Software agents in molecular computational biology

Progress made in applying agent systems to molecular computational biology is reviewed and strategies by which to exploit agent technology to greater advantage are investigated. Communities of software agents could play an important role in helping genome scientists design reagents for future research. The advent of genome sequencing in cattle and swine increases the complexity of data analysis required to conduct research in livestock genomics. Databases are always expanding and semantic differences among data are common. Agent platforms have been developed to deal with generic issues such as agent communication, life cycle management and advertisement of services (white and yellow pages). This frees computational biologists from the drudgery of having to re-invent the wheel on these common chores, giving them more time to focus on biology and bioinformatics. Agent platforms that comply with the Foundation for Intelligent Physical Agents (FIPA) standards are able to interoperate. In other words, agents developed on different platforms can communicate and cooperate with one another if domain-specific higher-level communication protocol details are agreed upon between different agent developers. Many software agent platforms are peer-to-peer, which means that even if some of the agents and data repositories are temporarily unavailable, a subset of the goals of the system can still be met. Past use of software agents in bioinformatics indicates that an agent approach should prove fruitful. Examination of current problems in bioinformatics indicates that existing agent platforms should be adaptable to novel situations.     

Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model

Several opensource or commercially available software platforms are widely used to develop dynamic simulations of movement. While computational approaches are conceptually similar across platforms, technical differences in implementation may influence output. We present a new upper limb dynamic model as a tool to evaluate potential differences in predictive behavior between platforms. We evaluated to what extent differences in technical implementations in popular simulation software environments result in differences in kinematic predictions for single and multijoint movements using EMG- and optimization-based approaches for deriving control signals. We illustrate the benchmarking comparison using SIMM–Dynamics Pipeline–SD/Fast and OpenSim platforms. The most substantial divergence results from differences in muscle model and actuator paths. This model is a valuable resource and is available for download by other researchers. The model, data, and simulation results presented here can be used by future researchers to benchmark other software platforms and software upgrades for these two platforms.     

R/Bioconductor在Affymetrix表达芯片中的应用

R是用于统计计算和数据作图的编程语言和程序设计环境,能在多种平台下编译和运行。Bioconductor也是免费开源的程序设计环境,它主要基于统计编程语言R,用于基因组数据的分析。我们通过已发表的数据,包含斑马鱼心肌再生的生物芯片数据,分析源码实例,较为详细地介绍和讲解了R/BioConductor的用法,为推广优秀的免费统计软件提供了一个简单的中文使用手册。     

Review of general algorithmic features for genome assemblers for next generation sequencers

In the realm of bioinformatics and computational biology,the most rudimentary data upon which all the analysis is built is the sequence data of genes,proteins and RNA.The sequence data of the entire genome is the solution to the genome assembly problem.The scope of this contribution is to provide an overview on the art of problem-solving applied within the domain of genome assembly in the nextgeneration sequencing(NGS) platforms.This article discusses the major genome assemblers that were proposed in the literature during the past decade by outlining their basic working principles.It is intended to act as a qualitative,not a quantitative,tutorial to all working on genome assemblers pertaining to the next generation of sequencers.We discuss the theoretical aspects of various genome assemblers,identifying their working schemes.We also discuss briefly the direction in which the area is headed towards along with discussing core issues on software simplicity.     

General statistical modeling of data from protein relative expression isobaric tags

Quantitative comparison of the protein content of biological samples is a fundamental tool of research. The TMT and iTRAQ isobaric labeling technologies allow the comparison of 2, 4, 6, or 8 samples in one mass spectrometric analysis. Sound statistical models that scale with the most advanced mass spectrometry (MS) instruments are essential for their efficient use. Through the application of robust statistical methods, we developed models that capture variability from individual spectra to biological samples. Classical experimental designs with a distinct sample in each channel as well as the use of replicates in multiple channels are integrated into a single statistical framework. We have prepared complex test samples including controlled ratios ranging from 100:1 to 1:100 to characterize the performance of our method. We demonstrate its application to actual biological data sets originating from three different laboratories and MS platforms. Finally, test data and an R package, named isobar, which can read Mascot, Phenyx, and mzIdentML files, are made available. The isobar package can also be used as an independent software that requires very little or no R programming skills.     

