GPMelt: Shedding light on the dark meltome
Welcome to GPMelt (Le Sueur, Rattray, and Savitski 2024), a versatile tool designed to analyse all kinds of time-series data, allowing us to compare dynamic behaviors across multiple conditions and replicates.
Unlike many tools, GPMelt is actionable and not just a ‘black box’!
You have full control, and this website is here to guide you through two key sections:
- The model explained simply – a friendly, video- and visualization-based overview without any complex maths.
- The tutorial – practical steps to help you use GPMelt effectively.
Whether you’re here for the big picture or the step-by-step guide, we’ve made sure everything is easy to follow — no advanced maths required.
Use the sidebar to navigate between the sections!
This image captures the transformative power of GPMelt to fully explore the complexity of melting curve behavior.
The mosaic of non-sigmoidal curves in the background symbolizes the diverse range of melting profiles that GPMelt can integrate. Unlike state-of-the-art methods, GPMelt is agnostic to curve shape, breaking free from the constraints of sigmoidal melting behavior. Every curve—no matter how irregular—finds a place in the analysis, allowing for a more inclusive understanding of the thermal proteome.
Illuminated by the X-ray viewer, two non-sigmoidal curves emerge from the shadows. No longer obscured or discarded, they become fully accessible and visible, ready for interpretation. GPMelt brings these previously overlooked curves into the spotlight, making them a valuable part of the TPP-TR dataset. This comprehensive vision transforms our capacity to analyze protein thermal stability, opening up new possibilities for discovery.
The stethoscope, lit by the X-ray viewer’s glow, serves as a metaphor for GPMelt’s diagnostic power. It symbolizes the newfound ability of users to uncover and diagnose these unconventional curves. GPMelt doesn’t just detect atypical melting behaviors; it empowers users to explore the underlying biology, leading them toward novel discoveries. By fitting all curve types, including the most complex, GPMelt opens the door to advanced analyses like clustering, which could inspire hypotheses about the origins of non-sigmoidality and reveal deeper biological insights.
In essence, GPMelt is not just a tool; it’s a breakthrough that exposes previously hidden part of the meltome, pushing the boundaries of thermal proteome profiling and giving access to biological phenomena that were previously out of reach.