Large shifts in abundance of proteins under different testing conditions can be easily detected by most methods. This is especially true for highly abundant proteins, or proteins that have a large number of identifications uniquely associated with them. The problem becomes more prominent in the hard cases where the detectable shift in abundance is both small and arises from very few measurements. It is in this context we introduce our tool called Maximal Aggregation of Good protein signal from Mass spectrometric data (MAGMa) that we show performs better across many metrics on various search tools in comparison to popular tools in detecting true signal without sacrificing its ability to distinguish and identify noise in parallel. Referred to as a tradeoff between sensitivity and specificity we show that MAGMa strikes a balance between both, is robust and applicable across various applications. Coupled with a user-friendly web server that allows the user to choose across various statistical assumptions that would be appropriate for their specific analysis, MAGMa also comes with the functionality of downstream visual assessment of the results in the form of volcano and network plots.

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