Effect Evaluation of Vascular Resection for Patients with Hilar Cholangiocarcinoma: Original Data and Meta-analysis

To evaluate the effect of vascular resection (VR) in surgical management of hilar cholangiocarcinoma (HCCA), this report is used in a clinical analysis and conducted a systematic review, combined other studies, based on meta-analysis. 238 HCCA patients underwent hepatectomy in the Eastern Hepatobiliary Surgery Hospital. Binary logistic regression analysis was performed to investigate the potentially complicated associated factors. Kaplan–Meier test was employed to compare the long-term survival of patients in four groups (R0+PVR-free, R0+PVR, R1, and R2). Meta-analysis was performed using RevMan 4.3.2 software. The results suggested that hepatectomy and HAR were important negative factors from complications (p < 0.01). Compared with patients in other groups, survival of patients in R0+PVR group was worse than R0+PVR-free group, better than R2 group, and similar to R1group with p = 0.001, 0.047, and 0.606, respectively. The results of meta-analysis suggested patients who underwent VR had higher complications rate and mortality rate than patients who did not. Moreover, patients with vascular resection had lower long-term survival rate. VR used to be considered effective to the patients with vascular invasion. However, our study suggests that the surgical decision of undergoing VR should be made cautiously, since VR could diminish the survival time in some cases.     

四种常用高通量测序拼接软件的应用比较

新一代测序平台的诞生推动了对全基因组鸟枪法测序数据的拼接算法和软件的研究,自2005年以来多种用于高通量测序的序列拼接软件已经被开发出来,并且在不断地进行改进以提高拼接效果.本文利用目前广泛使用的高通量测序拼接软件Velvet、AbySS、SOAPdenovo和CLC Genomic Workbench分别对本试验室分离的一株噬菌体IME08的高通量测序结果进行拼接,介绍这几种拼接软件的安装使用及参数优化,并对不同软件的拼接结果进行比较,针对不同的拼接软件得到优化的拼接参数,可为其他研究人员使用上述软件提供参考借鉴.     

Illumina-Solexa测序数据质量评估系统的构建

目的：针对下一代测序数据量大、序列长度短的特点,研究数据分析和质量评估方法。方法：选择已发布的Illumina-Solexa平台测序数据为研究对象,通过MAQ软件将测序数据与人类全基因组序列进行比对,并以外显子区域为例,在位点水平对测序数据质量进行评估。结果：结合已有软件系统和本文自创线性算法,建立了一套包括比对、拼接在内的测序数据质量评估系统。比对分析后,发现原始测序序列共覆盖了127,113,378个位点,涉及24条染色体上的64868个外显子。其中,每个位点都被测到的外显子为0.50%,位点平均测序深度大于等于1的外显子为3.98%。结论：成功构建了基于Illumina-Solexa测序平台的数据分析和质量评估方法,其可适用于其它第二代测序平台。研究者可在质量评估的基础上完善测序试验设计,并进行SNP和突变筛选及后续功能性研究。     

Biodiversity information platforms: From standards to interoperability

One of the most serious bottlenecks in the scientific workflows of biodiversity sciences is the need to integrate data from different sources, software applications, and services for analysis, visualisation and publication. For more than a quarter of a century the TDWG Biodiversity Information Standards organisation has a central role in defining and promoting data standards and protocols supporting interoperability between disparate and locally distributed systems.Although often not sufficiently recognized, TDWG standards are the foundation of many popular Biodiversity Informatics applications and infrastructures ranging from small desktop software solutions to large scale international data networks. However, individual scientists and groups of collaborating scientist have difficulties in fully exploiting the potential of standards that are often notoriously complex, lack non-technical documentations, and use different representations and underlying technologies. In the last few years, a series of initiatives such as Scratchpads, the EDIT Platform for Cybertaxonomy, and biowikifarm have started to implement and set up virtual work platforms for biodiversity sciences which shield their users from the complexity of the underlying standards. Apart from being practical work-horses for numerous working processes related to biodiversity sciences, they can be seen as information brokers mediating information between multiple data standards and protocols.The ViBRANT project will further strengthen the flexibility and power of virtual biodiversity working platforms by building software interfaces between them, thus facilitating essential information flows needed for comprehensive data exchange, data indexing, web-publication, and versioning. This work will make an important contribution to the shaping of an international, interoperable, and user-oriented biodiversity information infrastructure.     

