Stratalycs provides a computational framework and analytics for discovery-based LC-MS data analysis in terms of functionality, user interfaces, information input or output, and readily facilitates appropriate statistical data analysis.
LC-MS work is bottlenecked less by instruments than by the handling of what comes off them. We build the processing and analysis layer so more of the time goes into interpretation.
Our platform is complete and customisable, built for LC-MS-based proteomic workflows where off-the-shelf tools leave gaps. We also develop statistical algorithms, adapted from established microarray analysis methods, that suit LC-MS data.
What this includes
Data processing
Peak detection, alignment and normalisation across runs and batches.
Identification and quantification
Feature annotation against reference libraries, with confidence levels reported.
Statistical analysis
Univariate and multivariate approaches suited to discovery-scale datasets.
Interfaces and reporting
Tooling that lets analysts explore results without writing code for every question.
What you get
- Consistent processing across runs and batches
- Identifications reported with confidence levels attached
- Statistical analysis matched to the study design
- Less manual handling between instrument and interpretation