A system architecture for genomic data analysis

MOTIVATION: Most of diseases are caused by a set of gene defects, which occur in a complex association. The association scheme of expressed genes can be modelled by genetic networks. Genetic networks are efficiently facilities to understand the dynamic of pathogenic processes by modelling molecular reality of cell conditions. In this sense a genetic network consists of first, a set of genes of specified cells, tissues or species and second, causal relations between these genes determining the functional condition of the biological system, i. e. under disease. A relation between two genes will exist if they both are directly or indirectly associated with disease [8]. Our goal is to characterize diseases (especially autoimmune diseases like chronic pancreatitis CP, multiple sclerosis MS, rheumatoid arthritis RA) by genetic networks generated by a computer system. We want to introduce this practice as a bioinformatic approach for finding targets.     

A general purpose computer analysis system for chromatographic data

A manual integration system for the analysis of chromatographicdata is described. The analog output produced by an HPLC absorbancemonitor is passed to a non-inverting signal amplifier. Thisamplified signal is sent to an IBM PC where an analog to digitalconverter is used to digitize the data. A set of six computerprograms which collect, store and analyze these data are presented.This system was used to analyze the nucleotide content of theanaerobic organism Clostridium aceto-butylicum by strong anion-exchangeHPLC. Received on May 26, 1987; accepted on November 15, 1987     

An artificial immune system for data analysis

We present a simplified view of those parts of the human immune system which can be used to provide the basis for a data analysis tool. The motivation for and reasoning behind such a model is given and the desire for a 'transparent' model and meaningful visualization and interpretation techniques is noted. A minimalist formulation of an artificial immune system and some of its behaviour is described. A simple implementation and a suitable visualization technique are demonstrated using some trivial data and the famous 'iris' data set.     

Automating data manipulation for genetic analysis using a data base management system

Inefficient coding and manipulation of pedigree data have often hindered the progress of genetic studies. In this paper we present the methodology for interfacing a data base management system (DBMS) called MEGADATS with a linkage analysis program called LIPED. Two families that segregate a dominant trait and one test marker were used in a simulated exercise to demonstrate how a DBMS can be used to automate tedious clerical steps and improve the efficiency of a genetic analysis. The merits of this approach to data management are discussed. We conclude that a standardized format for genetic analysis programs would greatly facilitate data analysis.     

Computer video acquisition and analysis system for biological data

The BIAS (Biological Image Analysis System) was developed to:(i) permit accurate entry and image processing of biologicaldata; (ii) minimize the need for specialized hardware; and (iii)aid in the human genome mapping and other projects. The firstmouse/cursor key-driven module was designed to be user interactiveand readily accessible to many laboratories. It contains theDRSNDS programs which automate the entering of data in a systematicformat. The types of data that can be entered utilizing thismodule are DNA — RNA gels from either a positive or negativePolaroid^TM image, autoradiograms or biotinylated images fromSouthern, Northern and dot or slot blot hybridization analyses.The image is acquired using a video camera and then digitizedfor subsequent analysis. During the analysis graphical representationsof the intermediate results are provided to assure user confidence.At any point within the program the user may obtain on-linehelp with the current task. The output displays the mol. wtof each individual component in the appropriate context. Thepresent version of the program produces results comparable witha human interpreter for some data. Band shifting and opticaldensity calculations are in a prototype form to permit evaluationof various techniques. Future work is directed at expandingthe system's capabilities to interpret data from other biologicalanalyses including DNA sequencing gels. Received on December 1, 1987; accepted on December 12, 1987     

ESTAP--an automated system for the analysis of EST data

The EST Analysis Pipeline (ESTAP) is a set of analytical procedures that automatically verify, cleanse, store and analyze ESTs generated on high-throughput platforms. It uses a relational database to store sequence data and analysis results, which facilitates both the search for specific information and statistical analysis. ESTAP provides for easy viewing of the original and cleansed data, as well as the analysis results via a Web browser. It also allows the data owner to submit selected sequences to dbEST in a semi-automated fashion.     

A data management system for structural genomics

Background  

Structural genomics (SG) projects aim to determine thousands of protein structures by the development of high-throughput techniques for all steps of the experimental structure determination pipeline. Crucial to the success of such endeavours is the careful tracking and archiving of experimental and external data on protein targets.     

Joint analysis of longitudinal data and recurrent episodes data with application to medical cost analysis

This paper discusses regression analysis of longitudinal data in which the observation process may be related to the longitudinal process of interest. Such data have recently attracted a great deal of attention and some methods have been developed. However, most of those methods treat the observation process as a recurrent event process, which assumes that one observation can immediately follow another. Sometimes, this is not the case, as there may be some delay or observation duration. Such a process is often referred to as a recurrent episode process. One example is the medical cost related to hospitalization, where each hospitalization serves as a single observation. For the problem, we present a joint analysis approach for regression analysis of both longitudinal and observation processes and a simulation study is conducted that assesses the finite sample performance of the approach. The asymptotic properties of the proposed estimates are also given and the method is applied to the medical cost data that motivated this study.     