TOM software toolbox: acquisition and analysis for electron tomography

Automated data acquisition procedures have changed the perspectives of electron tomography (ET) in a profound manner. Elaborate data acquisition schemes with autotuning functions minimize exposure of the specimen to the electron beam and sophisticated image analysis routines retrieve a maximum of information from noisy data sets. "TOM software toolbox" integrates established algorithms and new concepts tailored to the special needs of low dose ET. It provides a user-friendly unified platform for all processing steps: acquisition, alignment, reconstruction, and analysis. Designed as a collection of computational procedures it is a complete software solution within a highly flexible framework. TOM represents a new way of working with the electron microscope and can serve as the basis for future high-throughput applications.     

Making more sense of the order: A review of Canoco for Windows 4.5, PC‐ORD version 4 and SYN‐TAX 2000

Abstract. Personal computers of ever‐increasing speed have motivated programmers of multivariate software to adapt their programs to be run in Microsoft Windows and Macintosh platforms. Updated versions of these multivariate programs appear more and more frequently and are marketed intensively. In this review we provide a comparative analysis of the most recent versions of three analytical software packages –Canoco for Windows 4.5, PC‐ORD version 4 and SYN‐TAX 2000. The three packages share two characteristics. First, the most recent versions are now compatible with the most recent Windows platforms and should therefore be accessible for use by virtually all vegetation scientists. Second, they have capabilities for numerous multivariate techniques, although each package has some unique techniques. Thus, any one of the packages will have much to offer the user.     

Codelink: an R package for analysis of GE healthcare gene expression bioarrays

MOTIVATION: Microarray-based expression profiles have become a standard methodology in any high-throughput analysis. Several commercial platforms are available, each with its strengths and weaknesses. The R platform for statistical analysis and graphics is a powerful environment for the analysis of microarray data, because it has many integrated statistical methods available as well as the specialized microarray analysis project Bioconductor. Many packages have been added in the last few years increasing the range of possible analysis. Here, we report the availability of a package for reading and analyzing data from GE Healthcare Gene Expression Bioarrays within the R environment. AVAILABILITY: The software is implemented in the R language, is open source and available for download free of charge through the Bioconductor (http://www.bioconductor.org) project.     

Microcoding: the second step in DNA barcoding

After the process of DNA barcoding has become well advanced in a group of organisms, as it has in the economically important fungi, the question then arises as to whether shorter and literally more barcode-like DNA segments should be utilized to facilitate rapid identification and, where applicable, detection. Through appropriate software analysis of typical full-length barcodes (generally over 500 base pairs long), uniquely distinctive oligonucleotide 'microcodes' of less than 25 bp can be found that allow rapid identification of circa 100-200 species on various array-like platforms. Microarrays can in principle fulfill the function of microcode-based species identification but, because of their high cost and low level of reusability, they tend to be less cost-effective. Two alternative platforms in current use in fungal identification are reusable nylon-based macroarrays and the Luminex system of specific, colour-coded DNA detection beads analysed by means of a flow cytometer. When the most efficient means of rapid barcode-based species identification is sought, a choice can be made either for one of these methodologies or for basic high-throughput sequencing, depending on the strategic outlook of the investigator and on current costs. Arrays and functionally similar platforms may have a particular advantage when a biologically complex material such as soil or a human respiratory secretion sample is analysed to give a census of relevant species present.     

DNA array analysis in a Microsoft Windows environment.

Microsoft Windows-based computers have evolved to the point that they provide sufficient computational and visualization power for robust analysis of DNA array data. In fact, smaller laboratories might prefer to carry out some or all of their analyses and visualization in a Windows environment, rather than alternative platforms such as UNIX. We have developed a series of manually executed macros written in Visual Basic for Microsoft Excel spreadsheets, that allows for rapid and comprehensive gene expression data analysis. The first macro assigns gene names to spots on the DNA array and normalizes individual hybridizations by expressing the signal intensity for each gene as a percentage of the sum of all gene intensities. The second macro streamlines statistical consideration of the confidence in individual gene measurements for sets of experimental replicates by calculating probability values with the Student's t test. The third macro introduces a threshold value, calculates expression ratios between experimental conditions, and calculates the standard deviation of the mean of the log ratio values. Selected columns of data are copied by a fourth macro to create a processed data set suitable for entry into a Microsoft Access database. An Access database structure is described that allows simple queries across multiple experiments and export of data into third-party data visualization software packages. These analysis tools can be used in their present form by others working with commercial E. coli membrane arrays, or they may be adapted for use with other systems. The Excel spreadsheets with embedded Visual Basic macros and detailed instructions for their use are available at http://www.ou.edu/microarray.     

