Effective peak alignment for mass spectrometry data analysis using two-phase clustering approach.

In recent years, mass spectrometry data analysis has become an important protein identification technique. The mass spectrometry technologies emerge as useful tools for biomarker discovery through studying protein profiles in various biological specimens. In mining mass spectrometry datasets, peak alignment is a critical issue among the preprocessing steps that affect the quality of analysis results. However, the existing peak alignment methods are sensitive to noise peaks across various mass spectrometry samples. In this paper, we proposed a novel algorithm named Two-Phase Clustering for peak Alignment (TPC-Align) to align mass spectrometry peaks across samples in the pre-processing phase. The TPC-Align algorithm sequentially considers the distribution of intensity values and the locations of mass-to-charge ratio values of peaks between samples. Moreover, TPC-Align algorithm can also report a list of significantly differential peaks between samples, which serve as the candidate biomarkers for further biological study. The proposed peak alignment method was compared to the current peak alignment approach based on one-dimension hierarchical clustering through experimental evaluations and the results show that TPC-Align outperforms the traditional method on the real dataset.

Decoding the disease-associated proteins encoded in the human chromosome 4.

Chromosome 4 is the fourth largest chromosome, containing approximately 191 megabases (~6.4% of the human genome) with 757 protein-coding genes. A number of marker genes for many diseases have been found in this chromosome, including genetic diseases (e.g., hepatocellular carcinoma) and biomedical research (cardiac system, aging, metabolic disorders, immune system, cancer and stem cell) related genes (e.g., oncogenes, growth factors). As a pilot study for the chromosome 4-centric human proteome project (Chr 4-HPP), we present here a systematic analysis of the disease association, protein isoforms, coding single nucleotide polymorphisms of these 757 protein-coding genes and their experimental evidence at the protein level. We also describe how the findings from the chromosome 4 project might be used to drive the biomarker discovery and validation study in disease-oriented projects, using the examples of secretomic and membrane proteomic approaches in cancer research. By integrating with cancer cell secretomes and several other existing databases in the public domain, we identified 141 chromosome 4-encoded proteins as cancer cell-secretable/shedable proteins. Additionally, we also identified 54 chromosome 4-encoded proteins that have been classified as cancer-associated proteins with successful selected or multiple reaction monitoring (SRM/MRM) assays developed. From literature annotation and topology analysis, 271 proteins were recognized as membrane proteins while 27.9% of the 757 proteins do not have any experimental evidence at the protein-level. In summary, the analysis revealed that the chromosome 4 is a rich resource for cancer-associated proteins for biomarker verification projects and for drug target discovery projects.

WF-MSB: a weighted fuzzy-based biclustering method for gene expression data.

Biclustering is an important analysis method on gene expression data for finding a subset of genes sharing compatible expression patterns. Although some biclustering algorithms have been proposed, few provided a query-driven approach for biologists to search the biclusters, which contain a certain gene of interest. In this paper, we proposed a generalised fuzzy-based approach, namely Weighted Fuzzy-based Maximum Similarity Biclustering (WF-MSB), for extracting a query-driven bicluster based on the user-defined reference gene. A fuzzy-based similarity measurement and condition weighting approach are used to extract significant biclusters in expression levels. Both of the most similar bicluster and the most dissimilar bicluster to the reference gene are discovered by WF-MSB. The proposed WF-MSB method was evaluated in comparison with MSBE on a real yeast microarray data and synthetic data sets. The experimental results show that WF-MSB can effectively find the biclusters with significant GO-based functional meanings.

Identification of tyrosine-phosphorylated proteins associated with lung cancer metastasis using label-free quantitative analyses.

