Han-Chang Sun

Tmod: toolbox of motif discovery

SUMMARY Motif discovery is an important topic in computational transcriptional regulation studies. In the past decade, many researchers have contributed to the field and many de novo motif-finding tools have been developed, each may have a different strength. However, most of these tools do not have a user-friendly interface and their results are not easily comparable. We present a software called Toolbox of Motif Discovery (Tmod) for Windows operating systems. The current version of Tmod integrates 12 widely used motif discovery programs: MDscan, BioProspector, AlignACE, Gibbs Motif Sampler, MEME, CONSENSUS, MotifRegressor, GLAM, MotifSampler, SeSiMCMC, Weeder and YMF. Tmod provides a unified interface to ease the use of these programs and help users to understand the tuning parameters. It allows plug-in motif-finding programs to run either separately or in a batch mode with predetermined parameters, and provides a summary comprising of outputs from multiple programs. Tmod is developed in C++ with the support of Microsoft Foundation Classes and Cygwin. Tmod can also be easily expanded to include future algorithms. AVAILABILITY Tmod is available for download at http://www.fas.harvard.edu/~junliu/Tmod/.

Advance of Peptide Detectability Prediction on Mass Spectrometry Platform in Proteomics

Abstract Mass spectrometry (MS) is one of the core technologies for proteome researches, by which large-scale, high-throughput qualitative and quantitative protein analysis can be carried out. Because of the complexity of samples and experimental processes, the repeatability of MS experiments is still not satisfactory; the results of peptide identification and quantification show a high randomicity, and the probability of peptides being detected by MS in proteome researches, especially in quantitative proteomic studies, has received considerable attention. Therefore, many experimental researches have been carried out, and a number of computational prediction methods have been developed. In this article, the important factors influencing the peptide detectability are summarized, the existing prediction methods are studied, and their application in experimental studies is reviewed.

A new outlier removal approach for cDNA microarray normalization.

Normalization is a critical step in the analysis of microarray gene expression data. For dual-labeled array, traditional normalization methods assume that the majority of genes are non-differentially expressed and that the number of overexpressed genes approximately equals the number of under-expressed genes. However, these assumptions are inappropriate in some particular conditions. Differentially expressed genes have a negative impact on normalization and are regarded as outliers in statistics. We propose a new outlier removal-based normalization method. Simulated and real data sets were analyzed, and our results demonstrate that our approach can significantly improve the precision of normalization by eliminating the impact of outliers, and efficiently identify candidates for differential expression.

TVNovo: De novo peptide sequencing for high resolution LTQ-FT mass spectrometry using virtual database searching

De novo peptide sequencing is one of the most challenging topics in the field of computational proteomics. In this manuscript, a novel method based on virtual database searching is presented to improve the performance of de novo sequencing for the data from high resolution LTQ-FT mass spectrometry. Our method directly generates a virtual database from each spectrum and applies a search engine to match spectrum against the calculated virtual database. Two datasets from different sources are employed to compare our method to other existing de novo sequencing algorithms and the results show that our method outperforms other methods.

Predicting potential disease-related genes using the network topological features

To help the biomedical scientist pre-confirm the disease-related genes, we considered these gene as a whole research set and analyzed the topological features of their interaction network. Two strategies had been proposed to construct the disease-related gene network from the OMIM database. Using these two constructed sets, we trained two support vector machine prediction models, the accuracy of which are 75.09% and 83.63%. As a result, we gained 27 and 2873 potential disease-related genes respectively. The intersection of the two predicted sets contains 19 genes. In addition, gene locuses with high appearance frequency were listed for further research.