GProX, a user-friendly platform for bioinformatics analysis and visualization of quantitative proteomics data

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net.     

Joint analysis of multiple phenotypes: summary of results and discussions from the Genetic Analysis Workshop 19

For Genetic Analysis Workshop 19, 2 extensive data sets were provided, including whole genome and whole exome sequence data, gene expression data, and longitudinal blood pressure outcomes, together with nongenetic covariates. These data sets gave researchers the chance to investigate different aspects of more complex relationships within the data, and the contributions in our working group focused on statistical methods for the joint analysis of multiple phenotypes, which is part of the research field of data integration. The analysis of data from different sources poses challenges to researchers but provides the opportunity to model the real-life situation more realistically.Our 4 contributions all used the provided real data to identify genetic predictors for blood pressure. In the contributions, novel multivariate rare variant tests, copula models, structural equation models and a sparse matrix representation variable selection approach were applied. Each of these statistical models can be used to investigate specific hypothesized relationships, which are described together with their biological assumptions.The results showed that all methods are ready for application on a genome-wide scale and can be used or extended to include multiple omics data sets. The results provide potentially interesting genetic targets for future investigation and replication. Furthermore, all contributions demonstrated that the analysis of complex data sets could benefit from modeling correlated phenotypes jointly as well as by adding further bioinformatics information.     

matchprobes: a Bioconductor package for the sequence-matching of microarray probe elements

SUMMARY: The nucleotide sequences of the probes on a microarray can be used for a variety of purposes in the analysis of microarray experiments. We describe software and a paradigm for the creation of data packages for curating, distributing and working with probe sequence data in a uniform, across-types-of-microarrays manner. While the implementation is specific to the Bioconductor project, the ideas and general strategies are more general and could be easily adopted by other projects. AVAILABILITY: The R package matchprobes is available under LGPL at http://www.bioconductor.org SUPPLEMENTARY INFORMATION: The package contains documentation in the form of a vignette and manual pages.     

BIGpre: a quality assessment package for next-generation sequencing data

The emergence of next-generation sequencing (NGS) technologies has significantly improved sequencing throughput and reduced costs. However, the short read length, duplicate reads and massive volume of data make the data processing much more difficult and complicated than the first-generation sequencing technology. Although there are some software packages developed to assess the data quality, those packages either are not easily available to users or require bioinformatics skills and computer resources. Moreover, almost all the quality assessment software currently available didn’t taken into account the sequencing errors when dealing with the duplicate assessment in NGS data. Here, we present a new user-friendly quality assessment software package called BIGpre, which works for both Illumina and 454 platforms. BIGpre contains all the functions of other quality assessment software, such as the correlation between forward and reverse reads, read GC-content distribution, and base Ns quality. More importantly, BIGpre incorporates associated programs to detect and remove duplicate reads after taking sequencing errors into account and trimming low quality reads from raw data as well. BIGpre is primarily written in Perl and integrates graphical capability from the statistics package R. This package produces both tabular and graphical summaries of data quality for sequencing datasets from Illumina and 454 platforms. Processing hundreds of millions reads within minutes, this package provides immediate diagnostic information for user to manipulate sequencing data for downstream analyses. BIGpre is freely available at http://bigpre.sourceforge.net/.     

The end of the microarray Tower of Babel: will universal standards lead the way?

Microarrays are the most common method of studying global gene expression, and may soon enter the realm of FDA-approved clinical/diagnostic testing of cancer and other diseases. However, the acceptance of array data has been made difficult by the proliferation of widely different array platforms with gene probes ranging in size from 25 bases (oligonucleotides) to several kilobases (complementary DNAs or cDNAs). The algorithms applied for image and data analysis are also as varied as the microarray platforms, perhaps more so. In addition, there is a total lack of universally accepted standards for use among the different platforms and even within the same array types. Due to this lack of coherency in array technologies, confusion in interpretation of data within and across platforms has often been the norm, and studies of the same biological phenomena have, in many cases, led to contradictory results. In this commentary/review, some of the causes of this confusion will be summarized, and progress in overcoming these obstacles will be described, with the goal of providing an optimistic view of the future for the use of array technologies in global expression profiling and other applications.