A statistical model providing comprehensive predictions for the mRNA differential display

Gene expression associated with the onset of hearing detected by differential display in rat organ of Corti

The exquisite performance of the mammalian hearing organ results from a finely orchestrated array of cell types, and their highly specialized functions are determined by their gene expression profile. In rodents, this profile is established mainly during the first 2 weeks of postnatal maturation. In this paper, we used the differential display technique on the rat organ of Corti to uncover transcripts upregulated in expression between postnatal stages P0 and P14. A total of 176 different genes were identified, the mRNA amount of which increased during early postnatal development. The transcripts code for proteins serving a broad spectrum of cellular functions including intracellular signaling, control of growth/differentiation, regulation of protein synthesis/degradation/modification, metabolism and synaptic function. In addition, the set of upregulated transcripts contained several proteins of yet unknown function, as well as hypothetical proteins and so far unknown mRNA sequences. Thus, this study unravels the broad and specific transcription program that operates the maturation of the mammalian hearing organ. Further, as 49 of the genes found here map to at least one unspecified deafness locus, our study provides candidate genes for these and novel deafness loci.

Confidence assessment for protein identification by using peptide-mass fingerprinting data

Protein identification using Peptide Mass Fingerprinting (PMF) data remains an important yet only partially solved problem. Current computational methods may lead to false positive identification since the top hit from a database search may not be the target protein. In addition, the identification scores assigned singly by a scoring function (raw scores) are not normalized. Therefore, the ranking based on raw scores may be biased. To address the above issue, we have developed a statistical model to evaluate the confidence of the raw score and to improve the ranking of proteins for identification. The results show that the statistical model better ranks the correct protein than the raw scores. Our study provides a new method to enhance the accuracy of protein identification by using PMF data. We incorporated the method into our software package "Protein-Decision" together with a user-friendly graphical interface. A standalone version of Protein-Decision is freely available at http://digbio.missouri.edu/ProteinDecision/.

Computer-assisted identification of multitrace electrophoretic patterns in differential display experiments

Modern multicapillary devices allow researchers to address increasingly complex biological questions involving comparisons of gene expression patterns across electrophoretic samples under various experimental conditions. As labor-intensive visual evaluation of the electrophoretic results is often the bottleneck of large-scale differential display (DD) studies, one way to further streamline this process is to focus only on a highly compressed list of the most potential patterns that are likely to provide reliable findings. To enable the identification of such candidate patterns, we present a computer-assisted method for objective ranking of multitrace peak patterns in DD experiments. The fundamental component of the multitrace pattern ranking method (MRANK) is the multiple alignment algorithm that allows for discovery of patterns involving sets of peak complexes from various electrophoretic samples. A score value is attached to each detected pattern which characterizes how accurately the pattern resembles the desired pattern query, freely defined by the researcher. The ranked pattern list produced by MRANK is validated against visual evaluation in terms of detecting and ranking a group of relevant patterns in a DD analysis of T-helper cell differentiation. We demonstrate high enrichment of the desired patterns on top of the score-ranked list (e.g., 90% of the visually selected patterns are discovered by looking through the first 3% of patterns in the ranked list of all patterns). The results suggest that a substantial amount of manual labor can be saved without compromising the accuracy of the findings by prioritizing the patterns according to MRANK output in the visual confirmation phase.