A system for de-identifying medical message board text

There are millions of public posts to medical message boards by users seeking support and information on a wide range of medical conditions. It has been shown that these posts can be used to gain a greater understanding of patients' experiences and concerns. As investigators continue to explore large corpora of medical discussion board data for research purposes, protecting the privacy of the members of these online communities becomes an important challenge that needs to be met. Extant entity recognition methods used for more structured text are not sufficient because message posts present additional challenges: the posts contain many typographical errors, larger variety of possible names, terms and abbreviations specific to Internet posts or a particular message board, and mentions of the authors' personal lives. The main contribution of this paper is a system to de-identify the authors of message board posts automatically, taking into account the aforementioned challenges. We demonstrate our system on two different message board corpora, one on breast cancer and another on arthritis. We show that our approach significantly outperforms other publicly available named entity recognition and de-identification systems, which have been tuned for more structured text like operative reports, pathology reports, discharge summaries, or newswire.     

Kibos: A microcomputerized system for the continuous collection and analysis of behavioral data

This paper describes the design and use of an observation and recording system in which data on behavioral patterns exhibited by socially-living primates are recorded as present or absent within recurring units of time. Behavioral observations and initial editing are carried out on-line on a microcomputer. The checklist of observed behavious is open-ended and modifiable, as required by the selection of subject species and by experimental goals. Summary statistical reports are available immediately after data entry has been completed. Further editing and organization of data files take plance when data are transferred to a large computer. The virtues of this system are its ease of use by observers who can be trained rapidly, its cost-effectiveness, and its ability to record simultaneously a larger number of behaviors than other systems usually can. Included are examples of data-analytic programs which are executed after data have been transferred to a large computer.     

Meta-Mesh——元基因组数据分析系统

随着元基因组数据的不断增多,建立一个包含高品质的元基因组样本(也称为"微生物群落")数据的集成化的分析平台成为可能,使得微生物群落样本能够被有效分析、比较与搜索,从中发现更加深入的生物学意义。然而,一方面目前大部分元基因组数据库仅仅提供了简单的数据存储,缺乏良好的样本注释或者仅仅提供了很少的分析功能。另一方面,用于计算微生物群落数据相似性的方法所能够接受的样本数据量非常有限。长期以来,科学家们一直在寻找有效的方法计算海量微生物群落之间的相似性,从而研究样本之间的相似度并发现元基因组数据信息的相关性。Meta-Mesh是一个全新的在线元基因组分析系统,它包括元基因组数据库和分析平台,可以对元基因组样本进行系统、有效地分析,并实现样本的群落结构比较和精确搜索。其中,元基因组数据库已经从公共领域和内部实验室收集了超过7 000个高品质、带有有效注释的样本。同时,Meta-Mesh的分析平台提供了多种在线分析工具,可以对元基因组样本进行群落的结构分析与注释,多角度比较,并能通过快速索引策略和群落结构相似性算法在数据库中高效搜索近似的样本。Meta-Mesh通过"人体微生物群落样本的数据库搜索识别"以及"基于相似度矩阵的样本的聚类"等一系列的元基因组研究案例证明了其分析方面的性能。作为一个在线的元基因组数据库和分析系统,Meta-Mesh将服务于元基因组样本的快速分析、识别、比对、搜索等相关领域。     

A statistical model for iTRAQ data analysis

We describe biological and experimental factors that induce variability in reporter ion peak areas obtained from iTRAQ experiments. We demonstrate how these factors can be incorporated into a statistical model for use in evaluating differential protein expression and highlight the benefits of using analysis of variance to quantify fold change. We demonstrate the model's utility based on an analysis of iTRAQ data derived from a spike-in study.     

Multi-user system for analysis of data from flow cytometry.

A new program is described for the analysis of DNA histograms from flow cytometry. The fundamental model representing the cell population is similar to one described previously. It assumes the population is grouped into compartments, each consisting of cells having approximately the same DNA content. After staining the cells with an appropriate fluorochrome, the fluorescence distribution of cells within each compartment is assumed to be Gaussian. In the present algorithm, the parameters of the model can either be computed directly by the program from the data, or can be specified as input by the user. When synchronous cell populations lacking distinct G1 and G2/M phases are analyzed, the parameter values must first be obtained using an appropriate control. Percentages of cells in the various compartments are computed using a gradient search method described by Bevington.