Organisms. The application of proteomics and related technologies for the analysis of proteome is severely hampered by the lack of publicly available sequence information for most of the unsequenced organisms [215] . Despite the precision of the mass information yielded by the SELDI technique, a significant number of proteins were found to have no similarity to known peptides, an aforementioned weakness of proteomics studies in nonmodel organisms [216] . In order to circumvent this limitation, different strategies and tools were developed to make unsequenced organisms amenable to high-throughput proteomics [217] (Figure 6 ). However, an evaluation of their performance in an integrated proteomics strategy using high-throughput shotgun MS data is currently missing. In principle, two different approaches can lead to an increase in protein identifications from unsequenced organisms. In the first approach, MS/MS data are searched against a protein database of an evolutionarily closely related organism. However, as a matter of principle of database-dependent searches, only proteins can be identified that contain at least one peptide with exactly the same sequence as the peptide from a protein in the database. With increasing evolutionary distance this will be an increasingly severe restriction [218] . In the second approach, the amino acid sequence of a peptide is extracted from the MS/MS spectrum for de novo sequencing, that is, in a fully databaseindependent manner using exclusively the information contained in the MS/MS spectrum. Several software tools for peptide de novo sequencing are now available and some of them provide sufficiently good results when applied to high-quality spectra [219] . A basic limitation of MS de novo sequencing methods is the necessity for backbone cleavage between each pair of adjacent amino acids; a mass value representing a terminal fragment containing only one of the two residues is a first requirement for ordering of a specific pair [220, 221] and this limitation urged the need for bioinformatics approaches that can help interpret the proteomics data [219] .