your highest priority peptides, whether for use as biomarkers, therapeutic targeting or just for further investigation. As new datasets that reference that peptide in any way are added, they are all collated into one overview which is dynamically updated upon data integration. This allows directed analyses to be performed, such as with the Analytics Framework, where any subset of peptides can be selected and analysed using clustering algorithms, logo generation and many other tools.
✕ Single, limited use of data
✕ Bottleneck for downstream research
✓ Ability to use and re-use data across organisation
✓ Automatic integration of data in downstream research pipeline
✕ Inability to analyse large data assets
✕ Inadvertent repetition of experiments
✓ Able to process and analyse large data in real-time
✓ Repetition of experiments to zero
|Quality||✕ Low visibility of data quality||✓ Automatic QA/QC run on all datasets providing clarity on data quality|