Statistical bioinformatics

This branch of bioinformatics is concerned with computational approaches to predict and analyse the spatial structure of proteins and nucleic acids. Whereas in many cases the primary sequence uniquely specifies the 3D structure, the specific rules are not well understood, and the protein folding problem remains largely unsolved. Some aspects of protein structure can already be predicted from amino acid content. Secondary structure can be deduced from the primary sequence with statistics or neural networks. When using a multiple sequence alignment, secondary structure can be predicted with an accuracy above 70%. 

However, there are a number of issues here. In the first place, stress itself is a somewhat nebulous concept, and there is continuing debate about how it should be defined. Second, even with the benefit of multivariate statistics and the techniques of bioinformatics, measuring stress from all sources in a meaningful way is dauntingly complex and may not be realizable in practice. 

Biochips produce huge data sets. Data collected from microarray experiments are random snapshots with errors, inherently noisy and incomplete. Extracting meaningful information from thousands of data points by means of bioinformatics and statistical analysis is sophisticated and calls for collaboration among researchers from different disciplines. An increasing number of image and data analysis tools, in part freely accessible ( ) to academic researchers and non-profit institutions, is available in the web. Some examples are found in Tables 3 and 4. 

For reproducible expression analysis and protein quantification MS methods based on isotopic labeling are available. They were designed in conjunction with two or more dimensional chromatographic peptide separation coupled online to MS and require advanced bioinformatics input to analyze the complex data sets in a reasonable time frame. This is also true for the alternative fluorescence-based technology of differential gel electrophoresis (DIGE Fig. 10.6) with tailor-made software which allows statistical validation of multiple data sets. 

Baldi P, Long AD. 2001. A Bayesian framework for the analysis of microarray expression data regularized t-test and statistical inference of gene changes. Bioinformatics 17 509. 

Pan W. 2002. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18 546. 

Tanay, A., Sharan, R, and Shamir, R (2002) Discovering statistically significant biclusters in gene expression data. Bioinformatics 18, S136-S144. 

Bioinformatics tools involving computer-based statistical analyses are essential for data management and analysis. When a complex biological sample containing thousands of different proteins is analyzed by multifaceted approaches, such as multidimensional protein identification technology, the identification of the proteins in the mixture is extremely complicated. Even multiple peptide identification methods, such as using both MS and... 

FIGURE 1 Example of a gel-free-oriented proteomics nano-LC/MS-MS workflow in which bacterial culture proteins digested to tryptic peptides are separated via LC and peptides subsequently analyzed by mass spectrometry. In the process, the spectrometer rapidly cycles every few seconds and examines a size window in which peptide-derived MSI ions are analyzed to define MS/MS (MS2) spectra. The MS/MS (MS2) spectrum generated for each peptide then enters a bioinformatic pipeline for sequence identification, statistical validation, and quantification. 

At present, there are advanced difference gel electrophoresis (DOGE) Systems and 2-D fluorescence difference gel electrophoresis (2-D DIGE) which enable the analyst to use simultaneously modern (more precise) methods of fluorescent analysis with 2-D electrophoresis (using internal patterns), aided by a fully integrated bioinformatics system. Such systems allow more complete differential protein analysis, while the application of internal standards eliminates differentiation between the intervals, thus ensuring that even the smallest differences will be detected irrespective of the multitude of components. This guarantees reproducibility of results and their statistical reliability. Such assays are one of the platforms employed in the research based on the proteomics method. 

The terms bioinformatics and cheminformatics refer to the use of computational methods in the study of biology and chemistry. Information from DNA or protein sequences, protein structure, and chemical structure is used to build models of biochemical systems or models of the interaction of a biochemical system with a small molecule (e.g., a drug). There are mathematical and statistical methods for analysis, public databases, and literature associated with each of these disciplines. However, there is substantial value in considering the interaction between these areas and in building computational models that integrate data from both sources. In the most... 

Previous discussions of multiplicity adjusted testing of gene expressions, by Dudoit et al. (2002) and (2003), for example, generally took a nonmodeling approach. Because the joint distribution of the test statistics is generally not available with this approach, multiplicity adjustments in these papers tend to be calculated based on conservative inequalities (for example, the Bonferroni inequality or Sidak s inequality) or on a joint distribution of independent test statistics. In contrast, here, we describe multiplicity adjustment based on the actual joint distribution of the test statistics. However, before describing such adjustments, we first address the construction principles to which all multiple tests should adhere, regardless of the approach taken. These principles do not appear to be as well known in the field of bioinformatics as they are in clinical trials. 

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