Biomedical subjects

Edward Uberbacher

Publications and source records attributed to Edward Uberbacher.

3 recordsLinked to original sources

PubMed2006

Detecting differential and correlated protein expression in label-free shotgun proteomics.

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.

Bacterial Proteins↗

PubMed2003

Background rareness-based iterative multiple sequence alignment algorithm for regulatory element detection.

MOTIVATION: Experimental methods capable of generating sets of co-regulated genes have become commonplace, however, recognizing the regulatory motifs responsible for this regulation remains difficult. As a result, computational detection of transcription factor binding sites in such data sets has been an active area of research. Most approaches have utilized either Gibbs sampling or greedy strategies to identify such elements in sets of sequences. These existing methods have varying degrees of success depending on the strength and length of the signals and the number of available sequences. We present a new deterministic iterative algorithm for regulatory element detection based on a Markov chain background. As in other methods, sequences in the entire genome and the training set are taken into account in order to discriminate against commonly occurring signals and produce patterns, which are significant in the training set. RESULTS: The results of the algorithm compare favorably with existing tools on previously known and newly compiled data sets. The iteration based search appears rather rigorous, not only finding the binding sites, but also showing how the binding site stands out from genomic background. The approach used to score the results is critical and a discussion of various scoring schemes and options is also presented. Benchmarking of several methods shows that while most tools are good at detecting strong signals, Gibbs sampling algorithms give inconsistent results when the regulatory element signal becomes weak. A Markov chain based background model alleviates the drawbacks of MAP (maximum a posteriori log likelihood) scores. AVAILABILITY: Available on request from the authors. SUPPLEMENTARY INFORMATION: Data and the results presented in this paper are available on the web at http://compbio.ornl.gov/mira/index.html

Algorithms↗

PubMed2003

Stepping up the pace of discovery: the genomes to life program.

Genome to life (GTL), the U.S Department of Energy Office of Science's systems biology program, focuses on environmental microbiology. Over the next 10 to 20 years, GTL's key goal is to understand the life processes of thousands of microbes and microbial systems in their native environments. This focus demands that we address huge gaps in knowledge, technology, computing, data capture and analysis, and systems-level integration. Distinguishing features include (1) strategies for unprecedented, comprehensive, and high-throughput data collection; (2) advanced computing, mathematics, algorithms, and data-management technologies; (3) a focus on potential microbial capabilities to help solve energy and environmental challenges; and (4) new research and management models that link production-scale systems biology facilities in an accessible environment. This unprecedented opportunity to provide the scientific foundation for solving urgent problems in energy, global climate change, and environmental cleanup demands that we take bold steps to achieve a much faster, more efficient pace of biological discovery.

Biological Science Disciplines↗