Bioinformatics and data mining in proteomics

Proteomic studies involve the identification as well as qualitative and quantitative comparison of proteins expressed under different conditions, and elucidation of their properties and functions, usually in a large-scale, high-throughput format. The high dimensionality of data generated from these studies will require the development of improved bioinformatics tools and data-mining approaches for efficient and accurate data analysis of biological specimens from healthy and diseased individuals. Mining large proteomics data sets provides a better understanding of the complexities between the normal and abnormal cell proteome of various biological systems, including environmental hazards, infectious agents (bioterrorism) and cancers. This review will shed light on recent developments in bioinformatics and data-mining approaches, and their limitations when applied to proteomics data sets, in order to strengthen the interdependence between proteomic technologies and bioinformatics tools.     

Bioinformatics in mass spectrometry data analysis for proteomics studies

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.     

Bioinformatics in mass spectrometry data analysis for proteomics studies

Mass spectrometry is a technique widely employed for the identification and characterization of proteins. The role of bioinformatics is fundamental for the elaboration of mass spectrometry data due to the amount of data that this technique can produce. To process data efficiently, new software packages and algorithms are continuously being developed to improve protein identification and characterization in terms of high-throughput and statistical accuracy. However, many limitations exist concerning bioinformatics spectral data elaboration. This review aims to critically cover the recent and future developments of new bioinformatics approaches in mass spectrometry data analysis for proteomics studies.     

Bioinformatics in proteomics

Several genome sequencing projects have recently been completed and the majority of human coding regions have been sequenced. In the next step many of the further studies will concentrate on proteins. Proteomics methods are essential for studying protein expression, activity, regulation and modifications. Bioinformatics is an integral part of proteomics research. The recent developments and applications in proteomics are discussed including mass spectrometry data analysis and interpretation, analysis and storage of the gel images to databases, gel comparison, and advanced methods to study e.g. protein co-expression, protein-protein interactions, as well as metabolic and cellular pathways. The significance of informatics in proteomics will gradually increase because of the advent of high-throughput methods relying on powerful data analysis.     

Bioinformatics analysis of mass spectrometry-based proteomics data sets

Proteomics has made tremendous progress, attaining throughput and comprehensiveness so far only seen in genomics technologies. The consequent avalanche of proteome level data poses great analytical challenges for downstream interpretation. We review bioinformatic analysis of qualitative and quantitative proteomic data, focusing on current and emerging paradigms employed for functional analysis, data mining and knowledge discovery from high resolution quantitative mass spectrometric data. Many bioinformatics tools developed for microarrays can be reused in proteomics, however, the uniquely quantitative nature of proteomics data also offers entirely novel analysis possibilities, which directly suggest and illuminate biological mechanisms.     

生物信息学及其在蛋白质组学中的应用

随着基因组学和蛋白质组学的发展,生物信息学在数据处理中的应用已经越来越广泛。作为数据处理中越来越重要的分析手段,蛋白质组学数据库是蛋白质组学的主要内容之一。本文分别从生物信息学的蛋白质双向电泳数据库和基于蛋白质质谱结果的数据库两个方面,概述了发展中的蛋白质数据库的最新动态和有关信息,同时对主要的热门蛋白质组学数据库站点和资源进行了评价和分析。     

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.     

Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.     

Bioinformatics support for high-throughput proteomics

In the "post-genome" era, mass spectrometry (MS) has become an important method for the analysis of proteome data. The rapid advancement of this technique in combination with other methods used in proteomics results in an increasing number of high-throughput projects. This leads to an increasing amount of data that needs to be archived and analyzed.To cope with the need for automated data conversion, storage, and analysis in the field of proteomics, the open source system ProDB was developed. The system handles data conversion from different mass spectrometer software, automates data analysis, and allows the annotation of MS spectra (e.g. assign gene names, store data on protein modifications). The system is based on an extensible relational database to store the mass spectra together with the experimental setup. It also provides a graphical user interface (GUI) for managing the experimental steps which led to the MS data. Furthermore, it allows the integration of genome and proteome data. Data from an ongoing experiment was used to compare manual and automated analysis. First tests showed that the automation resulted in a significant saving of time. Furthermore, the quality and interpretability of the results was improved in all cases.     

细菌生物被膜核心胞外多糖靶向水解酶的生物信息挖掘北大核心

【目的】胞外多糖是生物被膜不可或缺的重要成分,在细菌致病和耐药过程中发挥着重要作用。运用酶制剂针对生物被膜的核心胞外多糖进行靶向清除,能够从根本上破坏细菌生物被膜的核心骨架,有助于战胜细菌生物被膜导致的危害。【方法】本研究针对常见致病菌生物被膜核心胞外多糖Pel、Psl、褐藻胶、N-乙酰氨基葡萄糖(Poly-β(1,6)-N-acetyl-D-glucosamine,PNAG)和纤维素,基于NCBI数据库中丰富的基因序列信息,筛选靶向生物被膜核心胞外多糖的水解酶,进一步运用phyre2、SWISS-MODEL等生物信息工具,分析了这些水解酶的理化性质、遗传进化、功能域及三维结构。【结果】筛选获得了153个靶向生物被膜核心胞外多糖的水解酶及其序列信息。其中,靶向Pel胞外多糖的水解酶共30个,属于糖苷水解酶114家族(glycoside-hydrolase family GH114);靶向Psl胞外多糖的水解酶共25个,属于糖苷水解酶超家族(glyco_hydro super family);靶向褐藻胶胞外多糖的水解酶共33个,属于褐藻胶裂解酶超家族(Alg Lyase superfamily);靶向PNAG胞外多糖的水解酶共30个,属于糖苷水解酶13家族(glycoside-hydrolase family GH13);靶向纤维素胞外多糖的水解酶共35个,属于糖苷水解酶8家族(glycosyl hydrolases family GH8)。【结论】这些水解酶菌具备靶向瓦解生物被膜核心胞外多糖的潜力,亟待进一步开发与应用。本研究提供了迄今为止最为全面的生物被膜核心胞外多糖水解酶序列组成及生物信息,为生物被膜的精准预防和靶向控制奠定扎实的数据基础。     

