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Table 3 Advantages and disadvantages of computational and subproteomic approaches to localization analysis.

From: Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria

Computational methods Proteomics analysis
Rapid predictions for all proteins deduced to be encoded in a given sequence Can be performed under different conditions and provide condition-specific information
Detailed information about specific features of proteins, e.g. signal peptides, TMHs Confirms expression of hypothetical proteins
Identification of potential contaminants in subproteome analyses Large-scale source of data on SCL for hypothetical proteins that cannot be easily predicted computationally
Identification of hydrophobic integral membrane proteins  
Does not perform as well (less predictions) when analyzing an organism that is not similar to well studied/model organisms. Time-consuming
May miss flagging some multiply-localized proteins Low abundance and hydrophobic proteins not readily detected
Poorly predicts particular localizations for which there is little training data, or the proteins are computationally difficult to differentiate between localizations. Difficult to accurately identify all proteins found on the gel
Cannot identify condition-specific data on SCL, particularly proteins that change SCL depending on the condition. One subcellular fraction at once analyzed
  Subfractionation often results in contamination
  Cannot identify multiply localized proteins