Bioinformatics statistical tools

Some bioinformatics software tools for proteomics combine data analysis, statistics and artificial intelligence methods to manage MS data, to identify proteins and to update databases. In this section, specific tools used to identify proteins are reviewed. They use lists of peptide mass values from MS or MS/MS as input, and they may also combine this information with amino acid sequence tag information or amino acid composition to enhance the identification of proteins. Figure 6 shows a simplified flow chart of sample preparation and MS data collection. It also shows the techniques and tools for protein identification described in this section. 

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. 

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... 

After xenobiotic/toxin exposure, differentially expressed proteins are identified by the comparison of SELDI spectra from control and treated samples. By combining groupwise statistics with N-fold regulations, single biomarkers (m/z) can be selected. As to be expected from the complexity of the proteome, in many cases no single marker will be able to discriminate between the groups. Rather, a complex pattern of multiple markers will be acquired (Figure 8). Discovery of such markers/pattems can be successful by application of multivariate statistics methods on the data set. However, for the identification of specific protein expression patterns bioinformatics tools are... 

Artificial neural networks are versatile tools for a number of applications, including bioinformatics. However, they are not thinking machines nor are they black boxes to blindly feed data into with expectations of miraculous results. Neural networks are typically computer software implementations of algorithms, which fortunately may be represented by highly visual, often simple diagrams. Neural networks represent a powerful set of mathematical tools, usually highly nonlinear in nature, that can be used to perform a number of traditional statistical chores such as classification, pattern recognition and feature extraction. 

Metabolomic approaches use analytical techniques such as high-field NMR spectroscopy and MS to measure populations of low-molecular-weight metabolites in biological samples. Advanced statistical and bioinformatics tools are then employed to maximize the recovery of information and interpret the large data sets generated. 

Efficient data storage and retrieval, analysis, statistics, modeling, visualization, and informatics algorithms are the critical tools for biomedical discovery with arrays. A variety of powerful, established tools for biostatistical data analysis (e.g., SPSS, S-Plus, and SAS) and bioinformatics (e.g., GeneSpring, Genomax, and the NCBI web sites tools) are commercially available. However, the development of visualization, analysis, and modeling tools for time course data for arrays is needed. 

The successful application of array technology to will require continued technology development, the establishment of public databases with accepted standards for data annotation, and the development of powerful new statistical and bioinformatics tools. Clearly many challenges and promises lie ahead. 

We cannot be sure of the answers provided by statistical methods. But if they are developed appropriately, they provide a significance estimate, such as the P-value that was the main claim to fame of the BLAST program and its real innovation. This value tells us how much we can believe an analysis or prediction, in the case of BLAST a local sequence match witnessed by an alignment. Recently several other bioinformatics analysis methods have been equipped with theoretically founded or heuristically fitted significance estimates (see Chapters 2 and 6 of Volume 1). Only few bioinformatics tools come with this kind of significance estimate today, and we need more of them. 

One bioinformatics company that offers specialized siRNA software tools is called Ocimum Biosolutions. Their software, iRNAwiz, provides an environment for the design of successful siRNA molecules and is composed of several components. These components include a siRNA Search tool, a BLAST tool, a Motif Search tool, a Stemloop search, and a Statistical Analysis tool [32]. They claim the combination of these tools will result in the design of siRNA molecules with high efficiency. 

The module includes analysis tools for clustering and classification and statistical operations. It also integrates standard tools such as BLAST, ClustalW, and EMBOSS utilities. Interactive visualization software is available for viewing data structures relevant to the bioinformatics domain. 

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