Computational methods for protein identification from mass spectrometry data.

@article{citeulike:2787495, abstract = {Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology.}, address = {Discipline of Medicine and Sydney Bioinformatics, University of Sydney, Sydney, New South Wales, Australia.}, author = {McHugh, L. and Arthur, J. W. }, citeulike-article-id = {2787495}, doi = {10.1371/journal.pcbi.0040012}, issn = {1553-7358}, journal = {PLoS computational biology}, month = {February}, number = {2}, posted-at = {2008-05-15 10:27:15}, priority = {2}, title = {Computational methods for protein identification from mass spectrometry data.}, url = {http://dx.doi.org/10.1371/journal.pcbi.0040012}, volume = {4}, year = {2008} }

TY - JOUR ID - citeulike:2787495 L3 - citeulike-article-id:2787495 TI - Computational methods for protein identification from mass spectrometry data. JF - PLoS computational biology VL - 4 IS - 2 AD - Discipline of Medicine and Sydney Bioinformatics, University of Sydney, Sydney, New South Wales, Australia. SN - 1553-7358 N2 - Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology. AU - McHugh, L AU - Arthur, JW PY - 2008/02// UR - http://dx.doi.org/10.1371/journal.pcbi.0040012 ER -