Philip LoCascio | Search PubMed

PubMed2003

A computational pipeline for protein structure prediction and analysis at genome scale.

MOTIVATION: Experimental techniques alone cannot keep up with the production rate of protein sequences, while computational techniques for protein structure predictions have matured to such a level to provide reliable structural characterization of proteins at large scale. Integration of multiple computational tools for protein structure prediction can complement experimental techniques. RESULTS: We present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is our threading-based protein structure prediction system PROSPECT. The pipeline consists of a dozen tools for identification of protein domains and signal peptide, protein triage to determine the protein type (membrane or globular), protein fold recognition, generation of atomic structural models, prediction result validation, etc. Different processing and prediction branches are determined automatically by a prediction pipeline manager based on identified characteristics of the protein. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. Genome-scale applications on Caenorhabditis elegans, Pyrococcus furiosus and three cyanobacterial genomes are presented. AVAILABILITY: The pipeline is available at http://compbio.ornl.gov/proteinpipeline/

Algorithms↗

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↗