BACKGROUND: Bisulphite sequencing enables the detection of cytosine methylation. The sequence of the methylation states of cytosines on any given read forms a methylation pattern that carries substantially more information than merely studying the average methylation level at individual positions. In order to understand better the complexity of DNA methylation landscapes in biological samples, it is important to study the diversity of these methylation patterns. However, the accurate quantification of methylation patterns is subject to sequencing errors and spurious signals due to incomplete bisulphite conversion of cytosines. RESULTS: A statistical model is developed which accounts for the distribution of DNA methylation patterns at any given locus. The model incorporates the effects of sequencing errors and spurious reads, and enables estimation of the true underlying distribution of methylation patterns. CONCLUSIONS: Calculation of the estimated distribution over methylation patterns is implemented in the R Bioconductor package MPFE. Source code and documentation of the package are also available for download at http://bioconductor.org/packages/3.0/bioc/html/MPFE.html .

Tandem mass spectrometry-based iTRAQ protein quantification provides a powerful means for identifying disease biomarkers and plays an important role in developing new diagnosis and prognosis, new treatment, and personalised medicine. However, analyses of iTRAQ data encounter a number of statistical and computational challenges such as accurate protein identification, imputation of missing values, appropriate summarisation of protein quantification, among others. Therefore, a good evaluation dataset where raw spectra are provided, and actual composition and concentrations of the protein mixture are known, will enable better methodological development in this field. Unfortunately, there are limited evaluation datasets and existing ones are not sufficient for systematic evaluation of the existing analysis methods. To this end, we designed and performed a new Latin Square experiment that can be used for validating the accuracy of both protein identification and protein quantification.

Tandem mass spectrometry is a powerful tool for studying proteins and protein post-translational modifications. However, typically less than half of the proteins in a complex sample can be successfully identified. The low identification coverage is largely due to the presence of various protein modifications, which usually lead to incorrect protein identifications by existing methods. Therefore, how to effectively detect protein modifications simultaneously with protein identification is crucial for improving the identification coverage and accuracy. We have developed a new hybrid probability-based protein identification method to address this issue. Our method applies a new two-stage algorithmic framework that incorporates (i) spectra library searching and (ii) a more sophisticated scoring model. In the first stage, fast spectra library searching and simplified database searching are utilised to determine a reduced search space, which in the second stage is comprehensively explored to find the most likely protein and its modifications. Evaluated on large public datasets, our method is shown to identify more proteins and protein modifications than other popular protein identification engines.

BACKGROUND: Mass spectrometry-based protein identification is a very challenging task. The main identification approaches include de novo sequencing and database searching. Both approaches have shortcomings, so an integrative approach has been developed. The integrative approach firstly infers partial peptide sequences, known as tags, directly from tandem spectra through de novo sequencing, and then puts these sequences into a database search to see if a close peptide match can be found. However the current implementation of this integrative approach has several limitations. Firstly, simplistic de novo sequencing is applied and only very short sequence tags are used. Secondly, most integrative methods apply an algorithm similar to BLAST to search for exact sequence matches and do not accommodate sequence errors well. Thirdly, by applying these methods the integrated de novo sequencing makes a limited contribution to the scoring model which is still largely based on database searching. RESULTS: We have developed a new integrative protein identification method which can integrate de novo sequencing more efficiently into database searching. Evaluated on large real datasets, our method outperforms popular identification methods.

BACKGROUND: An ever increasing number of techniques are being used to find genes with similar profiles from microarray studies. Visualization of gene expression profiles can aid this process, potentially contributing to the identification of co-regulated genes and gene function as well as network development. RESULTS: We introduce the h-Profile plot to display gene expression profiles. Thumbnail versions of plots of gene expression profiles are plotted at coordinates such that profiles of similar shape are located in the same sector, with decreasing variance towards the origin. Negatively correlated profiles can easily be identified. A new method for selecting genes with fixed periodicity, but different phase and amplitude is described and used to demonstrate the use of the plots on cell cycle data. CONCLUSION: Visualization tools for gene expression data are important and h-profile plots provide a timely contribution to the field. They allow the simultaneous visualization of many gene expression profiles and can be used for the identification of genes with similar or reversed profiles, the foundation step in many analyses.

UNLABELLED: Most phylogenetic methods assume that the sequences evolved under homogeneous, stationary and reversible conditions. Compositional heterogeneity in data intended for studies of phylogeny suggests that the data did not evolve under these conditions. SeqVis, a Java application for analysis of nucleotide content, reads sequence alignments in several formats and plots the nucleotide content in a tetrahedron. Once plotted, outliers can be identified, thus allowing for decisions on the applicability of the data for phylogenetic analysis. AVAILABILITY: http://www.bio.usyd.edu.au/jermiin/programs.htm.