A Structured Hardware Software Architecture for Peptide Based Diagnosis of Baylisascaris Procyonis Infection (ICIAfS14)

TL;DR

This paper presents a hardware-accelerated model-based approach for protein inference from peptide data, significantly improving processing speed in proteomics workflows.

Contribution

It introduces a structured hardware-software architecture for peptide-based diagnosis, enabling rapid and reliable protein inference in complex biological samples.

Findings

Achieved 10x speed-up over software-only workflows

Validated on realistic mass spectrometry data

Demonstrated practical applicability in proteomics

Abstract

The problem of inferring proteins from complex peptide cocktails (digestion products of biological samples) in shotgun proteomic workflow sets extreme demands on computational resources in respect of the required very high processing throughputs, rapid processing rates and reliability of results. This is exacerbated by the fact that, in general, a given protein cannot be defined by a fixed sequence of amino acids due to the existence of splice variants and isoforms of that protein. Therefore, the problem of protein inference could be considered as one of identifying sequences of amino acids with some limited tolerance. In the current paper a model-based hardware acceleration of a structured and practical inference approach is developed and validated on a mass spectrometry experiment of realistic size. We have achieved 10 times maximum speed-up in the co-designed workflow compared to a similar software-only workflow run on the processor used for co-design.