Computational diagnostics based on proteomic data- review on approaches and algorithms

Protein identification using mass spectrometry is an indispensable tool for proteomics which inrecent days has evolved to give better understanding of the biology of cell and its functioning. Proteomicshas wide application in diagnosing diseases such as cancer, Alzheimer’s disease etc. The data obtainedfrom the diagnostic tools like LC-MS is to be interpreted accurately so as to obtain the correct qualitative andquantitative information about the peptides present in the biological sample. Such interpretation requires andexhaustive knowledge and review about different tools that can be employed and their comparison. Thisarticle focuses on comparison of different proteomic tools available for the MS data processing andinterpretation. The accuracy demanded during protein identification can be fulfilled by tag basedapproaches, than PMF or PFF systems. Although, there is a need of standardized matrices for thecomparison of the protein identification tools, identifying the single best package for each application fromthe available literature is at present extremely difficult as each package has its own advantage over other.The datasets and thresholds used in these kinds of comparisons have a critical importance on the outcomeof such experiments, and that the high variability in machine and experimental setups complicates analysis.The state of data standards and lack of benchmarks therefore makes it difficult to make an effectivecomparison. While the increasing availability of data in public repositories and tightening standards will nodoubt ameliorate the problem, until this basic benchmarking problem is overcome, no single package orapproach can conclusively be declared to outperform all others, expect, perhaps, in the specificcircumstances used in particular studies.