The need for national centers for proteomics

VDA, a method of choosing a better algorithm with fewer validations

The multitude of bioinformatics algorithms designed for performing a particular computational task presents end-users with the problem of selecting the most appropriate computational tool for analyzing their biological data. The choice of the best available method is often based on expensive experimental validation of the results. We propose an approach to design validation sets for method comparison and performance assessment that are effective in terms of cost and discrimination power.

Validation Discriminant Analysis (VDA) is a method for designing a minimal validation dataset to allow reliable comparisons between the performances of different algorithms. Implementation of our VDA approach achieves this reduction by selecting predictions that maximize the minimum Hamming distance between algorithmic predictions in the validation set. We show that VDA can be used to correctly rank algorithms according to their performances. These results are further supported by simulations and by realistic algorithmic comparisons in silico.

VDA is a novel, cost-efficient method for minimizing the number of validation experiments necessary for reliable performance estimation and fair comparison between algorithms.

Our VDA software is available at http://sourceforge.net/projects/klugerlab/files/VDA/

PROTEOMICS

The genome sequences of important model systems are available and the focus is now shifting to large-scale experiments enabled by this data. Following in the footsteps of genomics, we have functional genomics, proteomics, and even metabolomics, roughly paralleling the biological hierarchy of the transcription, translation, and production of small molecules. Proteomics is initially concerned with determining the structure, expression, localization, biochemical activity, interactions, and cellular roles of as many proteins as possible. There has been great progress owing to novel instrumentation, experimental strategies, and bioinformatics methods. The area of protein-protein interactions has been especially fruitful. First pass interaction maps of some model organisms exist, and the proteins in many important organelles are about to be determined. Researchers are also beginning to integrate large-scale data sets from various "omics" disciplines in targeted investigations of specific biomedical areas and in pursuit of a general framework for systems biology.

Bioinformatic analysis of the nucleolus

The nucleolus is a plurifunctional, nuclear organelle, which is responsible for ribosome biogenesis and many other functions in eukaryotes, including RNA processing, viral replication and tumour suppression. Our knowledge of the human nucleolar proteome has been expanded dramatically by the two recent MS studies on isolated nucleoli from HeLa cells [Andersen, Lyon, Fox, Leung, Lam, Steen, Mann and Lamond (2002) Curr. Biol. 12, 1-11; Scherl, Coute, Deon, Calle, Kindbeiter, Sanchez, Greco, Hochstrasser and Diaz (2002) Mol. Biol. Cell 13, 4100-4109]. Nearly 400 proteins were identified within the nucleolar proteome so far in humans. Approx. 12% of the identified proteins were previously shown to be nucleolar in human cells and, as expected, nearly all of the known housekeeping proteins required for ribosome biogenesis were identified in these analyses. Surprisingly, approx. 30% represented either novel or uncharacterized proteins. This review focuses on how to apply the derived knowledge of this newly recognized nucleolar proteome, such as their amino acid/peptide composition and their homologies across species, to explore the function and dynamics of the nucleolus, and suggests ways to identify, in silico, possible functions of the novel/uncharacterized proteins and potential interaction networks within the human nucleolus, or between the nucleolus and other nuclear organelles, by drawing resources from the public domain.

Noise filtering techniques for electrospray quadrupole time of flight mass spectra

The sensitivity of protein identification by peptide sequencing using a nanoelectrospray ion source is limited by our ability to identify peptide ions in the mass spectrum. Their intensity must be higher than the chemical noise level to allow a rapid localization in the spectrum. Multiply-charged peptide ions on or below this level can only be found because their isotopic pattern is denser than that of the mostly singly-charged chemical background ions. However, to find peptides by looking for multiply-charged isotope clusters can be very timeconsuming and may lead to misassignments of the first isotope. Here we present a software-based method to increase the signal to noise ratio of ion signals in an electrospray spectrum. The software has two elements, one to reduce the noise level and a second to increase the intensity of ion peaks. Both methods together generate a spectrum in which the signal to noise ratio of ion signals is considerably improved. Peptide ions previously hidden in the chemical background are dismantled and can now be localized and selected for fragmentation. The method has been used successfully to identify low level proteins separated by one dimensional gel electrophoresis.

Mass spectrometry-based proteomics

Recent successes illustrate the role of mass spectrometry-based proteomics as an indispensable tool for molecular and cellular biology and for the emerging field of systems biology. These include the study of protein-protein interactions via affinity-based isolations on a small and proteome-wide scale, the mapping of numerous organelles, the concurrent description of the malaria parasite genome and proteome, and the generation of quantitative protein profiles from diverse species. The ability of mass spectrometry to identify and, increasingly, to precisely quantify thousands of proteins from complex samples can be expected to impact broadly on biology and medicine.

From genomics to proteomics

Proteomics is the study of the function of all expressed proteins. Tremendous progress has been made in the past few years in generating large-scale data sets for protein-protein interactions, organelle composition, protein activity patterns and protein profiles in cancer patients. But further technological improvements, organization of international proteomics projects and open access to results are needed for proteomics to fulfil its potential.