The isotopic distribution conundrum

Although access to high-resolution mass spectrometry (MS), especially in the field of biomolecular MS, is becoming readily available due to recent advances in MS technology, the accompanied information on isotopic distribution in high-resolution spectra is not used at its full potential, mainly because of lack of knowledge and/or awareness. In this review, we give an insight into the practical problems related to calculating the isotopic distribution for large biomolecules, and present an overview of methods for the calculation of the isotopic distribution. We discuss the key events that triggered the development of various algorithms and explain the rationale of how and why the various isotopic-distribution calculations were performed. The review is focused around the developmental stages as briefly outlined below, starting with the first observation of an isotopic distribution. The observations of Beynon in the field of organic MS that chlorine appeared in a mass spectrum as two variants with odds 3:1 lie at the basis of the first wave of algorithms for the calculation of the isotopic distribution, based on the atomic composition of a molecule. From here on, we explain why more complex biomolecules such as peptides exhibit a highly complex isotope pattern when assayed by MS, and we discuss how combinatorial difficulties complicate the calculation of the isotopic distribution on computers. For this purpose, we highlight three methods, which were introduced in the 1980s. These are the stepwise procedure introduced by Kubinyi, the polynomial expansion from Brownawell and Fillippo, and the multinomial expansion from Yergey. The next development was instigated by Rockwood, who suggested to decompose the isotopic distribution in terms of their nucleon count instead of the exact mass. In this respect, we could claim that the term "aggregated" isotopic distribution is more appropriate. Due to the simplification of the isotopic distribution to its aggregated counterpart, Rockwood was able to use the convolution for the calculation of the "aggregated" isotopic distribution. Convolution methods are computationally efficient and economic in their memory usage. We spend a section on the work introduced by Rockwood during the 1990s. Due to recent breakthroughs in mass spectrometric technology and the widespread high-resolution instruments (e.g., FTICR-MS, FTOrbitrap-MS, and TOF-MS) that provide high-resolution, isotope-resolved, accurate mass data, there is an emerging need for algorithms that can calculate isotopic distributions for large biomolecules. The number of recent publications on this topic does witness this trend. The new methods are mostly based on complex mathematical developments such as, for example, cellular automata (Meija and Caruso [2004]. J Am Soc Mass Spectrom, 15(5):654-658), dynamic programming (Snider [2007]. J Am Soc Mass Spectrom, 18:1511-1515), and hierarchical models (Li et al. [2008] J Am Soc Mass Spectrom, 19:1867-1874). We also comment on the ideas to use Punnet squares and Pascal's triangle to introduce the concept of the isotopic distribution for educational and didactic purposes.     

国际上LIMS的最新进展与发展趋势

本文介绍了在经济全球化和IT热潮下国际上实验室信息管理系统（LIMS）的最新进展和发展趋势，在仪器联结、仪器控制、光谱采集、光谱处理和化学结构方面的突破，以及生物科学与IT结合诞生出来的新学科－－生物信息学（Bioinformatics）。     

Boosting and Microarray Data

We have found one reason why AdaBoost tends not to perform well on gene expression data, and identified simple modifications that improve its ability to find accurate class prediction rules. These modifications appear especially to be needed when there is a strong association between expression profiles and class designations. Cross-validation analysis of six microarray datasets with different characteristics suggests that, suitably modified, boosting provides competitive classification accuracy in general.Sometimes the goal in a microarray analysis is to find a class prediction rule that is not only accurate, but that depends on the level of expression of few genes. Because boosting makes an effort to find genes that are complementary sources of evidence of the correct classification of a tissue sample, it appears especially useful for such gene-efficient class prediction. This appears particularly to be true when there is a strong association between expression profiles and class designations, which is often the case for example when comparing tumor and normal samples.     