Lung cancer is a lethal disease, and early metastasis is the major cause of treatment failure and cancer-related death. Tyrosine phosphorylated (P-Tyr) proteins are involved in the invasive and metastatic behavior of lung cancer; however, only a limited number of targets were identified. We attempt to characterize P-Tyr proteins and events involved in the metastatic process. In a previous work, we have developed a strategy for identification of protein phosphorylation. Here, this strategy was used to characterize the tyrosine phosphoproteome of lung cancer cells that have different invasive abilities (CL1-0 vs. CL1-5). Using our analytical strategy, we report the identification of 335 P-Tyr sites from 276 phosphoproteins. Label-free quantitative analysis revealed that 36 P-Tyr peptides showed altered levels between CL1-0 and CL1-5 cells. From this list of sites, we extracted two novel consensus sequences and four known motifs for specific kinases and phosphatases including EGFR, Src, JAK2, and TC-PTP. Protein-protein interaction network analysis of the altered P-Tyr proteins illustrated that 11 proteins were linked to a network containing EGFR, c-Src, c-Myc, and STAT, which is known to be related to lung cancer metastasis. Among these 11 proteins, 7 P-Tyr proteins have not been previously reported to be associated with lung cancer metastasis and are of greatest interest for further study. The characterized tyrosine phosphoproteome and altered P-Tyr targets may provide a better comprehensive understanding of the mechanisms of lung cancer invasion/metastasis and discover potential therapies.

A statistical procedure to selectively detect metabolite signals in LC-MS data based on using variable isotope ratios.

The tracing of metabolite signals in LC-MS data using stable isotope-labeled compounds has been described in the literature. However, the filtering efficiency and confidence when mining metabolite signals in complex LC-MS datasets can be improved. Here, we propose an additional statistical procedure to increase the compound-derived signal mining efficiency. This method also provides a highly confident approach to screen out metabolite signals because the correlation of varying concentration ratios of native/stable isotope-labeled compounds and their instrumental response ratio is used. An in-house computational program [signal mining algorithm with isotope tracing (SMAIT)] was developed to perform the statistical procedure. To illustrate the SMAIT concept and its effectiveness for mining metabolite signals in LC-MS data, the plasticizer, di-(2-ethylhexyl) phthalate (DEHP), was used as an example. The statistical procedure effectively filtered 15 probable metabolite signals from 3617 peaks in the LC-MS data. These probable metabolite signals were considered structurally related to DEHP. Results obtained here suggest that the statistical procedure could be used to confidently facilitate the detection of probable metabolites from a compound-derived precursor presented in a complex LC-MS dataset.

Combining alkaline phosphatase treatment and hybrid linear ion trap/Orbitrap high mass accuracy liquid chromatography-mass spectrometry data for the efficient and confident identification of protein phosphorylation.

Protein phosphorylation is a vital post-translational modification that is involved in a variety of biological processes. Several mass spectrometry-based methods have been developed for phosphoprotein characterization. In our previous work, we demonstrated the capability of a computational algorithm in mining phosphopeptide signals in large LC-MS data sets by measuring the mass shifts due to phosphatase treatment (Wu, H. Y.; Tseng, V. S.; Liao, P. C. J. Proteome Res. 2007, 6, 1812-1821). Mass accuracy seems to play an important role in efficiently selecting out phosphopeptide signals. In recent years, the hybrid linear ion trap (LTQ)/Orbitrap mass spectrometer, which provides a high mass accuracy, has emerged as a powerful instrument in proteomic analysis. Here, we developed a process to incorporate LC-MS data that was generated from an LTQ/Orbitrap mass spectrometer into our strategy for taking advantage of the accurate mass measurement. LTQ/Orbitrap raw files were converted to the open file format mzXML via the ReAdW.exe program. To find peaks that were contained in each mzXML file, an open-source computer program, msInspect, was utilized to locate isotopes and assemble those isotopes into peptides. An in-house program, LcmsFormatConverter, was utilized for signal filtering and format transformation. A proposed in-house program, DeltaFinder, was modified and used for defining signals with an exact mass shift due to the dephosphorylation reaction, which generated a table that listed potential phosphopeptide signals. The retention times and m/z values of these selected LC-MS signals were used to program subsequent LC-MS/MS experiments to get high-confidence phosphorylation site determination. Compared to our previous work finished by using a quadrupole/time-of-flight mass spectrometer, a larger number of phosphopeptides in the casein mixture were identified by using LTQ/Orbitrap data, demonstrating the merit of high mass accuracy in our strategy. In addition, the characterization of the lung cancer cell tyrosine phosphoproteome revealed that the use of alkaline phosphatase treatment combined with accurate mass measurement in this strategy increased data repeatability and confidence.