Informatics and data management in proteomics

Proteomics has become dominated by large amounts of experimental data and interpreted results. This experimental data cannot be effectively used without understanding the fundamental structure of its information content and representing that information in such a way that knowledge can be extracted from it. This review explores the structure of this information with regard to three fundamental issues: the extraction of relevant information from raw data, the scale of the projects involved and the statistical significance of protein identification results.     

Bioinformatics,functional genomics,and proteomics study of Bacillus sp

The ability of bioinformatics to characterize genomic and proteomic sequences from bacteria Bacillus sp. for prediction of genes and proteins has been evaluated. Genomics coupling with proteomics, which is relied on integration of the significant advances recently achieved in two-dimensional (2-D) electrophoretic separation of proteins and mass spectrometry (MS), are now important and high throughput techniques for qualifying and analyzing gene and protein expression, discovering new gene or protein products, and understanding of gene and protein functions including post-genomic study. In addition, the bioinformatics of Bacillus sp. is embraced into many databases that will facilitate to rapidly search the information of Bacillus sp. in both genomics and proteomics. It is also possible to highlight sites for post-translational modifications based on the specific protein sequence motifs that play important roles in the structure, activity and compartmentalization of proteins. Moreover, the secreted proteins from Bacillus sp. are interesting and widely used in many applications especially biomedical applications that are the highly advantages for their potential therapeutic values.     

Challenges and opportunities in proteomics data analysis

Accurate, consistent, and transparent data processing and analysis are integral and critical parts of proteomics workflows in general and for biomarker discovery in particular. Definition of common standards for data representation and analysis and the creation of data repositories are essential to compare, exchange, and share data within the community. Current issues in data processing, analysis, and validation are discussed together with opportunities for improving the process in the future and for defining alternative workflows.     

Statistical data processing in clinical proteomics

This review discusses data analysis strategies for the discovery of biomarkers in clinical proteomics. Proteomics studies produce large amounts of data, characterized by few samples of which many variables are measured. A wealth of classification methods exists for extracting information from the data. Feature selection plays an important role in reducing the dimensionality of the data prior to classification and in discovering biomarker leads. The question which classification strategy works best is yet unanswered. Validation is a crucial step for biomarker leads towards clinical use. Here we only discuss statistical validation, recognizing that biological and clinical validation is of utmost importance. First, there is the need for validated model selection to develop a generalized classifier that predicts new samples correctly. A cross-validation loop that is wrapped around the model development procedure assesses the performance using unseen data. The significance of the model should be tested; we use permutations of the data for comparison with uninformative data. This procedure also tests the correctness of the performance validation. Preferably, a new set of samples is measured to test the classifier and rule out results specific for a machine, analyst, laboratory or the first set of samples. This is not yet standard practice. We present a modular framework that combines feature selection, classification, biomarker discovery and statistical validation; these data analysis aspects are all discussed in this review. The feature selection, classification and biomarker discovery modules can be incorporated or omitted to the preference of the researcher. The validation modules, however, should not be optional. In each module, the researcher can select from a wide range of methods, since there is not one unique way that leads to the correct model and proper validation. We discuss many possibilities for feature selection, classification and biomarker discovery. For validation we advice a combination of cross-validation and permutation testing, a validation strategy supported in the literature.     

Bioinformatic analysis of proteomics data

Most biochemical reactions in a cell are regulated by highly specialized proteins, which are the prime mediators of the cellular phenotype. Therefore the identification, quantitation and characterization of all proteins in a cell are of utmost importance to understand the molecular processes that mediate cellular physiology. With the advent of robust and reliable mass spectrometers that are able to analyze complex protein mixtures within a reasonable timeframe, the systematic analysis of all proteins in a cell becomes feasible. Besides the ongoing improvements of analytical hardware, standardized methods to analyze and study all proteins have to be developed that allow the generation of testable new hypothesis based on the enormous pre-existing amount of biological information. Here we discuss current strategies on how to gather, filter and analyze proteomic data sates using available software packages.     

Bioinformatics tools for genome mining of polyketide and non-ribosomal peptides

Microbial natural products have played a key role in the development of clinical agents in nearly all therapeutic areas. Recent advances in genome sequencing have revealed that there is an incredible wealth of new polyketide and non-ribosomal peptide natural product diversity to be mined from genetic data. The diversity and complexity of polyketide and non-ribosomal peptide biosynthesis has required the development of unique bioinformatics tools to identify, annotate, and predict the structures of these natural products from their biosynthetic gene clusters. This review highlights and evaluates web-based bioinformatics tools currently available to the natural product community for genome mining to discover new polyketides and non-ribosomal peptides.