A Reconfigurable Index FLASH Memory tailored to Seed-Based Genomic Sequence Comparison Algorithms

Genomic sequence comparison algorithms represent the basic toolbox for processing large volume of DNA or protein sequences. They are involved both in the systematic scan of databases, mostly for detecting similarities with an unknown sequence, and in preliminary processing before advanced bioinformatics analysis. Due to the exponential growth of genomic data, new solutions are required to keep the computation time reasonable. This paper presents a specific hardware architecture to speed-up seed-based algorithms which are currently the most popular heuristics for detecting alignments. The architecture regroups FLASH and FPGA technologies on a common support, allowing a large amount of data to be rapidly accessed and quickly processed. Experiments on database search and intensive sequence comparison demonstrate a good cost/performance ratio compared to standard approaches.

蛋白组学及其在食品科学研究中的应用

后基因组时代的主要研究任务之一即是蛋白组学研究,蛋白组学技术发展迅速,目前已有望成为用来解决生命科学领域中诸多问题包括食品品质与安全等食品科学问题的有力工具。蛋白组学为食品科学相关研究提供了新的思路和技术,在食品科学研究中已有广泛的应用。介绍了蛋白质组学研究的核心技术,即蛋白质组分分离、蛋白质组分鉴定、利用蛋白质组信息学进行结构和功能预测,综述了蛋白组学技术在食品科学研究中的进展,并展望了其应用前景。     

基于Web视图模型构建生物信息二级数据库

提出采用带根连通有向图来描述生物信息二级数据库的Web视图模型，以结合生物信息背景知识设计特定的生物信息Web视图及其问的相关关系．通过常量、静态和动态三类视图构成的Web视图模型解决了生物信息计算与数据资源共享的统一概念抽象．据此构建的硒蛋白相关生物信息二级数据库不仅可提供常规的数据管理和发布服务，并且将各类基因序列、蛋白质序列等生物信息数据的发布、蛋白质结构观察等Web计算有机地联系起来，符合分子生物学中信息流的观点．     

CMRS:聚类的多解析度字符串索引结构

随着基因测序技术和人类基因组计划的发展，从大量的生物数据中寻找相似的序列就越来越成为当前研究的热点问题．本文提出了一种聚类的多解析度字符串索引结构，用于解决生物序列的相似性查询问题．首先，以较小容量的MBR（最小绑定矩形）构造基因序列的多解析度字符串索引结构，然后通过对MBR的聚类以夏保序技术的应用，减小索引中MBR的平均体积，从而增加了查询向量到索引的空间距离，提高了索引的过滤能力．还给出了一种新的后处理方法，通过大量的减少编辑距离的计算，提高索引的性能．文中给出了该索引结构并详细介绍了索引的相关算法．实验表明，该索引结构是一种有效的处理生物数据的相似性查询的索引结构．     

INTRODUCTION TO THE SPECIAL ISSUE ON CASE-BASED REASONING IN THE HEALTH SCIENCES

There has been an explosion of interest in health sciences applications of case-based reasoning (CBR), not only in the traditional CBR in medicine domain, but also in bioinformatics, enabling home health-care technologies, CBR integration, and synergies between CBR and knowledge discovery. This special issue features the best papers from the third workshop on CBR in the health sciences, held at ICCBR-05 in Madrid. It is the third in a series of exciting workshops, the first two of which were held at ICCBR-03, in Trondheim, Norway, and at ECCBR-04, in Madrid, Spain. The nine high-quality papers introduced here represent the research and experience of twenty-two authors working in eight different countries on a wide range of problems and projects. These papers illustrate some of the major trends of current research in CBR in the health sciences, and represent overall an excellent sample of the most recent advances of CBR in the health sciences.     

桌面网格计算平台在多序列比对算法中的运用

提出了一种多序列比对ClustalW算法并行化处理的新方法ParaClustalW,该方法使用桌面网格计算平台作为高性能编程环境和运行平台.分析了多序列比对算法在桌面网格平台上的任务划分方式、并行化策略和实现技术.ParaClustalW策略考虑到序列的数目与序列的长度等因素,实现任务划分的均衡性.经实验证明,Para...