Biology Reference
In-Depth Information
Fig. 1.
This proteomics workflow highlights the chronological steps alternating
wet-lab and dry-lab (bioinformatics) operations. Each bioinformatics step matches
one of the three domains covered by the PIG. Their corresponding colors are used
in Fig. 3 to emphasize the bioinformatics developments specific to each domain.
Proteome informatics currently faces at least three important chal-
lenges. Firstly, a majority of mass spectra collected in high-throughput
experiments still cannot find confident molecular identification.
Explanations for this are twofold: they involve, on the one hand, the
quality of data and, on the other hand, the efficiency of mass spectra
data-matching procedures. The quality of the separation, the presence of
artifacts/contaminants/posttranslational modifications (PTMs), and
mass calibration status need assessing to ensure fruitful interpretation of
the data. This issue can be tackled by visually outputting two-dimensional
(2-D) LC-MS data and exploiting image intensities and contrasts for
monitoring data. More information from tandem mass spectrometry
(MS/MS) can be retrieved using workflows that combine several com-
plementary software tools. A platform supporting such workflows can
address the question of efficient MS/MS data-matching procedures.
In this case, efficiency is envisaged as increasing both the precision and
the speed of data processing. Sections 2 and 3 of this chapter show how
we implement these strategies.
Secondly, the large volume of data from proteomic experiments
needs to be integrated and exploited. As a first step, these datasets require
a standardized description and format. Data standardization efforts
undertaken up to now are expected to increasingly bear on future
