COMPUTATIONAL DIAGNOSTICS BASED ON PROTEOMIC DATA- REVIEW ON APPROACHES AND ALGORITHMS

Sreevatsa A.N.¹*, Badrunnisa S.²*, Shaukath Ali M.³*, Vinitha R Pai⁴*
¹BITM, Bellary, Karnataka, India
²Department of Biotechnology, BITM, Bellary, Karnataka, India
³College of applied medical science, Jazan University, Jizan
⁴Department of Biochemistry, Yenopoya Medical College Darelakatte Mangalore, Karnataka, India
* Corresponding Author : vinitharpai@gmail.com

Received : -     Accepted : -     Published : 15-06-2010
Volume : 2     Issue : 1       Pages : 56 - 66
Int J Bioinformatics Res 2.1 (2010):56-66
DOI : http://dx.doi.org/10.9735/0975-3087.2.1.56-66

Keywords : Aeropyrum pernix, Extremophiles, non-coding regions
Conflict of Interest : None declared

Protein identification using mass spectrometry is an indispensable tool for proteomics which in recent days has evolved to give better understanding of the biology of cell and its functioning. Proteomics has wide application in diagnosing diseases such as cancer, Alzheimer’s disease etc. The data obtained from the diagnostic tools like LC-MS is to be interpreted accurately so as to obtain the correct qualitative and quantitative information about the peptides present in the biological sample. Such interpretation requires and exhaustive knowledge and review about different tools that can be employed and their comparison. This article focuses on comparison of different proteomic tools available for the MS data processing and interpretation. The accuracy demanded during protein identification can be fulfilled by tag based approaches, than PMF or PFF systems. Although, there is a need of standardized matrices for the comparison of the protein identification tools, identifying the single best package for each application from the 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 outcome of 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 effective comparison. While the increasing availability of data in public repositories and tightening standards will no doubt ameliorate the problem, until this basic benchmarking problem is overcome, no single package or approach can conclusively be declared to outperform all others, expect, perhaps, in the specific circumstances used in particular studies.

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