  Protein inference plays a vital role in the proteomics study. Two major
approaches could be used to handle the problem of protein inference; top-down
and bottom-up. This paper presents a framework for protein inference, which
uses hardware accelerated protein inference framework for handling the most
important step in a bottom-up approach, viz. peptide identification during the
assembling process. In our framework, identified peptides and their
probabilities are used to predict the most suitable reference protein cluster
for a given input amino acid sequence with the probability of identified
peptides. The framework is developed on an FPGA where hardware software
co-design techniques are used to accelerate the computationally intensive parts
of the protein inference process. In the paper we have measured, compared and
reported the time taken for the protein inference process in our framework
against a pure software implementation.
